Human and vector behavioral determinants of malaria transmission dynamics in Nandi highlands, western Kenya

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Machani, Anoop Sunkara, Shehu Shagari Awandu, Maurice Ombok, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7478328/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 Dec, 2025 Read the published version in Malaria Journal → Version 1 posted 10 You are reading this latest preprint version Abstract Malaria transmission, characterised by spatial and temporal heterogeneity and complex vector behaviors, persists in Kenya’s highlands despite widespread use of Long-lasting insecticidal nets (LLINs). The role of human activity in exposure risk remains underexplored. Identifying vulnerable times and locations is crucial for designing and optimizing targeted control strategies that address the intricate interplay between human activity and local vector behavior that results in transmission. This study examined human-mosquito interactions in three different ecological settings in Nandi highlands in western Kenya. Methods: Malaria vector biting rates were monitored both indoors and outdoors from 18:00 to 06:00 over five consecutive nights in ten houses per village in three different ecological settings namely site close to the forest (Kipsamoite), neutral site neither close to forest nor swamp (Kebulonik), site close to the swamp and with past high malaria prevalence (Kapsisywa) using human landing catches (HLC) during the long (May 2018) and short (October 2018) rainy seasons. Concurrently, hourly human behavior observations (HBOs) were conducted to assess indoor versus outdoor presence, sleeping patterns and LLINs use. All Anopheles mosquitoes were first identified morphologically using standard anopheline keys and subsequently confirmed to species level through molecular sequencing of the internal transcribed spacer 2 (ITS2) region and cytochrome c oxidase subunit 1 (CO1) gene. Results: High Anopheles species diversity was observed, with site-specific dominance: An. arabiensis in Kipsamoite, An. christyi in Kebulonik, and the novel An. spp. 14 BSL-2014 in multiple sites. The majority of collections were indoors in Kipsamoite (67%) and Kebulonik (52.9%), while in Kapsisywa (58.3%) were outdoors. Mosquito exposure peaked outdoors in the early evening (1800-2100h) and indoors during the first half of the night (1900-0100h), coinciding with periods when people were awake or transitioning to or from sleep, with low LLIN use. Human behavior-adjusted exposure was highest outdoors in the early evening (1800-2100h) and indoors during the first half of the night (1900-0100h). Overall, most exposure occurred indoors for unprotected sleepers and individuals awake (53-55%), followed by outdoor exposure in the early evening and late morning (16-44%). LLINs prevented 24.5 to 44.9% of bites in Kipsamoite, 24.6 to 37% in Kebulonik, and 35.8% in Kapsisywa. Conclusion: This study demonstrates that human exposure to malaria vectors is shaped by the interplay between temporal and spatial human and vector behaviors, with the highest biting rates indoors for unprotected sleepers and awake individuals, and outdoor exposure peaking in the early evening and late morning. It also reveals diverse, behaviorally adaptable vector populations, including cryptic species, sustaining indoor and outdoor transmission. While LLINs use provide partial protection, significant gaps in protection remained during periods and in spaces where nets are not effective, highlighting persistent residual transmission and the need for vector characterization, behavior-informed interventions (e.g., spatial repellents and larviciding), community engagement, and strengthened entomological surveillance to guide effective malaria control. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Despite the declining malaria transmission, it remains a public concern in the highlands of East Africa, with an estimated 34 million people at risk of infection. In Kenya, nearly 7 million people (about 23% of the total population) live in 15 counties classified as highland malaria epidemicprone areas(1, 2 ). Since the mid-20th century, epidemic malaria has been reported more frequently in the Western highlands of Kenya than any other part of the country(3). However, malaria transmission in these areas is highly variable, with heterogeneities observed between years within the same locale. For instance, in the Nandi highlands, malaria incidence fluctuated between 2001 and 2005, with some villages experiencing sharp increases followed by declines(1, 4 ). The heterogeneity of malaria risk and transmission is largely influenced by vector species composition, local environmental condition, human behavior, social factors at the village or household level, and variations in healthcare service delivery ( 5 – 11 ). In 2015, WHO set a goal to reduce global malaria incidence and mortality by 90% by 2030, with many African countries aiming for elimination(12). Achieving this will require adaptive, cost-effective strategies tailored to local eco-epidemiological conditions. Despite the proven impact of vector control tools i.e. long-lasting insecticide treated nets (LLINs), indoor residual spraying (IRS) and artemisinin-based combination therapies (ACTs) in reducing malaria transmission( 13 ), persistent transmission threaten progress toward elimination( 14 , 15 ). These interventions not only protect individuals but also provide community-wide benefits to non-users( 16 ). However, their widespread use has driven behavioral adaptations in mosquito populations, including shifts in biting time, species composition, and host preference( 17 – 20 ). In Kenya’s highlands, targeted IRS and ACT use led to temporary malaria elimination between 2007 and 2008( 21 ), yet transmission rebounded( 22 ), underscoring the resilience of malaria vectors and the limitations of existing interventions. Residual malaria transmission remains a formidable barrier, as mosquitoes increasingly bite outdoors or earlier in the evening when humans are unprotected( 23 ). Additionally, socio-behavioral factors, such as inconsistent intervention use( 24 , 25 ) and variations in exposure risk( 26 , 27 ), further complicate control efforts. To address residual transmission in a particular location, requires a nuanced understanding of when and where vector and human behavior overlap. Building on existing evidence, this study examines human behavioral factors that contribute to mosquito exposure in a highland region of Kenya with ongoing but understudied malaria transmission. The challenges in this region is further compounded by high vector diversity as recent surveys identified 17 mosquito species, nine of which were previously undocumented in the databases ( 28 ) ( 29 ). Although only four of these novel species tested positive for Plasmodium falciparum , their presence underscores the ecological complexity of transmission dynamics in the highlands. As global malaria burden continues to decline, elimination will depend increasingly on finely targeted and locally adapted control strategies. Existing literature indicates that even though there is considerable outdoor mosquito biting (based on HLCs), much of the community exposure to malaria is indoors while people are asleep and unprotected ( 30 ). The present study examined whether findings hold true in the Kenyan highlands and identifies points of human-vector contact that may be disrupted to support elimination goals. Methods Study site This study was conducted in three rural sites Kapsisiywa (0°19'31" N, 35°4'39" E,), Kebulonik (0°22'50" N, 35°2'20" E,), and Kipsamoite (0°21'52"N, 35°5'20" E), with elevation ranging from 1900-2400m above sea level (Fig. 1 ) in the highlands of northern Nandi County Kenya. Historically, the climate is cool and wet with two rainy seasons: long rain between March and June and the short rains between October and November. The historical annual rainfall is between the range of 1,200 to 2,000 mm per year (100 to 166.66 mm per month), with average historical temperatures from 15–25°C. The main economic driver in this region is agriculture, including cash crops, and livestock( 31 ). These three sites were chosen for the diversity of geographical features that can be compared within a short distance. Specifically, Kapsisiywa is surrounded on to the East, South, and West by a swamp, and the Nandi North forest runs along the Western border of Kipsamoite. Kebulonik is a geographically neutral area that was chosen for the ability to compare the impact of forested and swampy areas to those without these features( 32 ). The three sites experience unstable and highly seasonal malaria transmission. Transmission typically peaks following the long rains between March and May; however, this peak is sometimes absent or may shift to a different time of the year( 2 ). Mosquito surveys indicate that the predominant indoor resting vector is Anopheles gambiae s.l . with occasional Anopheles funestus ( 33 ). Human Landing Catches (HLC) Mosquito collections were conducted between the hours of 1800 − 0600 hours local time in paired indoor and outdoor human landing catches (HLC) across all three sites in May 2018 (long rainy season) and October 2018 (short rainy season). In Kapsisywa and Kipsamoite, mosquito collections were conducted in 10 randomly selected houses each month, with sampling carried out for five nights per household. In Kebulonik, eight houses were randomly selected during each collection month and sampled for the same duration. All participating houses were chosen based on similar structural characteristics, including roof type, wall type, and the presence of open eaves. Collections were conducted for 45 minutes within each hour, followed by a 15-minute break to allow collectors to rest and change collection cups. Each hourly collection was kept separately in labeled paper cups, with the labels containing unique hourly codes that included the village code, house number, collection method, location, date, and time of collection. The trained collectors were tested for malaria by microscopy and rapid diagnostic tests and provided with a dose of malaria prophylaxis before collection. They were monitored for any signs and symptoms of malaria for two weeks after the collections. Supervisors were assigned to coordinate the collection activities and ensure volunteers were consistently engaged in mosquito collections throughout each collection night. Sample Processing Each morning, the anopheline mosquitoes collected were identified based on their morphological characteristics using taxonomic keys ( 34 ). They were sorted by species, location, and physiological status (unfed, fed, semi-gravid, and gravid) and placed with desiccant into labeled (collection hour, location, morphological identity, and house code) Eppendorf tubes for further analysis. Morphologically identified Anopheles were sequenced at the ribosomal DNA internal transcribed spacer region 2 (ITS2) and/or cytochrome oxidase subunit 1 ( CO1 ) loci towards species determination( 28 , 35 , 36 ). Human Behavior Observations In addition to the entomological surveys, direct observations of human behaviors ( 30 ) were conducted in parallel with mosquito collections, both inside and outside of HLC households. These observations aimed to identify the potential periods of overlap between human activity and vector biting, which may contribute to malaria transmission. Trained community recruited HLC collectors observed the number of household members in each behavioral category a) inside asleep with LLINs, b) inside asleep without LLINs, c) inside awake without LLINs, and d) outdoors, awake or asleep at the end of each HLC hour. HLC collectors were not household members. Data management and analysis Data was collected on paper forms, with a supervisor performing spot checks for quality control. Data was entered into Excel and compared to the paper forms for accuracy. Human behavior-adjusted biting (HBBR) rates were calculated based upon the vector landing (HLC) rates and human behavior observations as outlined in Monroe et al.