Modelling the impact of larviciding as a supplementary malaria vector control intervention in rural south-eastern Tanzania: A district-level simulation study

Modelling the impact of larviciding as a supplementary malaria vector control intervention in rural south-eastern Tanzania: A district-level simulation study | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (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];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Modelling the impact of larviciding as a supplementary malaria vector control intervention in rural south-eastern Tanzania: A district-level simulation study View ORCID Profile GloriaSalome Shirima , View ORCID Profile Emma L Fairbanks , Gavana Tegemeo , Gerald Kiwelu , Ismail Nambunga , Yeromin P Mlacha , Silas Mirau , Prosper Chaki , Nakul Chitnis , Samson Kiware doi: https://doi.org/10.1101/2025.11.13.25340176 GloriaSalome Shirima 1 Environmental Health and Ecological Science Department, Ifakara Health Institute , P.O. Box 78373, Ifakara, Tanzania 2 Computational and Communication Science and Engineering, The Nelson Mandela African Institution of Science and Technology, (NM-AIST) , P.O. Box 447, Tengeru, Arusha, Tanzania 3 Disease Modelling, Swiss Tropical and Public Health Institute , Kreuzstrasse 2, 4123, Allschwil, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for GloriaSalome Shirima For correspondence: gshirima{at}ihi.or.tz Emma L Fairbanks 3 Disease Modelling, Swiss Tropical and Public Health Institute , Kreuzstrasse 2, 4123, Allschwil, Switzerland 4 Department of Mathematics, University of Manchester , Manchester, UK 5 University of Basel , Petersplatz 1, 4001, Basel, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Emma L Fairbanks Gavana Tegemeo 1 Environmental Health and Ecological Science Department, Ifakara Health Institute , P.O. Box 78373, Ifakara, Tanzania 4 Department of Mathematics, University of Manchester , Manchester, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Gerald Kiwelu 1 Environmental Health and Ecological Science Department, Ifakara Health Institute , P.O. Box 78373, Ifakara, Tanzania Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ismail Nambunga 1 Environmental Health and Ecological Science Department, Ifakara Health Institute , P.O. Box 78373, Ifakara, Tanzania Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yeromin P Mlacha 1 Environmental Health and Ecological Science Department, Ifakara Health Institute , P.O. Box 78373, Ifakara, Tanzania Find this author on Google Scholar Find this author on PubMed Search for this author on this site Silas Mirau 2 Computational and Communication Science and Engineering, The Nelson Mandela African Institution of Science and Technology, (NM-AIST) , P.O. Box 447, Tengeru, Arusha, Tanzania Find this author on Google Scholar Find this author on PubMed Search for this author on this site Prosper Chaki 1 Environmental Health and Ecological Science Department, Ifakara Health Institute , P.O. Box 78373, Ifakara, Tanzania Find this author on Google Scholar Find this author on PubMed Search for this author on this site Nakul Chitnis 3 Disease Modelling, Swiss Tropical and Public Health Institute , Kreuzstrasse 2, 4123, Allschwil, Switzerland 5 University of Basel , Petersplatz 1, 4001, Basel, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Samson Kiware 1 Environmental Health and Ecological Science Department, Ifakara Health Institute , P.O. Box 78373, Ifakara, Tanzania 2 Computational and Communication Science and Engineering, The Nelson Mandela African Institution of Science and Technology, (NM-AIST) , P.O. Box 447, Tengeru, Arusha, Tanzania Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Combining larviciding with insecticide treated nets (ITNs) can reduce malaria transmission, but most modelling analyses use generalized scenarios rather than local contexts. In Tanzania and other countries, larviciding is increasingly being prioritized in national strategies, with growing advocacy for its broader implementation, to achieving sustained malaria reduction. District-specific modelling is therefore essential to capture variation in transmission ecology, seasonality, and varying coverage levels, providing evidence that is both rigorous and actionable for malaria control programs. The Vector Control Optimization Model (VCOM) was adapted and extended to incorporate local seasonality, simulating the impact of larviciding across a range of coverage levels combined with ITNs. The model was parameterized using district-level field-data on mosquito mortality collected before (2016-2017) and after (2019-2021) larviciding implementation. Mosquito mortality rates were estimated using Bayesian inference. Outcomes were evaluated specifically for Anopheles gambiae s.l. including annual entomological inoculation rates (EIR) and mosquito density. Sensitivity analysis explored the influence of key parameters driving transmission in this scenario study. The immature mosquito mortality rate due to larviciding is estimated to be 61% based on field data. VCOM simulation showed that, at 80%, ITNs coverage, larviciding substantially reduced mosquito densities and EIR. Specifically, combining ITNs at 80% and larviciding coverage ≥ 60% lowered EIR below 1, the threshold required to interrupt malaria transmission. Sensitivity analyses highlighted the high impact of targeting immature mosquitoes, suggesting larviciding can effectively complement ITNs to control vectors, including invasive