Higher abundance of the vector Aedes aegypti in rural areas than in urban areas in Managua, Nicaragua

Higher abundance of the vector Aedes aegypti in rural areas than in urban areas in Managua, Nicaragua | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Higher abundance of the vector Aedes aegypti in rural areas than in urban areas in Managua, Nicaragua Harold Suazo Laguna, Jacqueline Mojica Díaz, María M. Lopez, Angel Balmaseda, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6059011/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Ae. aegypti is the primary vector of dengue, chikungunya, and Zika viruses, traditionally associated with urban environments. However, its presence and abundance in rural settings remain understudied. This study compares Ae. aegypti populations between rural and urban communities in Managua, Nicaragua, across different seasons over multiple years. Methods Entomological surveys were conducted in 500 randomly selected households (250 rural, 250 urban) during the rainy and dry seasons of 2022 and 2023. Immature mosquitoes were collected from water-holding containers, and adult mosquitoes were sampled using aspirators. Entomological indices, including Stegomyia , pupal, and adult indices, were compared across seasons and localities. Results All entomological indices were significantly higher in rural communities than in urban areas across both years and seasons. Rural households had greater mosquito densities, with pupal productivity concentrated in large water storage containers. Adult mosquito collections confirmed a greater Ae. aegypti presence in rural areas, suggesting sustained transmission risk. We observed pupal thresholds in water-holding containers for female adult collections. Discussion Contrary to the conventional view of Ae. aegypti as an urban mosquito, our findings highlight its substantial presence in rural settings, likely driven by water storage practices and environmental conditions. These results align with findings from other regions reporting high mosquito abundance in rural areas, challenging assumptions about urban dominance. Conclusion Rural areas play a crucial role in sustaining Ae. aegypti populations. Vector control strategies should target both rural and urban communities, with seasonally tailored interventions to mitigate disease transmission risks. Aedes aegypti mosquitoes urban rural Nicaragua Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Arthropod-borne viruses (arboviruses) such as dengue, chikungunya and Zika viruses are the most important human viral diseases transmitted by mosquitoes ( 1 ). The public health impact of these diseases has increased over the past decades, especially due to dengue virus (DENV), which has spread to new geographic locations and places more than 4 billion people at risk of infection globally ( 2 ). These arboviruses are transmitted by mosquitoes of the genus Aedes , with Ae. aegypti (L.) being the main vector for disease transmission ( 3 ). Ae. aegypti is a highly anthropophilic mosquito, with its global distribution expanding due to the effects of climate change ( 4 ). As such, it is critical to fully understand how local populations of Ae. aegypti are modulated by spatiotemporal factors such as location and seasonality over time ( 5 – 7 ). However, several regions of Central America remain with scarce information regarding seasonality data for Ae. aegypti . In Nicaragua, dengue has been the most significant mosquito-borne disease since 1985, when the first epidemic was recorded, resulting in over 17,000 cases and seven deaths ( 8 ). Over the past decades, DENV has caused epidemics every 2–3 years ( 9 – 11 ). Previous work in Managua has shown the potential of improving ongoing vector control of Ae. aegypti by means of community engagement and source reduction ( 12 – 14 ). These studies have also provided information regarding productive containers for immature stages, seasonal trends, and risk factors that modulate local urban populations of Ae. aegypti . In these urban communities, we have previously observed the importance of water storage practices, waste management, and larvicides modulating entomological indices ( 12 , 15 ). Additionally, urban mosquito productivity has been associated with factors such as container type, environmental conditions, climate, and seasonality ( 5 , 16 , 17 ). However, limited information is currently available for seasonal mosquito abundance patterns in rural communities in Central America. Other regions have observed that density of children, lot size, water containers, and water storage practices impact mosquito abundance ( 18 – 21 ). Most studies in Nicaragua and elsewhere have focused on Ae. aegypti populations found in urban environments, largely because this mosquito is commonly associated with urban settings. It is thought that urbanicity issues such as a dilapidated infrastructure, erratic supply of water or water intermittency, poor sanitation services, and increasing human population density can produce ideal conditions for high Aedes mosquito densities. However, the importance of Ae. aegypti and the diseases it transmits has also been demonstrated in rural areas ( 18 – 21 ). Nonetheless, the definition of what is “urban” or “rural”, and where urbanicity starts and finishes can vary in both time and place, making comparison difficult. For this reason, it is critical to compare areas where entomological measurements are performed concurrently within a well-defined spatiotemporal framework. This project was carried out as part of the Asian-American Centers for Arbovirus Research and Enhanced Surveillance (A2CARES) of the NIH Centers for Research on Emerging Infectious Diseases (CREID) network. The objective of this study was to determine how Ae. aegypti entomological indices differ in urban and rural areas and identify which breeding sites were associated with higher productivity by area. We found that all entomological indices of Ae. aegypti were higher in “rural” as compared to “urban” areas in both dry and rainy seasons across multiple years, challenging the concept of Ae. aegypti as an urban mosquito. Material and Methods Study area This study was conducted in District III of Managua, Nicaragua (Fig. 1 ), during the dry season (February-March) and the rainy season (October-November) in both 2022 and 2023. The capital city of Managua comprises seven districts, of which District III is the largest, with an area of 73.2 km 2 and a population of ~ 187,000 inhabitants. It is located 100–400 meters above sea level, with a predominantly tropical climate characterized by two well-defined seasons (dry: December-May; rainy: June-November) and temperatures that range between 18 and 40°C ( 22 ). We worked in 21 neighborhoods of District III, within the catchment area of two health posts in Camilo Ortega and Nejapa areas. Camilo Ortega is an urban area with ~ 26,000 inhabitants, and Nejapa is designated as a rural area, with ~ 11,000 inhabitants. We used the official classification of the Nicaraguan Ministry of Health (MOH) to classify urbanicity within our sites ( 23 ). Study design and samples size The entomological surveillance program established is part of an ongoing community-based cohort study evaluating arboviruses. The parent cohort has > 2,000 participants living in > 1,000 households who undergo a yearly evaluation of arboviral infections and disease across a gradient of urbanicity. A total sample size of 468 households (234 per urban and rural communities) was estimated based on a 20% infestation rate during the dry season, at 95% confidence and a margin of error of 5%. We assumed a 5% non-response rate due to community members not available for a total roundup of 500 households (250 per community). These 500 households were randomly selected from the parent cohort of 1,000 households for serological surveillance. Household selection was performed using a framework for patches of risk based on landscape features ( i.e. , vegetation, house rooftops, and open areas). To create a spatially representative sampling design, we used satellite imagery to establish the rural and urban study areas in District III, using impervious surfaces (man-made structures) as a proxy for inhabited sampling locations, which were then selected randomly, taking into account close-pair points and allowances for misclassification and inaccessibility. Households from the previous visits that were found closed or un-inhabited were replaced with the closest house from the parent cohort. A total of 384 (77%) households had the complete set of 4 measurements. Of the 116 (23%) households that were replaced, the most common reason for drop-out was participant migration (> 60%). Entomological surveillance Field visits were conducted from 8 am to 5 pm Monday to Friday, with occasional Saturday visits for households that were not able to be visited during the weekdays. The MOH carried out routine Ae. aegypti control and surveillance activities at the study sites. Every two months, MOH vector control field technicians made inspection visits to houses to eliminate immature forms of vectors by placing larvicide in clean water containers in the household. Complementary activities carried out included pesticide fumigation and community clean-up campaigns. We scheduled our activities to avoid being in a community when MOH vector control efforts were being performed. All study data collected underwent a double entry procedure for quality assurance. Immature mosquito collections : Collection of larvae and pupae was conducted by inspecting indoor and outdoor containers that could hold water. This inspection was initiated outdoors and finished indoors; in each site, field technicians advanced from right to left, covering all grounds. For each container, we documented the following characteristics: type, location (inside or outside), use of water ( i.e ., consumption, human use, cleaning, irrigation, nothing), frequency of scrubbing, condition of covering (full, half, or none), material, presence of moss on the walls, and treatment with larvicide. Type of containers was classified into six categories: 1) barrels (volume of ≥ 200 liters of water), 2) cement laundry sinks, 3) tires, 4) buckets (volume of ≤ 20 liters), 5) other useful containers (jugs, animal water dishes, among others), and 6) non-useful containers (pieces of plastic, scrap metal, etc.). Immature mosquito stages, either larvae or pupae, found in containers were collected using a net, Pasteur pipette, and/or basin. The collection procedure lasted 15–20 minutes, depending on the property size and number of containers. The immature stages were stored in jars with 70% alcohol, labeled with the house unique Identifier, and transported to the study entomology laboratory for taxonomic classification. We performed taxonomic classification of larvae and pupae only for Ae. aegypti and Ae. albopictus . Adult collection : Adult mosquitoes were collected using backpack aspirators (Prokopacks®), with sampling also starting outdoors and moving indoors. The outdoor aspiration procedure was carried out on the exterior walls of the house focusing on plants, water barrels, tires, and animal water dishes. Indoor aspirations were thorough, with all living quarters inspected (bedrooms, living room, kitchen and bathrooms), sampling under, behind and over furniture, beds and closets. Different collection cups were used for indoor and outdoor collections, labeled with the house unique Identifier, and stored in a thermal bag carried by the field worker, maintaining a temperature of 4–8°C to avoid damage to the mosquitoes. All mosquitoes were transported to the laboratory field station and stored at -20°C until further processing. Mosquitoes were separated by species ( Ae. aegypti, Ae. albopictus, Culex quinquefasciatus, Culex coronator, Anopheles albimanus , others) ( 24 – 26 ), sex (male and female), and engorged state (bloodfed and unfed). This adult aspiration procedure lasted between 10–15 minutes per collection site. Entomological indices For each entomological visit, we calculated the Stegomyia indices (House Index, Container Index, and Breteau Index), pupal indices (pupae per person, pupae per container, and pupae per house), and adult indices (adult index, adults per person, adults per house, and females per person). Container productivity was also estimated as the percentage of total Ae. aegypti pupae collected by type of container ( 27 – 29 ). Stegomyia indices are defined as follows : House Index (HI): (# houses with immature Ae. aegypti /total houses inspected) *100 Container Index (CI): (# containers with immature Ae. aegypti /total containers per house) *100 Breteau Index (BI): (# of containers with immature Ae. aegypti /total houses inspected) *100 Pupal indices are defined as follows: Pupae per house index (PHI): # of Ae. aegypti pupae/inspected houses Pupae per container index (PCI): # of Ae. aegypti pupae/inspected containers Pupae per person index (PPI): # of Ae. aegypti pupae/total population of inspected houses Adult indices are defined as follows: Adult index (AI): (# of houses positive for adult Ae. aegypti /total of houses inspected) *100 Adults per person (AP): # of adult Ae. aegypti /total population of inspected houses Adults