( 30 ). Only female Anopheles mosquitoes were considered in the analysis of human behavior-adjusted biting rates. When no mosquitoes were collected during a particular hour, a man biting rate of 0.005 bites/person/hour or bites/person/night was assumed due to the realistic possibility of being bitten. Human landing catch rates were used in place of human biting rates, allowing determination of human biting rate per hour. To calculate HBBR, HLC was first divided by 2 to calculate man biting rate in bites/person/hour both indoors and outdoors. To adjust for human behavior, man biting rate was multiplied by the proportion of individuals in conditions a-d described in the previous HBOs section. The proportions of a-d must sum to 1 for any given hour, as that is implicit in proportion calculations. An example of HBBR determination would be the man biting rate indoors multiplied by the proportion of individuals indoors and asleep with LLINs for a given hour, producing the behavior adjusted biting rate indoors asleep with LLINs ( 30 ). Results Anopheles species composition and seasonal distribution A total of 98 female Anopheles mosquitoes were collected from indoor and outdoor locations across three different ecological study sites (Table 1 ). Of these, 89 samples were successfully sequenced and identified at the ITS2 and/or CO1 regions for molecular species identification. In Kipsamoite, 33 Anopheles mosquitoes were collected, with 32 successfully sequenced and identified. The most abundant species was Anopheles spp . 14 BSL-2014 (n = 11; 33.3%), followed by An. arabiensis (n = 10; 30.3%), An. christyi (n = 8; 24.2%), An. demelleioni (n = 2; 6.1%) and An. funestus (n = 2; 3%). The majority (n = 23; 67%) were collected indoors. In Kebulonik, 17 Anopheles mosquitoes were collected, comprising An. christyi (n = 7; 41.2%), An. demelleioni (n = 6; 35.3%), An. funestus (n = 3; 17.7%), and An. spp. 14 BSL-2014 (n = 1; 6%). Overall, most (52.9%) were collected indoors. Kapsisywa village accounted for the highest number of collections (n = 48; 48.9%), with 40 samples successfully sequenced and identified, while eight remained unidentified. The anopheline species comprised of An. spp. 14 BSL-2014 (n = 25; 52.1%), followed by An. coustani (n = 11; 22.9%), An. christyi (n = 2; 4.2%), and An. spp. 11 BSL-2014 (n = 2; 4.2%). In this village, most mosquitoes (n = 28; 58.3%) were collected from outdoors. Table 1 Total collected Anopheles (molecular confirmation) mosquitoes during HLCs per site. Site Anopheline Spp. Location Indoor Outdoor Total Kipsamoite Anopheles arabiensis 8 2 10 Anopheles funestus 1 0 1 Anopheles demelleioni 2 0 2 Anopheles christyi 4 4 8 Anopheles spp. 14 BSL-2014 7 4 11 unidentified 1 0 1 Total 23 10 33 Kebulonik Anopheles funestus 2 1 3 Anopheles demelleioni 4 2 6 Anopheles christyi 2 5 7 Anopheles spp . 14 BSL-2014 1 0 1 Total 9 8 17 Kapsisywa Anopheles christyi 0 2 2 Anopheles coustani 4 7 11 Anopheles spp. 11 BSL-2014 0 2 2 Anopheles spp. 14 BSL-2014 10 15 25 Unidentified 6 2 8 Total 20 28 48 Total (Anophelines) 52 46 98 The seasonal distribution of the most abundant human-biting Anopheles species, both indoors and outdoors, showed notable fluctuations between the long and short rainy season. In Kipsamoite, during May (long rainy season), An. arabiensis [52.6% (95% CI: 30.2–75.1%)] and An. christyi [42.1% (95% CI: 19.9–64.3%)] were the dominant species. In contrast, in October (short rainy season), An. spp. 14 BSL-2014 became the most prevalent, accounting for 84.6% (95% CI: 65–100%), while only one An. funestus was collected. An. demeilloni was present in both seasons, though in very low numbers (n = 1) (Fig. 2 A). In Kebulonik, An. demeilloni [60% (95% CI: 29.6–90.4%)] and An. funestus [30% (95% CI: 1.6–58.4%)] were dominant during the long rainy season. In this village, An. species 14 BSL-2014 was recorded only during the short rainy season in very low numbers (n = 1). An. christyi was present in both seasons but was most abundant in the short rainy season [85.7% (n = 6/7)] (Fig. 2 B). In Kapsisywa, An. species 14 BSL-2014 was the dominant species during the short rainy season [62.5% (95% CI: 47.5–77.5%)], followed by An. coustani [27.5% (95% CI: 13.6–41.3%)]. An. christyi and An. species 11 BSL-2014 were also present but in very low numbers (n = 2). No mosquitoes were collected in this village during the long rainy season (Fig. 2 C). Anopheles hourly biting patterns Overall, human-biting activity in Kipsamoite was higher indoors (mean 1.0 bites/person/hour) than outdoors (mean 0.4 bites/person/hour). During the long rainy season, indoor biting peaked in the early evening between 1900h-2000h (mean 2.5 bites/person/hour), while outdoor activity peaked slightly earlier, between 1800h-1900h (mean 1.5 bites/person/hour) (Fig. 3 A). In contrast, during the short rainy season, two indoor peaks were observed: one during the classical biting period (01:00–02:00 h, mean 1 bite/person/hour) and another in the late morning (04:00–05:00 h, mean 1 bite/person/hour). Outdoors, biting activity peaked between 21:00–23:00 h (mean 0.5 bites/person/hour), followed by a gradual rise in the late morning 04:00–05:00 h (mean 0.5 bites/person/hour) (Fig. 3 B). In Kebulonik, Anopheles biting activity was higher indoors (mean 0.3 bites/person/hour) than outdoors (mean 0.2 bites/person/hour), with a pronounced peak indoors early evening between 19:00–20:00 h (mean 2.5 bites/person/hour) during the long rainy season and a smaller peak toward the end of classical biting times (01:00–02:00 h, mean 0.5 bites/person/hour) (Fig. 3 C). Outdoor biting activity peaked between 20:00 and 21:00 h (mean 1 bite/person/hour), followed by a gradual rise late in the morning between 03:00–05:00 h (mean 0.5 bites/person/hour). During the short rainy season, biting activity in Kebulonik was minimal due to the low mosquito densities (Fig. 3 D). In contrast, in Kapsisywa during the short rainy season, human-biting activity by Anopheles mosquitoes was higher outdoors (mean 1.2 bites/person/hour) than indoors (mean 0.8 bites/person/hour). Three peaks were observed indoors: early evening (18:00–19:00 h) and a second peak between 20:00 and 21:00 h, both with similar intensity (mean 2.5 bites/person/hour). A third peak was recorded at midnight (00:00–01:00 h, mean 1.5 bites/person/hour), though it was less pronounced than the earlier peaks. Similarly, outdoors, three peaks were observed: the first occurred in the early evening at 18:00 h (mean 3.5 bites/person/hour), followed by a more pronounced peak between 19:00 and 21:00 h (mean 4 bites/person/hour). A third peak was recorded at the end of classical biting times between 02:00 and 03:00 h (mean 1.5 bites/person/hour) (Fig. 3 E). No mosquitoes were collected during the long rainy season. Human Behavior Observations (HBO) In Kipsamoite village during the long rainy season, there was a shift in human behavior activities from being outdoors (63.9% at 1800h to 0.8% at 0200h) and indoors awake (33.9% at 1800h to 0.8% at 0100h), to being indoors asleep either without a net (2.1% at 1800h to 41.4% at 0100h) or with a bed net (0% at 1800h to 57% at 0100h). The bulk of outdoor exposure occurred in the early evening hours between 1800h and 1900h, when most individuals were outside. Indoor exposure was slightly higher during the first half of the night between 1900h and 2100h, coinciding with the time most individuals were awake indoors (Fig. 3 A). A similar human behavioral pattern was observed during the short rainy season, with the proportion of individuals outdoors and indoors awake with no protection declining from 41.2% at 1800h to 0% at 0300h, and 47.1% at 1800h to 1.1% at 0300h respectively. Conversely the proportion of individuals indoors and asleep with a bed net increased from 11.8% at 1800h to 70.7% at 0300h) and those asleep without a net rose from 0% at 1800h to 28.1% at 0300h. From 0300h onwards, this trend reversed, with a gradual increase in exposure among individuals awake either outdoors or indoors, and a decrease among those asleep with or without nets. In contrast with long rains, outdoor exposure in the short rains peaked later in the evening between 2000h and 2300h, when a small proportion of individuals remained outside unprotected, and again late morning between 0400h and 0600h as people began to wake and go outdoors. Indoors, most exposure occurred between 0100h and 0400h, when a significant proportion of individuals were asleep without protection (Fig. 3 B). In Kebulonik village, during the peak rainy season, there was a shift from outdoors awake (64.4% at 1800h to 0% at 0100h) and indoors awake with no net (32.58% at 1800h to 5.7% at 0100h) to primary indoors asleep with net (rising from 3.0% at 1800h to 87.4% at 0100h). Notably, indoor exposure while asleep without a net rose slightly from 0% at 1800h to 6.9% at 0100h. After 0200h this pattern reversed, with both outdoors and indoors awake exposure increasing and the frequency of indoor exposure while sleeping with or without a net decreasing (Fig. 3 C). Most outdoor exposure occurred between 2000h and 2100h, when individuals were outdoors, with another less pronounced peak in the late morning hours between 0300h and 0600h, when most people were a wake. Indoor exposure peaked early evening between 1900h and 2000h, when the majority of individuals were awake indoors. A similar behavioral shift was observed during the short rainy season, with a considerable shift from outdoors (42.2% at 1800h to a low of 0% at 0200h) and indoors awake (54.7% at 1800h to 0% at 0200h) to indoors and asleep either with no net (rises from 1.6% at 1800h to 41.9% at 0200h) or with a bed net (rises from 1.6% at 1800h to 58.1% at 0200h). From 0300h to the end of collection at 0600h, there was a reversal of this trend, with outdoors and indoors awake increasing in exposure and sleeping with or without a net decreasing in frequency (Fig. 3 D). Notably, there was no measurable human landing catch (HLC) during this period in Kebulonik, resulting in no recorded man-biting rate. In Kapsisywa a marked shift in exposure was observed from outdoors (64.2% at 1800h to 0% at 0400h) and indoor awake (69.8% at 1900h to 0% at 0400h) to indoors asleep either without a net (0% at 1800h to 16.6% at 0400h) or with a net (9.4% at 1800h to 83.3% at 0400h during the short rainy season. Unlike other villages, Kapsisywa showed minimal reversal of this trend between 0200h and 0600h. Most outdoor exposure occurred in the early evening hours between 1800h and 2100h, continuing into the late night between 0200h and 0300h. Similarly, indoor exposure peaked when individuals were awake between 1800h and 2100h, and again between 2300h and 0100h as more people began sleeping without protection. A final increase in exposure was observed between 0400h and 0600h, coinciding with a rise in the number of unprotected sleepers indoors (Fig. 3 E). There were no human landing catches during the peak rainy season in this village. Human Behavior-Adjusted Biting Rates The overall human-biting rate was adjusted to reflect human behavior, accounting for spatial and temporal presence, awake and sleeping times as well as LLIN use. (Figs. 4 and 5 ). In Kipsamoite, indoor human-vector exposure was higher for