species like An. stephensi, regardless of feeding preference, resting, and biting behaviors, which hinder the effectiveness of most vector control tools. This study provides local evidence that larviciding is an effective complement to ITNs for interrupting malaria transmission. Implementation should leverage innovative approaches, such as drones for precise mapping and targeted application of biological larvicides, to maximize coverage, and scalability for district-level malaria control and elimination. Background Malaria, caused by the Plasmodium protozoan parasite and transmitted by a female anopheles’ mosquito, remains a significant global health threat ( 1 ). In 2023, there were an estimated 263 million cases globally, with the African region accounting for 89.7% of this burden ( 2 ). In Tanzania, malaria ranks among the top causes of mortality and morbidity; malaria case incidence increased by 5.6% from 121 in 2022 to 128 per 1000 of the population at risk ( 2 – 4 ). The Rufiji district, found in south-eastern Tanzania, is a recognized malaria hotspot within the country ( 5 ). The World Health Organization (WHO) recommends chemoprevention, case management, and vector control as core malaria interventions. However, the primary vector control tools, notably insecticide-treated nets (ITNs) and indoor residual spraying (IRS), are facing challenges. This is due to several factors, including insecticide resistance and changes in mosquito behavior, such as increased outdoor and early-evening biting, which reduce the effectiveness of these indoor-targeted tools ( 6 ). These challenges have contributed to a shortfall in achieving national and global targets such as the WHO’s 2030 goal to reduce malaria incidence and mortality by at least 90% from 2015 levels. As of 2023, globally, there were 60 cases per 1,000 people at risk and 13.7 deaths per 100,000 people at risk, compared to the targeted figures of 21.3 cases per 1,000 and 5.5 deaths per 100,000 people at risk ( 2 , 7 ). To address these limitations, the WHO promotes the Integrated Vector Management (IVM) strategy for sustainable malaria control and elimination ( 8 ). A combination of various interventions aims to target mosquitoes at multiple points in their lifecycle and in diverse settings, both indoors and outdoors. Key elements of IVM include evidence-based decision-making, the integration of non-chemical and chemical vector control methods, advocacy and social mobilization, and inter-sectoral collaboration and action. The key elements have recently been re-emphasized and endorsed by the World Health Assembly (WHA) in the form of pillars of action in the Global Vector Control Response framework for 2017–2030 ( 8 ). Innovative tools are needed to supplement existing ones rather than replace them. These include application of larviciding to affect the growth of immature mosquitoes ( 9 ), use of attractive toxic sugar baits (ATSBs) to lure and kill adult mosquitoes seeking sugar sources ( 10 ), treatment of cattle with endectocides to target mosquitoes that feed on livestock and reduce vector survival ( 11 ), house improvement and modification such as screening, sealing eaves, and using improved housing materials to minimize mosquito entry ( 12 ), and deployment of spatial repellents that create a protective barrier and reduce human–mosquito contact during outdoor activities ( 13 ). Further research is needed to understand the combined impact of these interventions in targeted areas. For instance, IVM programs incorporating larviciding with Bacillus thuringiensis israelensis (Bti) to the core vector interventions have been adopted in several African countries such as Kenya and Ethiopia contributing to significant malaria reductions ( 14 , 15 ). An alternative strategy to overcome the ITNs and IRS constraints is to focus on the larval phases of mosquitoes in their breeding areas, where they are densely populated, confined, and readily reachable ( 16 ). Larviciding is an intervention that aims at reducing the larval abundance, subsequently reducing the adult population by increasing the larval mortality. Larviciding process can include the use of chemicals, oils, or bio-larvicides ( 17 ). Bio-larvicides are currently advised for use, as they are environmentally friendly ( 18 ). Larviciding has shown a remarkable impact to complement ITNs and IRS targeting mosquitoes in the larval stage to interrupt malaria transmission ( 19 ), with different studies done around Sub-Saharan Africa showing that community-based involvement in the application of larviciding has brought a positive impact on the reduction of mosquito density ( 20 – 23 ). In Tanzania, larviciding has been added in the 2021-205 Malaria Strategic Plan as a supplementary vector control intervention alongside ITNs and IRS ( 5 ). There is growing advocacy for its broader implementation and its prioritization as one of the complementary interventions against vector control. Dar es Salaam, where larviciding was first implemented in Tanzania at a large scale, serves as a notable example, having contributed to the reduction of malaria transmission in this urban setting ( 24 ). Similarly, other countries have also prioritized larviciding; for instance, in Rwanda, drones are being used to implement larvicides in plantations ( 25 ), while in Malawi community-engagement plays a central role in larvicide application efforts ( 26 ). However, current evaluation methods for these interventions often rely on routine