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

Ratti, Vardayani; Chipman, Jonathan; Wallace, Dorothy

doi:10.1016/j.crpvbd.2022.100080

Cited by 3 publications

(2 citation statements)

References 59 publications

(81 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The quantitative effects of rainfall on malaria are less broadly understood compared to those of temperature. In some studies, an increase in rainfall has been associated with more malaria due to the increased availability of vector breeding sites, yet too much rainfall has also been associated with a reduction in malaria because it can flush larval habitats (Asare et al, 2016;Ratti et al, 2022). The functional form relating rainfall to malaria may vary across settings that differ in climate, hydrology, and land use.…”

Section: Discussionmentioning

confidence: 99%

Global malaria predictors at a localized scale

Skinner,

Childs,

Thomas

et al. 2024

Front. Malar.

View full text Add to dashboard Cite

Malaria is a life-threatening disease caused by Plasmodium parasites transmitted by Anopheles mosquitoes. In 2022, more than 249 million cases of malaria were reported worldwide, with an estimated 608,000 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 R0 model based on laboratory data, which predicts that malaria transmission peaks at 25°C and declines at lower and higher temperatures, 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.

show abstract

Section: Discussionmentioning

confidence: 99%

Global malaria predictors at a localized scale

Skinner,

Childs,

Thomas

et al. 2024

Front. Malar.

View full text Add to dashboard Cite

show abstract

“…However, these studies have alluded to the fact that assessment of the influence of different rainfall regimens on mosquito populations needs further examination as the rainfall linkage to mosquito habitats, in particular the larval habitat, could depend on factors such as slope, river routing, and availability of potential habitats for mosquitoes. Some have tackled this by using a lag in precipitation measurements or simulated rainfall to produce more accurate results ( Shone et al 2014 , Valdez et al 2017 , Ratti and Wallace 2020 , Ratti et al 2022 ). We hypothesize that including water gauge measurements in addition to temperature will improve predictions over temperature and precipitation (see Supp Fig.…”

mentioning

confidence: 99%

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

Shutt

Goodsman

Martinez

et al. 2022

Journal of Medical Entomology

View full text Add to dashboard Cite

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.

show abstract

Global malaria predictors at a localized scale

Skinner,

Childs,

Thomas

et al. 2023

Preprint

View full text Add to dashboard Cite

Malaria is a life-threatening disease caused byPlasmodiumparasites transmitted byAnophelesmosquitoes. 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 R0model 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.

show abstract

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

Cited by 3 publications

References 59 publications

Global malaria predictors at a localized scale

Global malaria predictors at a localized scale

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

Global malaria predictors at a localized scale

Contact Info

Product

Resources

About