Mosquito age-grading for malaria vector surveillance in sub-Saharan Africa: current practices and constraints

Mosquito age-grading for malaria vector surveillance in sub-Saharan Africa: current practices and constraints | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Mosquito age-grading for malaria vector surveillance in sub-Saharan Africa: current practices and constraints Doreen Josen Siria, Naomi Urio, Dickson Msaky, Jacqueline Mgaya, and 15 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8560704/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background Adult mosquito survival is a critical factor in malaria transmission because Anopheles vectors must live at least 11–14 days before they can transmit. However, there is no direct method to assess age. Traditional methods of mosquito age-grading rely on indirect proxies of reproductive history derived from dissection and observation of ovarian features (Detinova and Polovodova methods). Both approaches are labour-intensive, time-consuming, and demand specialized expertise; with little known about the extent to which are used in malaria surveillance programmes, and why they may be deprioritised. This study assessed current practices, capacity, and constraints related to mosquito age-grading for malaria vector surveillance in Africa, including knowledge and use of existing dissection-based methods and emerging alternatives such as infrared spectroscopy. Methods We conducted an online survey of 285 stakeholders from across Africa, including researchers, entomologists, and National Malaria Programme (NMP) personnel, to assess the practices, priorities, and barriers to mosquito age-grading. This was complemented with a series of in-depth interviews and focus group discussions to collect insights and perspectives from stakeholders regarding their familiarity and use of age-grading methods. Findings: More than 70% of survey respondents reported that malaria vector surveillance was routinely conducted by their institutions or countries, with the highest priority given to vector density, species identification, human biting rate, and insecticide resistance. Familiarity (awareness as opposed to knowledge) of age-grading methods was highest for the Detinova (55%) and Polovodova (44%), and lower for newer approaches, including infrared-spectroscopy. Only 50% of respondents indicated that they regularly assessed mosquito age (mostly Detinova method); with > 1/3 considering age-grading to be a high-priority in vector surveillance. Reported barriers to conducting mosquito-age-grading included insufficient technical expertise, perceived impracticality of ovary dissections for large-scale surveillance, inadequate tools, and limited funding. Conclusions Despite its critical role in malaria transmission, mosquito survival and age are rarely assessed in African vector surveillance programs. There is need for greater acknowledgment of these measures and their implications for disease transmission and control, and investment in tools, training, and funding to overcome current operational barriers. Integrating more practical and scalable age-assessment methods could enhance targeting of interventions. mosquito age-grading malaria surveillance parity assessment Detinova Polovodova infrared spectroscopy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Since 2000, malaria control interventions have averted 2.3 billion cases and 12.7 million deaths, yet the disease still causes an estimated 282 million cases and over 600,000 deaths (75% in children under 5) in 2024 [ 1 , 2 ]. Over the past two decades, insecticide-treated nets (ITNs), indoor residual spraying (IRS), and other vector control tools have been primarily responsible for reducing malaria transmission [ 2 , 3 ]. According to the latest WHO Malaria Vector Control Guidelines, surveillance (~ whether entomological or epidemiological) is now recognized as a core intervention against malaria control, on par with prevention and treatment strategies [ 4 ]. Effective vector surveillance provides essential data for decision-making, enabling malaria control programs to optimize the deployment of these interventions. It can help monitor changes in mosquito populations, detect insecticide resistance, and assess the effectiveness of control interventions [ 5 , 6 ]. To support malaria control and elimination efforts, national programs should establish a robust evidence base on the ecology, transmission potential and behaviour of mosquito vectors and their transmission potential. The World Health Organization’s Global Technical Strategy (WHO-GTS) for Malaria 2016–2030 designated surveillance as a core pillar of malaria control, with a recommendation of routine monitoring in all transmission-prone settings [ 7 , 8 ]. In line with this, the WHO manual for entomological surveillance in malaria emphasizes a set of priority indicators for programmatic decision-making, including vector species composition, density, infection rates, biting and resting behaviours, insecticide resistance status, and mosquito survival rates [ 8 , 9 ]. These metrics are parameters that are actually used to assess effectiveness of interventions and therefore correspond closely with the core entomological indicators typically monitored in national programs. However, many national programs face significant challenges in fully implementing the vector surveillance recommendations [ 9 , 10 ]. Limitations such as weak strategic frameworks, inadequate logistics, insufficient human resources, and financial constraints often hinder comprehensive surveillance [ 9 – 13 ]. This may result in surveillance relying on a single sampling tool, which can affect data reliability and interpretations, given that different methods come with their own inherent biases and limitations[ 12 , 13 ]. The intensity of malaria transmission depends on several mosquito population traits, including population size, human biting rate, gonotrophic cycle length, and adult female survival [ 14 – 16 ]. The survival of adult females has a particularly large impact because the ability of Anopheles mosquitoes to transmit is age dependent. The primary African malaria parasite Plasmodium falciparum requires 11 to 14 days to develop in mosquito vectors before transmission can occur [ 17 – 20 ]. Since this development period often matches or exceeds the median survival of Anopheles vector populations, only a small proportion of adult females live long enough to transmit the parasite [ 21 ]. Consequently, even modest reductions in adult survival can markedly reduce transmission [ 22 , 23 ]. For effective malaria control, it is therefore important to monitor how key vector control interventions impact adult mosquito survival and their age structure. The age of adult Anopheles mosquitoes can be expressed chronologically (days since emergence) or physiologically, as is most frequently assessed by changes in the reproductive history of females that occur across their lifespan. These are typically measured based on whether eggs have been laid (nulliparous or parous) and the number of gonotrophic cycles completed, defined as the period from blood feeding to oviposition[ 17 , 23 ]. Because robust methods for directly estimating chronological age are lacking, traditional age-grading approaches instead rely on assessing physiological age, inferred from morphological changes in the reproductive systems of adult female mosquitoes [ 24 , 25 ]. These methods include identifying whether a female mosquito has previously laid eggs (parous) or not (nulliparous), which can be determined by dissecting and examining the ovarian tracheoles using the Detinova method [ 25 , 26 ]. Another approach, the Polovodova method, estimates physiological age based on the number of gonotrophic cycles a female has completed as estimated from the number of ovarian dilatations observed [ 24 , 27 ]. Both techniques are considered standard methods for age-grading malaria vectors [ 28 ]. However, the adoption and application of these age-grading methods into vector surveillance vary widely, and often they are performed on only a limited basis (e.g., on only a subset of mosquitoes). This is largely due to these methods being labour-intensive and in the case of the Polovodova method, requiring significant technical expertise that is not widely accessible due to limited training opportunities [ 27 , 29 , 30 ]. A survey by Burkot et al. (2019) [ 11 ] reported that only 25% of National Malaria Control Programs (NMCPs) in African and Asian countries routinely conduct parity dissections to estimate mosquito survival [ 11 ]. This points to a significant gap in the operational use of mosquito survival-related data, despite its relevance for measuring transmission intensity and evaluating the impact of interventions. Several factors could explain this low uptake, including poor awareness of the importance of mosquito age-structure, limited access to training or dissection skills, inadequate infrastructure, and uncertainty around how to interpret or apply survival estimates in programmatic decision making [ 23 , 29 , 30 ]. While the Burkot study quantified the incorporation of parity dissections within NMCP surveillance, it did not explore the underlying reasons for low uptake. In addition to existing dissection-based methods, there is growing interest in the potential of alternative age grading technologies approaches which are less labour-intensive and potentially more accurate [ 31 – 33 ]. Understanding how end-users perceive and engage with these emerging methods is critical for informing their future integration into malaria surveillance. Several new methods for estimating the age of Anopheles mosquitoes are under development, including those that detect signals of aging based on chromatographic analysis of cuticular hydrocarbons [ 29 ], transcriptomic profiling [ 34 ], and infrared spectroscopy [ 31 , 32 , 35 , 36 ], and more recently, mass spectrometry techniques such as Matrix-Assisted Laser Desorption/Ionization–Time of Flight Mass Spectrometry (MALDI-TOF MS) [ 37 – 39 ]. The spectroscopy-based techniques have the greatest potential when combined with machine learning (ML), advanced methods to assist with complex analysis of cuticular spectral patterns to predict mosquito age categories [ 40 , 41 ]. However, despite the potential of these novel methods, they are currently not widely adopted for routine vector surveillance. Understanding how potential end-users perceive the value of both these emerging and traditional methods (Detinova and Polovodova) will be useful to overcome key barriers that limit their use, and guide strategies that promote their uptake and integration into malaria surveillance programs. The aim of this study was to assess the current practices and challenges of mosquito age-grading for malaria vector surveillance in Africa, as well as the insights and perspectives of key stakeholders including national malaria control programs (NMCPs), entomologists, and researchers from public and private institutions regarding the feasibility of different age-grading methods. NMCPs were targeted because they lead routine monitoring and programmatic decision-making in vector surveillance, while researchers often generate evidence of intervention impact, build capacity, or implement vector control activities. Specifically, the study focused on: (i) the extent to which mosquito age-grading is conducted within surveillance programs and reasons for non-implementation, (ii) stakeholder knowledge, skills, and familiarity with traditional and emerging age-grading methods, and (iii) operational and technical barriers limiting routine adoption. By providing evidence on stakeholder perceptions and priorities, this work aims to inform strategies that promote training on existing methods and facilitate the uptake of innovative age-grading tools, thereby enhancing the overall value of entomological surveillance for guiding malaria control and predicting transmission dynamics. Methods This study employed a mixed methods design, combining an online survey, semi-structured interviews, and a scoping review to guide data collection. The aim was to capture perspectives from malaria vector surveillance practitioners in African research institutions and national malaria control programmes, and to identify current practices, priorities, and barriers to mosquito age-grading. Scoping Review to Guide Data Collection For the development of the online survey and interview questions, a narrative review of existing literature review and targeted scoping analysis was conducted. Relevant studies on mosquito age-grading methods, particularly the Detinova and Polovodova dissection-based techniques were identified through searches of PubMed, Google Scholar, and institutional resources, including the University of Glasgow’s Library search platform [ 42 ]. Keywords included “mosquito age-grading”, “parity dissections”, “Detinova method”, “Polovodova method”, “gonotrophic cycle”, “vector survival”, and “entomological indicators.” Articles were selected based on relevance to mosquito age-grading and its application in surveillance, without formal inclusion or exclusion criteria. Online Survey The online survey targeted individuals and stakeholders involved in malaria surveillance and vector control programs in Africa. This included vector control leads operating within National Malaria Programs (NMPs) which included the (NMCP and National malaria elimination program, NMEP), as well as research scientists, entomologists affiliated with public health institutions, non-governmental organizations (NGOs), and academic institutions. Participants were selected based on current engagement in entomological surveillance or relevant expertise, ensuring informed perspectives on the importance, use, and challenges of mosquito age-grading. The survey link was distributed from July 2023 to December 2024 via social media platforms (X/Twitter, LinkedIn), institutional focal points, and the Pan-African Mosquito Control Association (PAMCA) network [ 43 ]. A QR code was shared at scientific meetings, including the 9th PAMCA conference (Addis Ababa, Ethiopia, 2023) and at the 29th American Society of Tropical Medicine and Hygiene (ASTMH) meeting (New Orleans, USA, 2024) [ 44 ]. A QR code for the survey was also shared with NMPs and other researchers and facilitators who attended the 8th Tanzania Vector Control Technical Working Group Meeting (December 2024). Additional outreach was conducted through personalized invitations to maximize participation. The survey was hosted on Microsoft Forms and is available on supplementary materials. The questionnaire comprised six sections (supplementary online material 1): (1) respondent demographics (age, education, job title); (2) frequency of mosquito surveillance activities and associated interventions, with frequency categories defined as frequent (~ weekly), occasional (~ monthly), rare (~ yearly), or never; (3) vector species surveyed and priority entomological indicators; (4) awareness, perceptions, and perceived importance of age-grading; (5) awareness and perspectives on other age-grading techniques such as the use of infrared (IR) spectroscopy-based; and (6) challenges and gaps affecting the use of age-grading techniques in vector surveillance. A total of 285 stakeholders from 18 African countries completed the survey (Fig. 1 ). In-depth interviews (IDIs) and Focus Group Discussions (FGDs) To complement the findings from the online stakeholder survey, additional qualitative data were collected by conducting 23 in-depth interviews (IDIs) and two (2) focus group discussions (FGDs). These follow-up methods were designed to gather deeper insights into stakeholder perceptions of mosquito age-grading techniques. A semi-structured discussion guide was used to explore participants’ roles and experience in malaria vector surveillance; current surveillance tools used and associated challenges; as well as general ideas on potential future improvements. Before each IDI or FGD, participants received a short presentation on age-grading methods to refresh their knowledge on the subject and ensure a shared understanding of the basics. Most participants in the IDIs and FGDs had completed the online survey beforehand, contributing to informed and interactive discussions. All interviews and discussions were conducted in person and included stakeholders from NMPs, research institutions, and vector surveillance units. The FGDs were held separately with NMPs representatives from mainland Tanzania and Zanzibar. These qualitative methods provided context to the survey results, offering additional perspectives on current surveillance and intervention approaches, the perceived value of age-grading and age-structure data, familiarity with existing methods, and challenges associated with traditional techniques. In all sessions, we sought participant consent and audio-recorded the discussions before transcribing and analysing them thematically. Data analysis Quantitative survey data were analysed using R version 4.2.2 [ 45 , 46 ], applying descriptive and univariate statistical methods. Demographic variables (sex, age group, and education level) were summarised to characterise the respondents. Further analysis focused on assessing the frequencies of surveillance and intervention types, reported prioritization of various entomological indicators, and familiarity and use of different age-grading methods. Categorical and Likert-scale responses were converted to ordered factors, with proportions and summary statistics calculated by “category” (e.g., intervention type, indicator). Visualisations included customised Likert plots and stacked bar charts. All audio recordings from the interviews and discussions were transcribed, then translated from local language (Swahili) to English when needed. Qualitative data from in-depth interviews, focus group discussions, and open-ended survey responses were analysed using NVivo 14.24.3 software [ 47 ]. Translated transcripts were reviewed iteratively to ensure familiarity, followed by systematic coding to identify meaningful units of information. Codes were linked to discussion guide sections and then organised into broader thematic categories based on recurring stakeholder narratives. To enhance consistency, findings were compared across data sources to identify areas of convergence and divergence. Representative quotes were selected to illustrate perspectives and support themes. Triangulation was employed by integrating insights from the online survey, interviews, and FGDs to validate findings, enhance analytical depth, and provide a more comprehensive understanding of stakeholder views on mosquito age-grading methods. Results Design of survey and interviews The literature review suggested that traditional age-grading are technically complex, require specialized training and are rarely prioritized in routine surveillance [ 8 ]. Also, emerging age-grading methods such as NIRS [ 36 , 48 ], MIRS [ 31 , 32 ] and matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) [ 37 , 39 ] could provide high-throughput age estimation but are not widely adopted in NMCPs. These observations informed the survey and interview questions, focusing on stakeholder familiarity, practical challenges, and feasibility of traditional and emerging age-grading methods. Characteristics of online survey study participants The study participants were diverse, encompassing various key stakeholders engaged in malaria vector surveillance across Africa. A total of 285 respondents took part in the survey, comprising 208 males (73%) and 77 females (27%). Most respondents had either a master’s degree (42%) or a bachelor’s degree (32%), with ~ 20% having doctoral-level education. Slightly more than half (55%) of participants were between 20–35 years old. In terms of stakeholder types and professional roles, respondents included representatives from NMCPs (14%), entomologists (17%), and researchers from academic, public, and private institutions (69%). The majority were employed either in national malaria programs, academic institutions, or research institutions, or both, with most of them having received training in entomology or related courses (Table 1 ). Geographically, most stakeholders were from Tanzania, Kenya, Uganda, Ghana, Cameroon, and Nigeria (Fig. 1 ). Table 1 Characteristics of online survey participants (n = 285). Categories Variables Number of participants (285) Sex Male Female 208 (73%) 77 (27%) Age-group 20–35 36–45 46–55 56–65 Above 65 157 (55.1%) 84 (29.5%) 38 (13.3%) 4 (1.4%) 2 (0.7%) Education level attained Bachelor (3 years post high school) Masters (4–5 years post high school) PhD (7–9 years post high school) Other forms of collage training 92 (32.3%) 119 (41.8%) 57 (20%) 17(5.9) Type of stakeholders NMCPs Entomologists Researchers 40 (14%) 49 (17%) 197 (69%) Frequency of different surveillance activities Respondents reported on the frequency of various malaria-related surveillance activities in their countries or institutions, including collection of entomological measures (e.g., adult mosquito density, larval surveys, insecticide resistance bioassays) and epidemiological measures (e.g., malaria prevalence in humans). Surveillance of adult mosquitoes was the most reported activity (87%), followed by larval surveillance (76%) and malaria infection monitoring in people (74%). Insecticide resistance testing was also frequently conducted (reported by 73% of respondents; Fig. 2 ). During FGDs and IDIs with stakeholders, respondents reported that malaria surveillance in their settings primarily relies on both vector and parasite surveillance. For vector surveillance, activities included entomological monitoring at the district level to track vector abundance, sporozoite rates, and entomological inoculation rates, as required to provide evidence for assessing the impact of interventions. National personnel also conducted insecticide resistance monitoring across multiple sites to ensure the continued effectiveness of WHO-recommended insecticides. For epidemiological surveillance, respondents