Spatial modeling of mosquito vectors for rift valley fever virus in northern Senegal: Integrating satellite-derived meteorological estimates in population dynamics models
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.
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Format: | article biblioteca |
Language: | eng |
Subjects: | L72 - Organismes nuisibles des animaux, L73 - Maladies des animaux, U10 - Informatique, mathématiques et statistiques, épidémiologie, vecteur de maladie, Virus de la fièvre de la vallée du Rift, modélisation, fièvre de la Vallée du Rift, dynamique des populations, télédétection, Aedes, Culex, http://aims.fao.org/aos/agrovoc/c_2615, http://aims.fao.org/aos/agrovoc/c_8164, http://aims.fao.org/aos/agrovoc/c_16463, http://aims.fao.org/aos/agrovoc/c_230ab86c, http://aims.fao.org/aos/agrovoc/c_b08d44fd, http://aims.fao.org/aos/agrovoc/c_6111, http://aims.fao.org/aos/agrovoc/c_6498, http://aims.fao.org/aos/agrovoc/c_146, http://aims.fao.org/aos/agrovoc/c_2015, http://aims.fao.org/aos/agrovoc/c_6970, |
Online Access: | http://agritrop.cirad.fr/592293/ http://agritrop.cirad.fr/592293/1/remotesensing-11-01024.pdf |
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dig-cirad-fr-5922932024-01-29T01:55:17Z http://agritrop.cirad.fr/592293/ http://agritrop.cirad.fr/592293/ Spatial modeling of mosquito vectors for rift valley fever virus in northern Senegal: Integrating satellite-derived meteorological estimates in population dynamics models. Tran Annelise, Fall Assane Gueye, Biteye Biram, Ciss Mamadou, Gimonneau Geoffrey, Castets Mathieu, Talla Seck Monar, Chevalier Véronique. 2019. Remote Sensing, 11 (9), 1024, 24 p.https://doi.org/10.3390/rs11091024 <https://doi.org/10.3390/rs11091024> Spatial modeling of mosquito vectors for rift valley fever virus in northern Senegal: Integrating satellite-derived meteorological estimates in population dynamics models Tran, Annelise Fall, Assane Gueye Biteye, Biram Ciss, Mamadou Gimonneau, Geoffrey Castets, Mathieu Talla Seck, Monar Chevalier, Véronique eng 2019 Remote Sensing L72 - Organismes nuisibles des animaux L73 - Maladies des animaux U10 - Informatique, mathématiques et statistiques épidémiologie vecteur de maladie Virus de la fièvre de la vallée du Rift modélisation fièvre de la Vallée du Rift dynamique des populations télédétection Aedes Culex http://aims.fao.org/aos/agrovoc/c_2615 http://aims.fao.org/aos/agrovoc/c_8164 http://aims.fao.org/aos/agrovoc/c_16463 http://aims.fao.org/aos/agrovoc/c_230ab86c http://aims.fao.org/aos/agrovoc/c_b08d44fd http://aims.fao.org/aos/agrovoc/c_6111 http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_146 http://aims.fao.org/aos/agrovoc/c_2015 Sénégal http://aims.fao.org/aos/agrovoc/c_6970 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. article info:eu-repo/semantics/article Journal Article info:eu-repo/semantics/publishedVersion http://agritrop.cirad.fr/592293/1/remotesensing-11-01024.pdf text cc_by_nc_nd info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-nd/4.0/ https://doi.org/10.3390/rs11091024 10.3390/rs11091024 info:eu-repo/semantics/altIdentifier/doi/10.3390/rs11091024 info:eu-repo/semantics/altIdentifier/purl/https://doi.org/10.3390/rs11091024 info:eu-repo/semantics/reference/purl/https://doi.org/10.18167/DVN1/IQ2J1L |
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L72 - Organismes nuisibles des animaux L73 - Maladies des animaux U10 - Informatique, mathématiques et statistiques épidémiologie vecteur de maladie Virus de la fièvre de la vallée du Rift modélisation fièvre de la Vallée du Rift dynamique des populations télédétection Aedes Culex http://aims.fao.org/aos/agrovoc/c_2615 http://aims.fao.org/aos/agrovoc/c_8164 http://aims.fao.org/aos/agrovoc/c_16463 http://aims.fao.org/aos/agrovoc/c_230ab86c http://aims.fao.org/aos/agrovoc/c_b08d44fd http://aims.fao.org/aos/agrovoc/c_6111 http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_146 http://aims.fao.org/aos/agrovoc/c_2015 http://aims.fao.org/aos/agrovoc/c_6970 L72 - Organismes nuisibles des animaux L73 - Maladies des animaux U10 - Informatique, mathématiques et statistiques épidémiologie vecteur de maladie Virus de la fièvre de la vallée du Rift modélisation fièvre de la Vallée du Rift dynamique des populations télédétection Aedes Culex http://aims.fao.org/aos/agrovoc/c_2615 http://aims.fao.org/aos/agrovoc/c_8164 http://aims.fao.org/aos/agrovoc/c_16463 http://aims.fao.org/aos/agrovoc/c_230ab86c http://aims.fao.org/aos/agrovoc/c_b08d44fd http://aims.fao.org/aos/agrovoc/c_6111 http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_146 http://aims.fao.org/aos/agrovoc/c_2015 http://aims.fao.org/aos/agrovoc/c_6970 |