entomological monitoring and field observations, which can be resource-intensive, spatially limited, and slow to detect impacts. Mathematical models are essential tools to guide stakeholders in making evidence-based decisions that are appropriate at a given scenario, the level of impact an intervention will bring, and predictions of future situations ( 27 ). However, many existing malaria models, including those of Kiware et al . which is Vector Control Optimization Model (VCOM), often rely on generalized parameters that may not capture local transmission dynamics, ecology, and seasonality. They use generalized scenarios simulating disease transmissions and impacts of interventions or predictions rather than local contexts that reflect the situationship and assist in making informed decisions. To address this gap, we aim to assess the impact of larviciding in combination with ITNs using a mathematical model based on district field-based data from Southeastern Tanzania, in malaria vector control, specifically An. gambiae s.l. in the Rufiji district as one of the dominant mosquito groups in the area. This will be achieved by using the field district-based data to inform the VCOM ( 28 ) model on the mortality rate of mosquitoes due to larviciding application. District-based modelling will ensure to capture variation in transmission ecology, seasonality impacts to the transmission and how different-intervention coverage levels impact malaria-vector control, providing evidence that is both rigorous and actionable for malaria control programs at that specific context. Methods Description of the study area The dataset used in this study was generated during a pilot project implemented in the Rufiji district, found in the southeastern part of Tanzania at 7.8387° S, 38.3481° E. The Rufiji district covers 14, 500 km 2 and the human activities in this area are mostly agriculture (rice) and fishing. The study took place in two wards that included 19 villages. The coverage of ITNs in this area is approximated to be 80% − 85% and the malaria prevalence of 26% [95% CI, 23.7-27.8] ( 29 ). There is no application of IRS in this area because in Tanzania IRS is applied in lake zone areas due to higher transmission in this zone. The Rufiji district can be seen from the Tanzania map in Fig 1 Download figure Open in new tab Fig 1: Project Implementation district Study design Modelling components was not part of the original objective for this study done in Rufiji, the use of this data to answer the question of assessing the impact of larviciding makes it a retrospective study Data Collection The project involved data collection during two phases, a baseline phase (2015–2016) where larviciding was not implemented in the Rufiji district, and an intervention phase (2020–2021) where larviciding was implemented. Both mature and immature mosquito population data were collected daily in the given areas of study. Other information collected with the number of mosquitoes includes the mosquito species, household identification number, location where the mosquito was collected if indoor or outdoor and the nature of the source in which the mosquito is collected if wet or dry. The malaria vectors were collected using three different methods: CDC-light traps, dipping, and clay pots. CDC-light traps were used to collect indoor matured mosquitoes while clay ports were used for both indoor and outdoor collection. Dipping was used for the collection of the immature mosquito population. Visualization of the monthly collected data of the female An. gambiae s.l. was done to show the visual difference in the monthly collections of the mosquitoes [fig4]. Parameter Estimation Both the first phase (control) and second phase (intervention) data were used to estimate the proportional increase in mortality of the mosquito population; the change in the mosquito density between the two phases stands as a proxy for the mortality estimated. Seasonality impact was accounted for when calibrating using a seasonal function (without meteorological effects) ( 30 , 31 ) and the data fitted. The proportion effect of larviciding was obtained from a comparison between the two dataset parameters. Bayesian inference was performed in Stan ( 32 ) in Rstudio ( 33 ). For each parameterized model, we run four Markov chains with 2000 iterations, removing the first 500 for burn-in. The convergence of chains was checked using the diagnostics available within Stan, including the R-statistic and effective sample size. Hyperparameter posteriors were plotted as pairwise scatter plots to identify any potential parameter identifiability issues. Sensitivity analysis Sensitivity analysis ( 34 ) is a method to determine how different dependent variables affect the output of the independent variable or model. For the parameters used in the VCOM model equations, sensitivity analysis was done to analyse how they drive malaria transmission in this scenario study. Knowing the sensitivity index of a parameter to the output will allow the control of the parameters that are responsible for increasing transmission ( 35 ). The values for the parameters and range variation that were used in the analysis can be seen in Table 1 . The normalized forward sensitivity index method is used. View this table: View inline View popup Table 1: Parameters used in the model and sensitivity analysis including their ranges. The Vector Control Optimization Model (VCOM) The VCOM model ( 28 ) was adapted by applying the study area-specific