described periodic malaria parasitological indicator surveys to evaluate disease burden and prevalence. They also highlighted programmatic surveillance, which involves tracking the use and performance of malaria commodities, such as monitoring the efficacy of antimalarial drugs and assessing the durability of bed nets post-distribution. Together, these surveillance activities form the evidence base for malaria control decision-making. As highlighted by one national malaria program respondent below, stakeholders frequently described surveillance as encompassing both entomological and epidemiological components, aimed at tracking transmission and evaluating the effectiveness of interventions: R1: “Our malaria surveillance relies on two main types: vector and parasite surveillance, monitoring mosquito density, infection, and resistance to insecticides, as well as tracking malaria prevalence through surveys and facility data. We also assess the performance of interventions like bednets and antimalarial drugs to ensure their continued effectiveness.” (NMP, male) Frequency of intervention activities implemented in different countries Respondents from the online survey were asked about their involvement in implementing or testing specific vector control methods. ITNs and IRS were the most frequently implemented interventions, reported by 67% and 66% of respondents, respectively. Larval source management (LSM); mostly consisting of larviciding was reported by 53% of respondents. Attractive Toxic Sugar Bait (ATSB) was the least frequently mentioned intervention. This is unsurprising because, unlike the other interventions considered, this intervention is still undergoing evaluation (with ongoing cluster randomized control trials in some countries) [ 49 , 50 ] and does not yet have a WHO recommendation (Fig. 3 ). Similarly, the stakeholders who participated in FGDs and IDIs were involved in a range of malaria control interventions, primarily focused on vector control and malaria elimination. In FGDs and IDIs, the most widely implemented intervention was the distribution and monitoring of ITNs, often delivered through mass campaigns or continuous distribution systems. Stakeholders were also engaged in efforts to improve malaria diagnosis through rapid diagnostic tests (RDTs) and microscopy, as well as ensuring the availability and appropriate use of antimalarial drugs. While a few participants mentioned past involvement in IRS, it was noted that this intervention is no longer routinely implemented in Tanzania (R2). Additionally, some respondents referred to experience with LSM in specific ecological settings (R3). R2: “In our district, most of the work revolves around distributing and monitoring the use of bednets. We used to conduct IRS some years ago, but that stopped due to changes in the national policy and funding. Nowadays, we focus on tracking net coverage, educating communities, and collecting mosquito data to inform other strategies.” (NMP, male) R3: “In Zanzibar, we have shifted focus more toward integrated vector management. LLINs remain our backbone, but we also conduct larval source management in selected urban areas. IRS was part of our strategy in the past, but it’s no longer active due to funding redirection. Our work now includes routine monitoring and adjusting based on entomological findings.” (NMP, female) Motivations for conducting mosquito surveillance Stakeholders from the online survey reported several motivations for conducting mosquito surveillance. The most frequently cited reason was to better understand mosquito ecology, including species composition, spatial distribution, population density, and behavioural patterns (n = 210, > 70% of respondents). Other commonly stated reasons for conducting vector surveillance (cited by n = 185, 65% of respondents) included evaluating malaria transmission and the effectiveness of vector control measures such as ITNs and IRS, and monitoring insecticide resistance to guide adjustments in control strategies (cited by n = 140, 49% of respondents). Moderate-frequency reasons given for entomological surveillance (cited by n = 120, 42% of respondents) included generating evidence to inform new public health policy, planning, resource allocation, and strengthening vector control capacity through academic research, training, and skill development. The least cited motivation for conducting surveillance (n = 45, 16% of the respondents) was for early warning and outbreak detection, and to support timely intervention (Table 2 ). Table 2 Stakeholders’ reasons and perspectives in conducting mosquito surveillance. Source: Online survey (2025) Opinions/Reasons Stakeholder Responses 1. Understanding mosquito ecology and population dynamics R4: “Monitoring mosquito density and seasonality helps us target control efforts effectively.” (Entomologist, Male) R5: “Knowing which mosquito species are present and how they behave informs our control strategies.” (NMP, Female) 2. Disease transmission monitoring and intervention evaluation R6: “To assess the impact of vector control interventions and measure malaria infection rates.” (Researcher, Female) R7: “We conduct surveillance primarily to reduce malaria cases by tracking vector populations and evaluating control tools.” (Senior Research Officer, Male) 3. Insecticide resistance monitoring R8: “To monitor insecticide resistance status of local malaria vector populations.” (Entomologist, Male) R9: “Insecticide resistance monitoring is critical to guide which insecticides to use in malaria control programs.” (Researcher, Female) 4. Supporting program planning, policy, and capacity building R10: “To generate vector data for evidence-based decision-making and support national malaria control programs.” (Researcher, Male) R11: “Our data informs policy decisions on insecticide selection and vector control strategies.” (NMP, Female) 5. Early warning and outbreak detection R12: “Early detection through vector surveillance is key to preventing disease spread.” (Researcher, Female) R13: “We monitor malaria transmission trends to identify hotspots and intervene before outbreaks occur.” (NMP, Male) Furthermore, stakeholders and participants from FGDs and IDIs echoed and emphasized that mosquito surveillance is a critical foundation for informed malaria control decision-making. Many highlighted that surveillance enables early detection of changes in mosquito population dynamics, such as species composition, density, and biting behaviour, which can influence the effectiveness of interventions. Others noted that entomological data can guide the deployment of vector control tools like ITNs (e.g., R14) and support monitoring of insecticide resistance trends. Several stakeholders also pointed out that routine mosquito surveillance helps assess the impact of interventions over time and ensures that national programs can respond adaptively to emerging threats, such as behavioural resistance or shifts in peak biting times. Overall, there was consensus that without regular entomological surveillance, malaria programs risk becoming reactive rather than proactive. R14 “ Mosquito surveillance is the eye of the vector control program. Without understanding where and how the mosquitoes are changing, we’re just guessing with our interventions.” (Researcher, male) Priority entomological indicators and vector species targeted Most respondents from the online survey stated that their vector surveillance programs are mostly focused on the surveillance of primary African malaria vectors: Anopheles gambiae complex (94%), and Anopheles funestus group (68.1%). Surveillance for the invasive Anopheles stephensi was reported by 39.3% of stakeholders, while 33% also monitored Aedes vectors responsible for transmission of arboviruses such as dengue fever, chikungunya, and yellow fever [ 51 ]. When asked about priority entomological indicators, vector species identification (78%), mosquito density (69%), and insecticide resistance (70%) ranked highest. Monitoring indoor human biting rate (60%) and sporozoite rates (57%) were also commonly reported (Fig. 4 ). In contrast, age-grading, by ovary dissection methods, was less frequently prioritized (Detinova method: 32%; Polovodova method: 21%), suggesting these techniques are of lower operational priority or face practical implementation challenges, (Fig. 4 ). Participants from the FGDs and IDIs similarly reported that their entomological surveillance efforts primarily targeted mosquitoes from the Anopheles gambiae complex and Anopheles funestus groups. Additionally, when asked about which entomological indicators were prioritized during surveillance in FGDs and IDIs, stakeholders consistently highlighted vector density, biting behaviour (indoor/outdoor), sporozoite infection rates, and insecticide resistance status as the most routinely monitored parameters (R15). These indicators were described as crucial for evaluating the effectiveness of vector control intervention and for guiding timely programmatic decisions. In contrast, mosquito age-grading or mosquito survivorship were rarely mentioned as being prioritized; with reasons cited primarily being limited technical capacity and resources, as well as a perception that such data were more relevant to research than to routine program operations (R16, R17). However, a few stakeholders acknowledged that integrating age-structure data could enhance the understanding of residual transmission risks, especially in settings with high insecticide-treated bed net coverage, yet persistent malaria remains. R15: “Our focus is mainly on vector density and biting time whether it’s indoors or outdoors. These help us adjust our interventions, especially where ITNs aren’t working well anymore.” (Entomologist, male) R16: “We routinely test for sporozoites and insecticide resistance, but we do not typically assess mosquito age. That’s usually left to researchers or bigger projects.” (NMP, female) R17: “Most of our data is on mosquito numbers and resistance. Age-grading is interesting, but we don’t have the equipment or training for that.” (NMP, male) Familiarity with mosquito age-grading methods Respondents from the online survey were asked about their familiarity with existing age-grading methods, such as the Polovodova and Detinova techniques, wing fray analysis, as well as newer approaches such as near-infrared (NIRS) and mid-infrared (MIRS) spectroscopy. Just over half (55%) reported familiarity with the Detinova method, and 44% with the Polovodova method. Familiarity with the other age-grading methods, NIRS, MIRS, and wing fray analysis, was much lower, ranging from 18% to 22% (Fig. 5 ). During FGDs and IDIs, participants expressed varying degrees of familiarity with different mosquito age-grading methods. Traditional techniques such as the Detinova and Polovodova methods were mostly known among research institutions and some national program staff/personnel. However, many other personnel and stakeholders had limited exposure to these methods, often viewing age-grading as a specialized research activity rather than a routine component of vector surveillance (R18-19). The awareness of newer and innovative technologies, such as NIRS and MIRS, was even more limited. While some participants engaged in entomological research or advanced surveillance programs were aware of these innovative methods and recognized their potential for rapid and accurate mosquito age estimation, others had little or no knowledge or experience with them (R20-21). This gap was due to a lack of training, limited access to specialized equipment, and low integration of these methods into routine surveillance activities (R22). Consequently, despite the promise of these new approaches, they remain largely unfamiliar and underutilized across many malaria control programs. R18: “I’ve seen ovary dissections done during training years ago, but it’s not something we use in our day-to-day work.” (NMP, male) R19: “Some of us know about parity dissections, but very few people are still practicing it actively. It’s more of a research thing.” (NMP, female) R20: “I’ve heard about infrared methods, but we’ve never used them here. They sound advanced, maybe too advanced for our current setup.” (NMP, male) R21: “To be honest, I wasn’t aware of NIRS or MIRS until this discussion. We’ve never had training on them or seen them in action.” (NMP, female) R22: “These new technologies are promising, but access is a major issue. We don’t have the machines or know how to use them.” (NMP, male) Stakeholder perceptions and the use of existing methods for mosquito age-grading in surveillance Half of the online survey respondents reported conducting mosquito age-grading as part of their surveillance activities, most commonly using the Detinova (mosquito parity) or Polovodova (gonotrophic cycle count) methods. In contrast, 39% stated they did not perform age-grading, and 11% were unsure whether such methods were applied within their programs or institutions. This question was designed to assess stakeholder awareness and overall use of traditional age-grading techniques as part of entomological surveillance. Overall, 82% of respondents agreed that estimating mosquito age or survival is an important indicator in vector surveillance. Open-ended responses from the online survey provided further insights into why stakeholders considered mosquito parity valuable, with thematic analysis revealing three main reasons: (1) assessing malaria transmission risk and supporting public health implications, (2) monitoring and evaluating vector control interventions, and lastly, (3) for research and laboratory applications. These findings illustrate a broad recognition of the importance of age-grading, even where routine practice is inconsistent. Below, we present selected quotes from open text boxes that highlight perceptions of each of these thematic areas. Theme 1: Assessing malaria transmission risk and public health implications (n = 212, 70%) : Majority of the stakeholders highlighted the importance of mosquito age in understanding malaria transmission potential and guiding control strategies. Older mosquitoes are more likely to transmit disease; thus, age data helps estimate transmission risk, target interventions, and assess the urgency of responses. R23: “The age of the mosquito population is a key determinant of pathogen transmission because only older mosquitoes can transmit malaria.” (Researcher, Male) R24: “Age-grading helps estimate the proportion of mosquitoes that are infectious, which is vital for malaria risk modelling.” (NMP, Female) R25: “Without knowing the age structure, we can’t accurately predict malaria transmission hotspots.” (Entomologist, Male) Theme 2: “Monitoring and evaluating vector control interventions (~ 180 to 150 respondents (approx. 50–60%) : Stakeholders emphasized using age-grading data in assessing the effectiveness of different vector control interventions, including ITNs and IRS, and to monitor insecticide resistance and mosquito population dynamics. R26 “Without age-grading, our understanding of vector ecology is incomplete. Monitoring mosquito survival after exposure to insecticides helps determine resistance and the effectiveness of control measures.” (Entomologist, Female) R27: “If interventions are effective, the mosquito population should be dominated by younger age classes. When older, resistant mosquitoes are still around after ITNs distribution, that’s a red flag for program managers” (Researcher, Male) R28 “Knowing mosquito age helps us understand population dynamics and environmental factors affecting vector abundance.” (NMP, Male) Theme 3: Research and laboratory applications (n = 80, 27%) : Stakeholders added that in conducting or understanding mosquito ages, the age-grading data support different laboratory studies such as vector competence assays, insecticide susceptibility tests, and epidemiological research, as age influences infection status, vector competence, and physiological traits. R29: “Collecting age data is important for lab assays, as mosquito age affects infection status and resistance profiling.” (Researcher, Female) R30: “When studying parasite development in mosquitoes, knowing the age is essential for interpreting results.” (Researcher, Male) R31: “We can’t run accurate transmission experiments without accounting for mosquito age. Lab data without age information is like reading a book with missing pages, you miss the context” (Researcher, Female) Existing and current age-grading methods Respondents from the online survey were asked which methods their institutions or projects currently use to estimate mosquito age. While the previous section captured general awareness and perceived value of mosquito age-grading, this question explored the specific techniques currently being applied by the stakeholders. The most commonly reported technique being used was parity rate assessment through the Detinova method (65.9%), followed by the Polovodova method (41.1%). Less frequently reported were wing fray analysis (12.6%), near-infrared spectroscopy (NIRS, 13%), and mid-infrared spectroscopy (MIRS, 11%). A further 15.8% indicated they did not use any of these methods, and 1.1% mentioned alternative approaches, such as MALDI-TOF MS. Awareness of novel infrared spectroscopy-based techniques for age-grading was moderate: 50.2% of respondents had heard of either NIRS or MIRS, 46.3% had not, and 3.5% were unsure. However, access remained a major barrier, with only 18.6% reporting availability of these technologies within their institution, compared to 74% who reported no access and 7.4% who were uncertain. These findings point to a clear gap between awareness of innovative age-grading tools and their operational adoption. Challenges and gaps associated with current age-grading methods In the online survey, stakeholders were asked to rank the challenges associated with different age-grading methods, considering whether a method: (a) could be taught to a non-expert, (b) was accurate and precise, (c) could be implemented at a large scale, and (d) would be cost-effective (Fig. 6 ). All stakeholders perceived that both existing standard age-grading methods (Polovodova and Detinova) and newer infrared methods (NIRS and MIRS) could be taught to a non-expert, were likely to be accurate and precise, cost-effective, and capable of implementation at a large scale. No consistent differences emerged in the perceived challenges associated with these methods (Fig. 6 ). However, unstructured responses from open-text responses within the survey, and discussions from the IDIs and FGDs revealed that some stakeholders viewed the Detinova and Polovodova methods as more difficult to implement (Table 3 ). These challenges were linked to their reliance on skilled personnel, the labour-intensive and time-consuming nature of dissections, and the complexity of data interpretation. Table 3 Key challenges in implementing age-grading methods for mosquito surveillance were identified from both the online survey, IDIs, and FGDs, with representative quotes illustrating stakeholder perception of the challenge. Challenges identified Responses Lack of necessary tools, funding, and expertise (Resource constraints) R32: “Because I don’t have tools for age grading” (NMP, male). R33: “In resource constrained environment, these activities are not possible” (Researcher, female) Labour-intensive, time-consuming, and require specialized skills (Technical complexity). R34: “Age-grading mosquitoes can be labour-intensive and require specialized skills and equipment” (Researcher, male) R35: “I think it is time consuming and labour intensive” (Researcher, male) Inaccurate or subjective, leading to unreliable age determination (Inaccuracy of the methods) R36: “I have a doubt on its accuracy due to some human errors” (Researcher, female) R37 : “ Some tools used to estimate age are biased giving misleading results” (Researcher, male) Impractical for large-scale field use in routine or large-scale field settings R38: “It is quite challenging to determine the age of a wild caught adult mosquito” (Researcher, female) R39: “Age-grading often requires specialized techniques, equipment, and expertise, making it resource-intensive and impractical for routine surveillance” (Researcher, female) May not directly inform immediate control strategies, reducing its priority. R40: “It does not give you an immediate result of whether a mosquito infected/infective with malaria but only the likelihood of infection” (Researcher, female), R41: “While age-grading can provide valuable insights into mosquito population dynamics, its utility in guiding immediate public health interventions may be limited” (Researcher, male) Discussion To our knowledge, this is the first comprehensive multi-country survey to identify, understand, and document the challenges and gaps in implementing mosquito age-grading for vector control and surveillance. Previous studies have comprehensively investigated the types of entomological data collected in malaria surveillance (e.g. Burkot et al [ 11 ], Russell et al [ 12 ], with this study further investigating the barriers to widespread incorporation of mosquito-age grading. By engaging key stakeholders, including the NMP, decision-makers, entomologists, and research scientists from NGOs, private institutions, and public sectors, we obtained valuable insights into the challenges that limit the collection of mosquito survival data. The key factors identified were limited prioritization of age-grading within routine surveillance, insufficient technical expertise to perform dissections or data analysis, and constraints in time and resources that hinder systematic implementation. Through understanding the factors that limit the uptake of both traditional and novel methods, this research can help guide strategies to promote and facilitate greater use of mosquito age-grading techniques in the future. This study found that the predominance of surveillance activities focused on adult mosquitoes and that most stakeholders prioritize adult mosquito density, species identification, and insecticide