per houses (AH): # of adult Ae. aegypti /total houses inspected Females per person (FP): # of adult Ae. aegypti /total population of inspected houses Data analysis Initial descriptive analyses were performed for data exploration. Container productivity analysis was only based on containers positive for Ae. aegypti pupae, as it has been shown that this entomological indicator is the most accurate to translate to adult abundance ( 28 , 30 ). Entomological indices were compared between rural and urban areas using either a z -test for two population proportions, two-sample independent t -test, or Rate Ratio. Significance was determined with a p-value of < 0.05, and 95% confidence intervals (95% CI) were calculated using OpenEpi ( 31 ). We evaluated female adult abundance using a generalized linear mixed model (GLMM) and generalized additive mixed model (GAMM) for count data. We employed mixed models for the potential lack of spatial independence (random effect for households) in our data ( 32 , 33 ). First, we evaluated mosquito counts assuming a Poisson distribution (variance = mean), then compared the fits with those of overdispersed counts (variance > mean) ( 34 , 35 ). The Quasi-Poisson and negative binomial (NB) distributions were used to evaluate if variance increased linearly or quadratically with the mean (referred to as NB type 1 and type 2 in the R packages used) ( 36 ). All models were generated with R 4.4.1 (R Core Team, Vienna, Austria) using the ‘glmmTMB’ ( 37 ) and ‘gamm4’ packages ( 38 ). All models tested main effects for community (2 levels = Urban and Rural), Season (2 levels = Dry and Rainy), and Year (2 levels = 2022 and 2023). Additionally, the GAMM models evaluated if stage 4 larvae (L4), pupae, a mixture of both (L4 + pupae), and containers with water had a non-linear relationship with female Ae. aegypti abundance. For a detailed step-by-step procedure, see Additional file: Text S1, which includes further explanation regarding model selection. Data heteroscedasticity was evaluated by plotting the residuals as a function of predicted values for the distribution models. Models were selected based on the lowest Akaike information criterion (AIC), a metric for model selection that balances goodness of fit and the number of parameters ( 39 ). Results Community demographic structure In each of the four surveys, we inspected 500 houses (250 urban houses and 250 rural houses), for a total of 2,000 inspections. In the urban communities, we observed an average of 5.1 (SD = 2.07) to 5.3 (SD = 2.46) people per house, of which approximately 39–41% were children ≤ 17 years of age. The proportion of adults aged 18–59 remained around 51–52%, and the proportion of adults aged ≥ 60 years ranged from 7 to 9%. In contrast, rural communities averaged 4.4 (SD = 2.01) to 4.7 (SD = 2.36) people per household. Approximately 37–38% of the rural population were children aged ≤ 17 years. The proportion of adults aged 18–59 years was slightly higher than in urban communities, ranging from 55–56%, while adults aged ≥ 60 represented 6–7% of the rural population. No statistical difference between communities by age structure was observed. Within the urban communities, 88–90% of the respondents were female, of which 6–8% did not have formal school studies. In rural communities, 82–84% of the respondents were female, and between 5–6% had no formal school studies (Additional file 1: Table S1 ). Productive pupal containers vary by rural vs. urban area We inspected a total of 6,727 containers throughout the course of the study, many of which underwent double, triple or quadruple inspections (Table 1 ). We did not mark containers; as such, we are unable to provide a measurement for container replacement in the communities. In urban communities, during the dry seasons of 2022 and 2023, on average we detected 2.4 (SD = 2.52) to 2.6 (SD = 2.15) containers per household, of which 2.8% (SD = 0.27) to 6.1% (SD = 0.45) were positive for pupae. In rural communities, the mean number of containers inspected was 3.8 (SD = 4.53) in 2022 and 4.2 (SD = 4.61) in 2023, of which between 2.7% (SD = 0.33) and 6.5% (SD = 0.57) were positive for pupae, respectively. During the rainy season of 2022 and 2023, in urban communities, an average of 2.4 (SD = 1.31) to 3.1 (SD = 2.50) containers were found per household, of which between 7.4% (SD = 0.45) and 10.8% (SD = 0.77) were positive for pupae. In rural communities, the average number of containers inspected per household was 3.1 (SD = 2.07) in 2022 and 5.2 (SD = 5.1) in 2023, with 13.8% (SD = 0.81) to 15.5% (SD = 1.49) containing pupae. Table 1 Entomological collections of immature Ae. aegypti in urban and rural settings of Managua, Nicaragua, during dry and rainy seasons of 2022 and 2023. Study site Season Year # Cont. a inspected # Cont. + pupae Larvae Pupae Rural Dry 2022 943 (3.8) 25 (2.7%) 3,303 (12.7%) 270 (9.1%) Rainy 2022 785 (3.1) 108 (13.8%) 7,684 (29.5%) 966 (32.6%) Dry 2023 1.047 (4.2) 68 (6.5%) 4,438 (17.0%) 466 (15.7%) Rainy 2023 1.294 (5.2) 200 (15.5%) 10,621 (40.8%) 1,260 (42.5%) Total 4.069 401 26,046 (100.0%) 2,962 (100.0%) Urban Dry 2022 604 (2.4) 17 (2.8%) 1,232 (11.0%) 57 (5.5%) Rainy 2022 608 (2.4) 45 (7.4%) 3,577 (31.9%) 228 (22.0%) Dry 2023 659 (2.6) 40 (6.1%) 1,708 (15.2%) 249 (24.0%) Rainy 2023 785 (3.1) 85 (10.8%) 4,692 (41.9%) 503 (48.5%) Total 2.656 187 11,209 (100.0%) 1,037 (100.0%) a Cont. = Containers; in () average containers per households unless stated differently In the dry season of 2022, we observed that only 1.6% (8/500) of all households in the study contributed 67.2% (3,268/4,862) of the total larvae and pupae collected. This pattern continued in the 2022 rainy season; 6.2% of households accumulated 63.8% (7,945/12,455) of the documented larvae and pupae. In the dry season of 2023, the distribution was similar: 3.2% of households concentrated 52.8% (3,624/6,861) of the total larvae and pupae collected. In the rainy season of 2023, 11.2% of households accumulated 68.6% of the larvae and pupae collected (11,710/17,076). These households had a high density of immature stages, with ≥ 100 larvae and at least one pupa in each household. In urban communities, the containers most productive for pupae were barrels (accounting for 55–79% of total pupae) and buckets (0.4–32%) across all seasons and years (Fig. 2 A), with cement laundry sinks accounting for 0%-9% of productivity during the dry seasons (Fig. 2 B). Similarly, barrels were also the most productive containers in rural communities, with 39–65% of productivity across all seasons and years. However, other types of containers, such as useful and non-useful containers, were documented to contribute 40% of overall productivity during the rainy seasons. It is important to highlight the importance of water intermittency in the region. Our results show that 51.1% of rural households faced interruptions in water supply, with an average of 6.87 hours (SD = 7.77) per day without running water, while in urban communities, this problem affected 34.8% of households, with average daily interruptions of 3.67 hours (SD = 5.77). This factor has been previously shown to be a risk factor for immature Aedes presence in households ( 15 ). Higher Ae. aegypti abundance in rural communities Immature stages Throughout the course of the study, a total of 41,254 immature (larvae: 37,255 and pupae: 3,999) Ae. aegypti were collected (Table 1 ). Larval collections in urban communities accounted for 30% of total larvae (n = 11,209), with distinct seasonal patterns of higher abundance during the rainy season 8,269 larvae compared to the dry season 2,940 larvae. Unsurprisingly, a similar pattern was also observed for pupal collections. Pupal collection in urban communities accounted for 26% of total pupae (n = 1,037), with 731 in the rainy season and 306 during the dry season. In rural communities, we collected 70% (n = 26,046) of total larvae, with 18,305 in the rainy season and 7,741 in the dry season. As in the urban area, pupal collections also had a marked seasonal pattern. Pupal collections in rural communities accounted for 74% of total pupae (n = 2,962) pupae, with 2,226 in the rainy season and 736 in the dry season. We observed that the traditional Stegomyia indices of HI (Fig. 3 A), CI (Fig. 3 B), and BI (Fig. 3 C) were statistically higher across all measurements for rural communities compared to urban areas, with the exception of the dry season of 2022 (see Additional file 1: Table S2-S7). Values for rural communities were highest during the rainy season, ranging from 59.2 to 58.8 for HI, 36.2 to 32.3 for CI, and 113.6 to 167.2 for BI in 2022 and 2023, respectively. In contrast, in urban communities for the same seasons, the indices ranged from 42.8 to 41.2 for HI, 28.1 to 23.9 for CI, and 68.4 to 75.2 for BI in 2022 and 2023, respectively. Additionally, the pupal indices of PHI (Fig. 3 D), PCI (Fig. 3 E), and PPI (Fig. 3 F), were also statistically higher for rural communities across all measurements. Values for rural communities were highest during the rainy season, ranging from 386.4 to 504 for PHI (no statistical difference for both summer seasons), 123.1 to 97.4 for PCI, and 82.3 to 111.6 for PPI in 2022 and 2023, respectively. In urban communities, these values were 91.2 to 201.2 for PHI, 37.5 to 63.9 for PCI, and 17.3 to 37.9 for PPI in 2022 and 2023, respectively. Adult Ae. aegypti collections We collected a total of 1,468 adult Ae. aegypti (Table 2 ). In urban communities, we collected 38% of all adult females (n = 274), of which 23% (n = 63) were collected in the dry season and 77% (n = 211) in the rainy season. In rural communities, we collected 62% of all adult females (n = 441) females, of which 27% (n = 120) were collected in the dry season and 73% (n = 321) in the rainy season. In urban communities, we collected 41% (n = 309) of all adult males, of which 26% (n = 81) were collected in the dry season and 74% (n = 228) in the rainy season. Rural communities accounted for 59% (n = 444) of the total adult males, of which 28% (n = 126) were during the dry season and 72% (n = 318) during the rainy season. Table 2 Entomological collections of adult Ae. aegypti in urban and rural settings of Managua, Nicaragua, during dry and rainy seasons of 2022 and 2023. Study site Season Year Females (Adult) Males (Adult) Rural Dry 2022 33 (7.5%) 48 (10.8%) Rainy 2022 142 (32.2%) 158 (35.6%) Dry 2023 87 (19.7%) 78 (17.6%) Rainy 2023 179 (40.6%) 160 (36.0%) Total 441 (100%) 444 (100%) Urban Dry 2022 18 (6.6%) 22 (7.1%) Rainy 2022 58 (21.2%) 87 (28.2%) Dry 2023 45 (16.4%) 59 (19.1%) Rainy 2023 153 (55.8%) 141 (45.6%) Total 274 (100%) 309 (100%) We observed distinct patterns of mosquito infestation and abundance in both urban and rural communities. The best fit models for the GLMM showed a Negative Binomial 2 type distribution with an AIC of 4341.6. We observed a statistically significant difference in the overall abundance of female mosquitoes, with urban communities having 36.5% less females than rural communities (OR = 0.635, 95%CI 0.53–0.77, p < 0.001). This statistically significant difference was observed across seasons and years (Additional file 1: Text S1-GLMM best fit model), with a consistently higher statistically significant abundance of female mosquitoes in rural communities. Regarding the proportion of houses positive for adult Ae. aegypti (AI) (Fig. 4 A), we observed that during the dry seasons, no significant differences were detected between rural and urban communities in 2022 (Rural: 18.8% and Urban: 14.0%) and 2023 (Rural: 32.0% and Urban: 25.2%). However, in the rainy season of 2022, we noted a higher proportion of rural houses positive for adult Ae. aegypti (Rural: 51.2% and Urban: 34.8%, z = 3.704, p = 0.0002). Nonetheless, in the rainy season of 2023, the proportion of houses positive for adult Ae. aegypti was similar between rural and urban areas (Rural: 53.2% and Urban: 50.8%). With respect to the number of adult Ae. aegypti collected per house (AH) (Fig. 4 B), we observed a significantly higher number of adults in rural compared to urban houses in the dry seasons of 2022 (Rural: 0.32 (SD = 0.818) and Urban: 0.16 (SD = 0.427), t = 2.74, df = 498, p = 0.006) and 2023 (Rural: 0.66 (SD = 1.338) and Urban: 0.42 (SD = 0.987), t = 2.28, df = 498, p = 0.02). We observed a similar pattern in the rainy season of 2022 with a significantly higher number of adults in rural houses (Rural: 1.2 (SD = 2.692) and Urban: 0.58 (SD = 1.07), t = 3.38, p < 0.001). However, in the rainy season of 2023, comparable results were observed in rural and urban houses (Rural: 1.36 (SD = 2.685) and Urban: 1.18 (SD = 2.079)). In relation to the number of adult Ae. aegypti per person (AP) (Fig. 5 A), we observed 2.4 (95%CI = 1.62–3.47, p < 0.0001) times higher vector density in rural communities compared to urban areas during the dry season of 2022. In the dry season of 2023, rural communities had 1.8 (95%CI = 1.40–2.29, p < 0.0001) times higher vector density than urban communities. This same pattern of higher vector abundance in rural communities was also observed in 2022 (2.3; 95%CI = 1.91–2.84, p < 0.0001) and 2023 (1.4; 95%CI = 1.16–1.58, p = 0.0001). Finally, a higher density of female Ae. aegypti per person (FP) (Fig. 5 B), was detected