parameters, which are mortality due to larviciding application and EIR for Rufiji. Other parameters from the literature and others originating from the VCOM model were used to study the impact of larviciding when added to ITNs [ Table 1 ]. The model was adapted because it simulates mosquito life stages and their interactions with humans, in addition to incorporating various vector control interventions. This allows changes in the intervention coverage to demonstrate their impact on malaria vector control [ fig 2 ]. The model was extended to capture the seasonality impact on malaria transmission ( 36 ). Several entomological outputs of the model can be considered as measures of the impact of intervention including, mosquito density, the reduction in human biting rate, and EIR ( 28 ). Download figure Open in new tab Fig 2: The schematic highlights opportunities for existing and novel vector control tools that can be used to target mosquitoes both indoors and outdoors and at all stages of the mosquito life and feeding cycles. Synergy and layering between interventions follow from this schematic diagram and how all the interventions are encoded from it ( 28 ). A one-year simulation, modeled on a daily scale, was performed to examine the effects of different larviciding coverage levels in combination with varying ITNs coverage scenarios. Because not all interventions are implemented simultaneously, it was assumed that ITNs deployment would occur first, followed by larviciding. to reflect on real field situations. Each mosquito state was simulated to demonstrate the reductions achieved when these interventions are applied at different times or coverage levels. These simulations can be automatically viewed in the available software open access available in the GUI [ http://skiware.github.io/VCOM/ ]. Results Sensitivity analysis Sensitivity analysis was conducted to evaluate the impact of various transmission parameters on the model’s output. Sensitivity indices were calculated for each parameter to quantify their influence on the model dynamics. The parameter definition and range are provided in Table 1 . The results, illustrated in Fig 3 , indicate that different parameters exhibit varying degrees of sensitivity. Specifically, the human recovery rate (recRate), duration of the latent period in mosquitos (durEV) and adult mosquito daily mortality in the presence of interventions (muVCom) have significant negative impacts, meaning that increases in these parameters lead to a reduction in transmission dynamics. Meanwhile, force of infection in vectors at equilibrium (lambdaV) and transmission efficiency from an infectious mosquito to an uninfected, susceptible human (bh) demonstrate a positive sensitivity index, suggesting that higher values of these parameters could potentially increase the transmission of malaria. Download figure Open in new tab Fig 3: Sensitivity indexes indicating how parameters impact the transmission Effect of Larviciding on Mortality Using the Bayesian inference approach, a proportion of 0.61 is obtained as an estimated impact to mortality. This value is directly used in the VCOM model as a proportional increase to the mortality value of the An. gambiae s.l species in the larval stage. The visualization of the two datasets, as shown in Fig 4 , clearly illustrates this reduction. Download figure Open in new tab Fig 4: Monthly collection of Anopheles gambiae in the two phases Simulating the population The results from the differential equations of the VCOM model were simulated according to mosquito populations and other entomological output measures. The simulation results for these two populations are in Fig 5 . Because not all the interventions were implemented from the beginning, larviciding was introduced after ITNs. The simulation was then conducted, assuming the interventions were implemented at different times. Out of the 365 days in a year, the ITNs starts at the beginning, and larviciding on the 150th day. Download figure Open in new tab Fig 5. Simulation of the population involved in the model. (A) shows the matured mosquito population. (B) shows the immature population. (C) a zoomed version to display the late instar reduction in the application of these two interventions. (D) indicated the bar plot for total mosquito population. All simulations were performed with the application of 80% ITNs and 60% larviciding to simulate these mosquito population dynamics. The mature mosquito population in Fig 5(a) indicates a vivid reduction in the susceptible population, while the infected and latent population also declines, though their numbers are small compared to the susceptible population. The small proportion of infected mosquitoes compared to the overall mosquito population is a result of the intervention’s impacts. For the immature mosquito population, the application of the intervention results in reducing the number of early larvae, along with other immature stages, as shown in Fig 5(b) . Due to the impact of larviciding on the larval stage, a reduction in the number of late instar stages can be observed, as depicted in Fig 5(c) , which is zoomed-in view from the previous Fig 5(b) . A bar graph can also effectively visualize the total populations of the mosquito stages, revealing that the infected mosquito population remains very small for the given simulation period. Mosquito Density Fig 6(b) shows that the mosquito-to-human ratio decreases more when two interventions are implemented compared