resistance testing; likely because these indicators are considered operationally straightforward and directly actionable. Indeed, WHO-endorsed interventions reflect the operational realities of NMCPs, where surveillance priorities are closely aligned with interventions that are already recommended, funded, and routinely implemented [ 8 , 9 ]. Similar patterns have been reported elsewhere, with entomological surveillance primarily designed to support monitoring of ITNs, IRS, and larval source management, rather than to evaluate emerging tools still under development [ 11 – 13 , 52 ]. This alignment likely reflects programmatic incentives, including donor reporting requirements, established technical guidance, and limited resources for piloting novel interventions [ 53 ]. Consequently, newer approaches, that would require different entomological or epidemiological indicators, may remain peripheral to routine surveillance systems until clear WHO recommendations and implementation frameworks are established, potentially delaying their integration into national monitoring strategies [ 54 – 56 ]. A significant proportion of responses from the online survey indicated that entomological surveillance primarily targets the major malaria vectors, An. gambiae s.l. and An. funestus s.l. both of which are species complexes comprising multiple morphologically similar sibling species. Consequently, some respondents may be monitoring subspecies such as An. arabiensis or An. coluzzii without explicitly distinguishing them in the survey or during the discussions. This is not surprising given that these are the primary malaria vector species in Africa, and the main target of most malaria control programmes. However, respondents from Ethiopia, Tanzania, Kenya, Uganda, Nigeria, Ghana, and Cameroon also reported involvement in surveillance for or in anticipation of An. stephensi , an invasive urban malaria vector originating from South Asia that has recently invaded several African countries [ 57 ]. Our study confirmed previous regional and global analyses showing that mosquito age-grading and survival estimation are rarely prioritized in routine surveillance, despite their epidemiological importance [ 11 , 13 , 52 ]. In a 2019 global survey, Burkot et al. found [ 11 ], that only about 25% of surveyed NMCPs across multiple regions reported conducting parity dissections to assess mosquito survivorship [ 11 ]. This indicates that direct age-structure monitoring remains rare in malaria-endemic settings, even among programmes with formal surveillance frameworks. Meanwhile, Russell et al. (2020) documented that just 8% of NMCPs reported having adequate capacity (in staffing, infrastructure, financing, and management) to implement comprehensive vector surveillance, with most programmes lacking the human resources or strategic prioritization to do so [ 12 ]. By engaging with frontline entomologists or programme staff we further documented user-reported barriers to age-grading, revealing that other entomological indicators are prioritized over age-grading. Our mixed-methods approach adds novel, empirical evidence on why age-grading remains underused: namely limited prioritization within surveillance programmes, insufficient technical expertise, and resource/time constraints prevent systematic implementation. Our study revealed specific barriers of adopting the traditional mosquito age-grading methods into routine surveillance. Indeed, while stakeholders were generally familiar with traditional approaches, such as the Detinova and Polovodova techniques, they consistently described these dissection-based methods as technically demanding and labour-intensive. Respondents emphasized that specialized training and access to microscopes often limited in programmatic settings are essential for implementing these methods. The Polovodova technique, in particular, was noted as challenging for assessing older, multiparous mosquitoes, consistent with previous studies reporting potential underestimation of parity in multi-cycle females [ 27 , 30 ]. Stakeholders also highlighted logistical constraints, such as the requirement for freshly collected mosquitoes, which complicates large-scale surveillance in remote or resource-limited areas [ 23 , 29 ]. While parity dissections are scientifically informative and can provide valuable data on mosquito survival, operational implementation in routine program settings may result in greater variability, especially when conducted by personnel with limited experience. This variability has been hypothesized based on documented methodological challenges such as difficulty distinguishing successive ovarian dilations even though quantitative inter-observer reliability data under programmatic conditions are not yet available [ 23 , 27 , 29 , 58 ]. These findings reinforce that, despite recognition of the epidemiological importance of mosquito age and survival metrics, practical barriers continue to restrict their systematic integration into routine vector surveillance. Our study showed a substantial research-to-practice gap for innovative age-grading methods. Indeed, although NIRS and MIRS are not new techniques - NIRS for age-grading was first demonstrated more than 15 years ago[ 36 ] and they have high surveillance potential - classifying species and age with high accuracy and high speed[ 31 , 35 , 36 , 59 ] - our survey showed that the awareness and use of innovative age-grading tools were minimal. Only 11–13% of respondents reported the use of spectroscopic methods such as NIRS or MIRS, which was largely attributed to the lack of training and restricted access to specialized equipment. Meanwhile, many newer molecular methods (e.g. Polymerase Chain Reaction-PCR or sequencing-based species identification or insecticide resistance assays) have already started to see field deployment. Additionally, the MALDI-TOF MS is a recent alternative method that enables simultaneous prediction of age and infection status[ 37 – 39 ] which was also mentioned as one of the methods by the respondents as a tool they wanted to use but have limited access to. All these innovations offer higher throughput, greater objectivity, and faster processing than dissections but remain largely confined to research institutions. Our study’s empirical data from frontline entomologists and programme staff highlights limited awareness, training, equipment access, and institutional buy-in remain major bottlenecks to operational implementation of spectroscopic age-grading methods in routine national vector surveillance. Importantly, while this study focuses on the role of mosquito age-grading in malaria vector surveillance in Africa, the implications extend beyond this context. Age-structure information is also crucial for understanding transmission dynamics of other vector-borne diseases, including arboviruses transmitted by Aedes and Culex species [ 60 ]. Yet, similar to malaria settings, age-grading is rarely incorporated into routine Aedes surveillance even in regions investing heavily in vector monitoring despite evidence that many of the same age-grading approaches can be applied to Aedes species [ 60 ]. Lessons from our work on malaria vectors therefore offer a foundation for strengthening the operational uptake of age-grading methods across diverse geographical settings and disease systems, helping build more robust, forward-looking vector surveillance frameworks globally. This study had several limitations. First, the number of survey respondents varied across countries, with some having fewer than five participants, which may limit the representativeness of the findings. Obtaining responses was particularly challenging due to the online survey format, which relied on participants’ willingness and ability to access and complete the survey. This means only those who accessed and responded resulted in their contribution. Additionally, countries with more extensive survey promotion, such as through project or NMCP meetings in Tanzania, had higher response rates, potentially introducing selection bias. Future studies should aim to engage a broader geographic range and include qualitative discussions in multiple countries to capture diverse perspectives on mosquito age-grading practices. Conclusions Taken together, our findings reveal a clear and persistent pattern: although methodological innovation in mosquito age-grading continues to progress, operational uptake remains limited by gaps in awareness, technical capacity, and access to equipment. Among these, the most immediate priority emerging from our data is improving practical training and hands-on capacity-building, as lack of expertise was the most frequently cited barrier by frontline entomologists and programme staff. Strengthening national and sub-national training programmes paired with provision of essential equipment and development of simplified, standardised protocols would offer the greatest short-term impact in enabling programmes to begin integrating age-structure data into routine surveillance. In parallel, broader institutional prioritisation is needed: validated spectroscopic and dissection-based methods should be incorporated into national surveillance guidelines and supported through sustainable financing mechanisms to ensure continuity beyond research settings. This study has demonstrated that strengthening malaria vector surveillance in Africa requires greater recognition of mosquito age-grading as a critical indicator of transmission potential and intervention impact. While stakeholders value the role of age-structured data, persistent barriers limited technical expertise, inadequate resources, and low prioritization in policy, continue to restrict its operational use. Addressing these challenges will demand targeted investments in training, infrastructure, and sustainable funding. Emerging tools such as infrared spectroscopy and other scalable methods hold promise for providing rapid, objective, and high-throughput age estimates, offering a practical complement to traditional dissections. Integrating such approaches into routine surveillance will not only enhance decision-making but could also accelerate progress toward malaria elimination by enabling more precise targeting of interventions. In conclusion, strengthening malaria vector surveillance in Africa requires greater recognition of mosquito age-grading as a critical indicator of transmission potential and intervention impact. Our findings reveal a clear pattern: although methodological innovation in age-grading is advancing, operational uptake remains constrained by gaps in awareness, technical capacity, and access to equipment. The most immediate priority is improving practical training and hands-on capacity-building. Strengthening national and sub-national training programmes, paired with provision of essential equipment and simplified, standardised protocols, would offer the greatest short-term impact in enabling programmes to integrate age-structure data into routine surveillance. In parallel, broader institutional prioritisation is needed. Validated spectroscopic and dissection-based methods should be incorporated into national surveillance guidelines and supported through sustainable financing to ensure continuity beyond research settings. Emerging tools such as infrared spectroscopy and other scalable approaches promise rapid, objective, high-throughput age estimates, complementing traditional dissections. Integrating these methods into routine surveillance will enhance decision-making and accelerate progress toward malaria elimination by enabling more precise targeting of interventions. Abbreviations IRS; Indoor residual spraying, ITN: Insecticide treated bed nets, NMCP: National malaria control personnel-program, PAMCA: Pan African mosquito control association, ASTMH: American Society of Tropical Medicine and Hygiene, ATSBs: Attractive toxic sugar baits, NMP: National malaria programs, NMEP: National malaria elimination program personnel, IHI: Ifakara health institute, CISM: Centro de Investigação em Saúde de Manhiça, NIMR: National Institute for Medical Research, FGD: Focus group discussion, IDI: In-depth interviews, IR: Infrared, ML: Machine learning, AI: Artificial intelligence, WHO-GTS: World Health Organization’s Global Technical Strategy, MALDITOF-MS: matrix-assisted laser desorption ionization time-of-flight mass spectrometry, WHO: World Health Organisation, IRSS: Institut de Recherche en Sciences de la Santé, RDTs: Rapid Diagnostic Tests. Declarations Authors contributions DJS was the lead study investigator who was involved in the study design, data collection, data entry and analysis, interpretation of the results, and writing of the manuscript. NU and DJS were also involved in FGD data collections. FO, FB, and HMF were involved in the study design, supervision, and critical revision of the manuscript. NU, DM, MF, HN, JM, YM, IM, EM, RM, EH, MM, MO, RS, AD, MS-L, contributed to the study design, and manuscript writing (review and editing). All authors read and approved the final manuscript. Funding This study was supported by the Bill and Melinda Gates Foundation (grant number INV-003079). Availability of data and materials All the data for this study is available upon reasonable request. Ethics approval Ethical consideration was sought prior to the work; ethical approval was sought from the Ifakara Health institutional review board (IRB), reference number (IHI/IRB/EXT/No: 02-2025), and National Institute for Medical Research reference number (NIMR/HQ/R.8a/Vol. IX/4112). Permission to publish was sought from the National Institute for Medical Research (NIMR) with a reference number Ref No. BD.242/437/01C/121. Conflict of interest The authors declare that there are no competing interests. References WHO. World Malaria Report 2025 Addressing the threat of antimalarial drug resistance [Internet]. World Health Organization. 2025 [cited 2025 Dec 17]. https://www.who.int/publications/i/item/9789240117822 . Accessed 17 Dec 2025. WHO. World Malaria Report. 2024 Addressing inequality in the global malaria response [Internet]. World Health Organization; 2024 [cited 2025 Dec 17]. https://www.who.int/teams/global-malaria-programme/reports/world-malaria-report-2024 . Accessed 17 Dec 2025. Dhiman S. Are malaria elimination efforts on right track? An analysis of gains achieved and challenges ahead. Infect Dis Poverty. BioMed Central Ltd.; 2019. https://doi.org/10.1186/s40249-019-0524-x . WHO. WHO Guidelines for malaria, 13 August 2025 [Internet]. World Health Organization; 2025. https://doi.org/10.2471/B09514 . Killeen GF, Tatarsky A, Diabate A, Chaccour CJ, Marshall JM, Okumu FO, et al. Developing an expanded vector control toolbox for malaria elimination. BMJ Glob Health BMJ Publishing Group. 2017;2. https://doi.org/10.1136/bmjgh-2016-000211 . Golding N, Wilson AL, Moyes CL, Cano J, Pigott DM, Velayudhan R, et al. Integrating vector control across diseases. BMC Med BioMed Cent Ltd. 2015;13. https://doi.org/10.1186/s12916-015-0491-4 . WHO. Global technical strategy for malaria, 2016–2030 [Internet]. Global Malaria Programme, World Health Organization. 2015 [cited 2025 Dec 17]. https://www.who.int/docs/default-source/documents/global-technical-strategy-for-malaria-2016-2030.pdf . Accessed 17 Dec 2025. WHO-GTS. Global technical strategy for malaria 2016–2030, 2021 update [Internet]. World Health Organ. 2021. http://apps.who.int/iris/bitstream/10665/176712/1/9789241564991_eng.pdf?ua=1 WHO. Malaria surveillance, monitoring & evaluation: a reference manual. [Internet]. 2018. https://www.who.int/publications/i/item/9789241565578 . Accessed 17 Dec 2025. WHO. Monitoring and evaluation indicators for integrated vector management. [Internet]. 2012. https://iris.who.int/bitstream/handle/10665/76504/9789241504027_eng.pdf?sequence=1 . Accessed 17 Dec 2025. Burkot TR, Farlow R, Min M, Espino E, Mnzava A, Russell TL. A global analysis of National Malaria Control Programme vector surveillance by elimination and control status in 2018. Malar J BioMed Cent Ltd. 2019;18. https://doi.org/10.1186/s12936-019-3041-2 . Russell TL, Farlow R, Min M, Espino E, Mnzava A, Burkot TR. Capacity of National Malaria Control Programmes to implement vector surveillance: a global analysis. Malar J BioMed Cent Ltd. 2020;19. https://doi.org/10.1186/s12936-020-03493-1 . Farlow R, Russell TL, Burkot TR. Nextgen Vector Surveillance Tools: sensitive, specific, cost-effective and epidemiologically relevant. Malar J BioMed Cent Ltd. 2020;19. https://doi.org/10.1186/s12936-020-03494-0 . Smith DL, Battle KE, Hay SI, Barker CM, Scott TW, McKenzie FE. Ross, Macdonald, and a theory for the dynamics and control of mosquito-transmitted pathogens. PLoS Pathog. 2012;8. https://doi.org/10.1371/journal.ppat.1002588 . Ross C. The prevention of malaria [Internet]. 1911. https://www.jstor.org/stable/714848 . Accessed 17 Dec 2025. Chernin E. Sir Ronald Ross, malaria, and the rewards of research. Med Hist. 1988;32:119–41. https://doi.org/10.1017/S0025727300047967 . Gillies MT. A Modified Technique for the Age-Grading of Populations of Anopheles gambiae . Ann Trop Med Parasitol [Internet] Taylor Francis. 1958;52:261–73. https://doi.org/10.1080/00034983.1958.11685867 . Gillies MT. The recognition of age-groups within populations of Anopheles gambiae by the pre-gravid rate and the sporozoite rate. Ann Trop Med Parasitol. 1954. https://doi.org/10.1080/00034983.1954.11685599 . Beier JC. Malaria parasite development in mosquitoes. Annu Rev Entomol. 1998;43:519–43. https://doi.org/10.1146/annurev.ento.43.1.519 . Aniedu I, Mutinga MJ, Mutero CM. Age composition and survival rate of Anopheles gambiae Giles complex (Dipt., Culicidae) in Baringo district, Kenya. J Appl Entomol. 1989;107:387–94. https://doi.org/10.1111/j.1439-0418.1989.tb00273.x . Ferguson HM, Maire N, Takken W, Lyimo IN, Brit O, Lindsay SW, et al. Selection of mosquito life-histories: A hidden weapon against malaria? Malar J. 2012;11:1–5. https://doi.org/10.1186/1475-2875-11-106 . Brady OJ, Godfray HCJ, Tatem AJ, Gething PW, Cohen JM, Ellis McKenzie F, et al. Vectorial capacity and vector control: Reconsidering sensitivity to parameters for malaria elimination. Trans R Soc Trop Med Hyg. 2016;110:107–17. https://doi.org/10.1093/trstmh/trv113 . Johnson BJ, Hugo LE, Churcher TS, Ong OTW, Devine GJ. Mosquito Age Grading and Vector-Control Programmes. Trends Parasitol. 2020;36:39–51. https://doi.org/10.1016/j.pt.2019.10.011 . Beklemishev WN, Detinova TS, Polovodova VP. Determination of physiological age in anophelines and of age distribution in anopheline populations in the USSR. Bull World Health Organ [Internet]. 1959;21:223–32. http://www.ncbi.nlm.nih.gov/pubmed/13798390 . Detinova TS. [Determination of the physiological age of Anopheles females by the changes in the tracheal system of the ovaries]. Med Parazitol (Mosk) [Internet]. 1945;14:45–9. http://www.ncbi.nlm.nih.gov/pubmed/20280604 . Detinova TS. Age-grouping methods in Diptera of medical importance with special reference to some vectors of malaria. Monogr Ser World Health Organ [Internet]. 1962;47:13–191. http://www.ncbi.nlm.nih.gov/pubmed/13885800 . Anagonou R, Agossa F, Azondékon R, Agbogan M, Oké-Agbo F, Gnanguenon V, et al. Application of Polovodova’s method for the determination of physiological age and relationship between the level of parity and infectivity of Plasmodium falciparum in Anopheles gambiae s.s, south-eastern Benin. Parasit Vectors. BioMed Central Ltd.; 2015. p. 8. https://doi.org/10.1186/s13071-015-0731-7 . Charlwood JD, Tomás EVE, Andegiorgish AK, Mihreteab S, LeClair C. We like it wet’: a comparison between dissection techniques for the assessment of parity in Anopheles arabiensis and determination of sac stage in mosquitoes alive or dead on collection. PeerJ. 2018;6:e5155. https://doi.org/10.7717/peerj.5155 . Hugo LE, Quick-Miles S, Kay BH, Ryan PA. Evaluations of mosquito Age grading techniques based on morphological changes. J Med Entomol. 2008;45:353–69. https://doi.org/10.1603/0022-2585(2008)45 . [353:EOMAGT]2.0.CO;2. Pretorius E, Kristan M, Bradley J, da Silva ET, Hutchins H, Barri F, et al. Validation of a method for the dry preservation and rehydration of Anopheles gambiae sensu lato for parity analysis to assess the impact of vector control measures in the field. Parasit Vectors [Internet] BioMed Cent. 2023;16:1–9. https://doi.org/10.1186/s13071-023-05866-2 . Siria DJ, Sanou R, Mitton J, Mwanga EP, Niang A, Sare I, et al. Rapid age-grading and species identification of natural mosquitoes for malaria surveillance. Nat Commun Springer US. 2022;13:1–9. https://doi.org/10.1038/s41467-022-28980-8 . Mwanga EP, Siria DJ, Mshani IH, Mwinyi SH, Abbasi S, Jimenez MG, et al. Rapid classification of epidemiologically relevant age categories of the malaria vector, Anopheles funestus. Parasit Vectors BioMed Cent Ltd. 2024;17. https://doi.org/10.1186/s13071-024-06209-5 . Pazmiño-Betancourth M, Ochoa-Gutiérrez V, Ferguson HM, González-Jiménez M, Wynne K, Baldini F, et al. Evaluation of diffuse reflectance spectroscopy for predicting age, species, and cuticular resistance of Anopheles gambiae s.l under laboratory conditions. Sci Rep [Internet] Nat Publishing Group UK. 2023;13:1–12. https://doi.org/10.1038/s41598-023-45696-x . Cook PE, Hugo LE, Williams CR, Chenoweth SF, Ritchie SA, Ryan PA et al. The use of transcriptional profiles to predict adult mosquito age under field conditions. 2006; https://doi.org/10.1073/pnas.0604875103 Sikulu-Lord MT, Devine GJ, Hugo LE, Dowell FE. First report on the application of near-infrared spectroscopy to predict the age of Aedes albopictus Skuse. Sci Rep. 2018. https://doi.org/10.1038/s41598-018-27998-7 . Sikulu M, Killeen GF, Hugo LE, Ryan PA, Dowell KM, Wirtz RA, et al. Near-infrared spectroscopy as a complementary age grading and species identification tool for African malaria vectors. Parasit Vectors. 