Effect of the Rural and Urban Microclimate on Mosquito Richness and Abundance in Yucatan State, Mexico

Diversity and Distribution of Culicidian Fauna in Urban and Rural Areas of Setif Region (North-East Algeria)

Objective: The study explored larval mosquito breeding sites in urban and rural areas from March 2021 to February 2022 in the North-East region of Algeria to provide information on larval biology and ecology of different mosquito species. Methodology: It focused on the effect of the physicochemical characteristics of the breeding sites (pH and water temperature), area, and months on the larval mosquito abundance. Diversity index and equitability were calculated for the registered mosquito species in both areas. Results: Months significantly affected the larval mosquito abundances, with a clear abundance increase from March to August 2021, and then, a clear decrease to February 2022. The interactions between rural and urban areas, water pH, and water temperature as well affected the mosquito abundance. Twelve species of Culicidae were identified and distributed in three genera: Culex, Culiseta, and Aedes. Culex pipiens was the most dominant species in rural and urban sites, with a rate of 69% and 48%, respectively. In rural site, Aedes caspius and Aedes vexans occupied the second position with value of 7-8% each. In urban sites, the second position was occupied by Culiseta longiareolata (36%) and then by Culex theileri (10%). In terms of site diversity, only one site was highly diversified in rural sites, El Hchichia (H' = 1.12 bits). In urban sites, Setif Center, Guellel an Ain Trick, had almost similar diversity with values between 1.03 and 1.04 bits. Equitability values were 0.48 in rural sites and 0.55 in urban sites. Therefore, the Culicidae species presented in these study sites are moderately balanced. Conclusion: The present study provides key tools for planning a better vector control through the information on the effect of some factors on the proliferation of mosquitoes in the urban and rural sites of the Setif region and on the Culicidae diversity and abundance.

Vector mosquito distribution and richness are predicted by socio-economic, and ecological variables

Mosquitoes of vectorial importance represent a ubiquitous and constant threat of potentially devastating arboviral outbreaks. Our ability to predict such outcomes is still restricted. To answer this, we have used an extensive data collection of 23 vector and 233 non-vector mosquito species distributed throughout the Mexican territory and linked them to social and environmental factors. Our aim was to predict vector and non-vector mosquitoes' distribution and species richness based on socioeconomic and environmental data. We found that lack of health services, human population variation, ecological degradation, and urban-rural categorization contributed significantly to explain the distribution of vector mosquitoes. mosquitoes. This phenomenon is probably attributed to the degradation of natural ecosystems as it creates favorable conditions for the proliferation of vector mosquitoes. The richness of vector mosquitoes was similarly explained by most of these variables as well as altitude. As for non-vector mosquitoes, social marginalization, ecological degradation, anthropogenic impact, and altitude explain species richness and distribution. These findings illustrate the complex interaction of environmental and socioeconomic factors behind the distribution of mosquitoes, and the potential for arboviral disease outbreaks. Areas with human populations at highest risk for mosquito-borne diseases should be primary targets for vector control.

Thermal tolerance of mosquito eggs is associated with urban adaptation and human interactions

Climate change is expected to profoundly affect mosquito distributions and their ability to serve as vectors for disease, specifically with the anticipated increase in heat waves. The rising temperature and frequent heat waves can accelerate mosquito life cycles, facilitating higher disease transmission. Conversely, higher temperatures could increase mosquito mortality as a negative consequence. Warmer temperatures are associated with increased human density, suggesting a need for anthropophilic mosquitoes to adapt to be more hardy to heat stress. Mosquito eggs provide an opportunity to study the biological impact of climate warming as this stage is stationary and must tolerate temperatures at the site of female oviposition. As such, egg thermotolerance is critical for survival in a specific habitat. In nature, Aedes mosquitoes exhibit different behavioral phenotypes, where specific populations prefer depositing eggs in tree holes and prefer feeding non-human vertebrates. In contrast, others, particularly human-biting specialists, favor laying eggs in artificial containers near human dwellings. This study examined the thermotolerance of eggs, along with adult stages, for Aedes aegypti and Ae. albopictus lineages associated with known ancestry and shifts in their relationship with humans. Mosquitoes collected from areas with higher human population density, displaying increased human preference, and having a human-associated ancestry profile have increased egg viability following high-temperature stress. Unlike eggs, thermal tolerance among adults showed no significant correlation based on the area of collection or human-associated ancestry. This study highlights that the egg stage is likely critical to mosquito survival when associated with humans and needs to be accounted when predicting future mosquito distribution.

Integration of population-level data sources into an individual-level clinical prediction model for dengue virus test positivity

The differentiation of dengue virus (DENV) infection, a major cause of acute febrile illness in tropical regions, from other etiologies, may help prioritize laboratory testing and limit the inappropriate use of antibiotics. While traditional clinical prediction models focus on individual patient-level parameters, we hypothesize that for infectious diseases, population-level data sources may improve predictive ability. To create a clinical prediction model that integrates patient-extrinsic data for identifying DENV among febrile patients presenting to a hospital in Thailand, we fit random forest classifiers combining clinical data with climate and population-level epidemiologic data. In cross-validation, compared to a parsimonious model with the top clinical predictors, a model with the addition of climate data, reconstructed susceptibility estimates, force of infection estimates, and a recent case clustering metric significantly improved model performance.

The influence of humidity and temperature on the vertical richness and abundance of blood-sucking flies (Culicidae and Ceratopogonidae) in a montane cloud forest in Mexico

Because the vertical distribution and diversity of blood-sucking flies are poorly known, we determined the diversity, structure, and composition of culicids between vertical vegetation strata. We evaluated the influence of microclimatic variables during different times of the day over a year. We used eight CDC traps baited with CO2 at a height of 1.5 m and 12-15 m. We conducted rank-abundance curves, similarity analysis (ANOSIM and SIMPER), and multivariate clustering with incidence and abundance data. We used GAM models to analyze the influence of strata (understory vs canopy), humidity, and temperature on insect richness and abundance. During the day, the difference between strata was mainly due to higher abundance of Wyeomyia arthrostigma and Wyeomyia ca. adelpha in the understory. During the night, the differences were mainly due to higher abundance of Culex stigmatosoma, Culex salinarius, and Aedes allotecnon in the canopy, and Wyeomyia arthrostigma in the understory. Seasonality played a role in the similarity between the strata. Diversity during the day was positively related to humidity and temperature, and nocturnal diversity increased with temperature but decreased with higher humidity. The effects of environmental factors on the spatiotemporal distribution of fly species are essential for epidemiological surveillance.

Integration of population-level data sources into an individual-level clinical prediction model for dengue virus test positivity

The differentiation of dengue virus (DENV) infection, a major cause of acute febrile illness in tropical regions, from other etiologies, may help prioritize laboratory testing and limit the inappropriate use of antibiotics. While traditional clinical prediction models focus on individual patient-level parameters, we hypothesize that for infectious diseases, population-level data sources may improve predictive ability. To create a clinical prediction model that integrates patient-extrinsic data for identifying DENV among febrile patients presenting to a hospital in Thailand, we fit random forest classifiers combining clinical data with climate and population-level epidemiologic data. In cross validation, compared to a parsimonious model with the top clinical predictors, a model with the addition of climate data, reconstructed susceptibility estimates, force of infection estimates, and a recent case clustering metric, significantly improved model performance.