in rural communities compared to urban communities during the dry seasons of 2022 (2.1, 95%CI = 1.20–3.86, p = 0.009) and 2023 (2.2; 95%CI = 1.53–3.15, p < 0.0001). The same was observed in the rainy seasons, with rural communities having a higher female vector density in both 2022 (2.8; 95%CI = 2.04–3.08, p < 0.0001) and 2023 (1.4; 95%CI = 1.11–1.70, p = 0.004). Additionally, as a whole, we observed a trend towards an increase of 1.72 (95% CI = 1.46–2.02, p < 0.0001) times the abundance of female Ae. aegypti in 2023 compared to 2022. Pupal thresholds for female Ae. aegypti We also evaluated the non-linear relationship (smooth terms) of L4 and/or pupae and water-holding containers with the abundance of female Ae. aegypti . The models showed that the immature variable of pupae had the best fit (AIC = 4599.8) for female mosquito abundance, with statistical significance of the pupae smooth term (edf = 3.48, Chi 2 = 46.07, p < 0.001, R 2 adjusted = 0.126). For the effect of containers with water, we did not observe statistical significance of the smooth function (p = 0.09); however, we observed a linear increase, which would imply a consistent positive association between total containers with water and female adults in the household. Next, we modeled pupae by community. In the rural communities, we observed that the relationship between female adult Ae. aegypti and total pupae increased in waves in response to the total number of females in the household. Specifically, there was an initial increase in pupae abundance from 1 to around 18 pupae, followed by a plateau between approximately 19 to 50 pupae, finalizing in a sharp increase beyond 50 pupae. This pattern suggests that as the count of pupae increases, there is an initial positive effect on number of female Ae. aegypti , followed by a stable phase, and then a strong increase, indicating possible thresholds in the effect of pupal abundance on the number of females in the household (Fig. 6 A). Our model prediction for adult female counts shows that from 1 to 13 pupae, we have a predicted increase from 0.24 (95%CI = 0.20–0.29) to 0.49 (95%CI = 0.36–0.66) females, a plateau of 0.49 to 0.48 (95%CI = 0.29–0.79) females from 13 to 41 pupae, and an increase to 2.03 (95%CI = 0.35–11.83) females from 42 to 84 pupae. In urban communities, we observed a moderate increase of the smooth peaking at 20 pupae, followed by a consistent decrease, suggesting that increases in pupal counts may not correspond to higher female captures in urban settings (Fig. 6 B). Discussion Ae. aegypti continues to be a major public health threat in tropical regions and an emerging threat to other parts of the globe due to its ecological range expansion as a consequence of climate change ( 1 , 4 , 40 ). With increasing dengue epidemics globally and billions of people at risk of arbovirus infection, it is critical to gain in-depth knowledge of the local distribution of Ae. aegypti and the risk that communities face. In particular, a deeper understanding is needed of communities that are not usually viewed as key for Ae. aegypti , such as rural areas ( 41 , 42 ). Our results in District III of Managua, Nicaragua, clearly show that Ae. aegypti had higher abundance in all traditional Stegomyia indices and higher mosquito densities in rural as compared to urban communities. Interestingly, households that were positive for adult presence had similar results in both rural and urban communities. These results emphasize the importance of evaluating Ae. aegypti infestation and disease transmission in areas that have traditionally not be considered ideal sites for Ae. aegypti , as this information can inform public health decision-makers. Our results highlight significant seasonal and spatial variations in container availability and pupal presence across urban and rural communities. Urban areas had fewer water-holding containers per household, yet pupal positivity was comparable or slightly lower than in rural settings, which suggests that both areas are ideal for mosquito development. Rural households, with greater reliance on water storage, exhibited higher pupal positivity, particularly during the rainy season. A similar result was observed in a longitudinal study in Kenya, with higher pupal density per container in rural sites ( 43 ). However, this contrasts with results observed in Colombia, where urban communities showed significantly higher pupal densities in containers than rural communities ( 21 ), and Sri Lanka, where higher container indices were reported in urban and suburban areas compared to rural areas ( 44 ). The results underscore the ecological differences found by geographic region and the plasticity of Ae. aegypty to adapt to changing environments. These highlights the importance of considering local environmental, socio-economic, and behavioral factors at a fine scale when designing vector control strategies. During the 2022 and 2023 dry seasons, barrels, cement laundry sinks, and buckets were the most productive pupal habitats across all households. In urban areas, these containers accounted for nearly all pupae collected, while in rural areas, they contributed to the majority. Similar trends have been reported in Mexico and Argentina, where large water storage tanks dominated pupal production ( 45 , 46 ). In the rainy seasons of both years, these container types remained the primary sources of pupae in urban areas, with tires, discarded containers, and useful containers also playing a role. In rural areas, barrels, cement laundry sinks, and buckets were still dominant, though alternative containers, including discarded items, gained importance. Observations from Sri Lanka align with our findings, emphasizing the significance of non-degradable garbage and tires as mosquito breeding sites ( 47 ). These results suggest the need for tailored intervention strategies to be implemented by season; while the dry season should focus on traditional water storage containers, the rainy season demands greater attention to eliminating discarded items in outdoor spaces. Additionally, overall we observe very low positive rates of water-holding containers with pupae regardless of rurality, which suggests that targeted elimination of key-breeding sites might be a viable control strategy for District III of Managua. Our observations over two consecutive years and across both rainy and dry seasons indicate that traditional Stegomyia indices in rural communities were significantly higher than those in urban areas. This suggests that rural environments not only support the reproduction of Ae. aegypti but also offer more available habitats and favorable conditions for their productivity, resulting in higher container indices throughout the year. Similar findings have been reported in other regions. For instance, a study in Zanzibar, Tanzania, found that rural settings had higher numbers of infested containers, as well as greater counts of Ae. aegypti immature forms and pupae compared to urban areas ( 48 ). Additionally, higher House and Breteau indices were recorded in rural settings. In contrast, a study in Cambodia did not observe differences in Stegomyia indices between rural and urban communities ( 49 ). Furthermore, several studies have shown a higher abundance of Ae. aegypti in urban areas ( 21 , 50 ), as it is thought that urbanization might favor the proliferation of this vector ( 51 ). These differences across studies might be related to several factors, such as variation in enviromental conditions, human behaviour, and mosquito control practices. Our pupal indices showed that over both rainy and dry seasons, Ae. aegypti populations are sustained, and even though the indices are lower during the dry season, they still are within the 50–150 PPI threshold and pose a threat for disease transmission ( 52 ). Another interesting observation is that a few houses in our study site contributed the vast majority of total pupae collections. This pattern was not observed in adult collections, which were more uniform across areas with high densities of adult Ae. aegypti in both years and seasons, potentially because of the dispersal of mosquitoes due to flight range ( 53 ). This could suggest that tackling key hot spots for breeding site (source) reduction might have a larger impact on vector control than area-wide management. It is undeniable that rural areas play a role in sustaining Ae. aegypti populations and are at risk of disease transmission; as such, Ae. aegypti vector control should also focus on rural communities. We observed a complex non-linear relationship between Ae. aegypti pupal abundance and female mosquito counts in both urban and rural communities. In rural communities, the GAMM models showed that pupae were a significant predictor of female Ae. aegypti populations, suggesting specific thresholds where accumulation of pupae leads to an increase in adult female mosquitoes, as observed in the initial rise followed by a plateau and then a sharp rise again above 50 pupae, pointing to a threshold effect. However, in urban communities, this effect was only observed in the initial rise to 20 pupae, before a decline in female abundance. This threshold effect could be modulated by density-dependent effects of pupae, such as pupal density and per capita growth, as previously observed in a laboratory setting as a non-linear relationship ( 54 ). Our results suggest that after reaching a pupal density of 20 pupae per container in urban areas, the impact on abundance of female adults will be limited ( 55 – 57 ). However, in rural communities, containers that may allow > 50 pupae per container would have enough resources to sustain the full development of more female mosquitoes. This also showcases the need for water-management in rural communities to avoid this threshold of pupal production. We also observed that the number of water-holding containers had a positive linear relationship trend with the total count of female adults, as we had observed previously in Managua ( 14 ). Our results highlight the potential ecological and environmental differences between rural and urban settings that influence mosquito abundance dynamics. Our study reveals some of the intricacies of urban and rural communities in two distinct seasons across two years of data collection. One of the limitations of our study was that the collection only occurred at specific time points; thus, the lack of longer-term temporal data prevents a deeper analysis of the ecological dynamics of these urban and rural communities. However, we compensate for this limitation with a robust sample size and a thorough entomological sampling of the households surveyed. We believe that even though our temporal collection events are restricted to seasons, there is enough evidence to highlight the importance of rural communities in sustaining Ae. aegypti populations and the potential to drive arboviral disease transmission in such areas. However, during this time-period, our mosquito-based arbovirus surveillance only yielded positive pools in the urban communities ( 58 ). We also acknowledge that this study did not evaluate non-household breeding sites, which can also play a role in sustaining mosquito populations in the area. We are currently evaluating the impact of non-household sites as drivers of population dynamics in District III. Of note, in the 2023 rainy season, we recorded for the first time Ae. albopictus within the communities of District III, showcasing the need for further surveillance efforts to evaluate the impact that Ae. albopictus might have in the ecology of Ae. aegypti and the transmission of arboviruses. Conclusion Our results demonstrate that rural communities had a higher density of adult Ae. aegypti and traditional Stegomyia indices in comparison to urban ones. We also observed that barrels, cement laundry sinks, and buckets should be treated as key container habitats within a vector control strategy, especially in communities facing water supply interruptions, where water storage is indispensable in dry and rainy seasons. Non-useful containers, tires, and animal water bowls were also found to be key productive containers during the rainy season. Additionally, we observed distinct threshold patterns for pupal density as predictors of adult female abundance in rural vs urban settings; further elucidation of these ecological patterns might be used for vector control strategies. We hope that this study can help guide public health control activities within the region, underscoring the importance of rural areas for Ae. aegypti propagation. Declarations Acknowledgements We would like to thank all the members of our A2CARES study team in Nicaragua, in particular Meyling Escobar Cárcamo and Carlos Santos Acevedo, who were involved in the collection of entomological samples and laboratory speciation procedures; Everts Morales Reyes for the support of customized informatic systems; Jorge Ruiz Salinas for support in database management; and Juan Carlos Mercado for support with material and logistics. We thank the residents of District III who allowed entomological collections in and around their homes. Additionally, we are grateful for valuable collaboration with community leaders and overall support from local health authorities. Funding This study was funded by the National Institute of Allergy and Infectious Diseases of the National Institutes of Health, grant U01 AI151788 (EH, JC), as part of the Centers for Research on Emerging Infectious Diseases (CREID) network. Availability of data and materials Data set is available in ZENODO: https://doi.org/10.5281/zenodo.14861397and R code can be found at: https://github.com/jgjuarez/UrbanRuralManagua Authors´contributions H.S.L., A.B., E.H., J.C., J.G.J. designed and guided the experiments. H.S.L., J.M.D., M.M.L. carried out the data collection and field activities. H.S.L., J.G.J. analyzed the data and generated the figures. H.S.L., E.H., J.G.J. wrote the manuscript. J.M.D., M.M.L., A.B., J.C. reviewed and edited the manuscript. All authors approved the final manuscript. Ethics approval and consent to participate Protocols for the entomological surveillance and socio-demographic questionnaires were reviewed and approved by the Institutional Review Boards (IRB) of the University of California, Berkeley (A2CARES: 2021-03-14191) and the Nicaraguan Ministry of Health (A2CARES: CIRE 02/08/21-114 Ver.4). 