2010;3:1–7. https://doi.org/10.1186/1756-3305-3-49 . Rakotonirina A, Pol M, Kainiu M, Barsac E, Tutagata J, Kilama S, et al. MALDI-TOF MS: Optimization for future uses in entomological surveillance and identification of mosquitoes from New Caledonia. Parasit Vectors [Internet] BioMed Cent. 2020;13:1–12. https://doi.org/10.1186/s13071-020-04234-8 . Raharimalala FN, Andrianinarivomanana TM, Rakotondrasoa A, Collard JM, Boyer S. Usefulness and accuracy of MALDI-TOF mass spectrometry as a supplementary tool to identify mosquito vector species and to invest in development of international database. Med Vet Entomol. 2017;31:289–98. https://doi.org/10.1111/mve.12230 . Tuwei M, Karisa J, Kiuru C, Ondieki Z, Odongo T, Muturi M, et al. Age structure and parity status determination of Afrotropical malaria vectors using MALDI-TOF MS. Sci Rep. 2025;15:1–11. https://doi.org/10.1038/s41598-025-00935-1 . Gonzalez-Jimenez M, Babayan SA, Khazaeli P, Doyle M, Walton F, Reedy E, et al. Prediction of malaria mosquito species and population age structure using mid-infrared spectroscopy and supervised machine learning. bioRxiv [Internet]. 2018;414342. https://doi.org/10.1101/414342 . Mwanga EP, Siria DJ, Mitton J, Mshani IH, González-Jiménez M, Selvaraj P, et al. Using transfer learning and dimensionality reduction techniques to improve generalisability of machine-learning predictions of mosquito ages from mid-infrared spectra. BMC Bioinf BioMed Cent Ltd. 2023;24. https://doi.org/10.1186/s12859-022-05128-5 . University of Glasgow(UofG). Summon Search Platform. University Library, University of Glasgow [Internet]. https://www.gla.ac.uk/myglasgow/library/ Lambert B, Sikulu-Lord MT, Mayagaya VS, Devine G, Dowell F, Churcher TS. Monitoring the Age of Mosquito Populations Using Near-Infrared Spectroscopy. Sci Rep [Internet] Springer US. 2018;8:1–9. https://doi.org/10.1038/s41598-018-22712-z . PAMCA, Pan-African. Mosquito Control Association Conference. 2023; https://pamca.org/en ASTMH AA book of the 2024. American Society of Tropical Medicine and Hygiene (ASTMH). 2024; https://tinyurl.com/5bv4t9de Team RC, The R Foundation for Statistical Computing. R Development Core Team. R: A Language and Environment for Statistical Computing [Internet]. 2017;55:275–86. https://doi.org/http://www.R-project.org R Core Team. 2019.: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/ . 2019. Dhakal K, NVivo. J Med Libr Association [Internet]. 2022;110:270–2. https://doi.org/10.5195/jmla.2022.1271 . Reegan AD, Joy S, Jambulingam P, Rahi M. First initiative to develop a standard methodology for the evaluation of Attractive Targeted Sugar Baits in different settings against targeted mosquito vectors: a methodological review. Malar J [Internet] BioMed Cent Ltd. 2025;24:323. https://doi.org/10.1186/s12936-025-05545-w . Obeagu EI, Obeagu GU. Emerging public health strategies in malaria control: innovations and implications. Annals of Medicine & Surgery. Ovid Technol (Wolters Kluwer Health). 2024;86:6576–84. https://doi.org/10.1097/ms9.0000000000002578 . Mnzava A, Monroe AC, Okumu F. Anopheles stephensi in Africa requires a more integrated response. Malar J BioMed Cent. 2022;21:4–9. https://doi.org/10.1186/s12936-022-04197-4 . van de Straat B, Russell TL, Staunton KM, Sinka ME, Burkot TR. A global assessment of surveillance methods for dominant malaria vectors. Sci Rep Nat Res. 2021;11. https://doi.org/10.1038/s41598-021-94656-w . Shretta R, Avanceña ALV, Hatefi A. The economics of malaria control and elimination: a systematic review. Malar J. 2016;15:593. https://doi.org/10.1186/s12936-016-1635-5 . Ashton RA, Saili K, Chishya C, Banda Yikona H, Arnzen A, Orange E, et al. Efficacy of attractive targeted sugar bait stations against malaria in Western Province Zambia: epidemiological findings from a two-arm cluster randomized phase III trial. Malar J BioMed Cent Ltd. 2024;23. https://doi.org/10.1186/s12936-024-05175-8 . Zembere K. The potential for attractive toxic sugar baits to complement core malaria interventions strategies: the need for more evidence. Malar J BioMed Cent Ltd. 2024. https://doi.org/10.1186/s12936-024-05161-0 . Steketee RW. Attractive targeted sugar baits series in Malaria Journal. Malar J BioMed Cent Ltd. 2025. https://doi.org/10.1186/s12936-025-05309-6 . WHO. Vector alert: Anopheles stephensi invasion and spread in Africa and Sri Lanka. Https://IrisWhoInt /Bitstream/Handle/10665/365710/9789240067714-EngPdf?Sequence = 1 . 2022. Gray L, Asay BC, Hephaestus B, McCabe R, Pugh G, Markle ED, et al. Back to the Future: Quantifying Wing Wear as a Method to Measure Mosquito Age. Am J Trop Med Hyg. 2022;107:689–700. https://doi.org/10.4269/ajtmh.21-1173 . Milali MP, Sikulu-Lord MT, Kiware SS, Dowell FE, Povinelli RJ, Corliss GF. Do NIR spectra collected from laboratory-reared mosquitoes differ from those collected from wild mosquitoes? PLoS One [Internet]. 2018;13:1–16. https://doi.org/10.1371/journal.pone.0198245 . Foti M, Micocci M, Pazmiño-Betancourth M, Casas Gomez-Uribarri I, Serini P, Caputo B, et al. Towards scalable age-grading of Aedes albopictus mosquito using mid-infrared spectroscopy and machine learning. Sci Rep [Internet]. 2025;15:40470. https://doi.org/10.1038/s41598-025-24404-x . Additional Declarations No competing interests reported. Supplementary Files OnlineSurveyFormSupplementary.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 18 May, 2026 Reviewers agreed at journal 11 May, 2026 Reviews received at journal 17 Mar, 2026 Reviewers agreed at journal 24 Feb, 2026 Reviewers invited by journal 24 Feb, 2026 Editor assigned by journal 09 Jan, 2026 Submission checks completed at journal 09 Jan, 2026 First submitted to journal 09 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8560704","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":597186503,"identity":"b5603ab9-dff6-4962-aa37-383b94343417","order_by":0,"name":"Doreen Josen Siria","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAs0lEQVRIiWNgGAWjYDCCAwdApA2PATOJWtJgWgyI0QImD8PUEqGF7+Dhwx8+7jgvY87OwPi4guGPPUEtkgeOpUnOPHObx7KZgdnwDAMRXjI4cMaMmbftNo/BYQY2yQYGAzZitBh//tt2Dq6FhxgtBtKMbQfgWiSI80tvWzJQC2OzYYOBMeEg47sBDLGfbXb2BucPH3zYUCFHOMQYJA7AWIwNxMUkA38DMapGwSgYBaNgRAMAFrc5NyKUsM4AAAAASUVORK5CYII=","orcid":"","institution":"Ifakara Health Institute","correspondingAuthor":true,"prefix":"","firstName":"Doreen","middleName":"Josen","lastName":"Siria","suffix":""},{"id":597186504,"identity":"3a42fb07-9c4b-439f-8e14-300f4f7e69b4","order_by":1,"name":"Naomi Urio","email":"","orcid":"","institution":"Ifakara Health Institute","correspondingAuthor":false,"prefix":"","firstName":"Naomi","middleName":"","lastName":"Urio","suffix":""},{"id":597186505,"identity":"0ad84dc9-ac53-49ad-9981-bd086429b505","order_by":2,"name":"Dickson Msaky","email":"","orcid":"","institution":"Ifakara Health Institute","correspondingAuthor":false,"prefix":"","firstName":"Dickson","middleName":"","lastName":"Msaky","suffix":""},{"id":597186506,"identity":"731373ce-61c0-4bde-a347-0581f37fb9d1","order_by":3,"name":"Jacqueline Mgaya","email":"","orcid":"","institution":"Ifakara Health Institute","correspondingAuthor":false,"prefix":"","firstName":"Jacqueline","middleName":"","lastName":"Mgaya","suffix":""},{"id":597186507,"identity":"35830e3e-b8de-41f7-bdb0-bfa7f4dbcfa5","order_by":4,"name":"Halfan Ngowo","email":"","orcid":"","institution":"Ifakara Health Institute","correspondingAuthor":false,"prefix":"","firstName":"Halfan","middleName":"","lastName":"Ngowo","suffix":""},{"id":597186508,"identity":"41846d4d-a5c5-4152-b81a-5280ca97edb7","order_by":5,"name":"Issa Mshani","email":"","orcid":"","institution":"Ifakara Health Institute","correspondingAuthor":false,"prefix":"","firstName":"Issa","middleName":"","lastName":"Mshani","suffix":""},{"id":597186509,"identity":"9ec6b714-0ea3-4f67-9a88-3842363347c8","order_by":6,"name":"Emmanuel Mwanga","email":"","orcid":"","institution":"Ifakara Health Institute","correspondingAuthor":false,"prefix":"","firstName":"Emmanuel","middleName":"","lastName":"Mwanga","suffix":""},{"id":597186511,"identity":"f8cb432d-5a22-436e-8c1e-77310315cf19","order_by":7,"name":"Emmanuel Hape","email":"","orcid":"","institution":"Ifakara Health Institute","correspondingAuthor":false,"prefix":"","firstName":"Emmanuel","middleName":"","lastName":"Hape","suffix":""},{"id":597186513,"identity":"6c1e9224-1372-4df4-8506-588dc804b293","order_by":8,"name":"Muanacha A. Mintade","email":"","orcid":"","institution":"Centro de Investigação em Saúde de Manhiça (CISM)","correspondingAuthor":false,"prefix":"","firstName":"Muanacha","middleName":"A.","lastName":"Mintade","suffix":""},{"id":597186516,"identity":"900db4ca-7dd5-4c3b-ab98-99a589501087","order_by":9,"name":"Rukiyah Mohammad","email":"","orcid":"","institution":"Ifakara Health Institute","correspondingAuthor":false,"prefix":"","firstName":"Rukiyah","middleName":"","lastName":"Mohammad","suffix":""},{"id":597186518,"identity":"c3add4d4-4a18-4fc5-aca9-19a79c85d33f","order_by":10,"name":"Yeromin P. Mlacha","email":"","orcid":"","institution":"Ifakara Health Institute","correspondingAuthor":false,"prefix":"","firstName":"Yeromin","middleName":"P.","lastName":"Mlacha","suffix":""},{"id":597186520,"identity":"342b6c85-575d-4dcf-9d58-eef59b8ae76f","order_by":11,"name":"Marceline Finda","email":"","orcid":"","institution":"Ifakara Health Institute","correspondingAuthor":false,"prefix":"","firstName":"Marceline","middleName":"","lastName":"Finda","suffix":""},{"id":597186521,"identity":"b83f4c3d-8bad-4974-83ec-09457c554376","order_by":12,"name":"Roger Sanou","email":"","orcid":"","institution":"Institut de Recherche en Sciences de la Santé-Direction Régionale Ouest (IRSS-DRO) /Centre Muraz","correspondingAuthor":false,"prefix":"","firstName":"Roger","middleName":"","lastName":"Sanou","suffix":""},{"id":597186522,"identity":"48174c60-a753-4b38-82b3-190cbf0ec58c","order_by":13,"name":"Maggy T. Sikulu-Lord","email":"","orcid":"","institution":"One Health Innovations Pty Ltd","correspondingAuthor":false,"prefix":"","firstName":"Maggy","middleName":"T.","lastName":"Sikulu-Lord","suffix":""},{"id":597186524,"identity":"2eb013b9-93ec-4251-b910-04d30667bc2c","order_by":14,"name":"Mercy Opiyo","email":"","orcid":"","institution":"Centro de Investigação em Saúde de Manhiça (CISM)","correspondingAuthor":false,"prefix":"","firstName":"Mercy","middleName":"","lastName":"Opiyo","suffix":""},{"id":597186527,"identity":"b2d5d468-29c5-4d5f-93ac-4c5347137f59","order_by":15,"name":"Abdoulaye Diabate","email":"","orcid":"","institution":"Institut de Recherche en Sciences de la Santé-Direction Régionale Ouest (IRSS-DRO) /Centre Muraz","correspondingAuthor":false,"prefix":"","firstName":"Abdoulaye","middleName":"","lastName":"Diabate","suffix":""},{"id":597186530,"identity":"6db4fd19-b461-4ee3-a4b2-23ed7fdd6cd9","order_by":16,"name":"Heather M. Ferguson","email":"","orcid":"","institution":"University of Glasgow","correspondingAuthor":false,"prefix":"","firstName":"Heather","middleName":"M.","lastName":"Ferguson","suffix":""},{"id":597186531,"identity":"429def16-d9ed-40e4-a109-aee72687bce8","order_by":17,"name":"Francesco Baldini","email":"","orcid":"","institution":"University of Glasgow","correspondingAuthor":false,"prefix":"","firstName":"Francesco","middleName":"","lastName":"Baldini","suffix":""},{"id":597186532,"identity":"785f009e-f4a5-4604-8fe2-1fc588a2f54d","order_by":18,"name":"Fredros Okumu","email":"","orcid":"","institution":"Ifakara Health Institute","correspondingAuthor":false,"prefix":"","firstName":"Fredros","middleName":"","lastName":"Okumu","suffix":""}],"badges":[],"createdAt":"2026-01-09 11:54:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8560704/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8560704/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103490611,"identity":"adb37c0b-64cc-4040-8552-0d4fe6c23e46","added_by":"auto","created_at":"2026-02-26 09:51:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1351726,"visible":true,"origin":"","legend":"\u003cp\u003eMap illustrating the countries represented in the study, along with the corresponding number of stakeholders who responded to the online survey.\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8560704/v1/af3fb082b5f0f6b575ae918e.png"},{"id":103490608,"identity":"292e8405-0702-461d-8a41-edfcbd08b691","added_by":"auto","created_at":"2026-02-26 09:51:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":129053,"visible":true,"origin":"","legend":"\u003cp\u003eThe frequency of different malaria surveillance activities reported by respondents as being used within their organisations. \u003cem\u003eSource: Online survey (2025).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8560704/v1/fa81a96260cc684a21de2ac5.png"},{"id":103507695,"identity":"7207bce7-d4c4-43c6-8f0a-095f0965c9c4","added_by":"auto","created_at":"2026-02-26 13:43:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":130482,"visible":true,"origin":"","legend":"\u003cp\u003eThe frequency of different intervention activities implemented during malaria surveillance.\u003cem\u003e Source: Online survey (2025).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8560704/v1/64f6ece0449d0bcb528e5c61.png"},{"id":103508125,"identity":"11309e1a-7b69-4fe7-b1ff-08e937e656d2","added_by":"auto","created_at":"2026-02-26 13:47:17","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":333985,"visible":true,"origin":"","legend":"\u003cp\u003ePriority ranking of entomological indicators used in malaria vector surveillance among 285 stakeholders from 18 African countries. Bars represent the proportion of respondents rating each indicator as high, intermediate, or low priority. Data are sorted by the percentage of respondents assigning high priority.\u003cem\u003e Source: Online survey (2025).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8560704/v1/503baf8a4c7f55a4195bfd85.png"},{"id":103490609,"identity":"a8f78f02-4c1d-462a-a29d-4de26b43fa41","added_by":"auto","created_at":"2026-02-26 09:51:40","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":157857,"visible":true,"origin":"","legend":"\u003cp\u003eThe proportions of familiarity with the age-grading methods. \u003cem\u003eSource: Online survey (2025).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-8560704/v1/21591ef35030310848c7d5d8.png"},{"id":103508279,"identity":"3c263a48-0644-4420-94e4-9701dcdf8790","added_by":"auto","created_at":"2026-02-26 13:48:03","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":491038,"visible":true,"origin":"","legend":"\u003cp\u003eThe percentage of the respondents on different challenges and gaps involved in age-grading methods (A) can be implemented at a large scale, (B) can be taught to a non-expert, (C) are the methods cost-effective, and (D) are the methods accurate and precise. \u003cem\u003eSource: Online survey (2025).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-8560704/v1/d282bdb25d552a81005c2860.png"},{"id":103510110,"identity":"4cf965bc-320f-4fc0-8381-337cc6c541d6","added_by":"auto","created_at":"2026-02-26 14:04:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3174737,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8560704/v1/31b27760-e6ee-4bb6-af99-8760e2cd5057.pdf"},{"id":103507474,"identity":"86c1e472-2601-4c05-9f70-67e683df7562","added_by":"auto","created_at":"2026-02-26 13:41:29","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":28945,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineSurveyFormSupplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-8560704/v1/14abb520085aadc898df184e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Mosquito age-grading for malaria vector surveillance in sub-Saharan Africa: current practices and constraints","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSince 2000, malaria control interventions have averted 2.3\u0026nbsp;billion cases and 12.7\u0026nbsp;million deaths, yet the disease still causes an estimated 282\u0026nbsp;million cases and over 600,000 deaths (75% in children under 5) in 2024 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Over the past two decades, insecticide-treated nets (ITNs), indoor residual spraying (IRS), and other vector control tools have been primarily responsible for reducing malaria transmission [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. According to the latest WHO Malaria Vector Control Guidelines, surveillance (~\u0026thinsp;whether entomological or epidemiological) is now recognized as a core intervention against malaria control, on par with prevention and treatment strategies [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Effective vector surveillance provides essential data for decision-making, enabling malaria control programs to optimize the deployment of these interventions. It can help monitor changes in mosquito populations, detect insecticide resistance, and assess the effectiveness of control interventions [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo support malaria control and elimination efforts, national programs should establish a robust evidence base on the ecology, transmission potential and behaviour of mosquito vectors and their transmission potential. The World Health Organization\u0026rsquo;s Global Technical Strategy (WHO-GTS) for Malaria 2016\u0026ndash;2030 designated surveillance as a core pillar of malaria control, with a recommendation of routine monitoring in all transmission-prone settings [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In line with this, the WHO manual for entomological surveillance in malaria emphasizes a set of priority indicators for programmatic decision-making, including vector species composition, density, infection rates, biting and resting behaviours, insecticide resistance status, and mosquito survival rates [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. These metrics are parameters that are actually used to assess effectiveness of interventions and therefore correspond closely with the core entomological indicators typically monitored in national programs. However, many national programs face significant challenges in fully implementing the vector surveillance recommendations [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Limitations such as weak strategic frameworks, inadequate logistics, insufficient human resources, and financial constraints often hinder comprehensive surveillance [\u003cspan additionalcitationids=\"CR10 CR11 CR12\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This may result in surveillance relying on a single sampling tool, which can affect data reliability and interpretations, given that different methods come with their own inherent biases and limitations[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe intensity of malaria transmission depends on several mosquito population traits, including population size, human biting rate, gonotrophic cycle length, and adult female survival [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The survival of adult females has a particularly large impact because the ability of \u003cem\u003eAnopheles\u003c/em\u003e mosquitoes to transmit is age dependent. The primary African malaria parasite \u003cem\u003ePlasmodium falciparum\u003c/em\u003e requires 11 to 14 days to develop in mosquito vectors before transmission can occur [\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Since this development period often matches or exceeds the median survival of \u003cem\u003eAnopheles\u003c/em\u003e vector populations, only a small proportion of adult females live long enough to transmit the parasite [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Consequently, even modest reductions in adult survival can markedly reduce transmission [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. For effective malaria control, it is therefore important to monitor how key vector control interventions impact adult mosquito survival and their age structure.\u003c/p\u003e \u003cp\u003eThe age of adult \u003cem\u003eAnopheles\u003c/em\u003e mosquitoes can be expressed chronologically (days since emergence) or physiologically, as is most frequently assessed by changes in the reproductive history of females that occur across their lifespan. These are typically measured based on whether eggs have been laid (nulliparous or parous) and the number of gonotrophic cycles completed, defined as the period from blood feeding to oviposition[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Because robust methods for directly estimating chronological age are lacking, traditional age-grading approaches instead rely on assessing physiological age, inferred from morphological changes in the reproductive systems of adult female mosquitoes [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. These methods include identifying whether a female mosquito has previously laid eggs (parous) or not (nulliparous), which can be determined by dissecting and examining the ovarian tracheoles using the Detinova method [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Another approach, the Polovodova method, estimates physiological age based on the number of gonotrophic cycles a female has completed as estimated from the number of ovarian dilatations observed [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Both techniques are considered standard methods for age-grading malaria vectors [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. However, the adoption and application of these age-grading methods into vector surveillance vary widely, and often they are performed on only a limited basis (e.g., on only a subset of mosquitoes). This is largely due to these methods being labour-intensive and in the case of the Polovodova method, requiring significant technical expertise that is not widely accessible due to limited training opportunities [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA survey by Burkot et al. (2019) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] reported that only 25% of National Malaria Control Programs (NMCPs) in African and Asian countries routinely conduct parity dissections to estimate mosquito survival [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This points to a significant gap in the operational use of mosquito survival-related data, despite its relevance for measuring transmission intensity and evaluating the impact of interventions. Several factors could explain this low uptake, including poor awareness of the importance of mosquito age-structure, limited access to training or dissection skills, inadequate infrastructure, and uncertainty around how to interpret or apply survival estimates in programmatic decision making [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. While the Burkot study quantified the incorporation of parity dissections within NMCP surveillance, it did not explore the underlying reasons for low uptake.