to using a single intervention. A noticeable reduction in mosquito density is observed with the deployment of ITNs at 80% coverage (before larviciding) and even greater reduction when both ITNs at 80% coverage and larviciding at 60% coverage are implemented on the 150 th day of the year (after larviciding). The combined interventions result in a 43.9% decrease in mosquito density. This reduction is attributed to the effect of larvicides, which prevent immature mosquitoes from developing into adults, thereby directly reducing the overall mosquito population. Download figure Open in new tab Fig 6: Simulated entomological outputs of the model. (A) Entomological inoculation rate (EIR) over time. (B) Adult mosquito density over time. (C) EIR under varying ITNs coverage with larviciding fixed at 60%. (D) EIR under varying larviciding coverage with ITNs fixed at 80%. Entomological Inoculation Rate The value of EIR decreases over time following the implementation of interventions. EIR is measured in relation to the proportion of infectious mosquitoes within the population. As shown in Fig 6(a) , a reduction in EIR is observed when ITNs are deployed at 80% coverage (before larviciding) and when both ITNs at 80% and larviciding at 60% coverage are implemented on the 150 th day of the year (after larviciding). The addition of larviciding results in a further 26.6% decrease in EIR. Increasing larviciding coverage in combination with ITNs leads to a reduction in EIR, allowing for analysis of different intervention scenarios’ output. The target is to achieve an EIR of less than one, which indicates interruption of malaria transmission. From Fig 6(d) , this threshold is reached at larviciding coverages greater than 40%. Meanwhile, when only ITNs are used, the EIR does not fall below this threshold throughout the simulation period, as shown by the blue line in Fig 6(a) . Although ITNs alone reduce EIR and lower transmission levels, residual transmission remains within the population. When varying ITNs coverage while maintaining larviciding at a constant 60%, Fig 6(c) shows that ITNs coverage above 60% can achieve an EIR below one. Discussion This study evaluated the impact of incorporating larviciding alongside the existing ITNs intervention for malaria vector control in Rufiji District. The findings indicate that ITNs alone are insufficient to achieve optimal vector control. The entomological component of this work examined the effects of these interventions through a modeling framework, enabling a quantitative assessment of their combined impact on transmission dynamics. To the best of our knowledge, this represents the first study in Rufiji District to undertake a direct, model-based comparison assessing the additional benefit of larviciding on malaria vector populations. The study observed a reduction in the relative mosquito density of An. gambiae s.l., following the application of larviciding in Rufiji, with an increase in mortality of 61%. From the simulation of the entomological outcomes, EIR was observed to drop by 22-fold in the use of both interventions and 9-fold in the use of only ITNs. A 7-fold drop in mosquito density was observed with the use of both interventions, while a 2-fold drop was observed with the use of only ITNs. This finding shows that additional complementary tools, such as larviciding, that ensure a reduction of mosquito density are essential. There is considerable heterogeneity in the outcome measures used to assess the impact of larviciding intervention. Most studies have reported effect on the EIR, human biting exposure, and the larval or adult mosquito densities. In urban Dar es Salaam, a 96% reduction in Anopheles larvae was observed after one year of intervention, accompanied by a 21.3% reduction in EIR. Additionally, the intervention is highly recommended to be used as a supplementary intervention but not replacing the primary tools which are LLINs and IRS ( 37 ). The aspect of timing was discussed by Fillinger et al., who reported larviciding to reduced Anopheles larval density by 95% and human exposure to bites from adults by 92% in rural Kenya ( 9 ), and his other study in western Kenya that reported a >90% of larval density with 73% reduction in EIR ( 38 ). The intervention is said to be seasonal, with advice on the application of larviciding in the dry season and at the beginning of the rainy season. In Yaoundé, Cameroon it was observed with over a 68% reduction of Anopheline densities collected using CDC-LT and 79% reduction of EIR ( 39 ). In Rwanda reported Ano pheles reduction in the rice farms due to application of Larviciding in different rounds ( 40 ). In Burkina Faso using Bti based larviciding ensured the number of female Anophelese species to drop by 70% ( 19 ). The application of larviciding has shown a positive impact on malaria vector control and is said to be a safe intervention with no adverse effects to other organisms ( 41 ). The IVM strategy, with involvement of larviciding as a supplementary tool will ensure reduction in malaria transmission by reducing the adult mosquito population. Moreover, the Bti used in this study to-date shows no resistance to its efficiency and is therefore highly advised ( 42 ). The limitation of this study includes, first, that attaining full larviciding coverage in a real-life scenario for all available habitats is difficult and requires extensive surveillance and community engagement. Nevertheless, the impact assessed by the model assumes proper