unprotected individuals who were asleep (HBBR = 2.7 adjusted b/p/n, 52%) and awake (1.7 adjusted b/p/n, 37%), with minimal outdoor exposure (0.5 adjusted b/p/n, 11%) (Fig. 4 A). During the short rainy season, indoor exposure for unprotected individuals decreased slightly while sleeping (1.13 adjusted b/p/n, 44%) and awake (0.5 adjusted b/p/n, 20%), while outdoor exposure increased (0.9 adjusted b/p/n, 36%) (Fig. 4 B). Over the course of the night, an estimated 24.5% and 44.9% of bites were prevented by LLINs during the long and short rainy seasons, respectively, based on the overlap of vector and human behaviors (Fig. 5 A&B). In Kebulonik, the trend was similar, with higher indoor exposure for unprotected individuals who were asleep (1.3 adjusted b/p/n, 55%) and awake (1.0 adjusted b/p/n, 43%) during the long rainy season, and minimal outdoor exposure (0.05 adjusted b/p/n, 2%) (Fig. 4 C). Despite the low Anopheles population during the short rainy season, outdoor exposure increased (0.02 adjusted b/p/n, 48%), compared to indoor exposure for unprotected individuals asleep (0.01 adjusted b/p/n, 36%) and awake (0.01 adjusted b/p/n, 16%) (Fig. 4 D). Over the course of the night, an estimated 24.6% and 37% of bites were prevented by LLINs during the long and short rainy seasons, respectively (Fig. 5 C&D). In Kapsisywa, during the short rainy season, indoor exposure for unprotected individuals who were asleep (3.7 adjusted b/p/n, 55%) and awake (1.9 adjusted b/p/n, 29%) was higher than outdoor exposure (1.1 adjusted b/p/n, 16%) (Fig. 4 E). No Anopheles mosquitoes were collected during the long rainy season. Use of LLINs was estimated to prevent 35.8% of bites during the short rainy season in this setting (Fig. 5 E). Discussion Vector and human drivers of exposure were documented across two transmission seasons at three sites in the Kenya Highlands. The intersection between mosquito and human behaviors, including LLIN use was demonstrated to outline where and when community exposure to vector bites occurs as well as what interventions may be needed to combat these exposures. The study observed high Anopheles species diversity, predominantly comprising cryptic species ( An. species 14 BSL-2014 and An. species 11 BSL-2014) alongside secondary vectors ( An. chrysti , An. demelleoni and An. coustani ) with notable seasonal shifts. Biting risk peaked during the early evening hours (18:00–21:00) across all sites, both indoors and outdoors, coinciding with the times when people were awake. These findings challenge the conventional focus on nighttime (when people are asleep) interventions and highlight the need for vector control strategies that address transmission during active human periods, providing evidence for targeted, behaviorally-informed interventions for improved control efficacy. Identifying peak exposure times is essential for optimizing vector control by targeting interventions to the highest-risk periods and locations. This study revealed distinct village-level differences in malaria exposure patterns across the three highland sites. In Kipsamoite and Kebulonik, indoor exposure was predominant, evidenced by higher indoor mosquito densities, while Kapsisiywa exhibited greater outdoor exposure. Across all villages, peak biting activity occurred during early evening hours (1800h–2100h), coinciding with times when most individuals were awake and active, either indoors or outdoors indicating a significant overlap between human behavior and vector activity. Notably, in Kapsisiywa, outdoor exposure was most consequential during early evening (3.5 bites/person/hour at 1800h), when 64.2% of individuals were outdoors. Although outdoor biting peaked later (4 bites/person/hour at 2100h), the proportion of people outdoors dropped sharply (to 7.1% at 2000h and 3.9% at 2100h), suggesting that late-evening HLC data may overestimate actual exposure, highlighting the importance of interpreting HLC data in the context of human behavior. These temporal and behavioral overlaps corroborate with findings from other studies in similar settings ( 37 – 41 ). The presence of biting exposure while individuals are awake, whether indoors or outdoors, highlights a gap in protection not addressed by LLINs alone. Therefore, complementary interventions such as spatial or topical repellents may be necessary to address residual transmission during these vulnerable periods and spaces. Analysis of human behavior-adjusted biting rates identified two high-risk groups indoors across the study sites: individuals who were asleep without the protection of LLINs and those who were awake indoors during peak biting periods. Our finding shows that outdoor exposure became more pronounced during the short rainy season in all villages. Interestingly, LLIN use was more protective during the short rains than the long rains, suggesting increased net use during this period. This is consistent with other studies from the region, which found that despite high LLIN ownership, consistent use remained low among some community members who had yet to fully recognize the importance of sleeping under nets for malaria prevention( 42 ). This behavioral shift implies that targeted social and behavioral change communication (SBCC) strategies could significantly reduce indoor exposure, particularly among those who sleep without nets. Given that LLINs remain one of the most effective tools for interrupting malaria transmission( 13 ), ensuring consistent and proper use is essential for maximizing their protective impact. However, the substantial biting risk among individuals who are awake indoors during early evening hours, when LLINs are typically not in use, highlights a persistent gap in protection. This population represents a key target for complementary interventions. Reducing residual transmission will thus require a dual strategy: reinforcing LLIN use during sleeping hours and deploying additional tools to protect those awake and active during vector biting times. The diversity of Anopheles mosquito species presents a substantial challenge to malaria control efforts, as different species exhibit distinct ecological and behavioral traits, particularly in relation to feeding/biting times and locations (e.g., indoor vs. outdoor) ( 43 ). Conventional vector control tools such as LLINs and IRS are primarily effective against endophagic (indoor-feeding) and endophilic (indoor-resting) species ( 44 ). However, their efficacy is limited when malaria vectors exhibit behaviors that do not overlap with human sleeping patterns or occur in locations where human-vector contact takes place outside of LLIN or IRS protection ( 45 ). This study identified considerable species diversity across the three highland villages, with site-specific variation in vector dominance. Of particular concern was the high abundance of cryptic and secondary vectors, many of which remain poorly characterized in terms of their bionomics. Early studies from western Kenya highlandshave also reported high mosquito species diversity, including cryptic species infected with Plasmodium , raising concerns about their potential role in local malaria transmission( 28 , 29 , 46 ). Notably, Anopheles species 14 BSL-2014 (a cryptic species) was dominant during the short rainy season across all sites and appears to be a key driver of transmission during this period. While primary vectors such as An. arabiensis was detected only in Kipsamoite, where it contributed substantially to indoor biting during the long rainy season, and An. funestus was caught indoors (though very low in numbers) during the short rains in Kipsamoite and both seasons in Kebulonik, these were not the predominant species. Secondary vectors like An. christyi , An. demeilloni , and An. coustani were present at lower densities but nonetheless may play a role in sustaining transmission, as these species have elsewhere been shown to be susceptible to Plasmodium infections ( 47 – 49 ). The predominance of outdoor and early evening biting species, though present in low numbers, coincides with the behavioral exposure patterns observed in this study, where human activity overlaps with vector activity in settings not protected by LLINs. These findings underscore a critical gap in current vector control strategies and highlight the need for continued vector surveillance and implementation of complementary tools that target behaviorally evasive vectors. The limitations of this study are: the sampling was limited to the wet seasons and involved a relatively narrow temporal scope; year-round data collection across both wet and dry seasons would have provided a more comprehensive understanding of vector dynamics and human behaviors under varying environmental conditions. Seasonal differences, particularly during the dry season, could significantly influence mosquito abundance, biting behavior, and human exposure patterns. Additionally, the study did not capture detailed information on the specific activities or social engagements individuals were involved in during peak exposure times, which could have provided valuable insights for designing targeted social and behavioral change interventions. Due to the low species-specific sample sizes, the analysis did not include disaggregated peak biting times for individual vector species, potentially overlooking species-specific transmission dynamics. Moreover, ecological factors such as the proximity of swamps and forested areas known to influence mosquito diversity and abundance were not fully explored, highlighting further investigation into their role in shaping vector populations and biting behavior. Conclusion This study underscores the complexity of malaria transmission in a close ecological highland setting, driven by diverse and behaviorally adaptable vector population that includes cryptic species often overlooked in routine surveillance. The analysis differentiates the raw HLC biting rates from human behavior-based (or community) exposure – the latter of which is more indicative of actual exposure in the community. The early evening biting peaks observed both indoors and outdoors, coinciding with periods when people are awake and therefore unprotected by LLINs, revealing vulnerabilities in current malaria control strategies. The finding that individuals both asleep without LLIN protection and those awake indoors during peak biting times face high exposure risk underscores the need to expand beyond conventional interventions. To effectively reduce transmission, integrated vector management approaches are essential for instance use of spatial repellents during unprotected hours and community-based behavior change interventions. Moreover, strengthening ecological entomological surveillance systems to detect and monitor emerging or understudied vectors will be key in adapting control measures to evolving transmission dynamics. Declarations Ethics approval and consent to participate: No human subject data was collected in this study as all data evaluating human behavior was collected without identifiers and aggregated at the household level. Project staff explained the study and received appropriate informed consent from participating households /individuals before any data collection. All individuals had the ability to remove themselves from the study at any point. Institutional review board (IRB) approval was received from Jaramogi Oginga Odinga Teaching and Referral hospital (IERC/JOOTRH/580/22). Availability of data and materials The dataset supporting the conclusions of this article is included within the article. Competing interest The authors have declared that no competing interest. Authors’ contribution SSA, LBT, GA, CCJ and NFL conceived and designed the study. SSA, GA, AA and EO were part of the data acquisition and implementation of the study. MGM, AS, SSA, LC, IR and NFL participated in analysis and interpretation; while MGM, AS, SSA, LC, IR, EO, and NFL participated in drafting and writing the manuscript. All authors read and approved the final version of the manuscript. Acknowledgments The authors wish to thank the volunteers for their participation in this study and the leadership of Nandi County for allowing us to conduct the study in the area. We acknowledge the Entomology Laboratory at Kenya Medical Research Institute, Kisumu, the field assistants in Nandi areas for providing technical support. Funding This study was supported by a Global Health Reciprocal Innovation Planning Grants sponsored by Indiana CTSI and Indiana University Center for Global Health. References Ernst KC, Adoka SO, Kowuor DO, Wilson ML, John CC. Malaria hotspot areas in a highland Kenya site are consistent in epidemic and non-epidemic years and are associated with ecological factors. Malaria Journal. 