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Fusing an agent-based model of mosquito population dynamics with a statistical reconstruction of spatio-temporal abundance patterns

The mosquito Aedes aegypti is the vector of a number of medically-important viruses, including dengue virus, yellow fever virus, chikungunya virus, and Zika virus, and as such vector control is a key approach to managing the diseases they cause. Understanding the impact of vector control on these di...

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Autores principales: Cavany, Sean M., España, Guido, Lloyd, Alun L., Vazquez-Prokopec, Gonzalo M., Astete, Helvio, Waller, Lance A., Kitron, Uriel, Scott, Thomas W., Morrison, Amy C., Reiner, Robert C., Perkins, T. Alex
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10168549/
https://www.ncbi.nlm.nih.gov/pubmed/37104528
http://dx.doi.org/10.1371/journal.pcbi.1010424
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author Cavany, Sean M.
España, Guido
Lloyd, Alun L.
Vazquez-Prokopec, Gonzalo M.
Astete, Helvio
Waller, Lance A.
Kitron, Uriel
Scott, Thomas W.
Morrison, Amy C.
Reiner, Robert C.
Perkins, T. Alex
author_facet Cavany, Sean M.
España, Guido
Lloyd, Alun L.
Vazquez-Prokopec, Gonzalo M.
Astete, Helvio
Waller, Lance A.
Kitron, Uriel
Scott, Thomas W.
Morrison, Amy C.
Reiner, Robert C.
Perkins, T. Alex
author_sort Cavany, Sean M.
collection PubMed
description The mosquito Aedes aegypti is the vector of a number of medically-important viruses, including dengue virus, yellow fever virus, chikungunya virus, and Zika virus, and as such vector control is a key approach to managing the diseases they cause. Understanding the impact of vector control on these diseases is aided by first understanding its impact on Ae. aegypti population dynamics. A number of detail-rich models have been developed to couple the dynamics of the immature and adult stages of Ae. aegypti. The numerous assumptions of these models enable them to realistically characterize impacts of mosquito control, but they also constrain the ability of such models to reproduce empirical patterns that do not conform to the models’ behavior. In contrast, statistical models afford sufficient flexibility to extract nuanced signals from noisy data, yet they have limited ability to make predictions about impacts of mosquito control on disease caused by pathogens that the mosquitoes transmit without extensive data on mosquitoes and disease. Here, we demonstrate how the differing strengths of mechanistic realism and statistical flexibility can be fused into a single model. Our analysis utilizes data from 176,352 household-level Ae. aegypti aspirator collections conducted during 1999–2011 in Iquitos, Peru. The key step in our approach is to calibrate a single parameter of the model to spatio-temporal abundance patterns predicted by a generalized additive model (GAM). In effect, this calibrated parameter absorbs residual variation in the abundance time-series not captured by other features of the mechanistic model. We then used this calibrated parameter and the literature-derived parameters in the agent-based model to explore Ae. aegypti population dynamics and the impact of insecticide spraying to kill adult mosquitoes. The baseline abundance predicted by the agent-based model closely matched that predicted by the GAM. Following spraying, the agent-based model predicted that mosquito abundance rebounds within about two months, commensurate with recent experimental data from Iquitos. Our approach was able to accurately reproduce abundance patterns in Iquitos and produce a realistic response to adulticide spraying, while retaining sufficient flexibility to be applied across a range of settings.
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spelling pubmed-101685492023-05-10 Fusing an agent-based model of mosquito population dynamics with a statistical reconstruction of spatio-temporal abundance patterns Cavany, Sean M. España, Guido Lloyd, Alun L. Vazquez-Prokopec, Gonzalo M. Astete, Helvio Waller, Lance A. Kitron, Uriel Scott, Thomas W. Morrison, Amy C. Reiner, Robert C. Perkins, T. Alex PLoS Comput Biol Research Article The mosquito Aedes aegypti is the vector of a number of medically-important viruses, including dengue virus, yellow fever virus, chikungunya virus, and Zika virus, and as such vector control is a key approach to managing the diseases they cause. Understanding the impact of vector control on these diseases is aided by first understanding its impact on Ae. aegypti population dynamics. A number of detail-rich models have been developed to couple the dynamics of the immature and adult stages of Ae. aegypti. The numerous assumptions of these models enable them to realistically characterize impacts of mosquito control, but they also constrain the ability of such models to reproduce empirical patterns that do not conform to the models’ behavior. In contrast, statistical models afford sufficient flexibility to extract nuanced signals from noisy data, yet they have limited ability to make predictions about impacts of mosquito control on disease caused by pathogens that the mosquitoes transmit without extensive data on mosquitoes and disease. Here, we demonstrate how the differing strengths of mechanistic realism and statistical flexibility can be fused into a single model. Our analysis utilizes data from 176,352 household-level Ae. aegypti aspirator collections conducted during 1999–2011 in Iquitos, Peru. The key step in our approach is to calibrate a single parameter of the model to spatio-temporal abundance patterns predicted by a generalized additive model (GAM). In effect, this calibrated parameter absorbs residual variation in the abundance time-series not captured by other features of the mechanistic model. We then used this calibrated parameter and the literature-derived parameters in the agent-based model to explore Ae. aegypti population dynamics and the impact of insecticide spraying to kill adult mosquitoes. The baseline abundance predicted by the agent-based model closely matched that predicted by the GAM. Following spraying, the agent-based model predicted that mosquito abundance rebounds within about two months, commensurate with recent experimental data from Iquitos. Our approach was able to accurately reproduce abundance patterns in Iquitos and produce a realistic response to adulticide spraying, while retaining sufficient flexibility to be applied across a range of settings. Public Library of Science 2023-04-27 /pmc/articles/PMC10168549/ /pubmed/37104528 http://dx.doi.org/10.1371/journal.pcbi.1010424 Text en https://creativecommons.org/publicdomain/zero/1.0/This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 (https://creativecommons.org/publicdomain/zero/1.0/) public domain dedication.
spellingShingle Research Article
Cavany, Sean M.
España, Guido
Lloyd, Alun L.
Vazquez-Prokopec, Gonzalo M.
Astete, Helvio
Waller, Lance A.
Kitron, Uriel
Scott, Thomas W.
Morrison, Amy C.
Reiner, Robert C.
Perkins, T. Alex
Fusing an agent-based model of mosquito population dynamics with a statistical reconstruction of spatio-temporal abundance patterns
title Fusing an agent-based model of mosquito population dynamics with a statistical reconstruction of spatio-temporal abundance patterns
title_full Fusing an agent-based model of mosquito population dynamics with a statistical reconstruction of spatio-temporal abundance patterns
title_fullStr Fusing an agent-based model of mosquito population dynamics with a statistical reconstruction of spatio-temporal abundance patterns
title_full_unstemmed Fusing an agent-based model of mosquito population dynamics with a statistical reconstruction of spatio-temporal abundance patterns
title_short Fusing an agent-based model of mosquito population dynamics with a statistical reconstruction of spatio-temporal abundance patterns
title_sort fusing an agent-based model of mosquito population dynamics with a statistical reconstruction of spatio-temporal abundance patterns
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10168549/
https://www.ncbi.nlm.nih.gov/pubmed/37104528
http://dx.doi.org/10.1371/journal.pcbi.1010424
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