implementation of larviciding to reach the required coverage, which is challenging in an operational field setting. This intervention is said to be applicable with an accurate measure of coverage in areas where mosquito habitats are few, fixed, and findable ( 16 , 17 ). This argument is also discussed by Tusting et al., who suggest that larviciding can be applied successfully and at reasonable cost even in areas not traditionally recommended by WHO ( 43 ). To ensure maximization of coverage and efficiency, implementation should leverage innovative approaches such as the use of drones for precise mapping and targeted application of biological larvicides. Drones can enhance surveillance by detecting and mapping potential breeding sites that are difficult to identify through ground-based surveys, such as those formed in flooded fields, irrigation channels, or water-filled cow hoof prints common in livestock-keeping areas like Rufiji. By providing high-resolution imagery and geographic data, drone technology can help vector control teams target interventions more efficiently, reduce missed habitats, and improve resource allocation. Furthermore, the involvement of communities in larvicide application can complement these technologies, ensuring access to smaller or temporary habitats that may not be captured by aerial mapping, thereby expanding coverage and promoting sustainability ( 21 ). Secondly, the control data and intervention data were collected at two different time points; this may lead to differences in some external factors that cannot be captured in the model and so inconsistencies. Thirdly, the issue of community involvement is essential in LSM and larviciding itself, this factor was not mostly considered in this modeling study. However, this study has demonstrated the additional advantage to the fight against malaria vector control that can be attained when larviciding is used in combination with ITNs. The effectiveness observed when larviciding is deployed complementing ITNs use ensures more interruption in malaria transmission in the area. Future study should consider impact assessment for the larval source management intervention including the quantification of source management component, this will require study to collect the data on how the SM was performed and its contribution to vector population, this will inform the model. However, the impact of this intervention beyond malaria vector species can be assessed in the future. Conclusion This study provides valuable insights into the potential of larviciding as an effective and key strategy for malaria control in the local context. While promising results were observed, the study also highlights the operational challenges of achieving optimal larviciding coverage as suggested by the model outcomes. Innovative solutions such as the use of drones could enhance the identification and mapping of mosquito breeding habitats, particularly in hard-to-reach or inaccessible areas, including those associated with livestock activity such as puddles formed by cow hooves in Rufiji. Strengthening community engagement will further support sustainability and ensure wider and more effective larviciding coverage. Overall, these findings contribute to the broader understanding of integrated vector control strategies and provide a foundation for future studies and policy development. Data Availability The datasets used and/or analyzed during the current study are available in an open-source database (mosquitodb.io), the corresponding author will give access to the specific data upon reasonable request. Acknowledgement We would like to express our sincere gratitude to the technicians, data collectors, volunteers, villagers where the study was conducted and organizations that generously provided access to their resources, which greatly contributed to the development of this manuscript. Their insights have significantly enhanced the quality of our work. Reference 1. ↵ Yoeli M . Sir Ronald Ross and the evolution of malaria research . Bull N Y Acad Med . 1973 ; 49 ( 8 ): 722 – 35 . OpenUrl PubMed 2. ↵ Organization WH . WHO , World malaria report 2024 . 2024 . 3. Kigadye ES , Nkwengulila G , Magesa SM , Abdulla S . Spatial variability in the density, distribution and vectorial capacity of anopheline species in Rufiji district, south-eastern Tanzania . Tanzan J Health Res . 2011 ; 13 ( 4 ): 112 – 8 . OpenUrl PubMed 4. ↵ Health TMo . Strategic master plan for the neglected tropical diseases control program Tanzania Mainland . Minist Heal . 2021 . 5. ↵ Program NMC . National Malaria Strategic plan 2021–2025 Transitioning to Malaria Elimination in Phases . 2020 . 6. ↵ N’Guessan R , Corbel V , Akogbeto M , Rowland M . Reduced efficacy of insecticide-treated nets and indoor residual spraying for malaria control in pyrethroid resistance area, Benin . Emerg Infect Dis . 2007 ; 13 ( 2 ): 199 – 206 . OpenUrl CrossRef PubMed Web of Science 7. ↵ Bhatt S , Weiss DJ , Cameron E , Bisanzio D , Mappin B , Dalrymple U , et al. The effect of malaria control on Plasmodium falciparum in Africa between 2000 and 2015 . Nature . 2015 ; 526 ( 7572 ): 207 – 11 . OpenUrl CrossRef PubMed 8. ↵ Organization WH , UNICEF . Global vector control response 2017-2030 . Global vector control response 2017 -20302017. 9. ↵ Fillinger U , Lindsay SW . Suppression of exposure to malaria vectors by an order of magnitude using microbial larvicides in rural Kenya . Trop Med Int Health . 