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Available from: https://planet.osm.org/ Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.docx Cite Share Download PDF Status: Posted Version 1 posted 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. 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Lopez","email":"","orcid":"","institution":"Sustainable Sciences Institute","correspondingAuthor":false,"prefix":"","firstName":"María","middleName":"M.","lastName":"Lopez","suffix":""},{"id":427499421,"identity":"33406e94-932b-4ae1-bd61-7c2cce62bac9","order_by":3,"name":"Angel Balmaseda","email":"","orcid":"","institution":"Sustainable Sciences Institute","correspondingAuthor":false,"prefix":"","firstName":"Angel","middleName":"","lastName":"Balmaseda","suffix":""},{"id":427499422,"identity":"f6c4b3b6-0f94-45ff-aaea-2eff9fd79c15","order_by":4,"name":"Eva Harris","email":"","orcid":"","institution":"University of California","correspondingAuthor":false,"prefix":"","firstName":"Eva","middleName":"","lastName":"Harris","suffix":""},{"id":427499423,"identity":"b7601b0f-77cb-46f0-af24-9a0e21e9b918","order_by":5,"name":"Josefina Coloma","email":"","orcid":"","institution":"University of California","correspondingAuthor":false,"prefix":"","firstName":"Josefina","middleName":"","lastName":"Coloma","suffix":""},{"id":427499424,"identity":"855db9bc-0d33-44a1-9fde-9751c4bbf857","order_by":6,"name":"Jose G. Juarez","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIiWNgGAWjYBACxoYDYFqODUQmkKLFmHgtMJDYQLRS5sYzhp8rKurS+9iPP3vwoMYun79/jeEHhop7drgMYWw4Yyx55gxbbhtPjrlBwrFkyxk33hhLMJwpTsajxUCysY0nt02Ch00isYHZgOHGGQMJxraEZFwOA9nys/GfRDqbBPszoJZ6A/kbZ4x/ENBiJtnYYJDAJsFgBtRy2MDgfI8ZyBY73FqOlVk2HEswBPrFTCLh2HEDwxtsZRYJZxIScGkxnHF4882Gmjp5+fbjzyR/1FQbyJ0/vPnGh4oEe9xaDqALSSSA4xRnTMnzY8jwQwzBacsoGAWjYBSMOAAA061YWNXOJwEAAAAASUVORK5CYII=","orcid":"","institution":"Sustainable Sciences Institute","correspondingAuthor":true,"prefix":"","firstName":"Jose","middleName":"G.","lastName":"Juarez","suffix":""}],"badges":[],"createdAt":"2025-02-18 21:08:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6059011/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6059011/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78359823,"identity":"f0e267b0-65c3-458e-9a78-b2bf7f290a09","added_by":"auto","created_at":"2025-03-12 12:09:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1431583,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEntomological collections conducted in dry and rainy seasons of 2022-2023 in communities of District 3 of Managua, Nicaragua\u003c/strong\u003e. \u003cstrong\u003eA\u003c/strong\u003e) Rural (Nejapa) and Urban (Camilo Ortega) communities of District III. \u003cstrong\u003eB\u003c/strong\u003e) Adult \u003cem\u003eAedes aegypti\u003c/em\u003e collections of the combined dry seasons of 2022-2023. \u003cstrong\u003eC\u003c/strong\u003e) Adult \u003cem\u003eAe. aegypti\u003c/em\u003e collections of the combined rainy seasons of 2022-2023. Maximum adult \u003cem\u003eAe. aegypti\u003c/em\u003e collection threshold was set at 28 for both dry and rainy seasons. Map was generated using Quantum GIS (QGIS 3.30) using freely available administrative boundaries and OpenStreetMap (59) for satellite imagery.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6059011/v1/05e5d2084e1532ed6993c5b4.png"},{"id":78360236,"identity":"ae3a170e-bb99-4d6b-89d3-5b3d45fce1d7","added_by":"auto","created_at":"2025-03-12 12:17:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":105724,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAe. aegypti\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e pupae collected in rural and urban communities of Managua, Nicaragua, during the rainy and dry seasons of 2022 and 2023.\u003c/strong\u003e \u003cstrong\u003eA\u003c/strong\u003e) Percentage of containers positive for pupae. \u003cstrong\u003eB\u003c/strong\u003e) Productivity of pupae per container.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6059011/v1/cce6d4bdcaba0c3870db556d.png"},{"id":78359826,"identity":"10c306be-9f67-4dd5-a300-2936ece1cb90","added_by":"auto","created_at":"2025-03-12 12:09:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":145879,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEntomological indices measured in rural and urban communities of District 3 of Managua, Nicaragua.\u003c/strong\u003e \u003cstrong\u003eA\u003c/strong\u003e) House Index (HI). \u003cstrong\u003eB\u003c/strong\u003e) Container Index (CI). \u003cstrong\u003eC\u003c/strong\u003e) Breteau Index (BI). \u003cstrong\u003eD\u003c/strong\u003e) Pupae per 100 house index (PHI). \u003cstrong\u003eE\u003c/strong\u003e) Pupae per container index (PCI). \u003cstrong\u003eF\u003c/strong\u003e) Pupae per person index (PPI).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6059011/v1/6d5ef07eb41c4c55276c7abc.png"},{"id":78361834,"identity":"018f969e-3185-4d0b-a51f-c9852b08bbd4","added_by":"auto","created_at":"2025-03-12 12:25:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":119932,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdult \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eAedes aegypti\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e household indices for rural and urban communities of District III of Managua, Nicaragua.\u003c/strong\u003e \u003cstrong\u003eA\u003c/strong\u003e) Households with adult \u003cem\u003eAe. aegypti\u003c/em\u003e (AI). \u003cstrong\u003eB\u003c/strong\u003e) Adult \u003cem\u003eAe. aegypti\u003c/em\u003eper house (AH).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6059011/v1/3ed86bae05e32f093da434ca.png"},{"id":78360237,"identity":"3566fd2b-10a9-42e9-9290-321214640381","added_by":"auto","created_at":"2025-03-12 12:17:06","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":112316,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdult \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eAedes aegypti\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e per person indices for rural and urban communities of District III of Managua, Nicaragua.\u003c/strong\u003e \u003cstrong\u003eA\u003c/strong\u003e) Adult \u003cem\u003eAe. aegypti \u003c/em\u003eper person (AP). \u003cstrong\u003eB\u003c/strong\u003e) Adult female \u003cem\u003eAe. aegypti\u003c/em\u003eper person (AF).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6059011/v1/ddf2fc2c69b5fcc5399e649e.png"},{"id":78359829,"identity":"531ca3fd-8c91-449a-89c3-723a4f7fead3","added_by":"auto","created_at":"2025-03-12 12:09:06","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":80098,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGeneralized Additive Mixed Model (GAMM) of pupal counts and female \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eAe. aegypti\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e captured in rural and urban communities of District 3 of Managua, Nicaragua.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6059011/v1/a18324c67003e93d9e89bb63.png"},{"id":84037390,"identity":"112c3bd3-0f59-4265-8bae-d3afc6140e5e","added_by":"auto","created_at":"2025-06-06 04:31:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3147093,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6059011/v1/03daee4e-d1cf-4396-a411-93f59fbd9e20.pdf"},{"id":78359824,"identity":"fb9ca2ab-deb3-417c-84b6-8c1a7580bb5c","added_by":"auto","created_at":"2025-03-12 12:09:06","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":35042,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6059011/v1/fba0de2302af45e149f20d74.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Higher abundance of the vector Aedes aegypti in rural areas than in urban areas in Managua, Nicaragua","fulltext":[{"header":"Background","content":"\u003cp\u003eArthropod-borne viruses (arboviruses) such as dengue, chikungunya and Zika viruses are the most important human viral diseases transmitted by mosquitoes (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The public health impact of these diseases has increased over the past decades, especially due to dengue virus (DENV), which has spread to new geographic locations and places more than 4\u0026nbsp;billion people at risk of infection globally (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). These arboviruses are transmitted by mosquitoes of the genus \u003cem\u003eAedes\u003c/em\u003e, with \u003cem\u003eAe. aegypti\u003c/em\u003e (L.) being the main vector for disease transmission (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). \u003cem\u003eAe. aegypti\u003c/em\u003e is a highly anthropophilic mosquito, with its global distribution expanding due to the effects of climate change (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). As such, it is critical to fully understand how local populations of \u003cem\u003eAe. aegypti\u003c/em\u003e are modulated by spatiotemporal factors such as location and seasonality over time (\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). However, several regions of Central America remain with scarce information regarding seasonality data for \u003cem\u003eAe. aegypti\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eIn Nicaragua, dengue has been the most significant mosquito-borne disease since 1985, when the first epidemic was recorded, resulting in over 17,000 cases and seven deaths (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Over the past decades, DENV has caused epidemics every 2\u0026ndash;3 years (\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Previous work in Managua has shown the potential of improving ongoing vector control of \u003cem\u003eAe. aegypti\u003c/em\u003e by means of community engagement and source reduction (\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). These studies have also provided information regarding productive containers for immature stages, seasonal trends, and risk factors that modulate local urban populations of \u003cem\u003eAe. aegypti\u003c/em\u003e. In these urban communities, we have previously observed the importance of water storage practices, waste management, and larvicides modulating entomological indices (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Additionally, urban mosquito productivity has been associated with factors such as container type, environmental conditions, climate, and seasonality (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). However, limited information is currently available for seasonal mosquito abundance patterns in rural communities in Central America. Other regions have observed that density of children, lot size, water containers, and water storage practices impact mosquito abundance (\u003cspan additionalcitationids=\"CR19 CR20\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMost studies in Nicaragua and elsewhere have focused on \u003cem\u003eAe. aegypti\u003c/em\u003e populations found in urban environments, largely because this mosquito is commonly associated with urban settings. It is thought that urbanicity issues such as a dilapidated infrastructure, erratic supply of water or water intermittency, poor sanitation services, and increasing human population density can produce ideal conditions for high \u003cem\u003eAedes\u003c/em\u003e mosquito densities. However, the importance of \u003cem\u003eAe. aegypti\u003c/em\u003e and the diseases it transmits has also been demonstrated in rural areas (\u003cspan additionalcitationids=\"CR19 CR20\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Nonetheless, the definition of what is \u0026ldquo;urban\u0026rdquo; or \u0026ldquo;rural\u0026rdquo;, and where urbanicity starts and finishes can vary in both time and place, making comparison difficult. For this reason, it is critical to compare areas where entomological measurements are performed concurrently within a well-defined spatiotemporal framework.