2006;5(1):78. 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Anopheles species diversity and breeding habitat distribution and the prospect for focused malaria control in the western highlands of Kenya. 2011. Gillies MT, Coetzee M. A supplement to the Anophelinae of Africa South of the Sahara. Publ S Afr Inst Med Res. 1987;55:1-143. Ratnasingham S, Hebert PD. bold: The Barcode of Life Data System (http://www.barcodinglife.org). Mol Ecol Notes. 2007;7(3):355-64. Lobo NF, St Laurent B, Sikaala CH, Hamainza B, Chanda J, Chinula D, et al. Unexpected diversity of Anopheles species in Eastern Zambia: implications for evaluating vector behavior and interventions using molecular tools. Scientific reports. 2015;5:17952. Dulacha D, Were V, Oyugi E, Kiptui R, Owiny M, Boru W, et al. Reduction in malaria burden following the introduction of indoor residual spraying in areas protected by long-lasting insecticidal nets in Western Kenya, 2016-2018. PLoS One. 2022;17(4):e0266736. Siteti MC, Injete SD, Wanyonyi WA. Malaria parasite species prevalence and transmission dynamics at selected sites in the Western highlands of Kenya. CHRISMED Journal of Health and Research. 2016;3(1):45-50. Debebe Y, Hill SR, Tekie H, Dugassa S, Hopkins RJ, Ignell R. Malaria hotspots explained from the perspective of ecological theory underlying insect foraging. Scientific Reports. 2020;10(1):21449. Zhou G, Githure J, Lee M-C, Zhong D, Wang X, Atieli H, et al. Malaria transmission heterogeneity in different eco-epidemiological areas of western Kenya: a region-wide observational and risk classification study for adaptive intervention planning. Malaria Journal. 2024;23(1):74. Esayas E, Gowelo S, Assefa M, Vajda EA, Thomsen E, Getachew A, et al. Impact of nighttime human behavior on exposure to malaria vectors and effectiveness of using long-lasting insecticidal nets in the Ethiopian lowlands and highlands. Parasites & Vectors. 2024;17(1):520. Mukisa MC, Kassano JJ, Mwalugelo YA, Ntege C, Kahamba NF, Finda MF, et al. Analysis of the 24-h biting patterns and human exposures to malaria vectors in south-eastern Tanzania. Parasites & Vectors. 2024;17(1):445. Nzioki I, Machani MG, Onyango SA, Kabui KK, Githeko AK, Ochomo E, et al. Differences in malaria vector biting behavior and changing vulnerability to malaria transmission in contrasting ecosystems of western Kenya. Parasites & Vectors. 2023;16(1):376. Tomas T, Eligo N, Tamiru G, Massebo F. Outdoor and early hour human biting activities of malaria mosquitoes and the suitability of clay pot for outdoor resting mosquito collection in malaria endemic villages of southern Rift valley, Ethiopia. Parasite epidemiology and control. 2022;19:e00278. Odero JI, Abong’o B, Moshi V, Ekodir S, Harvey SA, Ochomo E, et al. Early morning anopheline mosquito biting, a potential driver of malaria transmission in Busia County, western Kenya. Malaria journal. 2024;23(1):66. Gichuki PM, Mwatel CM, Njomo DW. Household Long-Lasting Insecticide Nets (LLINs) Ownership, Use, and Perceptions among a Community Living in the Malaria Epidemic Zone of Nandi County, Kenya. East African Journal of Health and Science. 2022;5(2):12-21. St. Laurent B. Mosquito vector diversity and malaria transmission. Frontiers in Malaria. 2025;3:1600850. WHO. World malaria report 2023. Geneva: : World Health Organization; 2023. Cooke MK, Kahindi SC, Oriango RM, Owaga C, Ayoma E, Mabuka D, et al. ‘A bite before bed’: exposure to malaria vectors outside the times of net use in the highlands of western Kenya. Malaria journal. 2015;14:1-15. Zhong D, Hemming-Schroeder E, Wang X, Kibret S, Zhou G, Atieli H, et al. Extensive new Anopheles cryptic species involved in human malaria transmission in western Kenya. Scientific reports. 2020;10(1):16139. Degefa T, Yewhalaw D, Zhou G, Lee M-c, Atieli H, Githeko AK, Yan G. Indoor and outdoor malaria vector surveillance in western Kenya: implications for better understanding of residual transmission. Malaria Journal. 2017;16(1):443. Lobo NF, Laurent BS, Sikaala CH, Hamainza B, Chanda J, Chinula D, et al. Unexpected diversity of Anopheles species in Eastern Zambia: implications for evaluating vector behavior and interventions using molecular tools. Scientific Reports. 2015;5(1):17952. Mustapha AM, Musembi S, Nyamache AK, Machani MG, Kosgei J, Wamuyu L, et al. Secondary malaria vectors in western Kenya include novel species with unexpectedly high densities and parasite infection rates. Parasites & Vectors. 2021;14(1):252. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 08 Dec, 2025 Read the published version in Malaria Journal → Version 1 posted Editorial decision: Revision requested 28 Oct, 2025 Reviews received at journal 28 Oct, 2025 Reviewers agreed at journal 14 Oct, 2025 Reviewers agreed at journal 12 Sep, 2025 Reviews received at journal 10 Sep, 2025 Reviewers agreed at journal 06 Sep, 2025 Reviewers invited by journal 03 Sep, 2025 Editor assigned by journal 29 Aug, 2025 Submission checks completed at journal 29 Aug, 2025 First submitted to journal 28 Aug, 2025 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-7478328","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":507808567,"identity":"8e86433a-abe2-4ee2-a4a0-96fdad4fe66a","order_by":0,"name":"Maxwell G. 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John","email":"","orcid":"","institution":"Ryan White Center for Pediatric Infectious Diseases and Global Health,","correspondingAuthor":false,"prefix":"","firstName":"Chandy","middleName":"C.","lastName":"John","suffix":""},{"id":507808579,"identity":"86cde8be-a804-49d6-903e-1b5de7383d95","order_by":11,"name":"Neil F. Lobo","email":"","orcid":"","institution":"University of Notre Dame","correspondingAuthor":false,"prefix":"","firstName":"Neil","middleName":"F.","lastName":"Lobo","suffix":""}],"badges":[],"createdAt":"2025-08-28 08:53:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7478328/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7478328/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12936-025-05681-3","type":"published","date":"2025-12-08T15:58:33+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":90481416,"identity":"ae7ec36c-981e-404a-a8b5-0b51e69ecc33","added_by":"auto","created_at":"2025-09-03 08:09:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1127408,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the study area with study sites namely Kipsamoite, Kebulonik, and Kapsisywa \u0026nbsp;in Nandi county in Kenya.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7478328/v1/4046c579af1b2b3e8b4d4d74.png"},{"id":90481806,"identity":"71b64447-8b3f-470d-9ab8-386158335ef6","added_by":"auto","created_at":"2025-09-03 08:17:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":29042,"visible":true,"origin":"","legend":"\u003cp\u003eSeasonal composition (proportional) and \u003cem\u003eAnopheles\u003c/em\u003e species composition biting indoors and outdoors in A) Kipsamoite B) Kebulonik, and C) Kapsisywa\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7478328/v1/aa9a6f9d5f8a52f196aae3b0.png"},{"id":90481405,"identity":"afffb615-c8fe-4787-9c1f-25cc1d5cec80","added_by":"auto","created_at":"2025-09-03 08:09:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":123147,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverall human landing rates and human behaviors at three sites. \u003c/strong\u003eIndoor (blue line) and outdoor (black line) biting rates are shown for Kipsamoite (A: May, B: October), Kebulonik (C: May, D: October), and Kapsisywa (E: October). These are overlaid with human observations, where bar graphs depict the proportion of people outdoors (green), indoors using LLINs (dotted), indoors awake without LLINs (orange), and indoors asleep without LLINs (blue).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7478328/v1/2a0f831f1feffa5e94abac81.png"},{"id":90481403,"identity":"f47e2c92-59fe-46dd-bc29-4e10770c2158","added_by":"auto","created_at":"2025-09-03 08:09:36","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":55072,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHuman adjusted exposure and protections in each village; \u003c/strong\u003eHuman behavior-adjusted biting rates per hour at each site, A) Kipsamoite- May B) Kipsamoite-October C) Kebulonik- May D) Kebulonik- October E) Kapsisywa- October. Protection by LLIN (spots) is found at all sites. Exposure indoors awake (blue), exposure indoors asleep (orange) and outdoors (yellow) is shown. Exposure indoors awake is most significant in the evenings, while exposure when asleep is relatively low. No adjusted bites are present for the October Kebulonik site, due to zero vectors being collected at that site in that timeframe.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7478328/v1/a01223d7057674bd99144764.png"},{"id":90481414,"identity":"5a42e6d6-0bf2-4e32-b0bc-60a19dfa4f52","added_by":"auto","created_at":"2025-09-03 08:09:40","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":39403,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdjusted exposure at each site based on human behavior-adjusted biting rates (excluding protection from ITN usage). \u003c/strong\u003eHuman behavioral observations in combination with vector biting rates were used to determine actual exposure at each site -, A) Kipsamoite- May B) Kipsamoite- October C) Kebulonik- May D) Kebulonik- October E) Kapsisywa- October. D) Kebulonik has anomalous results because of the lack of vectors collected at that site in October. Significant exposure occurs indoors, with significant exposure indoors while asleep without ITNs (grey) and exposure indoors while awake and unprotected (Dark blue). Bite exposure outdoors (green) is least impactful, but it is second highest and impactful for B.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7478328/v1/2218e13ae7ab9b5ae1c6fe77.png"},{"id":98243961,"identity":"0e335396-c2a7-46f2-a8fb-3645df63ab99","added_by":"auto","created_at":"2025-12-15 16:11:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2385712,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7478328/v1/96ad5d0d-5aa0-42c2-9f38-be845beb20de.