\u003c/p\u003e \u003cp\u003eIn addition to existing dissection-based methods, there is growing interest in the potential of alternative age grading technologies approaches which are less labour-intensive and potentially more accurate [\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Understanding how end-users perceive and engage with these emerging methods is critical for informing their future integration into malaria surveillance. Several new methods for estimating the age of \u003cem\u003eAnopheles\u003c/em\u003e mosquitoes are under development, including those that detect signals of aging based on chromatographic analysis of cuticular hydrocarbons [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], transcriptomic profiling [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], and infrared spectroscopy [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], and more recently, mass spectrometry techniques such as Matrix-Assisted Laser Desorption/Ionization\u0026ndash;Time of Flight Mass Spectrometry (MALDI-TOF MS) [\u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The spectroscopy-based techniques have the greatest potential when combined with machine learning (ML), advanced methods to assist with complex analysis of cuticular spectral patterns to predict mosquito age categories [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. However, despite the potential of these novel methods, they are currently not widely adopted for routine vector surveillance. Understanding how potential end-users perceive the value of both these emerging and traditional methods (Detinova and Polovodova) will be useful to overcome key barriers that limit their use, and guide strategies that promote their uptake and integration into malaria surveillance programs.\u003c/p\u003e \u003cp\u003eThe aim of this study was to assess the current practices and challenges of mosquito age-grading for malaria vector surveillance in Africa, as well as the insights and perspectives of key stakeholders including national malaria control programs (NMCPs), entomologists, and researchers from public and private institutions regarding the feasibility of different age-grading methods. NMCPs were targeted because they lead routine monitoring and programmatic decision-making in vector surveillance, while researchers often generate evidence of intervention impact, build capacity, or implement vector control activities. Specifically, the study focused on: (i) the extent to which mosquito age-grading is conducted within surveillance programs and reasons for non-implementation, (ii) stakeholder knowledge, skills, and familiarity with traditional and emerging age-grading methods, and (iii) operational and technical barriers limiting routine adoption. By providing evidence on stakeholder perceptions and priorities, this work aims to inform strategies that promote training on existing methods and facilitate the uptake of innovative age-grading tools, thereby enhancing the overall value of entomological surveillance for guiding malaria control and predicting transmission dynamics.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis study employed a mixed methods design, combining an online survey, semi-structured interviews, and a scoping review to guide data collection. The aim was to capture perspectives from malaria vector surveillance practitioners in African research institutions and national malaria control programmes, and to identify current practices, priorities, and barriers to mosquito age-grading.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eScoping Review to Guide Data Collection\u003c/h2\u003e \u003cp\u003eFor the development of the online survey and interview questions, a narrative review of existing literature review and targeted scoping analysis was conducted. Relevant studies on mosquito age-grading methods, particularly the Detinova and Polovodova dissection-based techniques were identified through searches of PubMed, Google Scholar, and institutional resources, including the University of Glasgow\u0026rsquo;s Library search platform [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Keywords included \u0026ldquo;mosquito age-grading\u0026rdquo;, \u0026ldquo;parity dissections\u0026rdquo;, \u0026ldquo;Detinova method\u0026rdquo;, \u0026ldquo;Polovodova method\u0026rdquo;, \u0026ldquo;gonotrophic cycle\u0026rdquo;, \u0026ldquo;vector survival\u0026rdquo;, and \u0026ldquo;entomological indicators.\u0026rdquo; Articles were selected based on relevance to mosquito age-grading and its application in surveillance, without formal inclusion or exclusion criteria.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eOnline Survey\u003c/h3\u003e\n\u003cp\u003eThe online survey targeted individuals and stakeholders involved in malaria surveillance and vector control programs in Africa. This included vector control leads operating within National Malaria Programs (NMPs) which included the (NMCP and National malaria elimination program, NMEP), as well as research scientists, entomologists affiliated with public health institutions, non-governmental organizations (NGOs), and academic institutions. Participants were selected based on current engagement in entomological surveillance or relevant expertise, ensuring informed perspectives on the importance, use, and challenges of mosquito age-grading.\u003c/p\u003e \u003cp\u003eThe survey link was distributed from July 2023 to December 2024 via social media platforms (X/Twitter, LinkedIn), institutional focal points, and the Pan-African Mosquito Control Association (PAMCA) network [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. A QR code was shared at scientific meetings, including the 9th PAMCA conference (Addis Ababa, Ethiopia, 2023) and at the 29th American Society of Tropical Medicine and Hygiene (ASTMH) meeting (New Orleans, USA, 2024) [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. A QR code for the survey was also shared with NMPs and other researchers and facilitators who attended the 8th Tanzania Vector Control Technical Working Group Meeting (December 2024). Additional outreach was conducted through personalized invitations to maximize participation. The survey was hosted on Microsoft Forms and is available on supplementary materials.\u003c/p\u003e \u003cp\u003eThe questionnaire comprised six sections (supplementary online material 1): (1) respondent demographics (age, education, job title); (2) frequency of mosquito surveillance activities and associated interventions, with frequency categories defined as frequent (~\u0026thinsp;weekly), occasional (~\u0026thinsp;monthly), rare (~\u0026thinsp;yearly), or never; (3) vector species surveyed and priority entomological indicators; (4) awareness, perceptions, and perceived importance of age-grading; (5) awareness and perspectives on other age-grading techniques such as the use of infrared (IR) spectroscopy-based; and (6) challenges and gaps affecting the use of age-grading techniques in vector surveillance.\u003c/p\u003e \u003cp\u003eA total of 285 stakeholders from 18 African countries completed the survey (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eIn-depth interviews (IDIs) and Focus Group Discussions (FGDs)\u003c/h3\u003e\n\u003cp\u003eTo complement the findings from the online stakeholder survey, additional qualitative data were collected by conducting 23 in-depth interviews (IDIs) and two (2) focus group discussions (FGDs). These follow-up methods were designed to gather deeper insights into stakeholder perceptions of mosquito age-grading techniques. A semi-structured discussion guide was used to explore participants\u0026rsquo; roles and experience in malaria vector surveillance; current surveillance tools used and associated challenges; as well as general ideas on potential future improvements. Before each IDI or FGD, participants received a short presentation on age-grading methods to refresh their knowledge on the subject and ensure a shared understanding of the basics. Most participants in the IDIs and FGDs had completed the online survey beforehand, contributing to informed and interactive discussions.\u003c/p\u003e \u003cp\u003eAll interviews and discussions were conducted in person and included stakeholders from NMPs, research institutions, and vector surveillance units. The FGDs were held separately with NMPs representatives from mainland Tanzania and Zanzibar. These qualitative methods provided context to the survey results, offering additional perspectives on current surveillance and intervention approaches, the perceived value of age-grading and age-structure data, familiarity with existing methods, and challenges associated with traditional techniques.\u003c/p\u003e \u003cp\u003e In all sessions, we sought participant consent and audio-recorded the discussions before transcribing and analysing them thematically.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eQuantitative survey data were analysed using R version 4.2.2 [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], applying descriptive and univariate statistical methods. Demographic variables (sex, age group, and education level) were summarised to characterise the respondents. Further analysis focused on assessing the frequencies of surveillance and intervention types, reported prioritization of various entomological indicators, and familiarity and use of different age-grading methods. Categorical and Likert-scale responses were converted to ordered factors, with proportions and summary statistics calculated by \u0026ldquo;category\u0026rdquo; (e.g., intervention type, indicator). Visualisations included customised Likert plots and stacked bar charts.\u003c/p\u003e \u003cp\u003e All audio recordings from the interviews and discussions were transcribed, then translated from local language (Swahili) to English when needed. Qualitative data from in-depth interviews, focus group discussions, and open-ended survey responses were analysed using NVivo 14.24.3 software [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Translated transcripts were reviewed iteratively to ensure familiarity, followed by systematic coding to identify meaningful units of information. Codes were linked to discussion guide sections and then organised into broader thematic categories based on recurring stakeholder narratives. To enhance consistency, findings were compared across data sources to identify areas of convergence and divergence. Representative quotes were selected to illustrate perspectives and support themes.\u003c/p\u003e \u003cp\u003eTriangulation was employed by integrating insights from the online survey, interviews, and FGDs to validate findings, enhance analytical depth, and provide a more comprehensive understanding of stakeholder views on mosquito age-grading methods.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDesign of survey and interviews\u003c/h2\u003e \u003cp\u003eThe literature review suggested that traditional age-grading are technically complex, require specialized training and are rarely prioritized in routine surveillance [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Also, emerging age-grading methods such as NIRS [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], MIRS [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] and matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] could provide high-throughput age estimation but are not widely adopted in NMCPs. These observations informed the survey and interview questions, focusing on stakeholder familiarity, practical challenges, and feasibility of traditional and emerging age-grading methods.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCharacteristics of online survey study participants\u003c/h3\u003e\n\u003cp\u003eThe study participants were diverse, encompassing various key stakeholders engaged in malaria vector surveillance across Africa. A total of 285 respondents took part in the survey, comprising 208 males (73%) and 77 females (27%). Most respondents had either a master\u0026rsquo;s degree (42%) or a bachelor\u0026rsquo;s degree (32%), with ~\u0026thinsp;20% having doctoral-level education. Slightly more than half (55%) of participants were between 20\u0026ndash;35 years old. In terms of stakeholder types and professional roles, respondents included representatives from NMCPs (14%), entomologists (17%), and researchers from academic, public, and private institutions (69%). The majority were employed either in national malaria programs, academic institutions, or research institutions, or both, with most of them having received training in entomology or related courses (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Geographically, most stakeholders were from Tanzania, Kenya, Uganda, Ghana, Cameroon, and Nigeria (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of online survey participants (n\u0026thinsp;=\u0026thinsp;285).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of participants (285)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e208 (73%)\u003c/p\u003e \u003cp\u003e77 (27%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge-group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026ndash;35\u003c/p\u003e \u003cp\u003e36\u0026ndash;45\u003c/p\u003e \u003cp\u003e46\u0026ndash;55\u003c/p\u003e \u003cp\u003e56\u0026ndash;65\u003c/p\u003e \u003cp\u003eAbove 65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e157 (55.1%)\u003c/p\u003e \u003cp\u003e84 (29.5%)\u003c/p\u003e \u003cp\u003e38 (13.3%)\u003c/p\u003e \u003cp\u003e4 (1.4%)\u003c/p\u003e \u003cp\u003e2 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level attained\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBachelor (3 years post high school)\u003c/p\u003e \u003cp\u003eMasters (4\u0026ndash;5 years post high school)\u003c/p\u003e \u003cp\u003ePhD (7\u0026ndash;9 years post high school)\u003c/p\u003e \u003cp\u003eOther forms of collage training\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92 (32.3%)\u003c/p\u003e \u003cp\u003e119 (41.8%)\u003c/p\u003e \u003cp\u003e57 (20%)\u003c/p\u003e \u003cp\u003e17(5.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of stakeholders\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNMCPs\u003c/p\u003e \u003cp\u003eEntomologists\u003c/p\u003e \u003cp\u003eResearchers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (14%)\u003c/p\u003e \u003cp\u003e49 (17%)\u003c/p\u003e \u003cp\u003e197 (69%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eFrequency of different surveillance activities\u003c/h3\u003e\n\u003cp\u003eRespondents reported on the frequency of various malaria-related surveillance activities in their countries or institutions, including collection of entomological measures (e.g., adult mosquito density, larval surveys, insecticide resistance bioassays) and epidemiological measures (e.g., malaria prevalence in humans). Surveillance of adult mosquitoes was the most reported activity (87%), followed by larval surveillance (76%) and malaria infection monitoring in people (74%). Insecticide resistance testing was also frequently conducted (reported by 73% of respondents; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDuring FGDs and IDIs with stakeholders, respondents reported that malaria surveillance in their settings primarily relies on both vector and parasite surveillance. For vector surveillance, activities included entomological monitoring at the district level to track vector abundance, sporozoite rates, and entomological inoculation rates, as required to provide evidence for assessing the impact of interventions. National personnel also conducted insecticide resistance monitoring across multiple sites to ensure the continued effectiveness of WHO-recommended insecticides. For epidemiological surveillance, respondents described periodic malaria parasitological indicator surveys to evaluate disease burden and prevalence. They also highlighted programmatic surveillance, which involves tracking the use and performance of malaria commodities, such as monitoring the efficacy of antimalarial drugs and assessing the durability of bed nets post-distribution. Together, these surveillance activities form the evidence base for malaria control decision-making. As highlighted by one national malaria program respondent below, stakeholders frequently described surveillance as encompassing both entomological and epidemiological components, aimed at tracking transmission and evaluating the effectiveness of interventions:\u003c/p\u003e \u003cp\u003e \u003cem\u003eR1: \u0026ldquo;Our malaria surveillance relies on two main types: vector and parasite surveillance, monitoring mosquito density, infection, and resistance to insecticides, as well as tracking malaria prevalence through surveys and facility data. We also assess the performance of interventions like bednets and antimalarial drugs to ensure their continued effectiveness.\u0026rdquo;\u003c/em\u003e (NMP, male)\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eFrequency of intervention activities implemented in different countries\u003c/h2\u003e \u003cp\u003eRespondents from the online survey were asked about their involvement in implementing or testing specific vector control methods. ITNs and IRS were the most frequently implemented interventions, reported by 67% and 66% of respondents, respectively. Larval source management (LSM); mostly consisting of larviciding was reported by 53% of respondents. Attractive Toxic Sugar Bait (ATSB) was the least frequently mentioned intervention. This is unsurprising because, unlike the other interventions considered, this intervention is still undergoing evaluation (with ongoing cluster randomized control trials in some countries) [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] and does not yet have a WHO recommendation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSimilarly, the stakeholders who participated in FGDs and IDIs were involved in a range of malaria control interventions, primarily focused on vector control and malaria elimination. In FGDs and IDIs, the most widely implemented intervention was the distribution and monitoring of ITNs, often delivered through mass campaigns or continuous distribution systems. Stakeholders were also engaged in efforts to improve malaria diagnosis through rapid diagnostic tests (RDTs) and microscopy, as well as ensuring the availability and appropriate use of antimalarial drugs. While a few participants mentioned past involvement in IRS, it was noted that this intervention is no longer routinely implemented in Tanzania (R2). Additionally, some respondents referred to experience with LSM in specific ecological settings (R3).\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003eR2: \u0026ldquo;In our district, most of the work revolves around distributing and monitoring the use of bednets. We used to conduct IRS some years ago, but that stopped due to changes in the national policy and funding. Nowadays, we focus on tracking net coverage, educating communities, and collecting mosquito data to inform other strategies.\u0026rdquo;\u003c/em\u003e (NMP, male)\u003c/p\u003e\u003cp\u003e \u003cem\u003eR3: \u0026ldquo;In Zanzibar, we have shifted focus more toward integrated vector management. LLINs remain our backbone, but we also conduct larval source management in selected urban areas. IRS was part of our strategy in the past, but it\u0026rsquo;s no longer active due to funding redirection. Our work now includes routine monitoring and adjusting based on entomological findings.\u0026rdquo;\u003c/em\u003e (NMP, female)\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMotivations for conducting mosquito surveillance\u003c/h2\u003e \u003cp\u003eStakeholders from the online survey reported several motivations for conducting mosquito surveillance. The most frequently cited reason was to better understand mosquito ecology, including species composition, spatial distribution, population density, and behavioural patterns (n\u0026thinsp;=\u0026thinsp;210, \u0026gt;\u0026thinsp;70% of respondents). Other commonly stated reasons for conducting vector surveillance (cited by n\u0026thinsp;=\u0026thinsp;185, 65% of respondents) included evaluating malaria transmission and the effectiveness of vector control measures such as ITNs and IRS, and monitoring insecticide resistance to guide adjustments in control strategies (cited by n\u0026thinsp;=\u0026thinsp;140, 49% of respondents). Moderate-frequency reasons given for entomological surveillance (cited by n\u0026thinsp;=\u0026thinsp;120, 42% of respondents) included generating evidence to inform new public health policy, planning, resource allocation, and strengthening vector control capacity through academic research, training, and skill development. The least cited motivation for conducting surveillance (n\u0026thinsp;=\u0026thinsp;45, 16% of the respondents) was for early warning and outbreak detection, and to support timely intervention (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStakeholders\u0026rsquo; reasons and perspectives in conducting mosquito surveillance.\u003c/p\u003e \u003cdiv class=\"Credit\"\u003e\u003cp\u003e\u003cem\u003eSource: Online survey (2025)\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOpinions/Reasons\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStakeholder Responses\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1. Understanding mosquito ecology and population dynamics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eR4: \u0026ldquo;Monitoring mosquito density and seasonality helps us target control efforts effectively.