Spatial Modeling of Mosquito Vectors for Rift Valley Fever Virus in Northern Senegal: Integrating Satellite-Derived Meteorological Estimates in Population Dynamics Models

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.

Rift Valley fever in northern Senegal : a modelling approach to analyse the processes underlying virus circulation recurrence

“…Instead, we used the outputs (computed with a daily time step) of the entomological model (EM) elaborated by Tran et al, and parameterized using mosquito trapping data collected in the same study area and period (Fig. 2) [2]. This model reproduced the demographic dynamics of Ae.…”

“…vexans population dynamics, resident and nomadic ruminant herd population dynamics, and nomadic herd movements. Vector population dynamics were based on outputs of an entomological model (EM) previously and independently developed, parameterized and validated using mosquito trapping data collected in the same area, during the same period [2]. Model comparison methods allowed analysing the scenarios that could explain the recurrent circulation of the virus in this ecosystem, incorporating VT in Ae.…”

“…To calibrate the model, serological surveys were performed in 2015-2016 on both resident and nomadic herds in the same area. Mosquito population dynamics were obtained from a published model trained in the same region [2]. Model comparison techniques were used to compare five different scenarios of virus introduction by nomadic herds associated or not with vertical transmission in Aedes vexans .…”

Modeling the Temporal Population Distribution of Ae. aegypti Mosquito using Big Earth Observation Data.

“…Mapping and predicting vector and diseases spread through EO data can provide reliable information in outbreak early warning, hotspots location, and public health budget optimization [19,20]. To this aim, there have been seminal works considering how to include major environmental variables obtainable from EO data such as surface temperature, vegetation condition, air quality, precipitation rate, humidity conditions, night-time lights, land cover types, degree of urbanization and velocity of the urban sprawl, among others [21][22][23][24].…”

“…Mapping and predicting vector and diseases spread through EO data can provide reliable information in outbreak early warning, hotspots location, and public health budget optimization [19,20]. To this aim, there have been seminal works considering how to include major environmental variables obtainable from EO data such as surface temperature, vegetation condition, air quality, precipitation rate, humidity conditions, night-time lights, land cover types, degree of urbanization and velocity of the urban sprawl, among others [21][22][23][24].According to the data provided by the World Health Organization, over 50% of the world population is exposed to the risk of mosquito-borne diseases [18,25], the ones with the greatest clinical importance being Zika, Chikungunya, Dengue and Yellow fever virus diseases. These diseases are transmitted mainly by the female Ae.…”