Abstract
Mosquitoes are vectors of major pathogen agents worldwide. Population dynamics models are useful tools to understand and predict mosquito abundances in space and time. To be used as forecasting tools over large areas, such models could benefit from integrating remote sensing data that describe the meteorological and environmental conditions driving mosquito population dynamics. The main objective of this study is to assess a process-based modeling framework for mosquito population dynamics using satellite-derived meteorological estimates as input variables. A generic weather-driven model of mosquito population dynamics was applied to Rift Valley fever vector species in northern Senegal, with rainfall, temperature, and humidity as inputs. The model outputs using meteorological data from ground weather station vs satellite-based estimates are compared, using longitudinal mosquito trapping data for validation at local scale in three different ecosystems. Model predictions were consistent with field entomological data on adult abundance, with a better fit between predicted and observed abundances for the Sahelian Ferlo ecosystem, and for the models using in-situ weather data as input. Based on satellite-derived rainfall and temperature data, dynamic maps of three potential Rift Valley fever vector species were then produced at regional scale on a weekly basis. When direct weather measurements are sparse, these resulting maps should be used to support policy-makers in optimizing surveillance and control interventions of Rift Valley fever in Senegal.
Highlights
Mosquitoes are vectors of many major pathogens worldwide, and the importance of the understanding and prediction of mosquito population dynamics to optimize surveillance and control of mosquito-borne diseases have long been acknowledged [1,2]
Our study focused on the population dynamics of the mosquito species vectors of Rift Valley fever virus (RVFV) in Senegal
Seicnreceasneodtirseeaatsmeeinntciedxeisntcsefoinr RbVotFh, hvaucmciannataionnd alinvdesvtoecctko.rOcobnvtirooulsrley,mpaeirniotdhes amnodstloecfafitcioiennst otof ooluttobrdeeackrseaasree dstirsoenasgelyinlicnidkeendcteoivnebcotothr ahbuumnadnanacneds. lHiveersetowcke.mOobdveioleudsltyh,epdeyrinoadms iacnsdofloCcuatliicoindsaeomf oousqtburietaokpsoapruelsattrioonngsliynltihnrkeeeddtioffevreecntot recaobsuynsdteamncseos.f Here we modeled the dynamics of Culicidae mosquito populations in three different ecosystems of Northern Senegal, at two different scales: (i) At local scale using in-situ weather data and (ii) at Northern Senegal, at two different scales: (i) At local scale using in-situ weather data and (ii) at regional scale using satellite-derived data
Summary
Mosquitoes are vectors of many major pathogens worldwide, and the importance of the understanding and prediction of mosquito population dynamics to optimize surveillance and control of mosquito-borne diseases have long been acknowledged [1,2]. Different population dynamics models have been developed for mosquito species belonging to the Anopheles, Aedes or Culex genera [3] involved in the transmission of pathogen agents responsible for malaria [4,5], dengue fever [6,7], Chikungunya [8,9], or Rift Valley fever [10,11,12] All of these models account for meteorological and environmental variables such as rainfall, temperature, humidity or flooding, as key drivers of mosquito population dynamics [13]. Land surface temperatures and vegetation indices from the Moderate Resolution Imaging Spectroradiometer (MODIS) were identified from statistical analyses as significant risk factors for malaria transmission [20] or abundances of malaria vector species [21,22] Most of these studies are based on statistical inference approaches, which may be not appropriate to provide simulation tools allowing testing different control strategies. In this study we developed a process-based modeling framework for mosquito population dynamics using satellite-derived meteorological estimates as input variables
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