\u003c/p\u003e \u003cp\u003eThis project was carried out as part of the Asian-American Centers for Arbovirus Research and Enhanced Surveillance (A2CARES) of the NIH Centers for Research on Emerging Infectious Diseases (CREID) network. The objective of this study was to determine how \u003cem\u003eAe. aegypti\u003c/em\u003e entomological indices differ in urban and rural areas and identify which breeding sites were associated with higher productivity by area. We found that all entomological indices of \u003cem\u003eAe. aegypti\u003c/em\u003e were higher in \u0026ldquo;rural\u0026rdquo; as compared to \u0026ldquo;urban\u0026rdquo; areas in both dry and rainy seasons across multiple years, challenging the concept of \u003cem\u003eAe. aegypti\u003c/em\u003e as an urban mosquito.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy area\u003c/h2\u003e \u003cp\u003eThis study was conducted in District III of Managua, Nicaragua (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), during the dry season (February-March) and the rainy season (October-November) in both 2022 and 2023. The capital city of Managua comprises seven districts, of which District III is the largest, with an area of 73.2 km\u003csup\u003e2\u003c/sup\u003e and a population of ~\u0026thinsp;187,000 inhabitants. It is located 100\u0026ndash;400 meters above sea level, with a predominantly tropical climate characterized by two well-defined seasons (dry: December-May; rainy: June-November) and temperatures that range between 18 and 40\u0026deg;C (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). We worked in 21 neighborhoods of District III, within the catchment area of two health posts in Camilo Ortega and Nejapa areas. Camilo Ortega is an urban area with ~\u0026thinsp;26,000 inhabitants, and Nejapa is designated as a rural area, with ~\u0026thinsp;11,000 inhabitants. We used the official classification of the Nicaraguan Ministry of Health (MOH) to classify urbanicity within our sites (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy design and samples size\u003c/h3\u003e\n\u003cp\u003eThe entomological surveillance program established is part of an ongoing community-based cohort study evaluating arboviruses. The parent cohort has \u0026gt;\u0026thinsp;2,000 participants living in \u0026gt;\u0026thinsp;1,000 households who undergo a yearly evaluation of arboviral infections and disease across a gradient of urbanicity. A total sample size of 468 households (234 per urban and rural communities) was estimated based on a 20% infestation rate during the dry season, at 95% confidence and a margin of error of 5%. We assumed a 5% non-response rate due to community members not available for a total roundup of 500 households (250 per community). These 500 households were randomly selected from the parent cohort of 1,000 households for serological surveillance. Household selection was performed using a framework for patches of risk based on landscape features (\u003cem\u003ei.e.\u003c/em\u003e, vegetation, house rooftops, and open areas). To create a spatially representative sampling design, we used satellite imagery to establish the rural and urban study areas in District III, using impervious surfaces (man-made structures) as a proxy for inhabited sampling locations, which were then selected randomly, taking into account close-pair points and allowances for misclassification and inaccessibility. Households from the previous visits that were found closed or un-inhabited were replaced with the closest house from the parent cohort. A total of 384 (77%) households had the complete set of 4 measurements. Of the 116 (23%) households that were replaced, the most common reason for drop-out was participant migration (\u0026gt;\u0026thinsp;60%).\u003c/p\u003e\n\u003ch3\u003eEntomological surveillance\u003c/h3\u003e\n\u003cp\u003eField visits were conducted from 8 am to 5 pm Monday to Friday, with occasional Saturday visits for households that were not able to be visited during the weekdays. The MOH carried out routine \u003cem\u003eAe. aegypti\u003c/em\u003e control and surveillance activities at the study sites. Every two months, MOH vector control field technicians made inspection visits to houses to eliminate immature forms of vectors by placing larvicide in clean water containers in the household. Complementary activities carried out included pesticide fumigation and community clean-up campaigns. We scheduled our activities to avoid being in a community when MOH vector control efforts were being performed. All study data collected underwent a double entry procedure for quality assurance.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eImmature mosquito collections\u003c/span\u003e: Collection of larvae and pupae was conducted by inspecting indoor and outdoor containers that could hold water. This inspection was initiated outdoors and finished indoors; in each site, field technicians advanced from right to left, covering all grounds. For each container, we documented the following characteristics: type, location (inside or outside), use of water (\u003cem\u003ei.e\u003c/em\u003e., consumption, human use, cleaning, irrigation, nothing), frequency of scrubbing, condition of covering (full, half, or none), material, presence of moss on the walls, and treatment with larvicide. Type of containers was classified into six categories: 1) barrels (volume of \u0026ge;\u0026thinsp;200 liters of water), 2) cement laundry sinks, 3) tires, 4) buckets (volume of \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026le;\u003c/span\u003e\u0026thinsp;20 liters), 5) other useful containers (jugs, animal water dishes, among others), and 6) non-useful containers (pieces of plastic, scrap metal, etc.). Immature mosquito stages, either larvae or pupae, found in containers were collected using a net, Pasteur pipette, and/or basin. The collection procedure lasted 15\u0026ndash;20 minutes, depending on the property size and number of containers. The immature stages were stored in jars with 70% alcohol, labeled with the house unique Identifier, and transported to the study entomology laboratory for taxonomic classification. We performed taxonomic classification of larvae and pupae only for \u003cem\u003eAe. aegypti\u003c/em\u003e and \u003cem\u003eAe. albopictus\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eAdult collection\u003c/span\u003e: Adult mosquitoes were collected using backpack aspirators (Prokopacks\u0026reg;), with sampling also starting outdoors and moving indoors. The outdoor aspiration procedure was carried out on the exterior walls of the house focusing on plants, water barrels, tires, and animal water dishes. Indoor aspirations were thorough, with all living quarters inspected (bedrooms, living room, kitchen and bathrooms), sampling under, behind and over furniture, beds and closets. Different collection cups were used for indoor and outdoor collections, labeled with the house unique Identifier, and stored in a thermal bag carried by the field worker, maintaining a temperature of 4\u0026ndash;8\u0026deg;C to avoid damage to the mosquitoes. All mosquitoes were transported to the laboratory field station and stored at -20\u0026deg;C until further processing. Mosquitoes were separated by species (\u003cem\u003eAe. aegypti, Ae. albopictus, Culex quinquefasciatus, Culex coronator, Anopheles albimanus\u003c/em\u003e, others) (\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e), sex (male and female), and engorged state (bloodfed and unfed). This adult aspiration procedure lasted between 10\u0026ndash;15 minutes per collection site.\u003c/p\u003e\n\u003ch3\u003eEntomological indices\u003c/h3\u003e\n\u003cp\u003eFor each entomological visit, we calculated the \u003cem\u003eStegomyia\u003c/em\u003e indices (House Index, Container Index, and Breteau Index), pupal indices (pupae per person, pupae per container, and pupae per house), and adult indices (adult index, adults per person, adults per house, and females per person). Container productivity was also estimated as the percentage of total \u003cem\u003eAe. aegypti\u003c/em\u003e pupae collected by type of container (\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eStegomyia\u003c/span\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eindices are defined as follows\u003c/span\u003e:\u003c/p\u003e \u003cp\u003eHouse Index (HI): (# houses with immature \u003cem\u003eAe. aegypti\u003c/em\u003e/total houses inspected) *100\u003c/p\u003e \u003cp\u003eContainer Index (CI): (# containers with immature \u003cem\u003eAe. aegypti\u003c/em\u003e/total containers per house) *100\u003c/p\u003e \u003cp\u003eBreteau Index (BI): (# of containers with immature \u003cem\u003eAe. aegypti\u003c/em\u003e/total houses inspected) *100\u003c/p\u003e\n\u003ch3\u003ePupal indices are defined as follows:\u003c/h3\u003e\n\u003cp\u003ePupae per house index (PHI): # of \u003cem\u003eAe. aegypti\u003c/em\u003e pupae/inspected houses\u003c/p\u003e \u003cp\u003ePupae per container index (PCI): # of \u003cem\u003eAe. aegypti\u003c/em\u003e pupae/inspected containers\u003c/p\u003e \u003cp\u003ePupae per person index (PPI): # of \u003cem\u003eAe. aegypti\u003c/em\u003e pupae/total population of inspected houses\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAdult indices are defined as follows:\u003c/h2\u003e \u003cp\u003eAdult index (AI): (# of houses positive for adult \u003cem\u003eAe. aegypti\u003c/em\u003e/total of houses inspected) *100\u003c/p\u003e \u003cp\u003eAdults per person (AP): # of adult \u003cem\u003eAe. aegypti\u003c/em\u003e/total population of inspected houses\u003c/p\u003e \u003cp\u003eAdults per houses (AH): # of adult \u003cem\u003eAe. aegypti\u003c/em\u003e/total houses inspected\u003c/p\u003e \u003cp\u003eFemales per person (FP): # of adult \u003cem\u003eAe. aegypti\u003c/em\u003e/total population of inspected houses\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eInitial descriptive analyses were performed for data exploration. Container productivity analysis was only based on containers positive for \u003cem\u003eAe. aegypti\u003c/em\u003e pupae, as it has been shown that this entomological indicator is the most accurate to translate to adult abundance (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Entomological indices were compared between rural and urban areas using either a \u003cem\u003ez\u003c/em\u003e-test for two population proportions, two-sample independent \u003cem\u003et\u003c/em\u003e-test, or Rate Ratio. Significance was determined with a p-value of \u0026lt;\u0026thinsp;0.05, and 95% confidence intervals (95% CI) were calculated using OpenEpi (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). We evaluated female adult abundance using a generalized linear mixed model (GLMM) and generalized additive mixed model (GAMM) for count data. We employed mixed models for the potential lack of spatial independence (random effect for households) in our data (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). First, we evaluated mosquito counts assuming a Poisson distribution (variance\u0026thinsp;=\u0026thinsp;mean), then compared the fits with those of overdispersed counts (variance\u0026thinsp;\u0026gt;\u0026thinsp;mean) (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). The Quasi-Poisson and negative binomial (NB) distributions were used to evaluate if variance increased linearly or quadratically with the mean (referred to as NB type 1 and type 2 in the R packages used) (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). All models were generated with R 4.4.1 (R Core Team, Vienna, Austria) using the \u0026lsquo;glmmTMB\u0026rsquo; (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e) and \u0026lsquo;gamm4\u0026rsquo; packages (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAll models tested main effects for community (2 levels\u0026thinsp;=\u0026thinsp;Urban and Rural), Season (2 levels\u0026thinsp;=\u0026thinsp;Dry and Rainy), and Year (2 levels\u0026thinsp;=\u0026thinsp;2022 and 2023). Additionally, the GAMM models evaluated if stage 4 larvae (L4), pupae, a mixture of both (L4\u0026thinsp;+\u0026thinsp;pupae), and containers with water had a non-linear relationship with female \u003cem\u003eAe. aegypti\u003c/em\u003e abundance. For a detailed step-by-step procedure, see Additional file: Text S1, which includes further explanation regarding model selection. Data heteroscedasticity was evaluated by plotting the residuals as a function of predicted values for the distribution models. Models were selected based on the lowest Akaike information criterion (AIC), a metric for model selection that balances goodness of fit and the number of parameters (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCommunity demographic structure\u003c/h2\u003e \u003cp\u003eIn each of the four surveys, we inspected 500 houses (250 urban houses and 250 rural houses), for a total of 2,000 inspections. In the urban communities, we observed an average of 5.1 (SD\u0026thinsp;=\u0026thinsp;2.07) to 5.3 (SD\u0026thinsp;=\u0026thinsp;2.46) people per house, of which approximately 39\u0026ndash;41% were children\u0026thinsp;\u0026le;\u0026thinsp;17 years of age. The proportion of adults aged 18\u0026ndash;59 remained around 51\u0026ndash;52%, and the proportion of adults aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years ranged from 7 to 9%. In contrast, rural communities averaged 4.4 (SD\u0026thinsp;=\u0026thinsp;2.01) to 4.7 (SD\u0026thinsp;=\u0026thinsp;2.36) people per household. Approximately 37\u0026ndash;38% of the rural population were