spellingShingle |
L72 - Organismes nuisibles des animaux L73 - Maladies des animaux U10 - Informatique, mathématiques et statistiques épidémiologie vecteur de maladie Virus de la fièvre de la vallée du Rift modélisation fièvre de la Vallée du Rift dynamique des populations télédétection Aedes Culex http://aims.fao.org/aos/agrovoc/c_2615 http://aims.fao.org/aos/agrovoc/c_8164 http://aims.fao.org/aos/agrovoc/c_16463 http://aims.fao.org/aos/agrovoc/c_230ab86c http://aims.fao.org/aos/agrovoc/c_b08d44fd http://aims.fao.org/aos/agrovoc/c_6111 http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_146 http://aims.fao.org/aos/agrovoc/c_2015 http://aims.fao.org/aos/agrovoc/c_6970 L72 - Organismes nuisibles des animaux L73 - Maladies des animaux U10 - Informatique, mathématiques et statistiques épidémiologie vecteur de maladie Virus de la fièvre de la vallée du Rift modélisation fièvre de la Vallée du Rift dynamique des populations télédétection Aedes Culex http://aims.fao.org/aos/agrovoc/c_2615 http://aims.fao.org/aos/agrovoc/c_8164 http://aims.fao.org/aos/agrovoc/c_16463 http://aims.fao.org/aos/agrovoc/c_230ab86c http://aims.fao.org/aos/agrovoc/c_b08d44fd http://aims.fao.org/aos/agrovoc/c_6111 http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_146 http://aims.fao.org/aos/agrovoc/c_2015 http://aims.fao.org/aos/agrovoc/c_6970 Tran, Annelise Fall, Assane Gueye Biteye, Biram Ciss, Mamadou Gimonneau, Geoffrey Castets, Mathieu Talla Seck, Monar Chevalier, Véronique Spatial modeling of mosquito vectors for rift valley fever virus in northern Senegal: Integrating satellite-derived meteorological estimates in population dynamics models |
description |
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. |
format |
article |
topic_facet |
L72 - Organismes nuisibles des animaux L73 - Maladies des animaux U10 - Informatique, mathématiques et statistiques épidémiologie vecteur de maladie Virus de la fièvre de la vallée du Rift modélisation fièvre de la Vallée du Rift dynamique des populations télédétection Aedes Culex http://aims.fao.org/aos/agrovoc/c_2615 http://aims.fao.org/aos/agrovoc/c_8164 http://aims.fao.org/aos/agrovoc/c_16463 http://aims.fao.org/aos/agrovoc/c_230ab86c http://aims.fao.org/aos/agrovoc/c_b08d44fd http://aims.fao.org/aos/agrovoc/c_6111 http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_146 http://aims.fao.org/aos/agrovoc/c_2015 http://aims.fao.org/aos/agrovoc/c_6970 |
author |
Tran, Annelise Fall, Assane Gueye Biteye, Biram Ciss, Mamadou Gimonneau, Geoffrey Castets, Mathieu Talla Seck, Monar Chevalier, Véronique |
author_facet |
Tran, Annelise Fall, Assane Gueye Biteye, Biram Ciss, Mamadou Gimonneau, Geoffrey Castets, Mathieu Talla Seck, Monar Chevalier, Véronique |
author_sort |
Tran, Annelise |
title |
Spatial modeling of mosquito vectors for rift valley fever virus in northern Senegal: Integrating satellite-derived meteorological estimates in population dynamics models |
title_short |
Spatial modeling of mosquito vectors for rift valley fever virus in northern Senegal: Integrating satellite-derived meteorological estimates in population dynamics models |
title_full |
Spatial modeling of mosquito vectors for rift valley fever virus in northern Senegal: Integrating satellite-derived meteorological estimates in population dynamics models |
title_fullStr |
Spatial modeling of mosquito vectors for rift valley fever virus in northern Senegal: Integrating satellite-derived meteorological estimates in population dynamics models |
title_full_unstemmed |
Spatial modeling of mosquito vectors for rift valley fever virus in northern Senegal: Integrating satellite-derived meteorological estimates in population dynamics models |
title_sort |
spatial modeling of mosquito vectors for rift valley fever virus in northern senegal: integrating satellite-derived meteorological estimates in population dynamics models |
url |
http://agritrop.cirad.fr/592293/ http://agritrop.cirad.fr/592293/1/remotesensing-11-01024.pdf |
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