2006 ; 11 ( 11 ): 1629 – 42 . OpenUrl CrossRef PubMed Web of Science 10. ↵ Traore MM , Junnila A , Traore SF , Doumbia S , Revay EE , Kravchenko VD , et al. Large-scale field trial of attractive toxic sugar baits (ATSB) for the control of malaria vector mosquitoes in Mali, West Africa . Malar J . 2020 ; 19 ( 1 ): 72 . OpenUrl CrossRef PubMed 11. ↵ Lyimo IN , Kessy ST , Mbina KF , Daraja AA , Mnyone LL . Ivermectin-treated cattle reduces blood digestion, egg production and survival of a free-living population of Anopheles arabiensis under semi-field condition in south-eastern Tanzania . Malar J . 2017 ; 16 ( 1 ): 239 . OpenUrl PubMed 12. ↵ Tusting LS , Ippolito MM , Willey BA , Kleinschmidt I , Dorsey G , Gosling RD , et al. The evidence for improving housing to reduce malaria: a systematic review and meta-analysis . Malar J . 2015 ; 14 : 209 . OpenUrl CrossRef PubMed 13. ↵ Achee NL , Bangs MJ , Farlow R , Killeen GF , Lindsay S , Logan JG , et al. Spatial repellents: from discovery and development to evidence-based validation . Malar J . 2012 ; 11 : 164 . 14. ↵ Mutero CM , Okoyo C , Girma M , Mwangangi J , Kibe L , Ng’ang’a P , et al. Evaluating the impact of larviciding with Bti and community education and mobilization as supplementary integrated vector management interventions for malaria control in Kenya and Ethiopia . Malaria Journal . 2020 ; 19 : 1 – 17 . OpenUrl CrossRef PubMed 15. ↵ Teshome A , Erko B , Golassa L , Yohannes G , Irish SR , Zohdy S , et al. Laboratory-based efficacy evaluation of Bacillus thuringiensis var. israelensis and temephos larvicides against larvae of Anopheles stephensi in ethiopia . Malar J . 2023 ; 22 ( 1 ): 48 . OpenUrl PubMed 16. ↵ Okumu F , Moore SJ , Selvaraj P , Yafin AH , Juma EO , Shirima GG , et al. Elevating larval source management as a key strategy for controlling malaria and other vector-borne diseases in Africa . Parasites & Vectors . 2025 ; 18 ( 1 ): 45 . OpenUrl PubMed 17. ↵ Salon V , IFRC G. Draft Outline of an Operational Manual. 18. ↵ Shinde L , Goyal M , Bayas R , Patil S . Review on Potential Eco-friendly Biolarvicides against Dengue Vector: Aedes aegypti (Linn)(Diptera: Culicidae) . International J Life Sci Res . 2018 ; 6 : 61 – 80 . OpenUrl 19. ↵ Dambach P , Baernighausen T , Traore I , Ouedraogo S , Sie A , Sauerborn R , et al. Reduction of malaria vector mosquitoes in a large-scale intervention trial in rural Burkina Faso using Bti based larval source management . Malar J . 2019 ; 18 ( 1 ): 311 . OpenUrl PubMed 20. ↵ Derua YA , Kahindi SC , Mosha FW , Kweka EJ , Atieli HE , Wang X , et al. Microbial larvicides for mosquito control: Impact of long lasting formulations of Bacillus thuringiensis var. israelensis and Bacillus sphaericus on non-target organisms in western Kenya highlands . Ecol Evol . 2018 ; 8 ( 15 ): 7563 – 73 . OpenUrl PubMed 21. ↵ Shirima G , Masserey T , Gervas H , Chitnis N , Kiware S , Mirau S . Assessing the role of community involvement and capacity building in larviciding applications for malaria control in Africa: A scoping review . Curr Res Parasitol Vector Borne Dis . 2025 ; 8 : 100307 . OpenUrl PubMed 22. Maheu-Giroux M , Castro MC . Impact of community-based larviciding on the prevalence of malaria infection in Dar es Salaam, Tanzania . PLoS One . 2013 ; 8 ( 8 ): e71638 . OpenUrl CrossRef PubMed 23. ↵ Ochomo E , Rund SSC , Mthawanji RS , Antonio-Nkondjio C , Machani M , Samake S , et al. Mosquito control by abatement programmes in the United States: perspectives and lessons for countries in sub-Saharan Africa . Malar J . 2024 ; 23 ( 1 ): 8 . OpenUrl CrossRef PubMed 24. ↵ Vanek MJ , Shoo B , Mtasiwa D , Kiama M , Lindsay SW , Fillinger U , et al. Community-based surveillance of malaria vector larval habitats: a baseline study in urban Dar es Salaam, Tanzania . BMC Public Health . 2006 ; 6 : 154 . OpenUrl CrossRef PubMed 25. ↵ Munyakanage D , Niyituma E , Mutabazi A , Misago X , Musanabaganwa C , Remera E , et al. The impact of Bacillus thuringiensis var. israelensis (Vectobac((R)) WDG) larvicide sprayed with drones on the bio-control of malaria vectors in rice fields of sub-urban Kigali, Rwanda . Malar J . 2024 ; 23 ( 1 ): 281 . OpenUrl PubMed 26. ↵ McCann RS , Kabaghe AN , Moraga P , Gowelo S , Mburu MM , Tizifa T , et al. The effect of community-driven larval source management and house improvement on malaria transmission when added to the standard malaria control strategies in Malawi: a cluster-randomized controlled trial . Malaria Journal . 2021 ; 20 ( 1 ): 232 . OpenUrl PubMed 27. ↵ Chitnis N , Schapira A , Smith DL , Smith T , Hay SI , Steketee R . Mathematical modelling to support malaria control and elimination . Roll Back Malaria Progress and Impact Series . 2010 ; 5 : 48 . 28. ↵ Kiware SS , Chitnis N , Tatarsky A , Wu S , Castellanos HMS , Gosling R , et al. Attacking the mosquito on multiple fronts: Insights from the Vector Control Optimization Model (VCOM) for malaria elimination . PLoS One . 2017 ; 12 ( 12 ): e0187680 . OpenUrl CrossRef PubMed 29. ↵ Mlacha YP , Wang DQ , Chaki PP , Gavana T , Zhou ZB , Michael MG , et al. Effectiveness of the innovative 1,7-malaria reactive community-based testing and response (1, 7-mRCTR) approach on malaria burden reduction in Southeastern Tanzania . Malaria Journal. 2020 ; 19 ( 1 ). 30. ↵ TPH S. OpenMalaria: A simulator of malaria epidemiology and control . Available from: https://githubcom/SwissTPH/openmalaria . Available from: https://github.com/SwissTPH/openmalaria2021 . 