\u0026rdquo;\u003c/em\u003e (Entomologist, Male)\u003c/p\u003e \u003cp\u003e\u003cem\u003eR5: \u0026ldquo;Knowing which mosquito species are present and how they behave informs our control strategies.\u0026rdquo;\u003c/em\u003e (NMP, Female)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2. Disease transmission monitoring and intervention evaluation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eR6: \u0026ldquo;To assess the impact of vector control interventions and measure malaria infection rates.\u0026rdquo;\u003c/em\u003e (Researcher, Female)\u003c/p\u003e \u003cp\u003e\u003cem\u003eR7: \u0026ldquo;We conduct surveillance primarily to reduce malaria cases by tracking vector populations and evaluating control tools.\u0026rdquo;\u003c/em\u003e (Senior Research Officer, Male)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3. Insecticide resistance monitoring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eR8: \u0026ldquo;To monitor insecticide resistance status of local malaria vector populations.\u0026rdquo;\u003c/em\u003e (Entomologist, Male)\u003c/p\u003e \u003cp\u003e\u003cem\u003eR9: \u0026ldquo;Insecticide resistance monitoring is critical to guide which insecticides to use in malaria control programs.\u0026rdquo;\u003c/em\u003e (Researcher, Female)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4. Supporting program planning, policy, and capacity building\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eR10: \u0026ldquo;To generate vector data for evidence-based decision-making and support national malaria control programs.\u0026rdquo;\u003c/em\u003e (Researcher, Male)\u003c/p\u003e \u003cp\u003e\u003cem\u003eR11: \u0026ldquo;Our data informs policy decisions on insecticide selection and vector control strategies.\u0026rdquo;\u003c/em\u003e (NMP, Female)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5. Early warning and outbreak detection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eR12: \u0026ldquo;Early detection through vector surveillance is key to preventing disease spread.\u0026rdquo;\u003c/em\u003e (Researcher, Female)\u003c/p\u003e \u003cp\u003e\u003cem\u003eR13: \u0026ldquo;We monitor malaria transmission trends to identify hotspots and intervene before outbreaks occur.\u0026rdquo;\u003c/em\u003e (NMP, Male)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFurthermore, stakeholders and participants from FGDs and IDIs echoed and emphasized that mosquito surveillance is a critical foundation for informed malaria control decision-making. Many highlighted that surveillance enables early detection of changes in mosquito population dynamics, such as species composition, density, and biting behaviour, which can influence the effectiveness of interventions. Others noted that entomological data can guide the deployment of vector control tools like ITNs (e.g., R14) and support monitoring of insecticide resistance trends. Several stakeholders also pointed out that routine mosquito surveillance helps assess the impact of interventions over time and ensures that national programs can respond adaptively to emerging threats, such as behavioural resistance or shifts in peak biting times. Overall, there was consensus that without regular entomological surveillance, malaria programs risk becoming reactive rather than proactive.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eR14\u003c/strong\u003e \u003cp\u003e\u0026ldquo;\u003cem\u003eMosquito surveillance is the eye of the vector control program. Without understanding where and how the mosquitoes are changing, we\u0026rsquo;re just guessing with our interventions.\u0026rdquo;\u003c/em\u003e (Researcher, male)\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePriority entomological indicators and vector species targeted\u003c/h2\u003e \u003cp\u003eMost respondents from the online survey stated that their vector surveillance programs are mostly focused on the surveillance of primary African malaria vectors: \u003cem\u003eAnopheles gambiae complex\u003c/em\u003e (94%), and \u003cem\u003eAnopheles funestus\u003c/em\u003e group (68.1%). Surveillance for the invasive \u003cem\u003eAnopheles stephensi\u003c/em\u003e was reported by 39.3% of stakeholders, while 33% also monitored \u003cem\u003eAedes\u003c/em\u003e vectors responsible for transmission of arboviruses such as dengue fever, chikungunya, and yellow fever [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. When asked about priority entomological indicators, vector species identification (78%), mosquito density (69%), and insecticide resistance (70%) ranked highest. Monitoring indoor human biting rate (60%) and sporozoite rates (57%) were also commonly reported (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In contrast, age-grading, by ovary dissection methods, was less frequently prioritized (Detinova method: 32%; Polovodova method: 21%), suggesting these techniques are of lower operational priority or face practical implementation challenges, (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eParticipants from the FGDs and IDIs similarly reported that their entomological surveillance efforts primarily targeted mosquitoes from the \u003cem\u003eAnopheles gambiae\u003c/em\u003e complex and \u003cem\u003eAnopheles funestus\u003c/em\u003e groups. Additionally, when asked about which entomological indicators were prioritized during surveillance in FGDs and IDIs, stakeholders consistently highlighted vector density, biting behaviour (indoor/outdoor), sporozoite infection rates, and insecticide resistance status as the most routinely monitored parameters (R15). These indicators were described as crucial for evaluating the effectiveness of vector control intervention and for guiding timely programmatic decisions. In contrast, mosquito age-grading or mosquito survivorship were rarely mentioned as being prioritized; with reasons cited primarily being limited technical capacity and resources, as well as a perception that such data were more relevant to research than to routine program operations (R16, R17). However, a few stakeholders acknowledged that integrating age-structure data could enhance the understanding of residual transmission risks, especially in settings with high insecticide-treated bed net coverage, yet persistent malaria remains.\u003c/p\u003e \u003cp\u003e \u003cem\u003eR15: \u0026ldquo;Our focus is mainly on vector density and biting time whether it\u0026rsquo;s indoors or outdoors. These help us adjust our interventions, especially where ITNs aren\u0026rsquo;t working well anymore.\u0026rdquo;\u003c/em\u003e (Entomologist, male)\u003c/p\u003e \u003cp\u003e \u003cem\u003eR16: \u0026ldquo;We routinely test for sporozoites and insecticide resistance, but we do not typically assess mosquito age. That\u0026rsquo;s usually left to researchers or bigger projects.\u0026rdquo;\u003c/em\u003e \u003c/p\u003e \u003cp\u003e(NMP, female)\u003c/p\u003e \u003cp\u003e \u003cem\u003eR17: \u0026ldquo;Most of our data is on mosquito numbers and resistance. Age-grading is interesting, but we don\u0026rsquo;t have the equipment or training for that.\u0026rdquo;\u003c/em\u003e (NMP, male)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eFamiliarity with mosquito age-grading methods\u003c/h2\u003e \u003cp\u003eRespondents from the online survey were asked about their familiarity with existing age-grading methods, such as the Polovodova and Detinova techniques, wing fray analysis, as well as newer approaches such as near-infrared (NIRS) and mid-infrared (MIRS) spectroscopy. Just over half (55%) reported familiarity with the Detinova method, and 44% with the Polovodova method. Familiarity with the other age-grading methods, NIRS, MIRS, and wing fray analysis, was much lower, ranging from 18% to 22% (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDuring FGDs and IDIs, participants expressed varying degrees of familiarity with different mosquito age-grading methods. Traditional techniques such as the Detinova and Polovodova methods were mostly known among research institutions and some national program staff/personnel. However, many other personnel and stakeholders had limited exposure to these methods, often viewing age-grading as a specialized research activity rather than a routine component of vector surveillance (R18-19). The awareness of newer and innovative technologies, such as NIRS and MIRS, was even more limited. While some participants engaged in entomological research or advanced surveillance programs were aware of these innovative methods and recognized their potential for rapid and accurate mosquito age estimation, others had little or no knowledge or experience with them (R20-21). This gap was due to a lack of training, limited access to specialized equipment, and low integration of these methods into routine surveillance activities (R22). Consequently, despite the promise of these new approaches, they remain largely unfamiliar and underutilized across many malaria control programs.\u003c/p\u003e \u003cp\u003e \u003cem\u003eR18: \u0026ldquo;I\u0026rsquo;ve seen ovary dissections done during training years ago, but it\u0026rsquo;s not something we use in our day-to-day work.\u0026rdquo;\u003c/em\u003e (NMP, male)\u003c/p\u003e \u003cp\u003e \u003cem\u003eR19: \u0026ldquo;Some of us know about parity dissections, but very few people are still practicing it actively. It\u0026rsquo;s more of a research thing.\u0026rdquo;\u003c/em\u003e (NMP, female)\u003c/p\u003e \u003cp\u003e \u003cem\u003eR20: \u0026ldquo;I\u0026rsquo;ve heard about infrared methods, but we\u0026rsquo;ve never used them here. They sound advanced, maybe too advanced for our current setup.\u0026rdquo;\u003c/em\u003e (NMP, male)\u003c/p\u003e \u003cp\u003e \u003cem\u003eR21: \u0026ldquo;To be honest, I wasn\u0026rsquo;t aware of NIRS or MIRS until this discussion. We\u0026rsquo;ve never had training on them or seen them in action.\u0026rdquo;\u003c/em\u003e (NMP, female)\u003c/p\u003e \u003cp\u003e \u003cem\u003eR22: \u0026ldquo;These new technologies are promising, but access is a major issue. We don\u0026rsquo;t have the machines or know how to use them.\u0026rdquo;\u003c/em\u003e (NMP, male)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eStakeholder perceptions and the use of existing methods for mosquito age-grading in surveillance\u003c/h2\u003e \u003cp\u003eHalf of the online survey respondents reported conducting mosquito age-grading as part of their surveillance activities, most commonly using the Detinova (mosquito parity) or Polovodova (gonotrophic cycle count) methods. In contrast, 39% stated they did not perform age-grading, and 11% were unsure whether such methods were applied within their programs or institutions. This question was designed to assess stakeholder awareness and overall use of traditional age-grading techniques as part of entomological surveillance.\u003c/p\u003e \u003cp\u003eOverall, 82% of respondents agreed that estimating mosquito age or survival is an important indicator in vector surveillance. Open-ended responses from the online survey provided further insights into why stakeholders considered mosquito parity valuable, with thematic analysis revealing three main reasons: (1) assessing malaria transmission risk and supporting public health implications, (2) monitoring and evaluating vector control interventions, and lastly, (3) for research and laboratory applications. These findings illustrate a broad recognition of the importance of age-grading, even where routine practice is inconsistent. Below, we present selected quotes from open text boxes that highlight perceptions of each of these thematic areas.\u003c/p\u003e \u003cp\u003e \u003cem\u003eTheme 1: Assessing malaria transmission risk and public health implications (n\u0026thinsp;=\u0026thinsp;212, 70%)\u003c/em\u003e: Majority of the stakeholders highlighted the importance of mosquito age in understanding malaria transmission potential and guiding control strategies. Older mosquitoes are more likely to transmit disease; thus, age data helps estimate transmission risk, target interventions, and assess the urgency of responses.\u003c/p\u003e \u003cp\u003e \u003cem\u003eR23: \u0026ldquo;The age of the mosquito population is a key determinant of pathogen transmission because only older mosquitoes can transmit malaria.\u0026rdquo;\u003c/em\u003e (Researcher, Male)\u003c/p\u003e \u003cp\u003e \u003cem\u003eR24: \u0026ldquo;Age-grading helps estimate the proportion of mosquitoes that are infectious, which is vital for malaria risk modelling.\u0026rdquo;\u003c/em\u003e (NMP, Female)\u003c/p\u003e \u003cp\u003e \u003cem\u003eR25: \u0026ldquo;Without knowing the age structure, we can\u0026rsquo;t accurately predict malaria transmission hotspots.\u0026rdquo;\u003c/em\u003e (Entomologist, Male)\u003c/p\u003e \u003cp\u003e \u003cem\u003eTheme 2: \u0026ldquo;Monitoring and evaluating vector control interventions (~\u0026thinsp;180 to 150 respondents (approx. 50\u0026ndash;60%)\u003c/em\u003e: Stakeholders emphasized using age-grading data in assessing the effectiveness of different vector control interventions, including ITNs and IRS, and to monitor insecticide resistance and mosquito population dynamics.\u003c/p\u003e \u003cp\u003e \u003cem\u003eR26 \u0026ldquo;Without age-grading, our understanding of vector ecology is incomplete. Monitoring mosquito survival after exposure to insecticides helps determine resistance and the effectiveness of control measures.\u0026rdquo;\u003c/em\u003e (Entomologist, Female)\u003c/p\u003e \u003cp\u003e \u003cem\u003eR27: \u0026ldquo;If interventions are effective, the mosquito population should be dominated by younger age classes. When older, resistant mosquitoes are still around after ITNs distribution, that\u0026rsquo;s a red flag for program managers\u0026rdquo;\u003c/em\u003e (Researcher, Male)\u003c/p\u003e \u003cp\u003e \u003cem\u003eR28 \u0026ldquo;Knowing mosquito age helps us understand population dynamics and environmental factors affecting vector abundance.\u0026rdquo;\u003c/em\u003e (NMP, Male)\u003c/p\u003e \u003cp\u003e \u003cem\u003eTheme 3: Research and laboratory applications (n\u0026thinsp;=\u0026thinsp;80, 27%)\u003c/em\u003e: Stakeholders added that in conducting or understanding mosquito ages, the age-grading data support different laboratory studies such as vector competence assays, insecticide susceptibility tests, and epidemiological research, as age influences infection status, vector competence, and physiological traits.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003eR29: \u0026ldquo;Collecting age data is important for lab assays, as mosquito age affects infection status and resistance profiling.\u0026rdquo;\u003c/em\u003e (Researcher, Female)\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eR30: \u0026ldquo;When studying parasite development in mosquitoes, knowing the age is essential for interpreting results.\u0026rdquo;\u003c/em\u003e (Researcher, Male)\u003c/p\u003e \u003cp\u003e \u003cem\u003eR31: \u0026ldquo;We can\u0026rsquo;t run accurate transmission experiments without accounting for mosquito age. Lab data without age information is like reading a book with missing pages, you miss the context\u0026rdquo;\u003c/em\u003e (Researcher, Female)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eExisting and current age-grading methods\u003c/h2\u003e \u003cp\u003eRespondents from the online survey were asked which methods their institutions or projects currently use to estimate mosquito age. While the previous section captured general awareness and perceived value of mosquito age-grading, this question explored the specific techniques currently being applied by the stakeholders. The most commonly reported technique being used was parity rate assessment through the Detinova method (65.9%), followed by the Polovodova method (41.1%). Less frequently reported were wing fray analysis (12.6%), near-infrared spectroscopy (NIRS, 13%), and mid-infrared spectroscopy (MIRS, 11%). A further 15.8% indicated they did not use any of these methods, and 1.1% mentioned alternative approaches, such as MALDI-TOF MS. Awareness of novel infrared spectroscopy-based techniques for age-grading was moderate: 50.2% of respondents had heard of either NIRS or MIRS, 46.3% had not, and 3.5% were unsure. However, access remained a major barrier, with only 18.6% reporting availability of these technologies within their institution, compared to 74% who reported no access and 7.4% who were uncertain. These findings point to a clear gap between awareness of innovative age-grading tools and their operational adoption.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eChallenges and gaps associated with current age-grading methods\u003c/h2\u003e \u003cp\u003eIn the online survey, stakeholders were asked to rank the challenges associated with different age-grading methods, considering whether a method: (a) could be taught to a non-expert, (b) was accurate and precise, (c) could be implemented at a large scale, and (d) would be cost-effective (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). All stakeholders perceived that both existing standard age-grading methods (Polovodova and Detinova) and newer infrared methods (NIRS and MIRS) could be taught to a non-expert, were likely to be accurate and precise, cost-effective, and capable of implementation at a large scale. No consistent differences emerged in the perceived challenges associated with these methods (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). However, unstructured responses from open-text responses within the survey, and discussions from the IDIs and FGDs revealed that some stakeholders viewed the Detinova and Polovodova methods as more difficult to implement (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These challenges were linked to their reliance on skilled personnel, the labour-intensive and time-consuming nature of dissections, and the complexity of data interpretation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eKey challenges in implementing age-grading methods for mosquito surveillance were identified from both the online survey, IDIs, and FGDs, with representative quotes illustrating stakeholder perception of the challenge.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChallenges identified\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResponses\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLack of necessary tools, funding, and expertise (Resource constraints)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eR32: \u0026ldquo;Because I don\u0026rsquo;t have tools for age grading\u0026rdquo;\u003c/em\u003e (NMP, male).\u003c/p\u003e \u003cp\u003e\u003cem\u003eR33: \u0026ldquo;In resource constrained environment, these activities are not possible\u0026rdquo;\u003c/em\u003e (Researcher, female)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLabour-intensive, time-consuming, and require specialized skills (Technical complexity).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eR34: \u0026ldquo;Age-grading mosquitoes can be labour-intensive and require specialized skills and equipment\u0026rdquo;\u003c/em\u003e (Researcher, male)\u003c/p\u003e \u003cp\u003e\u003cem\u003eR35: \u0026ldquo;I think it is time consuming and labour intensive\u0026rdquo;\u003c/em\u003e (Researcher, male)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInaccurate or subjective, leading to unreliable age determination (Inaccuracy of the methods)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eR36: \u0026ldquo;I have a doubt on its accuracy due to some human errors\u0026rdquo;\u003c/em\u003e (Researcher, female)\u003c/p\u003e \u003cp\u003e\u003cem\u003eR37\u003c/em\u003e: \u0026ldquo;\u003cem\u003eSome tools used to estimate age are biased giving misleading results\u0026rdquo;\u003c/em\u003e (Researcher, male)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImpractical for large-scale field use in routine or large-scale field settings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eR38: \u0026ldquo;It is quite challenging to determine the age of a wild caught adult mosquito\u0026rdquo;\u003c/em\u003e (Researcher, female)\u003c/p\u003e \u003cp\u003e\u003cem\u003eR39: \u0026ldquo;Age-grading often requires specialized techniques, equipment, and expertise, making it resource-intensive and impractical for routine surveillance\u0026rdquo;\u003c/em\u003e (Researcher, female)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMay not directly inform immediate control strategies, reducing its priority.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eR40: \u0026ldquo;It does not give you an immediate result of whether a mosquito infected/infective with malaria but only the likelihood of infection\u0026rdquo;\u003c/em\u003e (Researcher, female),\u003c/p\u003e \u003cp\u003e\u003cem\u003eR41: \u0026ldquo;While age-grading can provide valuable insights into mosquito population dynamics, its utility in guiding immediate public health interventions may be limited\u0026rdquo;\u003c/em\u003e (Researcher, male)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo our knowledge, this is the first comprehensive multi-country survey to identify, understand, and document the challenges and gaps in implementing mosquito age-grading for vector control and surveillance. Previous studies have comprehensively investigated the types of entomological data collected in malaria surveillance (e.g. Burkot et al [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], Russell et al [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], with this study further investigating the barriers to widespread incorporation of mosquito-age grading. By engaging key stakeholders, including the NMP, decision-makers, entomologists, and research scientists from NGOs, private institutions, and public sectors, we obtained valuable insights into the challenges that limit the collection of mosquito survival data. The key factors identified were limited prioritization of age-grading within routine surveillance, insufficient technical expertise to perform dissections or data analysis, and constraints in time and resources that hinder systematic implementation. Through understanding the factors that limit the uptake of both traditional and novel methods, this research can help guide strategies to promote and facilitate greater use of mosquito age-grading techniques in the future.