children aged\u0026thinsp;\u0026le;\u0026thinsp;17 years. The proportion of adults aged 18\u0026ndash;59 years was slightly higher than in urban communities, ranging from 55\u0026ndash;56%, while adults aged\u0026thinsp;\u0026ge;\u0026thinsp;60 represented 6\u0026ndash;7% of the rural population. No statistical difference between communities by age structure was observed. Within the urban communities, 88\u0026ndash;90% of the respondents were female, of which 6\u0026ndash;8% did not have formal school studies. In rural communities, 82\u0026ndash;84% of the respondents were female, and between 5\u0026ndash;6% had no formal school studies (Additional file 1: Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eProductive pupal containers vary by rural vs. urban area\u003c/h2\u003e \u003cp\u003eWe inspected a total of 6,727 containers throughout the course of the study, many of which underwent double, triple or quadruple inspections (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). We did not mark containers; as such, we are unable to provide a measurement for container replacement in the communities. In urban communities, during the dry seasons of 2022 and 2023, on average we detected 2.4 (SD\u0026thinsp;=\u0026thinsp;2.52) to 2.6 (SD\u0026thinsp;=\u0026thinsp;2.15) containers per household, of which 2.8% (SD\u0026thinsp;=\u0026thinsp;0.27) to 6.1% (SD\u0026thinsp;=\u0026thinsp;0.45) were positive for pupae. In rural communities, the mean number of containers inspected was 3.8 (SD\u0026thinsp;=\u0026thinsp;4.53) in 2022 and 4.2 (SD\u0026thinsp;=\u0026thinsp;4.61) in 2023, of which between 2.7% (SD\u0026thinsp;=\u0026thinsp;0.33) and 6.5% (SD\u0026thinsp;=\u0026thinsp;0.57) were positive for pupae, respectively. During the rainy season of 2022 and 2023, in urban communities, an average of 2.4 (SD\u0026thinsp;=\u0026thinsp;1.31) to 3.1 (SD\u0026thinsp;=\u0026thinsp;2.50) containers were found per household, of which between 7.4% (SD\u0026thinsp;=\u0026thinsp;0.45) and 10.8% (SD\u0026thinsp;=\u0026thinsp;0.77) were positive for pupae. In rural communities, the average number of containers inspected per household was 3.1 (SD\u0026thinsp;=\u0026thinsp;2.07) in 2022 and 5.2 (SD\u0026thinsp;=\u0026thinsp;5.1) in 2023, with 13.8% (SD\u0026thinsp;=\u0026thinsp;0.81) to 15.5% (SD\u0026thinsp;=\u0026thinsp;1.49) containing pupae.\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\u003eEntomological collections of immature \u003cem\u003eAe. aegypti\u003c/em\u003e in urban and rural settings of Managua, Nicaragua, during dry and rainy seasons of 2022 and 2023.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy site\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeason\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e# Cont. \u003csup\u003ea\u003c/sup\u003e inspected\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e# Cont. + pupae\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLarvae\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePupae\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRural\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e943 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25 (2.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3,303 (12.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e270 (9.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRainy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e785 (3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e108 (13.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7,684 (29.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e966 (32.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.047 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68 (6.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4,438 (17.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e466 (15.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRainy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.294 (5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e200 (15.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10,621 (40.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1,260 (42.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eTotal\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e4.069\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e401\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e26,046 (100.0%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e2,962 (100.0%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUrban\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e604 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17 (2.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1,232 (11.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e57 (5.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRainy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e608 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45 (7.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3,577 (31.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e228 (22.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e659 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40 (6.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1,708 (15.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e249 (24.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRainy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e785 (3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e85 (10.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4,692 (41.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e503 (48.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eTotal\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e2.656\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e187\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e11,209 (100.0%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e1,037 (100.0%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003ea\u003c/sup\u003eCont. = Containers; in () average containers per households unless stated differently\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the dry season of 2022, we observed that only 1.6% (8/500) of all households in the study contributed 67.2% (3,268/4,862) of the total larvae and pupae collected. This pattern continued in the 2022 rainy season; 6.2% of households accumulated 63.8% (7,945/12,455) of the documented larvae and pupae. In the dry season of 2023, the distribution was similar: 3.2% of households concentrated 52.8% (3,624/6,861) of the total larvae and pupae collected. In the rainy season of 2023, 11.2% of households accumulated 68.6% of the larvae and pupae collected (11,710/17,076). These households had a high density of immature stages, with \u0026ge;\u0026thinsp;100 larvae and at least one pupa in each household.\u003c/p\u003e \u003cp\u003eIn urban communities, the containers most productive for pupae were barrels (accounting for 55\u0026ndash;79% of total pupae) and buckets (0.4\u0026ndash;32%) across all seasons and years (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), with cement laundry sinks accounting for 0%-9% of productivity during the dry seasons (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Similarly, barrels were also the most productive containers in rural communities, with 39\u0026ndash;65% of productivity across all seasons and years. However, other types of containers, such as useful and non-useful containers, were documented to contribute 40% of overall productivity during the rainy seasons.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIt is important to highlight the importance of water intermittency in the region. Our results show that 51.1% of rural households faced interruptions in water supply, with an average of 6.87 hours (SD\u0026thinsp;=\u0026thinsp;7.77) per day without running water, while in urban communities, this problem affected 34.8% of households, with average daily interruptions of 3.67 hours (SD\u0026thinsp;=\u0026thinsp;5.77). This factor has been previously shown to be a risk factor for immature \u003cem\u003eAedes\u003c/em\u003e presence in households (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eHigher Ae. aegypti abundance in rural communities\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003eImmature stages\u003c/h2\u003e \u003cp\u003eThroughout the course of the study, a total of 41,254 immature (larvae: 37,255 and pupae: 3,999) \u003cem\u003eAe. aegypti\u003c/em\u003e were collected (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Larval collections in urban communities accounted for 30% of total larvae (n\u0026thinsp;=\u0026thinsp;11,209), with distinct seasonal patterns of higher abundance during the rainy season 8,269 larvae compared to the dry season 2,940 larvae. Unsurprisingly, a similar pattern was also observed for pupal collections. Pupal collection in urban communities accounted for 26% of total pupae (n\u0026thinsp;=\u0026thinsp;1,037), with 731 in the rainy season and 306 during the dry season.\u003c/p\u003e \u003cp\u003eIn rural communities, we collected 70% (n\u0026thinsp;=\u0026thinsp;26,046) of total larvae, with 18,305 in the rainy season and 7,741 in the dry season. As in the urban area, pupal collections also had a marked seasonal pattern. Pupal collections in rural communities accounted for 74% of total pupae (n\u0026thinsp;=\u0026thinsp;2,962) pupae, with 2,226 in the rainy season and 736 in the dry season.\u003c/p\u003e \u003cp\u003eWe observed that the traditional \u003cem\u003eStegomyia\u003c/em\u003e indices of HI (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), CI (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), and BI (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC) were statistically higher across all measurements for rural communities compared to urban areas, with the exception of the dry season of 2022 (see Additional file 1: Table S2-S7). Values for rural communities were highest during the rainy season, ranging from 59.2 to 58.8 for HI, 36.2 to 32.3 for CI, and 113.6 to 167.2 for BI in 2022 and 2023, respectively. In contrast, in urban communities for the same seasons, the indices ranged from 42.8 to 41.2 for HI, 28.1 to 23.9 for CI, and 68.4 to 75.2 for BI in 2022 and 2023, respectively. Additionally, the pupal indices of PHI (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD), PCI (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE), and PPI (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF), were also statistically higher for rural communities across all measurements. Values for rural communities were highest during the rainy season, ranging from 386.4 to 504 for PHI (no statistical difference for both summer seasons), 123.1 to 97.4 for PCI, and 82.3 to 111.6 for PPI in 2022 and 2023, respectively. In urban communities, these values were 91.2 to 201.2 for PHI, 37.5 to 63.9 for PCI, and 17.3 to 37.9 for PPI in 2022 and 2023, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eAdult\u003c/span\u003e \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eAe. aegypti\u003c/span\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ecollections\u003c/span\u003e\u003c/p\u003e \u003cp\u003eWe collected a total of 1,468 adult \u003cem\u003eAe. aegypti\u003c/em\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In urban communities, we collected 38% of all adult females (n\u0026thinsp;=\u0026thinsp;274), of which 23% (n\u0026thinsp;=\u0026thinsp;63) were collected in the dry season and 77% (n\u0026thinsp;=\u0026thinsp;211) in the rainy season. In rural communities, we collected 62% of all adult females (n\u0026thinsp;=\u0026thinsp;441) females, of which 27% (n\u0026thinsp;=\u0026thinsp;120) were collected in the dry season and 73% (n\u0026thinsp;=\u0026thinsp;321) in the rainy season. In urban communities, we collected 41% (n\u0026thinsp;=\u0026thinsp;309) of all adult males, of which 26% (n\u0026thinsp;=\u0026thinsp;81) were collected in the dry season and 74% (n\u0026thinsp;=\u0026thinsp;228) in the rainy season. Rural communities accounted for 59% (n\u0026thinsp;=\u0026thinsp;444) of the total adult males, of which 28% (n\u0026thinsp;=\u0026thinsp;126) were during the dry season and 72% (n\u0026thinsp;=\u0026thinsp;318) during the rainy season.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEntomological collections of adult \u003cem\u003eAe. aegypti\u003c/em\u003e in urban and rural settings of Managua, Nicaragua, during dry and rainy seasons of 2022 and 2023.\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\"\u003e \u003cp\u003eStudy site\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeason\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFemales (Adult)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMales (Adult)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRural\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33 (7.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48 (10.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRainy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e142 (32.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e158 (35.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e87 (19.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78 (17.