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Human and vector behavioral determinants of malaria transmission dynamics in Nandi highlands, western Kenya","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDespite the declining malaria transmission, it remains a public concern in the highlands of East Africa, with an estimated 34\u0026nbsp;million people at risk of infection. In Kenya, nearly 7\u0026nbsp;million people (about 23% of the total population) live in 15 counties classified as highland malaria epidemicprone areas(1, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Since the mid-20th century, epidemic malaria has been reported more frequently in the Western highlands of Kenya than any other part of the country(3). However, malaria transmission in these areas is highly variable, with heterogeneities observed between years within the same locale. For instance, in the Nandi highlands, malaria incidence fluctuated between 2001 and 2005, with some villages experiencing sharp increases followed by declines(1, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). The heterogeneity of malaria risk and transmission is largely influenced by vector species composition, local environmental condition, human behavior, social factors at the village or household level, and variations in healthcare service delivery (\u003cspan additionalcitationids=\"CR6 CR7 CR8 CR9 CR10\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). In 2015, WHO set a goal to reduce global malaria incidence and mortality by 90% by 2030, with many African countries aiming for elimination(12). Achieving this will require adaptive, cost-effective strategies tailored to local eco-epidemiological conditions.\u003c/p\u003e\u003cp\u003eDespite the proven impact of vector control tools i.e. long-lasting insecticide treated nets (LLINs), indoor residual spraying (IRS) and artemisinin-based combination therapies (ACTs) in reducing malaria transmission(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), persistent transmission threaten progress toward elimination(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). These interventions not only protect individuals but also provide community-wide benefits to non-users(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). However, their widespread use has driven behavioral adaptations in mosquito populations, including shifts in biting time, species composition, and host preference(\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). In Kenya\u0026rsquo;s highlands, targeted IRS and ACT use led to temporary malaria elimination between 2007 and 2008(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), yet transmission rebounded(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), underscoring the resilience of malaria vectors and the limitations of existing interventions. Residual malaria transmission remains a formidable barrier, as mosquitoes increasingly bite outdoors or earlier in the evening when humans are unprotected(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Additionally, socio-behavioral factors, such as inconsistent intervention use(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) and variations in exposure risk(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), further complicate control efforts. To address residual transmission in a particular location, requires a nuanced understanding of when and where vector and human behavior overlap.\u003c/p\u003e\u003cp\u003eBuilding on existing evidence, this study examines human behavioral factors that contribute to mosquito exposure in a highland region of Kenya with ongoing but understudied malaria transmission. The challenges in this region is further compounded by high vector diversity as recent surveys identified 17 mosquito species, nine of which were previously undocumented in the databases (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Although only four of these novel species tested positive for \u003cem\u003ePlasmodium falciparum\u003c/em\u003e, their presence underscores the ecological complexity of transmission dynamics in the highlands. As global malaria burden continues to decline, elimination will depend increasingly on finely targeted and locally adapted control strategies. Existing literature indicates that even though there is considerable outdoor mosquito biting (based on HLCs), much of the community exposure to malaria is indoors while people are asleep and unprotected (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). The present study examined whether findings hold true in the Kenyan highlands and identifies points of human-vector contact that may be disrupted to support elimination goals.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy site\u003c/h2\u003e\u003cp\u003eThis study was conducted in three rural sites Kapsisiywa (0\u0026deg;19'31\" N, 35\u0026deg;4'39\" E,), Kebulonik (0\u0026deg;22'50\" N, 35\u0026deg;2'20\" E,), and Kipsamoite (0\u0026deg;21'52\"N, 35\u0026deg;5'20\" E), with elevation ranging from 1900-2400m above sea level (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) in the highlands of northern Nandi County Kenya. Historically, the climate is cool and wet with two rainy seasons: long rain between March and June and the short rains between October and November. The historical annual rainfall is between the range of 1,200 to 2,000 mm per year (100 to 166.66 mm per month), with average historical temperatures from 15\u0026ndash;25\u0026deg;C. The main economic driver in this region is agriculture, including cash crops, and livestock(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). These three sites were chosen for the diversity of geographical features that can be compared within a short distance. Specifically, Kapsisiywa is surrounded on to the East, South, and West by a swamp, and the Nandi North forest runs along the Western border of Kipsamoite. Kebulonik is a geographically neutral area that was chosen for the ability to compare the impact of forested and swampy areas to those without these features(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). The three sites experience unstable and highly seasonal malaria transmission. Transmission typically peaks following the long rains between March and May; however, this peak is sometimes absent or may shift to a different time of the year(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Mosquito surveys indicate that the predominant indoor resting vector is \u003cem\u003eAnopheles gambiae s.l\u003c/em\u003e. with occasional \u003cem\u003eAnopheles funestus\u003c/em\u003e (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eHuman Landing Catches (HLC)\u003c/h3\u003e\n\u003cp\u003eMosquito collections were conducted between the hours of 1800\u0026thinsp;\u0026minus;\u0026thinsp;0600 hours local time in paired indoor and outdoor human landing catches (HLC) across all three sites in May 2018 (long rainy season) and October 2018 (short rainy season). In Kapsisywa and Kipsamoite, mosquito collections were conducted in 10 randomly selected houses each month, with sampling carried out for five nights per household. In Kebulonik, eight houses were randomly selected during each collection month and sampled for the same duration. All participating houses were chosen based on similar structural characteristics, including roof type, wall type, and the presence of open eaves. Collections were conducted for 45 minutes within each hour, followed by a 15-minute break to allow collectors to rest and change collection cups. Each hourly collection was kept separately in labeled paper cups, with the labels containing unique hourly codes that included the village code, house number, collection method, location, date, and time of collection. The trained collectors were tested for malaria by microscopy and rapid diagnostic tests and provided with a dose of malaria prophylaxis before collection. They were monitored for any signs and symptoms of malaria for two weeks after the collections. Supervisors were assigned to coordinate the collection activities and ensure volunteers were consistently engaged in mosquito collections throughout each collection night.\u003c/p\u003e\n\u003ch3\u003eSample Processing\u003c/h3\u003e\n\u003cp\u003eEach morning, the anopheline mosquitoes collected were identified based on their morphological characteristics using taxonomic keys (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). They were sorted by species, location, and physiological status (unfed, fed, semi-gravid, and gravid) and placed with desiccant into labeled (collection hour, location, morphological identity, and house code) Eppendorf tubes for further analysis. Morphologically identified \u003cem\u003eAnopheles\u003c/em\u003e were sequenced at the ribosomal DNA internal transcribed spacer region 2 (ITS2) and/or cytochrome oxidase subunit 1 (\u003cem\u003eCO1\u003c/em\u003e) loci towards species determination(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eHuman Behavior Observations\u003c/h3\u003e\n\u003cp\u003eIn addition to the entomological surveys, direct observations of human behaviors (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) were conducted in parallel with mosquito collections, both inside and outside of HLC households. These observations aimed to identify the potential periods of overlap between human activity and vector biting, which may contribute to malaria transmission. Trained community recruited HLC collectors observed the number of household members in each behavioral category a) inside asleep with LLINs, b) inside asleep without LLINs, c) inside awake without LLINs, and d) outdoors, awake or asleep at the end of each HLC hour. HLC collectors were not household members.\u003c/p\u003e\n\u003ch3\u003eData management and analysis\u003c/h3\u003e\n\u003cp\u003eData was collected on paper forms, with a supervisor performing spot checks for quality control. Data was entered into Excel and compared to the paper forms for accuracy. Human behavior-adjusted biting (HBBR) rates were calculated based upon the vector landing (HLC) rates and human behavior observations as outlined in Monroe et al.(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Only female \u003cem\u003eAnopheles\u003c/em\u003e mosquitoes were considered in the analysis of human behavior-adjusted biting rates. When no mosquitoes were collected during a particular hour, a man biting rate of 0.005 bites/person/hour or bites/person/night was assumed due to the realistic possibility of being bitten. Human landing catch rates were used in place of human biting rates, allowing determination of human biting rate per hour. To calculate HBBR, HLC was first divided by 2 to calculate man biting rate in bites/person/hour both indoors and outdoors. To adjust for human behavior, man biting rate was multiplied by the proportion of individuals in conditions a-d described in the previous HBOs section. The proportions of a-d must sum to 1 for any given hour, as that is implicit in proportion calculations. An example of HBBR determination would be the man biting rate indoors multiplied by the proportion of individuals indoors and asleep with LLINs for a given hour, producing the behavior adjusted biting rate indoors asleep with LLINs (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eAnopheles\u003c/b\u003e \u003cb\u003especies composition and seasonal distribution\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA total of 98 female \u003cem\u003eAnopheles\u003c/em\u003e mosquitoes were collected from indoor and outdoor locations across three different ecological study sites (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Of these, 89 samples were successfully sequenced and identified at the ITS2 and/or CO1 regions for molecular species identification. In Kipsamoite, 33 \u003cem\u003eAnopheles\u003c/em\u003e mosquitoes were collected, with 32 successfully sequenced and identified. The most abundant species was \u003cem\u003eAnopheles spp\u003c/em\u003e. 14 BSL-2014 (n\u0026thinsp;=\u0026thinsp;11; 33.3%), followed by \u003cem\u003eAn. arabiensis\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;10; 30.3%), \u003cem\u003eAn. christyi\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;8; 24.2%), \u003cem\u003eAn. demelleioni\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;2; 6.1%) and \u003cem\u003eAn. funestus\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;2; 3%). The majority (n\u0026thinsp;=\u0026thinsp;23; 67%) were collected indoors. In Kebulonik, 17 \u003cem\u003eAnopheles\u003c/em\u003e mosquitoes were collected, comprising \u003cem\u003eAn. christyi\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;7; 41.2%), \u003cem\u003eAn. demelleioni\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;6; 35.3%), \u003cem\u003eAn. funestus\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;3; 17.7%), and \u003cem\u003eAn. spp.