31. ↵ Sanders CJ , Shortall CR , Gubbins S , Burgin L , Gloster J , Harrington R , et al. Influence of season and meteorological parameters on flight activity of Culicoides biting midges . Journal of Applied Ecology . 2011 ; 48 ( 6 ): 1355 – 64 . OpenUrl Web of Science 32. ↵ Team SD. RStan: the R interface to Stan. R package version 2.32.7 : https://mc-stan.org/ ; 2025 . 33. ↵ Allaire J . RStudio: integrated development environment for R. Boston , MA . 2012 ; 770 ( 394 ): 165 – 71 . OpenUrl 34. ↵ Saltelli A , Annoni P , Azzini I , Campolongo F , Ratto M , Tarantola S . Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index . Computer physics communications . 2010 ; 181 ( 2 ): 259 – 70 . OpenUrl CrossRef PubMed Web of Science 35. ↵ Smith T , Maire N , Ross A , Penny M , Chitnis N , Schapira A , et al. Towards a comprehensive simulation model of malaria epidemiology and control . Parasitology . 2008 ; 135 ( 13 ): 1507 – 16 . OpenUrl CrossRef PubMed Web of Science 36. ↵ White MT , Griffin JT , Churcher TS , Ferguson NM , Basanez MG , Ghani AC . Modelling the impact of vector control interventions on Anopheles gambiae population dynamics . Parasit Vectors . 2011 ; 4 : 153 . OpenUrl CrossRef PubMed 37. ↵ Geissbühler Y , Kannady K , Chaki PP , Emidi B , Govella NJ , Mayagaya V , et al. Microbial larvicide application by a large-scale, community-based program reduces malaria infection prevalence in urban Dar es Salaam, Tanzania . PLOS ONE . 2009 ; 4 ( 3 ): e5107 . OpenUrl CrossRef PubMed 38. ↵ Fillinger U , Ndenga B , Githeko A , Lindsay SW . Integrated malaria vector control with microbial larvicides and insecticide-treated nets in western Kenya: a controlled trial . Bull World Health Organ . 2009 ; 87 ( 9 ): 655 – 65 . OpenUrl CrossRef PubMed Web of Science 39. ↵ Antonio-Nkondjio C , Doumbe-Belisse P , Djamouko-Djonkam L , Ngadjeu CS , Talipouo A , Kopya E , et al. High efficacy of microbial larvicides for malaria vectors control in the city of Yaounde Cameroon following a cluster randomized trial . Sci Rep . 2021 ; 11 ( 1 ): 17101 . OpenUrl CrossRef PubMed 40. ↵ Hakizimana E , Ingabire CM , Rulisa A , Kateera F , van den Borne B , Muvunyi CM , et al. Community-based control of malaria vectors using Bacillus thuringiensis var. Israelensis (Bti) in Rwanda . International Journal of Environmental Research and Public Health . 2022 ; 19 ( 11 ): 6699 . OpenUrl 41. ↵ Lagadic L , Roucaute M , Caquet T . Bti sprays do not adversely affect non-target aquatic invertebrates in F rench A tlantic coastal wetlands . Journal of applied ecology . 2014 ; 51 ( 1 ): 102 – 13 . OpenUrl 42. ↵ Becker N . Bacterial control of vector-mosquitoes and black flies . Entomopathogenic bacteria: from laboratory to field application: Springer ; 2000 . p. 383 – 98 . 43. ↵ Tusting LS , Thwing J , Sinclair D , Fillinger U , Gimnig J , Bonner KE , et al. Mosquito larval source management for controlling malaria . Cochrane Database of Systematic Reviews . 2013 ( 8 ). View the discussion thread. Back to top Previous Next Posted November 15, 2025. Download PDF Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. 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Share Modelling the impact of larviciding as a supplementary malaria vector control intervention in rural south-eastern Tanzania: A district-level simulation study GloriaSalome Shirima , Emma L Fairbanks , Gavana Tegemeo , Gerald Kiwelu , Ismail Nambunga , Yeromin P Mlacha , Silas Mirau , Prosper Chaki , Nakul Chitnis , Samson Kiware medRxiv 2025.11.13.25340176; doi: https://doi.org/10.1101/2025.11.13.25340176 Share This Article: Copy Citation Tools Modelling the impact of larviciding as a supplementary malaria vector control intervention in rural south-eastern Tanzania: A district-level simulation study GloriaSalome Shirima , Emma L Fairbanks , Gavana Tegemeo , Gerald Kiwelu , Ismail Nambunga , Yeromin P Mlacha , Silas Mirau , Prosper Chaki , Nakul Chitnis , Samson Kiware medRxiv 2025.11.13.25340176; doi: https://doi.org/10.1101/2025.11.13.25340176 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Epidemiology Subject Areas All Articles Addiction Medicine (568) Allergy and Immunology (863) Anesthesia (300) Cardiovascular Medicine (4435) Dentistry and Oral Medicine (444) Dermatology (382) Emergency Medicine (608) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1509) Epidemiology (15229) Forensic Medicine (30) Gastroenterology (1124) Genetic and Genomic Medicine (6600) Geriatric Medicine (668) Health Economics (997) Health Informatics (4536) Health Policy (1368) Health Systems and Quality Improvement (1613) Hematology (541) HIV/AIDS (1264) Infectious Diseases (except HIV/AIDS) (15916) Intensive Care and Critical Care Medicine (1103) Medical Education (623) Medical Ethics (146) Nephrology (667) Neurology (6599) Nursing (346) Nutrition (998) Obstetrics and Gynecology (1144) Occupational and Environmental Health (957) Oncology (3332) Ophthalmology (974) Orthopedics (369) Otolaryngology (420) Pain Medicine (436) Palliative Medicine (130) Pathology (663) Pediatrics (1693) Pharmacology and Therapeutics (691) Primary Care Research (711) Psychiatry and Clinical Psychology (5447) Public and Global Health (9232) Radiology and Imaging (2198) Rehabilitation Medicine and Physical Therapy (1370) Respiratory Medicine (1196) Rheumatology (593) Sexual and Reproductive Health (712) Sports Medicine (530) Surgery (712) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a00aa3b2ff464ece',t:'MTc3OTYwODI5Mg=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();