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRainy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e179 (40.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e160 (36.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eTotal\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e441 (100%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e444 (100%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUrban\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (6.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22 (7.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRainy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58 (21.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e87 (28.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45 (16.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59 (19.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRainy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e153 (55.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e141 (45.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eTotal\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e274 (100%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e309 (100%)\u003c/em\u003e\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\u003eWe observed distinct patterns of mosquito infestation and abundance in both urban and rural communities. The best fit models for the GLMM showed a Negative Binomial 2 type distribution with an AIC of 4341.6. We observed a statistically significant difference in the overall abundance of female mosquitoes, with urban communities having 36.5% less females than rural communities (OR\u0026thinsp;=\u0026thinsp;0.635, 95%CI 0.53\u0026ndash;0.77, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This statistically significant difference was observed across seasons and years (Additional file 1: Text S1-GLMM best fit model), with a consistently higher statistically significant abundance of female mosquitoes in rural communities.\u003c/p\u003e \u003cp\u003eRegarding the proportion of houses positive for adult \u003cem\u003eAe. aegypti\u003c/em\u003e (AI) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), we observed that during the dry seasons, no significant differences were detected between rural and urban communities in 2022 (Rural: 18.8% and Urban: 14.0%) and 2023 (Rural: 32.0% and Urban: 25.2%). However, in the rainy season of 2022, we noted a higher proportion of rural houses positive for adult \u003cem\u003eAe. aegypti\u003c/em\u003e (Rural: 51.2% and Urban: 34.8%, \u003cem\u003ez\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.704, p\u0026thinsp;=\u0026thinsp;0.0002). Nonetheless, in the rainy season of 2023, the proportion of houses positive for adult \u003cem\u003eAe. aegypti\u003c/em\u003e was similar between rural and urban areas (Rural: 53.2% and Urban: 50.8%). With respect to the number of adult \u003cem\u003eAe. aegypti\u003c/em\u003e collected per house (AH) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB), we observed a significantly higher number of adults in rural compared to urban houses in the dry seasons of 2022 (Rural: 0.32 (SD\u0026thinsp;=\u0026thinsp;0.818) and Urban: 0.16 (SD\u0026thinsp;=\u0026thinsp;0.427), \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.74, df\u0026thinsp;=\u0026thinsp;498, p\u0026thinsp;=\u0026thinsp;0.006) and 2023 (Rural: 0.66 (SD\u0026thinsp;=\u0026thinsp;1.338) and Urban: 0.42 (SD\u0026thinsp;=\u0026thinsp;0.987), \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.28, df\u0026thinsp;=\u0026thinsp;498, p\u0026thinsp;=\u0026thinsp;0.02). We observed a similar pattern in the rainy season of 2022 with a significantly higher number of adults in rural houses (Rural: 1.2 (SD\u0026thinsp;=\u0026thinsp;2.692) and Urban: 0.58 (SD\u0026thinsp;=\u0026thinsp;1.07), \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.38, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, in the rainy season of 2023, comparable results were observed in rural and urban houses (Rural: 1.36 (SD\u0026thinsp;=\u0026thinsp;2.685) and Urban: 1.18 (SD\u0026thinsp;=\u0026thinsp;2.079)).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn relation to the number of adult \u003cem\u003eAe. aegypti\u003c/em\u003e per person (AP) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA), we observed 2.4 (95%CI\u0026thinsp;=\u0026thinsp;1.62\u0026ndash;3.47, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) times higher vector density in rural communities compared to urban areas during the dry season of 2022. In the dry season of 2023, rural communities had 1.8 (95%CI\u0026thinsp;=\u0026thinsp;1.40\u0026ndash;2.29, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) times higher vector density than urban communities. This same pattern of higher vector abundance in rural communities was also observed in 2022 (2.3; 95%CI\u0026thinsp;=\u0026thinsp;1.91\u0026ndash;2.84, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and 2023 (1.4; 95%CI\u0026thinsp;=\u0026thinsp;1.16\u0026ndash;1.58, p\u0026thinsp;=\u0026thinsp;0.0001).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFinally, a higher density of female \u003cem\u003eAe. aegypti\u003c/em\u003e per person (FP) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB), was detected in rural communities compared to urban communities during the dry seasons of 2022 (2.1, 95%CI\u0026thinsp;=\u0026thinsp;1.20\u0026ndash;3.86, p\u0026thinsp;=\u0026thinsp;0.009) and 2023 (2.2; 95%CI\u0026thinsp;=\u0026thinsp;1.53\u0026ndash;3.15, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). The same was observed in the rainy seasons, with rural communities having a higher female vector density in both 2022 (2.8; 95%CI\u0026thinsp;=\u0026thinsp;2.04\u0026ndash;3.08, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and 2023 (1.4; 95%CI\u0026thinsp;=\u0026thinsp;1.11\u0026ndash;1.70, p\u0026thinsp;=\u0026thinsp;0.004). Additionally, as a whole, we observed a trend towards an increase of 1.72 (95% CI\u0026thinsp;=\u0026thinsp;1.46\u0026ndash;2.02, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) times the abundance of female \u003cem\u003eAe. aegypti\u003c/em\u003e in 2023 compared to 2022.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ePupal thresholds for female Ae. aegypti\u003c/h2\u003e \u003cp\u003eWe also evaluated the non-linear relationship (smooth terms) of L4 and/or pupae and water-holding containers with the abundance of female \u003cem\u003eAe. aegypti\u003c/em\u003e. The models showed that the immature variable of pupae had the best fit (AIC\u0026thinsp;=\u0026thinsp;4599.8) for female mosquito abundance, with statistical significance of the pupae smooth term (edf\u0026thinsp;=\u0026thinsp;3.48, Chi\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;46.07, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, R\u003csup\u003e2\u003c/sup\u003e adjusted\u0026thinsp;=\u0026thinsp;0.126). For the effect of containers with water, we did not observe statistical significance of the smooth function (p\u0026thinsp;=\u0026thinsp;0.09); however, we observed a linear increase, which would imply a consistent positive association between total containers with water and female adults in the household. Next, we modeled pupae by community. In the rural communities, we observed that the relationship between female adult \u003cem\u003eAe. aegypti\u003c/em\u003e and total pupae increased in waves in response to the total number of females in the household. Specifically, there was an initial increase in pupae abundance from 1 to around 18 pupae, followed by a plateau between approximately 19 to 50 pupae, finalizing in a sharp increase beyond 50 pupae. This pattern suggests that as the count of pupae increases, there is an initial positive effect on number of female \u003cem\u003eAe. aegypti\u003c/em\u003e, followed by a stable phase, and then a strong increase, indicating possible thresholds in the effect of pupal abundance on the number of females in the household (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Our model prediction for adult female counts shows that from 1 to 13 pupae, we have a predicted increase from 0.24 (95%CI\u0026thinsp;=\u0026thinsp;0.20\u0026ndash;0.29) to 0.49 (95%CI\u0026thinsp;=\u0026thinsp;0.36\u0026ndash;0.66) females, a plateau of 0.49 to 0.48 (95%CI\u0026thinsp;=\u0026thinsp;0.29\u0026ndash;0.79) females from 13 to 41 pupae, and an increase to 2.03 (95%CI\u0026thinsp;=\u0026thinsp;0.35\u0026ndash;11.83) females from 42 to 84 pupae. In urban communities, we observed a moderate increase of the smooth peaking at 20 pupae, followed by a consistent decrease, suggesting that increases in pupal counts may not correspond to higher female captures in urban settings (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003e \u003cem\u003eAe. aegypti\u003c/em\u003e continues to be a major public health threat in tropical regions and an emerging threat to other parts of the globe due to its ecological range expansion as a consequence of climate change (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). With increasing dengue epidemics globally and billions of people at risk of arbovirus infection, it is critical to gain in-depth knowledge of the local distribution of \u003cem\u003eAe. aegypti\u003c/em\u003e and the risk that communities face. In particular, a deeper understanding is needed of communities that are not usually viewed as key for \u003cem\u003eAe. aegypti\u003c/em\u003e, such as rural areas (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Our results in District III of Managua, Nicaragua, clearly show that \u003cem\u003eAe. aegypti\u003c/em\u003e had higher abundance in all traditional \u003cem\u003eStegomyia\u003c/em\u003e indices and higher mosquito densities in rural as compared to urban communities. Interestingly, households that were positive for adult presence had similar results in both rural and urban communities. These results emphasize the importance of evaluating \u003cem\u003eAe. aegypti\u003c/em\u003e infestation and disease transmission in areas that have traditionally not be considered ideal sites for \u003cem\u003eAe. aegypti\u003c/em\u003e, as this information can inform public health decision-makers.\u003c/p\u003e \u003cp\u003eOur results highlight significant seasonal and spatial variations in container availability and pupal presence across urban and rural communities. Urban areas had fewer water-holding containers per household, yet pupal positivity was comparable or slightly lower than in rural settings, which suggests that both areas are ideal for mosquito development. Rural households, with greater reliance on water storage, exhibited higher pupal positivity, particularly during the rainy season. A similar result was observed in a longitudinal study in Kenya, with higher pupal density per container in rural sites (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). However, this contrasts with results observed in Colombia, where urban communities showed significantly higher pupal densities in containers than rural communities (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), and Sri Lanka, where higher container indices were reported in urban and suburban areas compared to rural areas (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). The results underscore the ecological differences found by geographic region and the plasticity of \u003cem\u003eAe. aegypty\u003c/em\u003e to adapt to changing environments. These highlights the importance of considering local environmental, socio-economic, and behavioral factors at a fine scale when designing vector control strategies.\u003c/p\u003e \u003cp\u003eDuring the 2022 and 2023 dry seasons, barrels, cement laundry sinks, and buckets were the most productive pupal habitats across all households. In urban areas, these containers accounted for nearly all pupae collected, while in rural areas, they contributed to the majority. Similar trends have been reported in Mexico and Argentina, where large water storage tanks dominated pupal production (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). In the rainy seasons of both years, these container types remained the primary sources of pupae in urban areas, with tires, discarded containers, and useful containers also playing a role. In rural areas, barrels, cement laundry sinks, and buckets were still dominant, though alternative containers, including discarded items, gained importance. Observations from Sri Lanka align with our findings, emphasizing the significance of non-degradable garbage and tires as mosquito breeding sites (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). These results suggest the need for tailored intervention strategies to be implemented by season; while the dry season should focus on traditional water storage containers, the rainy season demands greater attention to eliminating discarded items in outdoor spaces. Additionally, overall we observe very low positive rates of water-holding containers with pupae regardless of rurality, which suggests that targeted elimination of key-breeding sites might be a viable control strategy for District III of Managua.