\u003c/p\u003e \u003cp\u003eThis study found that the predominance of surveillance activities focused on adult mosquitoes and that most stakeholders prioritize adult mosquito density, species identification, and insecticide resistance testing; likely because these indicators are considered operationally straightforward and directly actionable. Indeed, WHO-endorsed interventions reflect the operational realities of NMCPs, where surveillance priorities are closely aligned with interventions that are already recommended, funded, and routinely implemented [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Similar patterns have been reported elsewhere, with entomological surveillance primarily designed to support monitoring of ITNs, IRS, and larval source management, rather than to evaluate emerging tools still under development [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. This alignment likely reflects programmatic incentives, including donor reporting requirements, established technical guidance, and limited resources for piloting novel interventions [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Consequently, newer approaches, that would require different entomological or epidemiological indicators, may remain peripheral to routine surveillance systems until clear WHO recommendations and implementation frameworks are established, potentially delaying their integration into national monitoring strategies [\u003cspan additionalcitationids=\"CR55\" citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA significant proportion of responses from the online survey indicated that entomological surveillance primarily targets the major malaria vectors, \u003cem\u003eAn. gambiae s.l.\u003c/em\u003e and \u003cem\u003eAn. funestus s.l.\u003c/em\u003e both of which are species complexes comprising multiple morphologically similar sibling species. Consequently, some respondents may be monitoring subspecies such as \u003cem\u003eAn. arabiensis\u003c/em\u003e or \u003cem\u003eAn. coluzzii\u003c/em\u003e without explicitly distinguishing them in the survey or during the discussions. This is not surprising given that these are the primary malaria vector species in Africa, and the main target of most malaria control programmes. However, respondents from Ethiopia, Tanzania, Kenya, Uganda, Nigeria, Ghana, and Cameroon also reported involvement in surveillance for or in anticipation of \u003cem\u003eAn. stephensi\u003c/em\u003e, an invasive urban malaria vector originating from South Asia that has recently invaded several African countries [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur study confirmed previous regional and global analyses showing that mosquito age-grading and survival estimation are rarely prioritized in routine surveillance, despite their epidemiological importance [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. In a 2019 global survey, Burkot et al. found [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], that only about 25% of surveyed NMCPs across multiple regions reported conducting parity dissections to assess mosquito survivorship [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This indicates that direct age-structure monitoring remains rare in malaria-endemic settings, even among programmes with formal surveillance frameworks. Meanwhile, Russell et al. (2020) documented that just 8% of NMCPs reported having adequate capacity (in staffing, infrastructure, financing, and management) to implement comprehensive vector surveillance, with most programmes lacking the human resources or strategic prioritization to do so [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. By engaging with frontline entomologists or programme staff we further documented user-reported barriers to age-grading, revealing that other entomological indicators are prioritized over age-grading. Our mixed-methods approach adds novel, empirical evidence on why age-grading remains underused: namely limited prioritization within surveillance programmes, insufficient technical expertise, and resource/time constraints prevent systematic implementation.\u003c/p\u003e \u003cp\u003eOur study revealed specific barriers of adopting the traditional mosquito age-grading methods into routine surveillance. Indeed, while stakeholders were generally familiar with traditional approaches, such as the Detinova and Polovodova techniques, they consistently described these dissection-based methods as technically demanding and labour-intensive. Respondents emphasized that specialized training and access to microscopes often limited in programmatic settings are essential for implementing these methods. The Polovodova technique, in particular, was noted as challenging for assessing older, multiparous mosquitoes, consistent with previous studies reporting potential underestimation of parity in multi-cycle females [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Stakeholders also highlighted logistical constraints, such as the requirement for freshly collected mosquitoes, which complicates large-scale surveillance in remote or resource-limited areas [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. While parity dissections are scientifically informative and can provide valuable data on mosquito survival, operational implementation in routine program settings may result in greater variability, especially when conducted by personnel with limited experience. This variability has been hypothesized based on documented methodological challenges such as difficulty distinguishing successive ovarian dilations even though quantitative inter-observer reliability data under programmatic conditions are not yet available [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. These findings reinforce that, despite recognition of the epidemiological importance of mosquito age and survival metrics, practical barriers continue to restrict their systematic integration into routine vector surveillance.\u003c/p\u003e \u003cp\u003eOur study showed a substantial research-to-practice gap for innovative age-grading methods. Indeed, although NIRS and MIRS are not new techniques - NIRS for age-grading was first demonstrated more than 15 years ago[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] and they have high surveillance potential - classifying species and age with high accuracy and high speed[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e] - our survey showed that the awareness and use of innovative age-grading tools were minimal. Only 11\u0026ndash;13% of respondents reported the use of spectroscopic methods such as NIRS or MIRS, which was largely attributed to the lack of training and restricted access to specialized equipment. Meanwhile, many newer molecular methods (e.g. Polymerase Chain Reaction-PCR or sequencing-based species identification or insecticide resistance assays) have already started to see field deployment. Additionally, the MALDI-TOF MS is a recent alternative method that enables simultaneous prediction of age and infection status[\u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] which was also mentioned as one of the methods by the respondents as a tool they wanted to use but have limited access to. All these innovations offer higher throughput, greater objectivity, and faster processing than dissections but remain largely confined to research institutions. Our study\u0026rsquo;s empirical data from frontline entomologists and programme staff highlights limited awareness, training, equipment access, and institutional buy-in remain major bottlenecks to operational implementation of spectroscopic age-grading methods in routine national vector surveillance.\u003c/p\u003e \u003cp\u003eImportantly, while this study focuses on the role of mosquito age-grading in malaria vector surveillance in Africa, the implications extend beyond this context. Age-structure information is also crucial for understanding transmission dynamics of other vector-borne diseases, including arboviruses transmitted by \u003cem\u003eAedes\u003c/em\u003e and \u003cem\u003eCulex\u003c/em\u003e species [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Yet, similar to malaria settings, age-grading is rarely incorporated into routine \u003cem\u003eAedes\u003c/em\u003e surveillance even in regions investing heavily in vector monitoring despite evidence that many of the same age-grading approaches can be applied to \u003cem\u003eAedes\u003c/em\u003e species [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Lessons from our work on malaria vectors therefore offer a foundation for strengthening the operational uptake of age-grading methods across diverse geographical settings and disease systems, helping build more robust, forward-looking vector surveillance frameworks globally.\u003c/p\u003e \u003cp\u003eThis study had several limitations. First, the number of survey respondents varied across countries, with some having fewer than five participants, which may limit the representativeness of the findings. Obtaining responses was particularly challenging due to the online survey format, which relied on participants\u0026rsquo; willingness and ability to access and complete the survey. This means only those who accessed and responded resulted in their contribution. Additionally, countries with more extensive survey promotion, such as through project or NMCP meetings in Tanzania, had higher response rates, potentially introducing selection bias. Future studies should aim to engage a broader geographic range and include qualitative discussions in multiple countries to capture diverse perspectives on mosquito age-grading practices.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eTaken together, our findings reveal a clear and persistent pattern: although methodological innovation in mosquito age-grading continues to progress, operational uptake remains limited by gaps in awareness, technical capacity, and access to equipment. Among these, the most immediate priority emerging from our data is improving practical training and hands-on capacity-building, as lack of expertise was the most frequently cited barrier by frontline entomologists and programme staff. Strengthening national and sub-national training programmes paired with provision of essential equipment and development of simplified, standardised protocols would offer the greatest short-term impact in enabling programmes to begin integrating age-structure data into routine surveillance. In parallel, broader institutional prioritisation is needed: validated spectroscopic and dissection-based methods should be incorporated into national surveillance guidelines and supported through sustainable financing mechanisms to ensure continuity beyond research settings.\u003c/p\u003e \u003cp\u003eThis study has demonstrated that strengthening malaria vector surveillance in Africa requires greater recognition of mosquito age-grading as a critical indicator of transmission potential and intervention impact. While stakeholders value the role of age-structured data, persistent barriers limited technical expertise, inadequate resources, and low prioritization in policy, continue to restrict its operational use. Addressing these challenges will demand targeted investments in training, infrastructure, and sustainable funding. Emerging tools such as infrared spectroscopy and other scalable methods hold promise for providing rapid, objective, and high-throughput age estimates, offering a practical complement to traditional dissections. Integrating such approaches into routine surveillance will not only enhance decision-making but could also accelerate progress toward malaria elimination by enabling more precise targeting of interventions.\u003c/p\u003e \u003cp\u003eIn conclusion, strengthening malaria vector surveillance in Africa requires greater recognition of mosquito age-grading as a critical indicator of transmission potential and intervention impact. Our findings reveal a clear pattern: although methodological innovation in age-grading is advancing, operational uptake remains constrained by gaps in awareness, technical capacity, and access to equipment. The most immediate priority is improving practical training and hands-on capacity-building. Strengthening national and sub-national training programmes, paired with provision of essential equipment and simplified, standardised protocols, would offer the greatest short-term impact in enabling programmes to integrate age-structure data into routine surveillance. In parallel, broader institutional prioritisation is needed. Validated spectroscopic and dissection-based methods should be incorporated into national surveillance guidelines and supported through sustainable financing to ensure continuity beyond research settings. Emerging tools such as infrared spectroscopy and other scalable approaches promise rapid, objective, high-throughput age estimates, complementing traditional dissections. Integrating these methods into routine surveillance will enhance decision-making and accelerate progress toward malaria elimination by enabling more precise targeting of interventions.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eIRS; Indoor residual spraying, ITN: Insecticide treated bed nets, NMCP: National malaria control personnel-program, PAMCA: Pan African mosquito control association, ASTMH: American Society of Tropical Medicine and Hygiene, ATSBs: Attractive toxic sugar baits, NMP: National malaria programs, NMEP: National malaria elimination program personnel, IHI: Ifakara health institute, CISM: Centro de Investiga\u0026ccedil;\u0026atilde;o em Sa\u0026uacute;de de Manhi\u0026ccedil;a, NIMR: National Institute for Medical Research, FGD: Focus group discussion, IDI: In-depth interviews, IR: Infrared, ML: Machine learning, AI: Artificial intelligence, WHO-GTS: World Health Organization\u0026rsquo;s Global Technical Strategy, MALDITOF-MS: matrix-assisted laser desorption ionization time-of-flight mass spectrometry, WHO: World Health Organisation, IRSS: Institut de Recherche en Sciences de la Sant\u0026eacute;, RDTs: Rapid Diagnostic Tests. \u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDJS was the lead study investigator who was involved in the study design, data collection, data entry and analysis, interpretation of the results, and writing of the manuscript. NU and DJS were also involved in FGD data collections. FO, FB, and HMF were involved in the study design, supervision, and critical revision of the manuscript. NU, DM, MF, HN, JM, YM, IM, EM, RM, EH, MM, MO, RS, AD, MS-L, contributed to the study design, and manuscript writing (review and editing). All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Bill and Melinda Gates Foundation (grant number INV-003079).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the data for this study is available upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical consideration was sought prior to the work; ethical approval was sought from the Ifakara Health institutional review board (IRB), reference number (IHI/IRB/EXT/No: 02-2025), and National Institute for Medical Research reference number (NIMR/HQ/R.8a/Vol. IX/4112). Permission to publish was sought from the National Institute for Medical Research (NIMR) with a reference number\u0026nbsp;Ref No. BD.242/437/01C/121.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no competing interests.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWHO. World Malaria Report 2025 Addressing the threat of antimalarial drug resistance [Internet]. World Health Organization. 2025 [cited 2025 Dec 17]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/publications/i/item/9789240117822\u003c/span\u003e\u003cspan address=\"https://www.who.int/publications/i/item/9789240117822\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 17 Dec 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO. World Malaria Report. 2024 Addressing inequality in the global malaria response [Internet]. World Health Organization; 2024 [cited 2025 Dec 17]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/teams/global-malaria-programme/reports/world-malaria-report-2024\u003c/span\u003e\u003cspan address=\"https://www.who.int/teams/global-malaria-programme/reports/world-malaria-report-2024\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 17 Dec 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDhiman S. Are malaria elimination efforts on right track? An analysis of gains achieved and challenges ahead. Infect Dis Poverty. BioMed Central Ltd.; 2019. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s40249-019-0524-x\u003c/span\u003e\u003cspan address=\"10.1186/s40249-019-0524-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO. WHO Guidelines for malaria, 13 August 2025 [Internet]. World Health Organization; 2025. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2471/B09514\u003c/span\u003e\u003cspan address=\"10.2471/B09514\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKilleen GF, Tatarsky A, Diabate A, Chaccour CJ, Marshall JM, Okumu FO, et al. Developing an expanded vector control toolbox for malaria elimination. BMJ Glob Health BMJ Publishing Group. 2017;2. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/bmjgh-2016-000211\u003c/span\u003e\u003cspan address=\"10.1136/bmjgh-2016-000211\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGolding N, Wilson AL, Moyes CL, Cano J, Pigott DM, Velayudhan R, et al. Integrating vector control across diseases. BMC Med BioMed Cent Ltd. 2015;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12916-015-0491-4\u003c/span\u003e\u003cspan address=\"10.1186/s12916-015-0491-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO. Global technical strategy for malaria, 2016\u0026ndash;2030 [Internet]. Global Malaria Programme, World Health Organization. 2015 [cited 2025 Dec 17]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/docs/default-source/documents/global-technical-strategy-for-malaria-2016-2030.pdf\u003c/span\u003e\u003cspan address=\"https://www.who.int/docs/default-source/documents/global-technical-strategy-for-malaria-2016-2030.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 17 Dec 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO-GTS. Global technical strategy for malaria 2016\u0026ndash;2030, 2021 update [Internet]. World Health Organ. 2021. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://apps.who.int/iris/bitstream/10665/176712/1/9789241564991_eng.pdf?ua=1\u003c/span\u003e\u003cspan address=\"http://apps.who.int/iris/bitstream/10665/176712/1/9789241564991_eng.pdf?ua=1\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO. Malaria surveillance, monitoring \u0026amp; evaluation: a reference manual. [Internet]. 2018. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/publications/i/item/9789241565578\u003c/span\u003e\u003cspan address=\"https://www.who.int/publications/i/item/9789241565578\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 17 Dec 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO. Monitoring and evaluation indicators for integrated vector management. [Internet]. 2012. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://iris.who.int/bitstream/handle/10665/76504/9789241504027_eng.pdf?sequence=1\u003c/span\u003e\u003cspan address=\"https://iris.who.int/bitstream/handle/10665/76504/9789241504027_eng.pdf?sequence=1\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 17 Dec 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurkot TR, Farlow R, Min M, Espino E, Mnzava A, Russell TL. A global analysis of National Malaria Control Programme vector surveillance by elimination and control status in 2018. Malar J BioMed Cent Ltd. 2019;18. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12936-019-3041-2\u003c/span\u003e\u003cspan address=\"10.1186/s12936-019-3041-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRussell TL, Farlow R, Min M, Espino E, Mnzava A, Burkot TR. Capacity of National Malaria Control Programmes to implement vector surveillance: a global analysis. Malar J BioMed Cent Ltd. 2020;19. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12936-020-03493-1\u003c/span\u003e\u003cspan address=\"10.1186/s12936-020-03493-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFarlow R, Russell TL, Burkot TR. Nextgen Vector Surveillance Tools: sensitive, specific, cost-effective and epidemiologically relevant. Malar J BioMed Cent Ltd. 2020;19. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12936-020-03494-0\u003c/span\u003e\u003cspan address=\"10.1186/s12936-020-03494-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmith DL, Battle KE, Hay SI, Barker CM, Scott TW, McKenzie FE. Ross, Macdonald, and a theory for the dynamics and control of mosquito-transmitted pathogens. PLoS Pathog. 2012;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.ppat.1002588\u003c/span\u003e\u003cspan address=\"10.1371/journal.ppat.1002588\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoss C. The prevention of malaria [Internet]. 1911. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.jstor.org/stable/714848\u003c/span\u003e\u003cspan address=\"https://www.jstor.org/stable/714848\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 17 Dec 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChernin E. Sir Ronald Ross, malaria, and the rewards of research. Med Hist. 1988;32:119\u0026ndash;41. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/S0025727300047967\u003c/span\u003e\u003cspan address=\"10.1017/S0025727300047967\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGillies MT. A Modified Technique for the Age-Grading of Populations of \u003cem\u003eAnopheles gambiae\u003c/em\u003e. Ann Trop Med Parasitol [Internet] Taylor Francis. 1958;52:261\u0026ndash;73. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/00034983.1958.11685867\u003c/span\u003e\u003cspan address=\"10.1080/00034983.1958.11685867\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGillies MT. The recognition of age-groups within populations of \u003cem\u003eAnopheles gambiae\u003c/em\u003e by the pre-gravid rate and the sporozoite rate. Ann Trop Med Parasitol. 1954. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/00034983.1954.11685599\u003c/span\u003e\u003cspan address=\"10.1080/00034983.1954.11685599\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeier JC. Malaria parasite development in mosquitoes. Annu Rev Entomol. 1998;43:519\u0026ndash;43. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1146/annurev.ento.43.1.519\u003c/span\u003e\u003cspan address=\"10.1146/annurev.ento.43.1.519\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAniedu I, Mutinga MJ, Mutero CM. Age composition and survival rate of \u003cem\u003eAnopheles gambiae Giles complex\u003c/em\u003e (Dipt., Culicidae) in Baringo district, Kenya. J Appl Entomol. 1989;107:387\u0026ndash;94. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1439-0418.1989.tb00273.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1439-0418.1989.tb00273.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFerguson HM, Maire N, Takken W, Lyimo IN, Brit O, Lindsay SW, et al. Selection of mosquito life-histories: A hidden weapon against malaria? Malar J. 2012;11:1\u0026ndash;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1475-2875-11-106\u003c/span\u003e\u003cspan address=\"10.1186/1475-2875-11-106\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrady OJ, Godfray HCJ, Tatem AJ, Gething PW, Cohen JM, Ellis McKenzie F, et al. Vectorial capacity and vector control: Reconsidering sensitivity to parameters for malaria elimination. Trans R Soc Trop Med Hyg. 2016;110:107\u0026ndash;17. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/trstmh/trv113\u003c/span\u003e\u003cspan address=\"10.1093/trstmh/trv113\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJohnson BJ, Hugo LE, Churcher TS, Ong OTW, Devine GJ. Mosquito Age Grading and Vector-Control Programmes. Trends Parasitol. 2020;36:39\u0026ndash;51. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.pt.2019.10.011\u003c/span\u003e\u003cspan address=\"10.1016/j.pt.2019.10.011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeklemishev WN, Detinova TS, Polovodova VP. Determination of physiological age in anophelines and of age distribution in anopheline populations in the USSR. Bull World Health Organ [Internet]. 1959;21:223\u0026ndash;32. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/pubmed/13798390\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/pubmed/13798390\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDetinova TS. [Determination of the physiological age of Anopheles females by the changes in the tracheal system of the ovaries]. Med Parazitol (Mosk) [Internet]. 1945;14:45\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/pubmed/20280604\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/pubmed/20280604\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDetinova TS. Age-grouping methods in Diptera of medical importance with special reference to some vectors of malaria. Monogr Ser World Health Organ [Internet]. 1962;47:13\u0026ndash;191. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/pubmed/13885800\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/pubmed/13885800\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnagonou R, Agossa F, Azond\u0026eacute;kon R, Agbogan M, Ok\u0026eacute;-Agbo F, Gnanguenon V, et al. Application of Polovodova\u0026rsquo;s method for the determination of physiological age and relationship between the level of parity and infectivity of Plasmodium falciparum in Anopheles gambiae s.s, south-eastern Benin. Parasit Vectors. BioMed Central Ltd.; 2015. p. 8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13071-015-0731-7\u003c/span\u003e\u003cspan address=\"10.1186/s13071-015-0731-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCharlwood JD, Tom\u0026aacute;s EVE, Andegiorgish AK, Mihreteab S, LeClair C. We like it wet\u0026rsquo;: a comparison between dissection techniques for the assessment of parity in Anopheles arabiensis and determination of sac stage in mosquitoes alive or dead on collection. PeerJ. 2018;6:e5155. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7717/peerj.5155\u003c/span\u003e\u003cspan address=\"10.7717/peerj.5155\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHugo LE, Quick-Miles S, Kay BH, Ryan PA. Evaluations of mosquito Age grading techniques based on morphological changes. J Med Entomol. 2008;45:353\u0026ndash;69. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1603/0022-2585(2008)45\u003c/span\u003e\u003cspan address=\"10.1603/0022-2585(2008)45\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. [353:EOMAGT]2.0.CO;2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePretorius E, Kristan M, Bradley J, da Silva ET, Hutchins H, Barri F, et al. Validation of a method for the dry preservation and rehydration of \u003cem\u003eAnopheles gambiae sensu lato\u003c/em\u003e for parity analysis to assess the impact of vector control measures in the field. Parasit Vectors [Internet] BioMed Cent. 2023;16:1\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13071-023-05866-2\u003c/span\u003e\u003cspan address=\"10.1186/s13071-023-05866-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSiria DJ, Sanou R, Mitton J, Mwanga EP, Niang A, Sare I, et al. Rapid age-grading and species identification of natural mosquitoes for malaria surveillance. Nat Commun Springer US. 2022;13:1\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41467-022-28980-8\u003c/span\u003e\u003cspan address=\"10.1038/s41467-022-28980-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMwanga EP, Siria DJ, Mshani IH, Mwinyi SH, Abbasi S, Jimenez MG, et al. Rapid classification of epidemiologically relevant age categories of the malaria vector, Anopheles funestus. Parasit Vectors BioMed Cent Ltd. 2024;17. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13071-024-06209-5\u003c/span\u003e\u003cspan address=\"10.1186/s13071-024-06209-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePazmi\u0026ntilde;o-Betancourth M, Ochoa-Guti\u0026eacute;rrez V, Ferguson HM, Gonz\u0026aacute;lez-Jim\u0026eacute;nez M, Wynne K, Baldini F, et al. Evaluation of diffuse reflectance spectroscopy for predicting age, species, and cuticular resistance of Anopheles gambiae s.l under laboratory conditions. Sci Rep [Internet] Nat Publishing Group UK. 2023;13:1\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-023-45696-x\u003c/span\u003e\u003cspan address=\"10.1038/s41598-023-45696-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCook PE, Hugo LE, Williams CR, Chenoweth SF, Ritchie SA, Ryan PA et al. The use of transcriptional profiles to predict adult mosquito age under field conditions. 2006; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.0604875103\u003c/span\u003e\u003cspan address=\"10.1073/pnas.0604875103\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSikulu-Lord MT, Devine GJ, Hugo LE, Dowell FE. First report on the application of near-infrared spectroscopy to predict the age of \u003cem\u003eAedes albopictus\u003c/em\u003e Skuse. Sci Rep. 2018. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-018-27998-7\u003c/span\u003e\u003cspan address=\"10.1038/s41598-018-27998-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSikulu M, Killeen GF, Hugo LE, Ryan PA, Dowell KM, Wirtz RA, et al. Near-infrared spectroscopy as a complementary age grading and species identification tool for African malaria vectors. Parasit Vectors. 2010;3:1\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1756-3305-3-49\u003c/span\u003e\u003cspan address=\"10.1186/1756-3305-3-49\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRakotonirina A, Pol M, Kainiu M, Barsac E, Tutagata J, Kilama S, et al. MALDI-TOF MS: Optimization for future uses in entomological surveillance and identification of mosquitoes from New Caledonia. Parasit Vectors [Internet] BioMed Cent. 2020;13:1\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13071-020-04234-8\u003c/span\u003e\u003cspan address=\"10.1186/s13071-020-04234-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaharimalala FN, Andrianinarivomanana TM, Rakotondrasoa A, Collard JM, Boyer S. Usefulness and accuracy of MALDI-TOF mass spectrometry as a supplementary tool to identify mosquito vector species and to invest in development of international database. Med Vet Entomol. 2017;31:289\u0026ndash;98. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/mve.12230\u003c/span\u003e\u003cspan address=\"10.1111/mve.12230\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTuwei M, Karisa J, Kiuru C, Ondieki Z, Odongo T, Muturi M, et al. Age structure and parity status determination of Afrotropical malaria vectors using MALDI-TOF MS. Sci Rep. 2025;15:1\u0026ndash;11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-025-00935-1\u003c/span\u003e\u003cspan address=\"10.1038/s41598-025-00935-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGonzalez-Jimenez M, Babayan SA, Khazaeli P, Doyle M, Walton F, Reedy E, et al. Prediction of malaria mosquito species and population age structure using mid-infrared spectroscopy and supervised machine learning. bioRxiv [Internet]. 2018;414342. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1101/414342\u003c/span\u003e\u003cspan address=\"10.1101/414342\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMwanga EP, Siria DJ, Mitton J, Mshani IH, Gonz\u0026aacute;lez-Jim\u0026eacute;nez M, Selvaraj P, et al. Using transfer learning and dimensionality reduction techniques to improve generalisability of machine-learning predictions of mosquito ages from mid-infrared spectra. BMC Bioinf BioMed Cent Ltd. 2023;24. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12859-022-05128-5\u003c/span\u003e\u003cspan address=\"10.1186/s12859-022-05128-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUniversity of Glasgow(UofG). Summon Search Platform. University Library, University of Glasgow [Internet]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gla.ac.uk/myglasgow/library/\u003c/span\u003e\u003cspan address=\"https://www.gla.ac.uk/myglasgow/library/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLambert B, Sikulu-Lord MT, Mayagaya VS, Devine G, Dowell F, Churcher TS. Monitoring the Age of Mosquito Populations Using Near-Infrared Spectroscopy. Sci Rep [Internet] Springer US. 2018;8:1\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-018-22712-z\u003c/span\u003e\u003cspan address=\"10.1038/s41598-018-22712-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePAMCA, Pan-African. Mosquito Control Association Conference. 2023; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pamca.org/en\u003c/span\u003e\u003cspan address=\"https://pamca.org/en\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eASTMH AA book of the 2024. American Society of Tropical Medicine and Hygiene (ASTMH). 2024; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://tinyurl.com/5bv4t9de\u003c/span\u003e\u003cspan address=\"https://tinyurl.com/5bv4t9de\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTeam RC, The R Foundation for Statistical Computing. R Development Core Team. R: A Language and Environment for Statistical Computing [Internet]. 2017;55:275\u0026ndash;86. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/http://www.R-project.org\u003c/span\u003e\u003cspan address=\"https://doi.org/http://www.R-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR Core Team. 2019.: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.R-project.org/\u003c/span\u003e\u003cspan address=\"http://www.R-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. 2019.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDhakal K, NVivo. J Med Libr Association [Internet]. 2022;110:270\u0026ndash;2. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5195/jmla.2022.1271\u003c/span\u003e\u003cspan address=\"10.5195/jmla.2022.1271\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReegan AD, Joy S, Jambulingam P, Rahi M. First initiative to develop a standard methodology for the evaluation of Attractive Targeted Sugar Baits in different settings against targeted mosquito vectors: a methodological review. Malar J [Internet] BioMed Cent Ltd. 2025;24:323. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12936-025-05545-w\u003c/span\u003e\u003cspan address=\"10.1186/s12936-025-05545-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eObeagu EI, Obeagu GU. Emerging public health strategies in malaria control: innovations and implications. Annals of Medicine \u0026amp; Surgery. Ovid Technol (Wolters Kluwer Health). 2024;86:6576\u0026ndash;84. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/ms9.0000000000002578\u003c/span\u003e\u003cspan address=\"10.1097/ms9.0000000000002578\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMnzava A, Monroe AC, Okumu F. \u003cem\u003eAnopheles stephensi\u003c/em\u003e in Africa requires a more integrated response. Malar J BioMed Cent. 2022;21:4\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12936-022-04197-4\u003c/span\u003e\u003cspan address=\"10.1186/s12936-022-04197-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan de Straat B, Russell TL, Staunton KM, Sinka ME, Burkot TR. A global assessment of surveillance methods for dominant malaria vectors. Sci Rep Nat Res. 2021;11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-021-94656-w\u003c/span\u003e\u003cspan address=\"10.1038/s41598-021-94656-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShretta R, Avance\u0026ntilde;a ALV, Hatefi A. The economics of malaria control and elimination: a systematic review. Malar J. 2016;15:593. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12936-016-1635-5\u003c/span\u003e\u003cspan address=\"10.1186/s12936-016-1635-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAshton RA, Saili K, Chishya C, Banda Yikona H, Arnzen A, Orange E, et al. Efficacy of attractive targeted sugar bait stations against malaria in Western Province Zambia: epidemiological findings from a two-arm cluster randomized phase III trial. Malar J BioMed Cent Ltd. 2024;23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12936-024-05175-8\u003c/span\u003e\u003cspan address=\"10.1186/s12936-024-05175-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZembere K. The potential for attractive toxic sugar baits to complement core malaria interventions strategies: the need for more evidence. Malar J BioMed Cent Ltd. 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12936-024-05161-0\u003c/span\u003e\u003cspan address=\"10.1186/s12936-024-05161-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSteketee RW. Attractive targeted sugar baits series in Malaria Journal. Malar J BioMed Cent Ltd. 2025. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12936-025-05309-6\u003c/span\u003e\u003cspan address=\"10.1186/s12936-025-05309-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO. Vector alert: \u003cem\u003eAnopheles stephensi\u003c/em\u003e invasion and spread in Africa and Sri Lanka. Https://IrisWhoInt\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e/Bitstream/Handle/10665/365710/9789240067714-EngPdf?Sequence\u0026thinsp;=\u0026thinsp;1\u003c/span\u003e\u003cspan address=\"http:///Bitstream/Handle/10665/365710/9789240067714-EngPdf?Sequence\u0026thinsp;=\u0026thinsp;1\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGray L, Asay BC, Hephaestus B, McCabe R, Pugh G, Markle ED, et al. Back to the Future: Quantifying Wing Wear as a Method to Measure Mosquito Age. Am J Trop Med Hyg. 2022;107:689\u0026ndash;700. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4269/ajtmh.21-1173\u003c/span\u003e\u003cspan address=\"10.4269/ajtmh.21-1173\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMilali MP, Sikulu-Lord MT, Kiware SS, Dowell FE, Povinelli RJ, Corliss GF. Do NIR spectra collected from laboratory-reared mosquitoes differ from those collected from wild mosquitoes? PLoS One [Internet]. 2018;13:1\u0026ndash;16. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0198245\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0198245\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFoti M, Micocci M, Pazmi\u0026ntilde;o-Betancourth M, Casas Gomez-Uribarri I, Serini P, Caputo B, et al. Towards scalable age-grading of \u003cem\u003eAedes albopictus\u003c/em\u003e mosquito using mid-infrared spectroscopy and machine learning. Sci Rep [Internet]. 2025;15:40470. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-025-24404-x\u003c/span\u003e\u003cspan address=\"10.1038/s41598-025-24404-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"malaria-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"malj","sideBox":"Learn more about [Malaria Journal](http://malariajournal.biomedcentral.com/)","snPcode":"12936","submissionUrl":"https://submission.nature.com/new-submission/12936/3","title":"Malaria Journal","twitterHandle":"@malariajournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"mosquito age-grading, malaria surveillance, parity assessment, Detinova, Polovodova, infrared spectroscopy","lastPublishedDoi":"10.21203/rs.3.rs-8560704/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8560704/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAdult mosquito survival is a critical factor in malaria transmission because \u003cem\u003eAnopheles\u003c/em\u003e vectors must live at least 11\u0026ndash;14 days before they can transmit. However, there is no direct method to assess age. Traditional methods of mosquito age-grading rely on indirect proxies of reproductive history derived from dissection and observation of ovarian features (Detinova and Polovodova methods). Both approaches are labour-intensive, time-consuming, and demand specialized expertise; with little known about the extent to which are used in malaria surveillance programmes, and why they may be deprioritised. This study assessed current practices, capacity, and constraints related to mosquito age-grading for malaria vector surveillance in Africa, including knowledge and use of existing dissection-based methods and emerging alternatives such as infrared spectroscopy.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted an online survey of 285 stakeholders from across Africa, including researchers, entomologists, and National Malaria Programme (NMP) personnel, to assess the practices, priorities, and barriers to mosquito age-grading. This was complemented with a series of in-depth interviews and focus group discussions to collect insights and perspectives from stakeholders regarding their familiarity and use of age-grading methods.\u003c/p\u003e\u003ch2\u003eFindings:\u003c/h2\u003e \u003cp\u003eMore than 70% of survey respondents reported that malaria vector surveillance was routinely conducted by their institutions or countries, with the highest priority given to vector density, species identification, human biting rate, and insecticide resistance. Familiarity (awareness as opposed to knowledge) of age-grading methods was highest for the Detinova (55%) and Polovodova (44%), and lower for newer approaches, including infrared-spectroscopy. Only 50% of respondents indicated that they regularly assessed mosquito age (mostly Detinova method); with \u0026gt;\u0026thinsp;1/3 considering age-grading to be a high-priority in vector surveillance. Reported barriers to conducting mosquito-age-grading included insufficient technical expertise, perceived impracticality of ovary dissections for large-scale surveillance, inadequate tools, and limited funding.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eDespite its critical role in malaria transmission, mosquito survival and age are rarely assessed in African vector surveillance programs. There is need for greater acknowledgment of these measures and their implications for disease transmission and control, and investment in tools, training, and funding to overcome current operational barriers. Integrating more practical and scalable age-assessment methods could enhance targeting of interventions.\u003c/p\u003e","manuscriptTitle":"Mosquito age-grading for malaria vector surveillance in sub-Saharan Africa: current practices and constraints","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-26 09:51:34","doi":"10.21203/rs.3.rs-8560704/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"243753852457416541722604401717154972880","date":"2026-05-18T16:31:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"294984224847755022353208546925384911467","date":"2026-05-11T18:06:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-17T19:01:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"8539690834861688762041569765148547831","date":"2026-02-24T15:03:28+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-24T12:22:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-09T20:06:53+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-09T18:19:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"Malaria Journal","date":"2026-01-09T11:43:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"malaria-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"malj","sideBox":"Learn more about [Malaria Journal](http://malariajournal.biomedcentral.com/)","snPcode":"12936","submissionUrl":"https://submission.nature.com/new-submission/12936/3","title":"Malaria Journal","twitterHandle":"@malariajournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"61cfe93f-4854-4701-afc7-aa1a19430b37","owner":[],"postedDate":"February 26th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"243753852457416541722604401717154972880","date":"2026-05-18T16:31:37+00:00","index":41,"fulltext":""},{"type":"reviewerAgreed","content":"294984224847755022353208546925384911467","date":"2026-05-11T18:06:41+00:00","index":39,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-26T09:51:35+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-26 09:51:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8560704","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8560704","identity":"rs-8560704","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}