\u003c/em\u003e 14 BSL-2014 (n\u0026thinsp;=\u0026thinsp;1; 6%). Overall, most (52.9%) were collected indoors. Kapsisywa village accounted for the highest number of collections (n\u0026thinsp;=\u0026thinsp;48; 48.9%), with 40 samples successfully sequenced and identified, while eight remained unidentified. The anopheline species comprised of \u003cem\u003eAn. spp.\u003c/em\u003e 14 BSL-2014 (n\u0026thinsp;=\u0026thinsp;25; 52.1%), followed by \u003cem\u003eAn. coustani\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;11; 22.9%), \u003cem\u003eAn. christyi\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;2; 4.2%), and \u003cem\u003eAn. spp.\u003c/em\u003e 11 BSL-2014 (n\u0026thinsp;=\u0026thinsp;2; 4.2%). In this village, most mosquitoes (n\u0026thinsp;=\u0026thinsp;28; 58.3%) were collected from outdoors.\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\u003eTotal collected \u003cem\u003eAnopheles\u003c/em\u003e (molecular confirmation) mosquitoes during HLCs per site.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSite\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eAnopheline Spp.\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003eLocation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIndoor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOutdoor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003e\u003cb\u003eKipsamoite\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eAnopheles arabiensis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eAnopheles funestus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eAnopheles demelleioni\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eAnopheles christyi\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eAnopheles spp.\u003c/em\u003e 14 BSL-2014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eunidentified\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e23\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e10\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e\u003cb\u003eKebulonik\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eAnopheles funestus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eAnopheles demelleioni\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eAnopheles christyi\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eAnopheles spp\u003c/em\u003e. 14 BSL-2014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e9\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003e\u003cb\u003eKapsisywa\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eAnopheles christyi\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eAnopheles coustani\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eAnopheles spp.\u003c/em\u003e 11 BSL-2014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eAnopheles spp.\u003c/em\u003e 14 BSL-2014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnidentified\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e20\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e28\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal (Anophelines)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e52\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e46\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e98\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\u003eThe seasonal distribution of the most abundant human-biting \u003cem\u003eAnopheles\u003c/em\u003e species, both indoors and outdoors, showed notable fluctuations between the long and short rainy season. In Kipsamoite, during May (long rainy season), \u003cem\u003eAn. arabiensis\u003c/em\u003e [52.6% (95% CI: 30.2\u0026ndash;75.1%)] and \u003cem\u003eAn. christyi\u003c/em\u003e [42.1% (95% CI: 19.9\u0026ndash;64.3%)] were the dominant species. In contrast, in October (short rainy season), \u003cem\u003eAn. spp. 14 BSL-2014\u003c/em\u003e became the most prevalent, accounting for 84.6% (95% CI: 65\u0026ndash;100%), while only one \u003cem\u003eAn. funestus\u003c/em\u003e was collected. \u003cem\u003eAn. demeilloni\u003c/em\u003e was present in both seasons, though in very low numbers (n\u0026thinsp;=\u0026thinsp;1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). In Kebulonik, \u003cem\u003eAn. demeilloni\u003c/em\u003e [60% (95% CI: 29.6\u0026ndash;90.4%)] and \u003cem\u003eAn. funestus\u003c/em\u003e [30% (95% CI: 1.6\u0026ndash;58.4%)] were dominant during the long rainy season. In this village, \u003cem\u003eAn. species 14 BSL-2014\u003c/em\u003e was recorded only during the short rainy season in very low numbers (n\u0026thinsp;=\u0026thinsp;1). \u003cem\u003eAn. christyi\u003c/em\u003e was present in both seasons but was most abundant in the short rainy season [85.7% (n\u0026thinsp;=\u0026thinsp;6/7)] (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). In Kapsisywa, \u003cem\u003eAn. species 14 BSL-2014\u003c/em\u003e was the dominant species during the short rainy season [62.5% (95% CI: 47.5\u0026ndash;77.5%)], followed by \u003cem\u003eAn. coustani\u003c/em\u003e [27.5% (95% CI: 13.6\u0026ndash;41.3%)]. \u003cem\u003eAn. christyi\u003c/em\u003e and \u003cem\u003eAn. species 11 BSL-2014\u003c/em\u003e were also present but in very low numbers (n\u0026thinsp;=\u0026thinsp;2). No mosquitoes were collected in this village during the long rainy season (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eAnopheles\u003c/b\u003e \u003cb\u003ehourly biting patterns\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOverall, human-biting activity in Kipsamoite was higher indoors (mean 1.0 bites/person/hour) than outdoors (mean 0.4 bites/person/hour). During the long rainy season, indoor biting peaked in the early evening between 1900h-2000h (mean 2.5 bites/person/hour), while outdoor activity peaked slightly earlier, between 1800h-1900h (mean 1.5 bites/person/hour) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). In contrast, during the short rainy season, two indoor peaks were observed: one during the classical biting period (01:00\u0026ndash;02:00 h, mean 1 bite/person/hour) and another in the late morning (04:00\u0026ndash;05:00 h, mean 1 bite/person/hour). Outdoors, biting activity peaked between 21:00\u0026ndash;23:00 h (mean 0.5 bites/person/hour), followed by a gradual rise in the late morning 04:00\u0026ndash;05:00 h (mean 0.5 bites/person/hour) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn Kebulonik, \u003cem\u003eAnopheles\u003c/em\u003e biting activity was higher indoors (mean 0.3 bites/person/hour) than outdoors (mean 0.2 bites/person/hour), with a pronounced peak indoors early evening between 19:00\u0026ndash;20:00 h (mean 2.5 bites/person/hour) during the long rainy season and a smaller peak toward the end of classical biting times (01:00\u0026ndash;02:00 h, mean 0.5 bites/person/hour) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Outdoor biting activity peaked between 20:00 and 21:00 h (mean 1 bite/person/hour), followed by a gradual rise late in the morning between 03:00\u0026ndash;05:00 h (mean 0.5 bites/person/hour). During the short rainy season, biting activity in Kebulonik was minimal due to the low mosquito densities (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003eIn contrast, in Kapsisywa during the short rainy season, human-biting activity by \u003cem\u003eAnopheles\u003c/em\u003e mosquitoes was higher outdoors (mean 1.2 bites/person/hour) than indoors (mean 0.8 bites/person/hour). Three peaks were observed indoors: early evening (18:00\u0026ndash;19:00 h) and a second peak between 20:00 and 21:00 h, both with similar intensity (mean 2.5 bites/person/hour). A third peak was recorded at midnight (00:00\u0026ndash;01:00 h, mean 1.5 bites/person/hour), though it was less pronounced than the earlier peaks. Similarly, outdoors, three peaks were observed: the first occurred in the early evening at 18:00 h (mean 3.5 bites/person/hour), followed by a more pronounced peak between 19:00 and 21:00 h (mean 4 bites/person/hour). A third peak was recorded at the end of classical biting times between 02:00 and 03:00 h (mean 1.5 bites/person/hour) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). No mosquitoes were collected during the long rainy season.\u003c/p\u003e\n\u003ch3\u003eHuman Behavior Observations (HBO)\u003c/h3\u003e\n\u003cp\u003eIn Kipsamoite village during the long rainy season, there was a shift in human behavior activities from being outdoors (63.9% at 1800h to 0.8% at 0200h) and indoors awake (33.9% at 1800h to 0.8% at 0100h), to being indoors asleep either without a net (2.1% at 1800h to 41.4% at 0100h) or with a bed net (0% at 1800h to 57% at 0100h). The bulk of outdoor exposure occurred in the early evening hours between 1800h and 1900h, when most individuals were outside. Indoor exposure was slightly higher during the first half of the night between 1900h and 2100h, coinciding with the time most individuals were awake indoors (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). A similar human behavioral pattern was observed during the short rainy season, with the proportion of individuals outdoors and indoors awake with no protection declining from 41.2% at 1800h to 0% at 0300h, and 47.1% at 1800h to 1.1% at 0300h respectively. Conversely the proportion of individuals indoors and asleep with a bed net increased from 11.8% at 1800h to 70.7% at 0300h) and those asleep without a net rose from 0% at 1800h to 28.1% at 0300h. From 0300h onwards, this trend reversed, with a gradual increase in exposure among individuals awake either outdoors or indoors, and a decrease among those asleep with or without nets. In contrast with long rains, outdoor exposure in the short rains peaked later in the evening between 2000h and 2300h, when a small proportion of individuals remained outside unprotected, and again late morning between 0400h and 0600h as people began to wake and go outdoors. Indoors, most exposure occurred between 0100h and 0400h, when a significant proportion of individuals were asleep without protection (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003eIn Kebulonik village, during the peak rainy season, there was a shift from outdoors awake (64.4% at 1800h to 0% at 0100h) and indoors awake with no net (32.58% at 1800h to 5.7% at 0100h) to primary indoors asleep with net (rising from 3.0% at 1800h to 87.4% at 0100h). Notably, indoor exposure while asleep without a net rose slightly from 0% at 1800h to 6.9% at 0100h. After 0200h this pattern reversed, with both outdoors and indoors awake exposure increasing and the frequency of indoor exposure while sleeping with or without a net decreasing (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Most outdoor exposure occurred between 2000h and 2100h, when individuals were outdoors, with another less pronounced peak in the late morning hours between 0300h and 0600h, when most people were a wake. Indoor exposure peaked early evening between 1900h and 2000h, when the majority of individuals were awake indoors. A similar behavioral shift was observed during the short rainy season, with a considerable shift from outdoors (42.2% at 1800h to a low of 0% at 0200h) and indoors awake (54.7% at 1800h to 0% at 0200h) to indoors and asleep either with no net (rises from 1.6% at 1800h to 41.9% at 0200h) or with a bed net (rises from 1.6% at 1800h to 58.1% at 0200h). From 0300h to the end of collection at 0600h, there was a reversal of this trend, with outdoors and indoors awake increasing in exposure and sleeping with or without a net decreasing in frequency (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Notably, there was no measurable human landing catch (HLC) during this period in Kebulonik, resulting in no recorded man-biting rate.