\u003c/p\u003e \u003cp\u003eOur observations over two consecutive years and across both rainy and dry seasons indicate that traditional \u003cem\u003eStegomyia\u003c/em\u003e indices in rural communities were significantly higher than those in urban areas. This suggests that rural environments not only support the reproduction of \u003cem\u003eAe. aegypti\u003c/em\u003e but also offer more available habitats and favorable conditions for their productivity, resulting in higher container indices throughout the year. Similar findings have been reported in other regions. For instance, a study in Zanzibar, Tanzania, found that rural settings had higher numbers of infested containers, as well as greater counts of \u003cem\u003eAe. aegypti\u003c/em\u003e immature forms and pupae compared to urban areas (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). Additionally, higher House and Breteau indices were recorded in rural settings. In contrast, a study in Cambodia did not observe differences in \u003cem\u003eStegomyia\u003c/em\u003e indices between rural and urban communities (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). Furthermore, several studies have shown a higher abundance of \u003cem\u003eAe. aegypti\u003c/em\u003e in urban areas (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e), as it is thought that urbanization might favor the proliferation of this vector (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). These differences across studies might be related to several factors, such as variation in enviromental conditions, human behaviour, and mosquito control practices. Our pupal indices showed that over both rainy and dry seasons, \u003cem\u003eAe. aegypti\u003c/em\u003e populations are sustained, and even though the indices are lower during the dry season, they still are within the 50\u0026ndash;150 PPI threshold and pose a threat for disease transmission (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). Another interesting observation is that a few houses in our study site contributed the vast majority of total pupae collections. This pattern was not observed in adult collections, which were more uniform across areas with high densities of adult \u003cem\u003eAe. aegypti\u003c/em\u003e in both years and seasons, potentially because of the dispersal of mosquitoes due to flight range (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). This could suggest that tackling key hot spots for breeding site (source) reduction might have a larger impact on vector control than area-wide management. It is undeniable that rural areas play a role in sustaining \u003cem\u003eAe. aegypti\u003c/em\u003e populations and are at risk of disease transmission; as such, \u003cem\u003eAe. aegypti\u003c/em\u003e vector control should also focus on rural communities.\u003c/p\u003e \u003cp\u003eWe observed a complex non-linear relationship between \u003cem\u003eAe. aegypti\u003c/em\u003e pupal abundance and female mosquito counts in both urban and rural communities. In rural communities, the GAMM models showed that pupae were a significant predictor of female \u003cem\u003eAe. aegypti\u003c/em\u003e populations, suggesting specific thresholds where accumulation of pupae leads to an increase in adult female mosquitoes, as observed in the initial rise followed by a plateau and then a sharp rise again above 50 pupae, pointing to a threshold effect. However, in urban communities, this effect was only observed in the initial rise to 20 pupae, before a decline in female abundance. This threshold effect could be modulated by density-dependent effects of pupae, such as pupal density and \u003cem\u003eper capita\u003c/em\u003e growth, as previously observed in a laboratory setting as a non-linear relationship (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). Our results suggest that after reaching a pupal density of 20 pupae per container in urban areas, the impact on abundance of female adults will be limited (\u003cspan additionalcitationids=\"CR56\" citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e). However, in rural communities, containers that may allow\u0026thinsp;\u0026gt;\u0026thinsp;50 pupae per container would have enough resources to sustain the full development of more female mosquitoes. This also showcases the need for water-management in rural communities to avoid this threshold of pupal production. We also observed that the number of water-holding containers had a positive linear relationship trend with the total count of female adults, as we had observed previously in Managua (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Our results highlight the potential ecological and environmental differences between rural and urban settings that influence mosquito abundance dynamics.\u003c/p\u003e \u003cp\u003eOur study reveals some of the intricacies of urban and rural communities in two distinct seasons across two years of data collection. One of the limitations of our study was that the collection only occurred at specific time points; thus, the lack of longer-term temporal data prevents a deeper analysis of the ecological dynamics of these urban and rural communities. However, we compensate for this limitation with a robust sample size and a thorough entomological sampling of the households surveyed. We believe that even though our temporal collection events are restricted to seasons, there is enough evidence to highlight the importance of rural communities in sustaining \u003cem\u003eAe. aegypti\u003c/em\u003e populations and the potential to drive arboviral disease transmission in such areas. However, during this time-period, our mosquito-based arbovirus surveillance only yielded positive pools in the urban communities (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e). We also acknowledge that this study did not evaluate non-household breeding sites, which can also play a role in sustaining mosquito populations in the area. We are currently evaluating the impact of non-household sites as drivers of population dynamics in District III. Of note, in the 2023 rainy season, we recorded for the first time \u003cem\u003eAe. albopictus\u003c/em\u003e within the communities of District III, showcasing the need for further surveillance efforts to evaluate the impact that \u003cem\u003eAe. albopictus\u003c/em\u003e might have in the ecology of \u003cem\u003eAe. aegypti\u003c/em\u003e and the transmission of arboviruses.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur results demonstrate that rural communities had a higher density of adult \u003cem\u003eAe. aegypti\u003c/em\u003e and traditional \u003cem\u003eStegomyia\u003c/em\u003e indices in comparison to urban ones. We also observed that barrels, cement laundry sinks, and buckets should be treated as key container habitats within a vector control strategy, especially in communities facing water supply interruptions, where water storage is indispensable in dry and rainy seasons. Non-useful containers, tires, and animal water bowls were also found to be key productive containers during the rainy season. Additionally, we observed distinct threshold patterns for pupal density as predictors of adult female abundance in rural vs urban settings; further elucidation of these ecological patterns might be used for vector control strategies. We hope that this study can help guide public health control activities within the region, underscoring the importance of rural areas for \u003cem\u003eAe. aegypti\u003c/em\u003e propagation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank all the members of our A2CARES study team in Nicaragua, in particular Meyling Escobar C\u0026aacute;rcamo and Carlos Santos Acevedo, who were involved in the collection of entomological samples and laboratory speciation procedures; Everts Morales Reyes for the support of customized informatic systems; Jorge Ruiz Salinas for support in database management; and Juan Carlos Mercado for support with material and logistics. We thank the residents of District III who allowed entomological collections in and around their homes. Additionally, we are grateful for valuable collaboration with community leaders and overall support from local health authorities.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the National Institute of Allergy and Infectious Diseases of the National Institutes of Health, grant U01 AI151788 (EH, JC), as part of the Centers for Research on Emerging Infectious Diseases (CREID) network.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData set is available in ZENODO: https://doi.org/10.5281/zenodo.14861397and R code can be found at: https://github.com/jgjuarez/UrbanRuralManagua\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026acute;contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eH.S.L., A.B., E.H., J.C., J.G.J. designed and guided the experiments. H.S.L., J.M.D., M.M.L. carried out the data collection and field activities. H.S.L., J.G.J. analyzed the data and generated the figures. H.S.L., E.H., J.G.J. wrote the manuscript. J.M.D., M.M.L., A.B., J.C. reviewed and edited the manuscript. All authors approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProtocols for the entomological surveillance and socio-demographic questionnaires were reviewed and approved by the Institutional Review Boards (IRB) of the University of California, Berkeley (A2CARES: 2021-03-14191) and the Nicaraguan Ministry of Health (A2CARES: CIRE 02/08/21-114 Ver.4). Household owners provided signed consent during the initial visit and verbal consent for each subsequent visit.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors declare no competing interests\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWHO. Promoting dengue vector surveillance and control. 2023. Available from: https://www.who.int/activities/promoting-dengue-vector-surveillance-and-control\u003c/li\u003e\n\u003cli\u003eCDC. Dengue. 2024. Areas with Risk of Dengue. Available from: https://www.cdc.gov/dengue/areas-with-risk/index.html\u003c/li\u003e\n\u003cli\u003eGubler DJ. Aedes aegypti and the \u003cem\u003eAedes aegypti-\u003c/em\u003eborne disease control in the 1990s: top down or bottom up. 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Available from: https://planet.osm.org/\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Aedes aegypti, mosquitoes, urban, rural, Nicaragua","lastPublishedDoi":"10.21203/rs.3.rs-6059011/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6059011/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAe. aegypti\u003c/em\u003e is the primary vector of dengue, chikungunya, and Zika viruses, traditionally associated with urban environments. However, its presence and abundance in rural settings remain understudied. This study compares \u003cem\u003eAe. aegypti\u003c/em\u003epopulations between rural and urban communities in Managua, Nicaragua, across different seasons over multiple years.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEntomological surveys were conducted in 500 randomly selected households (250 rural, 250 urban) during the rainy and dry seasons of 2022 and 2023. Immature mosquitoes were collected from water-holding containers, and adult mosquitoes were sampled using aspirators. Entomological indices, including \u003cem\u003eStegomyia\u003c/em\u003e, pupal, and adult indices, were compared across seasons and localities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll entomological indices were significantly higher in rural communities than in urban areas across both years and seasons. Rural households had greater mosquito densities, with pupal productivity concentrated in large water storage containers. Adult mosquito collections confirmed a greater \u003cem\u003eAe. aegypti\u003c/em\u003e presence in rural areas, suggesting sustained transmission risk. We observed pupal thresholds in water-holding containers for female adult collections.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiscussion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eContrary to the conventional view of \u003cem\u003eAe. aegypti\u003c/em\u003e as an urban mosquito, our findings highlight its substantial presence in rural settings, likely driven by water storage practices and environmental conditions. These results align with findings from other regions reporting high mosquito abundance in rural areas, challenging assumptions about urban dominance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRural areas play a crucial role in sustaining \u003cem\u003eAe. aegypti\u003c/em\u003e populations. Vector control strategies should target both rural and urban communities, with seasonally tailored interventions to mitigate disease transmission risks.\u003c/p\u003e","manuscriptTitle":"Higher abundance of the vector Aedes aegypti in rural areas than in urban areas in Managua, Nicaragua","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-12 12:09:02","doi":"10.21203/rs.3.rs-6059011/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2722186b-e837-4297-b726-e23908dac4ab","owner":[],"postedDate":"March 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-06T04:23:38+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-12 12:09:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6059011","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6059011","identity":"rs-6059011","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}