Larval flushing alters malaria endemicity patterns in regions with similar habitat abundance

Species abundance and density of malaria vectors in Western Thailand and implications for disease transmission

Understanding the dynamics of malaria vectors and their interactions with environmental factors is crucial for effective malaria control. This study investigated the abundance, species composition, seasonal variations, and malaria infection status of female mosquitoes in malaria transmission and non-transmission areas in Western Thailand. Additionally, the susceptibility of malaria vectors to pyrethroid insecticides was assessed. Entomological field surveys were conducted during the hot, wet, and cold seasons in both malaria transmission areas (TA) and non-transmission areas (NTA). The abundance and species composition of malaria vectors were compared between TA and NTA. The availability of larval habitats and the impact of seasonality on vector abundance were analyzed. Infection with Plasmodium spp. in primary malaria vectors was determined using molecular techniques. Furthermore, the susceptibility of malaria vectors to pyrethroids was evaluated using the World Health Organization (WHO) susceptibility test. A total of 9799 female mosquitoes belonging to 54 species and 11 genera were collected using various trapping methods. The number of malaria vectors was significantly higher in TA compared to NTA (P < 0.001). Anopheles minimus and An. aconitus were the predominant species in TA, comprising over 50% and 30% of the total mosquitoes collected, respectively. Seasonality had a significant effect on the availability of larval habitats in both areas (P < 0.05) but did not impact the abundance of adult vectors (P > 0.05). The primary malaria vectors tested were not infected with Plasmodium spp. The WHO susceptibility test revealed high susceptibility of malaria vectors to pyrethroids, with mortality rates of 99-100% at discriminating concentrations. The higher abundance of malaria vectors in the transmission areas underscores the need for targeted control measures in these regions. The susceptibility of malaria vectors to pyrethroids suggests the continued effectiveness of this class of insecticides for vector control interventions. Other factors influencing malaria transmission risk in the study areas are discussed. These findings contribute to our understanding of malaria vectors and can inform evidence-based strategies for malaria control and elimination efforts in Western Thailand.

Global malaria predictors at a localized scale

Malaria is a life-threatening disease caused by Plasmodium parasites transmitted by Anopheles mosquitoes. In 2021, more than 247 million cases of malaria were reported worldwide, with an estimated 619,000 deaths. While malaria incidence has decreased globally in recent decades, some public health gains have plateaued, and many endemic hotspots still face high transmission rates. Understanding local drivers of malaria transmission is crucial but challenging due to the complex interactions between climate, entomological and human variables, and land use. This study focuses on highly climatically suitable and endemic areas in Côte d'Ivoire to assess the explanatory power of coarse climatic predictors of malaria transmission at a fine scale. Using data from 40 villages participating in a randomized controlled trial of a household malaria intervention, the study examines the effects of climate variation over time on malaria transmission. Through panel regressions and statistical modeling, the study investigates which variable (temperature, precipitation, or entomological inoculation rate) and its form (linear or unimodal) best explains seasonal malaria transmission and the factors predicting spatial variation in transmission. The results highlight the importance of temperature and rainfall, with quadratic temperature and all precipitation models performing well, but the causal influence of each driver remains unclear due to their strong correlation. Further, an independent, mechanistic temperature-dependent R 0 model based on laboratory data aligns well with observed malaria incidence rates, emphasizing the significance and predictability of temperature suitability across scales. By contrast, entomological variables, such as entomological inoculation rate, were not strong predictors of human incidence in this context. Finally, the study explores the predictors of spatial variation in malaria, considering land use, intervention, and entomological variables. The findings contribute to a better understanding of malaria transmission dynamics at local scales, aiding in the development of effective control strategies in endemic regions.

A Process-based Model with Temperature, Water, and Lab-derived Data Improves Predictions of Daily Culex pipiens/restuans Mosquito Density

While the number of human cases of mosquito-borne diseases has increased in North America in the last decade, accurate modeling of mosquito population density has remained a challenge. Longitudinal mosquito trap data over the many years needed for model calibration, and validation is relatively rare. In particular, capturing the relative changes in mosquito abundance across seasons is necessary for predicting the risk of disease spread as it varies from year to year. We developed a discrete, semi-stochastic, mechanistic process-based mosquito population model that captures life-cycle egg, larva, pupa, adult stages, and diapause for Culex pipiens (Diptera, Culicidae) and Culex restuans (Diptera, Culicidae) mosquito populations. This model combines known models for development and survival into a fully connected age-structured model that can reproduce mosquito population dynamics. Mosquito development through these stages is a function of time, temperature, daylight hours, and aquatic habitat availability. The time-dependent parameters are informed by both laboratory studies and mosquito trap data from the Greater Toronto Area. The model incorporates city-wide water-body gauge and precipitation data as a proxy for aquatic habitat. This approach accounts for the nonlinear interaction of temperature and aquatic habitat variability on the mosquito life stages. We demonstrate that the full model predicts the yearly variations in mosquito populations better than a statistical model using the same data sources. This improvement in modeling mosquito abundance can help guide interventions for reducing mosquito abundance in mitigating mosquito-borne diseases like West Nile virus.