\u003c/p\u003e\u003cp\u003eIn Kapsisywa a marked shift in exposure was observed from outdoors (64.2% at 1800h to 0% at 0400h) and indoor awake (69.8% at 1900h to 0% at 0400h) to indoors asleep either without a net (0% at 1800h to 16.6% at 0400h) or with a net (9.4% at 1800h to 83.3% at 0400h during the short rainy season. Unlike other villages, Kapsisywa showed minimal reversal of this trend between 0200h and 0600h. Most outdoor exposure occurred in the early evening hours between 1800h and 2100h, continuing into the late night between 0200h and 0300h. Similarly, indoor exposure peaked when individuals were awake between 1800h and 2100h, and again between 2300h and 0100h as more people began sleeping without protection. A final increase in exposure was observed between 0400h and 0600h, coinciding with a rise in the number of unprotected sleepers indoors (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). There were no human landing catches during the peak rainy season in this village.\u003c/p\u003e\n\u003ch3\u003eHuman Behavior-Adjusted Biting Rates\u003c/h3\u003e\n\u003cp\u003eThe overall human-biting rate was adjusted to reflect human behavior, accounting for spatial and temporal presence, awake and sleeping times as well as LLIN use. (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). In Kipsamoite, indoor human-vector exposure was higher for unprotected individuals who were asleep (HBBR\u0026thinsp;=\u0026thinsp;2.7 adjusted b/p/n, 52%) and awake (1.7 adjusted b/p/n, 37%), with minimal outdoor exposure (0.5 adjusted b/p/n, 11%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). During the short rainy season, indoor exposure for unprotected individuals decreased slightly while sleeping (1.13 adjusted b/p/n, 44%) and awake (0.5 adjusted b/p/n, 20%), while outdoor exposure increased (0.9 adjusted b/p/n, 36%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Over the course of the night, an estimated 24.5% and 44.9% of bites were prevented by LLINs during the long and short rainy seasons, respectively, based on the overlap of vector and human behaviors (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA\u0026amp;B).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn Kebulonik, the trend was similar, with higher indoor exposure for unprotected individuals who were asleep (1.3 adjusted b/p/n, 55%) and awake (1.0 adjusted b/p/n, 43%) during the long rainy season, and minimal outdoor exposure (0.05 adjusted b/p/n, 2%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Despite the low \u003cem\u003eAnopheles\u003c/em\u003e population during the short rainy season, outdoor exposure increased (0.02 adjusted b/p/n, 48%), compared to indoor exposure for unprotected individuals asleep (0.01 adjusted b/p/n, 36%) and awake (0.01 adjusted b/p/n, 16%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Over the course of the night, an estimated 24.6% and 37% of bites were prevented by LLINs during the long and short rainy seasons, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC\u0026amp;D).\u003c/p\u003e\u003cp\u003eIn Kapsisywa, during the short rainy season, indoor exposure for unprotected individuals who were asleep (3.7 adjusted b/p/n, 55%) and awake (1.9 adjusted b/p/n, 29%) was higher than outdoor exposure (1.1 adjusted b/p/n, 16%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). No \u003cem\u003eAnopheles\u003c/em\u003e mosquitoes were collected during the long rainy season. Use of LLINs was estimated to prevent 35.8% of bites during the short rainy season in this setting (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eVector and human drivers of exposure were documented across two transmission seasons at three sites in the Kenya Highlands. The intersection between mosquito and human behaviors, including LLIN use was demonstrated to outline where and when community exposure to vector bites occurs as well as what interventions may be needed to combat these exposures. The study observed high \u003cem\u003eAnopheles\u003c/em\u003e species diversity, predominantly comprising cryptic species (\u003cem\u003eAn. species\u003c/em\u003e 14 BSL-2014 and \u003cem\u003eAn. species\u003c/em\u003e 11 BSL-2014) alongside secondary vectors (\u003cem\u003eAn. chrysti\u003c/em\u003e, \u003cem\u003eAn. demelleoni\u003c/em\u003e and \u003cem\u003eAn. coustani\u003c/em\u003e) with notable seasonal shifts. Biting risk peaked during the early evening hours (18:00\u0026ndash;21:00) across all sites, both indoors and outdoors, coinciding with the times when people were awake. These findings challenge the conventional focus on nighttime (when people are asleep) interventions and highlight the need for vector control strategies that address transmission during active human periods, providing evidence for targeted, behaviorally-informed interventions for improved control efficacy.\u003c/p\u003e\u003cp\u003eIdentifying peak exposure times is essential for optimizing vector control by targeting interventions to the highest-risk periods and locations. This study revealed distinct village-level differences in malaria exposure patterns across the three highland sites. In Kipsamoite and Kebulonik, indoor exposure was predominant, evidenced by higher indoor mosquito densities, while Kapsisiywa exhibited greater outdoor exposure. Across all villages, peak biting activity occurred during early evening hours (1800h\u0026ndash;2100h), coinciding with times when most individuals were awake and active, either indoors or outdoors indicating a significant overlap between human behavior and vector activity. Notably, in Kapsisiywa, outdoor exposure was most consequential during early evening (3.5 bites/person/hour at 1800h), when 64.2% of individuals were outdoors. Although outdoor biting peaked later (4 bites/person/hour at 2100h), the proportion of people outdoors dropped sharply (to 7.1% at 2000h and 3.9% at 2100h), suggesting that late-evening HLC data may overestimate actual exposure, highlighting the importance of interpreting HLC data in the context of human behavior. These temporal and behavioral overlaps corroborate with findings from other studies in similar settings (\u003cspan additionalcitationids=\"CR38 CR39 CR40\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). The presence of biting exposure while individuals are awake, whether indoors or outdoors, highlights a gap in protection not addressed by LLINs alone. Therefore, complementary interventions such as spatial or topical repellents may be necessary to address residual transmission during these vulnerable periods and spaces.\u003c/p\u003e\u003cp\u003eAnalysis of human behavior-adjusted biting rates identified two high-risk groups indoors across the study sites: individuals who were asleep without the protection of LLINs and those who were awake indoors during peak biting periods. Our finding shows that outdoor exposure became more pronounced during the short rainy season in all villages. Interestingly, LLIN use was more protective during the short rains than the long rains, suggesting increased net use during this period. This is consistent with other studies from the region, which found that despite high LLIN ownership, consistent use remained low among some community members who had yet to fully recognize the importance of sleeping under nets for malaria prevention(\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). This behavioral shift implies that targeted social and behavioral change communication (SBCC) strategies could significantly reduce indoor exposure, particularly among those who sleep without nets. Given that LLINs remain one of the most effective tools for interrupting malaria transmission(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), ensuring consistent and proper use is essential for maximizing their protective impact. However, the substantial biting risk among individuals who are awake indoors during early evening hours, when LLINs are typically not in use, highlights a persistent gap in protection. This population represents a key target for complementary interventions. Reducing residual transmission will thus require a dual strategy: reinforcing LLIN use during sleeping hours and deploying additional tools to protect those awake and active during vector biting times.\u003c/p\u003e\u003cp\u003eThe diversity of \u003cem\u003eAnopheles\u003c/em\u003e mosquito species presents a substantial challenge to malaria control efforts, as different species exhibit distinct ecological and behavioral traits, particularly in relation to feeding/biting times and locations (e.g., indoor vs. outdoor) (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). Conventional vector control tools such as LLINs and IRS are primarily effective against endophagic (indoor-feeding) and endophilic (indoor-resting) species (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). However, their efficacy is limited when malaria vectors exhibit behaviors that do not overlap with human sleeping patterns or occur in locations where human-vector contact takes place outside of LLIN or IRS protection (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). This study identified considerable species diversity across the three highland villages, with site-specific variation in vector dominance. Of particular concern was the high abundance of cryptic and secondary vectors, many of which remain poorly characterized in terms of their bionomics. Early studies from western Kenya highlandshave also reported high mosquito species diversity, including cryptic species infected with \u003cem\u003ePlasmodium\u003c/em\u003e, raising concerns about their potential role in local malaria transmission(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). Notably, \u003cem\u003eAnopheles\u003c/em\u003e species 14 BSL-2014 (a cryptic species) was dominant during the short rainy season across all sites and appears to be a key driver of transmission during this period. While primary vectors such as \u003cem\u003eAn. arabiensis\u003c/em\u003e was detected only in Kipsamoite, where it contributed substantially to indoor biting during the long rainy season, and \u003cem\u003eAn. funestus\u003c/em\u003e was caught indoors (though very low in numbers) during the short rains in Kipsamoite and both seasons in Kebulonik, these were not the predominant species. Secondary vectors like \u003cem\u003eAn. christyi\u003c/em\u003e, \u003cem\u003eAn. demeilloni\u003c/em\u003e, and \u003cem\u003eAn. coustani\u003c/em\u003e were present at lower densities but nonetheless may play a role in sustaining transmission, as these species have elsewhere been shown to be susceptible to \u003cem\u003ePlasmodium\u003c/em\u003e infections (\u003cspan additionalcitationids=\"CR48\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). The predominance of outdoor and early evening biting species, though present in low numbers, coincides with the behavioral exposure patterns observed in this study, where human activity overlaps with vector activity in settings not protected by LLINs. These findings underscore a critical gap in current vector control strategies and highlight the need for continued vector surveillance and implementation of complementary tools that target behaviorally evasive vectors.\u003c/p\u003e\u003cp\u003eThe limitations of this study are: the sampling was limited to the wet seasons and involved a relatively narrow temporal scope; year-round data collection across both wet and dry seasons would have provided a more comprehensive understanding of vector dynamics and human behaviors under varying environmental conditions. Seasonal differences, particularly during the dry season, could significantly influence mosquito abundance, biting behavior, and human exposure patterns. Additionally, the study did not capture detailed information on the specific activities or social engagements individuals were involved in during peak exposure times, which could have provided valuable insights for designing targeted social and behavioral change interventions. Due to the low species-specific sample sizes, the analysis did not include disaggregated peak biting times for individual vector species, potentially overlooking species-specific transmission dynamics. Moreover, ecological factors such as the proximity of swamps and forested areas known to influence mosquito diversity and abundance were not fully explored, highlighting further investigation into their role in shaping vector populations and biting behavior.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study underscores the complexity of malaria transmission in a close ecological highland setting, driven by diverse and behaviorally adaptable vector population that includes cryptic species often overlooked in routine surveillance. The analysis differentiates the raw HLC biting rates from human behavior-based (or community) exposure \u0026ndash; the latter of which is more indicative of actual exposure in the community. The early evening biting peaks observed both indoors and outdoors, coinciding with periods when people are awake and therefore unprotected by LLINs, revealing vulnerabilities in current malaria control strategies. The finding that individuals both asleep without LLIN protection and those awake indoors during peak biting times face high exposure risk underscores the need to expand beyond conventional interventions. To effectively reduce transmission, integrated vector management approaches are essential for instance use of spatial repellents during unprotected hours and community-based behavior change interventions. Moreover, strengthening ecological entomological surveillance systems to detect and monitor emerging or understudied vectors will be key in adapting control measures to evolving transmission dynamics.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo human subject data was collected in this study as all data evaluating human behavior was collected without identifiers and aggregated at the household level. Project staff explained the study and received appropriate informed consent from participating households /individuals before any data collection. All individuals had the ability to remove themselves from the study at any point. Institutional review board (IRB) approval was received from Jaramogi Oginga Odinga Teaching and Referral hospital (IERC/JOOTRH/580/22).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset supporting the conclusions of this article is included within the article.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have declared that no competing interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contribution\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSSA, LBT, GA, CCJ and NFL\u0026nbsp;conceived and designed the study. SSA, GA, AA and EO\u0026nbsp;were part of the data acquisition and implementation of the study. MGM, AS, SSA, LC, IR and NFL participated in analysis and interpretation; while MGM, AS, SSA, LC, IR, EO, and NFL participated in drafting and writing the manuscript.\u0026nbsp;All authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors wish to thank the volunteers for their participation in this study and the leadership of Nandi County for allowing us to conduct the study in the area. We acknowledge the Entomology Laboratory at Kenya Medical Research Institute, Kisumu, the field assistants in Nandi areas for providing technical support.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by a Global Health Reciprocal Innovation Planning Grants sponsored by Indiana CTSI and Indiana University Center for Global Health.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eErnst KC, Adoka SO, Kowuor DO, Wilson ML, John CC. Malaria hotspot areas in a highland Kenya site are consistent in epidemic and non-epidemic years and are associated with ecological factors. Malaria Journal. 2006;5(1):78.\u003c/li\u003e\n\u003cli\u003eGithinji GK, Odhiambo FO, Andala CM, Chepkwony D, Sang JK, Owiny M, et al. 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Geneva: : World Health Organization; 2023.\u003c/li\u003e\n\u003cli\u003eCooke MK, Kahindi SC, Oriango RM, Owaga C, Ayoma E, Mabuka D, et al. \u0026lsquo;A bite before bed\u0026rsquo;: exposure to malaria vectors outside the times of net use in the highlands of western Kenya. Malaria journal. 2015;14:1-15.\u003c/li\u003e\n\u003cli\u003eZhong D, Hemming-Schroeder E, Wang X, Kibret S, Zhou G, Atieli H, et al. Extensive new Anopheles cryptic species involved in human malaria transmission in western Kenya. Scientific reports. 2020;10(1):16139.\u003c/li\u003e\n\u003cli\u003eDegefa T, Yewhalaw D, Zhou G, Lee M-c, Atieli H, Githeko AK, Yan G. Indoor and outdoor malaria vector surveillance in western Kenya: implications for better understanding of residual transmission. Malaria Journal. 2017;16(1):443.\u003c/li\u003e\n\u003cli\u003eLobo NF, Laurent BS, Sikaala CH, Hamainza B, Chanda J, Chinula D, et al. 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Parasites \u0026amp; Vectors. 2021;14(1):252.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"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":"","lastPublishedDoi":"10.21203/rs.3.rs-7478328/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7478328/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMalaria transmission, characterised by spatial and temporal heterogeneity and complex vector behaviors, persists in Kenya’s highlands despite widespread use of Long-lasting insecticidal nets (LLINs). The role of human activity in exposure risk remains underexplored. Identifying vulnerable times and locations is crucial for designing and optimizing targeted control strategies that address the intricate interplay between human activity and local vector behavior that results in transmission. This study examined human-mosquito interactions in three different ecological settings in Nandi highlands in western Kenya.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Malaria vector biting rates were monitored both indoors and outdoors from 18:00 to 06:00 over five consecutive nights in ten houses per village in three different ecological settings namely site close to the forest (Kipsamoite), neutral site neither close to forest nor swamp (Kebulonik), site close to the swamp and with past high malaria prevalence (Kapsisywa) using human landing catches (HLC) during the long (May 2018) and short (October 2018) rainy seasons. Concurrently, hourly human behavior observations (HBOs) were conducted to assess indoor versus outdoor presence, sleeping patterns and LLINs use. All \u003cem\u003eAnopheles\u003c/em\u003emosquitoes were first identified morphologically using standard anopheline keys and subsequently confirmed to species level through molecular sequencing of the internal transcribed spacer 2 (ITS2) region and cytochrome c oxidase subunit 1 (CO1) gene.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003e\u0026nbsp;High \u003cem\u003eAnopheles\u003c/em\u003especies diversity was observed, with site-specific dominance: \u003cem\u003eAn. arabiensis\u003c/em\u003e in Kipsamoite, \u003cem\u003eAn. christyi\u003c/em\u003e in Kebulonik, and the novel \u003cem\u003eAn. spp. 14 BSL-2014\u003c/em\u003e in multiple sites. The majority of collections were indoors in Kipsamoite (67%) and Kebulonik (52.9%), while in Kapsisywa (58.3%) were outdoors. Mosquito exposure peaked outdoors in the early evening (1800-2100h) and indoors during the first half of the night (1900-0100h), coinciding with periods when people were awake or transitioning to or from sleep, with low LLIN use. Human behavior-adjusted exposure was highest outdoors in the early evening (1800-2100h) and indoors during the first half of the night (1900-0100h). Overall, most exposure occurred indoors for unprotected sleepers and individuals awake (53-55%), followed by outdoor exposure in the early evening and late morning (16-44%). LLINs prevented 24.5 to 44.9% of bites in Kipsamoite, 24.6 to 37% in Kebulonik, and 35.8% in Kapsisywa.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e This study demonstrates that human exposure to malaria vectors is shaped by the interplay between temporal and spatial human and vector behaviors, with the highest biting rates indoors for unprotected sleepers and awake individuals, and outdoor exposure peaking in the early evening and late morning. It also reveals diverse, behaviorally adaptable vector populations, including cryptic species, sustaining indoor and outdoor transmission. While LLINs use provide partial protection, significant gaps in protection remained during periods and in spaces where nets are not effective, highlighting persistent residual transmission and the need for vector characterization, behavior-informed interventions (e.g., spatial repellents and larviciding), community engagement, and strengthened entomological surveillance to guide effective malaria control.\u003c/p\u003e","manuscriptTitle":"Human and vector behavioral determinants of malaria transmission dynamics in Nandi highlands, western Kenya","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-03 08:09:30","doi":"10.21203/rs.3.rs-7478328/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-29T00:01:51+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-28T21:33:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"247706419553821826572517754005322238490","date":"2025-10-14T15:23:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"258841731287641335025695245092951363452","date":"2025-09-12T04:10:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-11T03:51:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"97856395133527156523925485170063234203","date":"2025-09-06T18:59:33+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-03T15:38:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-29T08:01:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-29T07:59:13+00:00","index":"","fulltext":""},{"type":"submitted","content":"Malaria Journal","date":"2025-08-28T08:42:53+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":"e826b47c-a6b4-4a23-8021-c59481684814","owner":[],"postedDate":"September 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-15T16:03:55+00:00","versionOfRecord":{"articleIdentity":"rs-7478328","link":"https://doi.org/10.1186/s12936-025-05681-3","journal":{"identity":"malaria-journal","isVorOnly":false,"title":"Malaria Journal"},"publishedOn":"2025-12-08 15:58:33","publishedOnDateReadable":"December 8th, 2025"},"versionCreatedAt":"2025-09-03 08:09:30","video":"","vorDoi":"10.1186/s12936-025-05681-3","vorDoiUrl":"https://doi.org/10.1186/s12936-025-05681-3","workflowStages":[]},"version":"v1","identity":"rs-7478328","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7478328","identity":"rs-7478328","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}