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Assessing the potential impact of vector-borne disease transmission following heavy rainfall events: a mathematical framework

Predicting the impact of natural disasters such as hurricanes on the transmission dynamics of infectious diseases poses significant challenges. In this paper, we put forward a simple modelling framework to investigate the impact of heavy rainfall events (HREs) on mosquito-borne disease transmission...

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Detalles Bibliográficos
Autores principales: Chowell, G., Mizumoto, K., Banda, J. M., Poccia, S., Perrings, C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6553605/
https://www.ncbi.nlm.nih.gov/pubmed/31056044
http://dx.doi.org/10.1098/rstb.2018.0272
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author Chowell, G.
Mizumoto, K.
Banda, J. M.
Poccia, S.
Perrings, C.
author_facet Chowell, G.
Mizumoto, K.
Banda, J. M.
Poccia, S.
Perrings, C.
author_sort Chowell, G.
collection PubMed
description Predicting the impact of natural disasters such as hurricanes on the transmission dynamics of infectious diseases poses significant challenges. In this paper, we put forward a simple modelling framework to investigate the impact of heavy rainfall events (HREs) on mosquito-borne disease transmission in temperate areas of the world such as the southern coastal areas of the USA. In particular, we explore the impact of the timing of HREs relative to the transmission season via analyses that test the sensitivity of HRE-induced epidemics to variation in the effects of rainfall on the dynamics of mosquito breeding capacity, and the intensity and temporal profile of human population displacement patterns. The recent Hurricane Harvey in Texas motivates the simulations reported. Overall, we find that the impact of vector-borne disease transmission is likely to be greater the earlier the HREs occur in the transmission season. Simulations based on data for Hurricane Harvey suggest that the limited impact it had on vector-borne disease transmission was in part because of when it occurred (late August) relative to the local transmission season, and in part because of the mitigating effect of the displacement of people. We also highlight key data gaps related to models of vector-borne disease transmission in the context of natural disasters. This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’. This issue is linked with the subsequent theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’.
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spelling pubmed-65536052019-06-19 Assessing the potential impact of vector-borne disease transmission following heavy rainfall events: a mathematical framework Chowell, G. Mizumoto, K. Banda, J. M. Poccia, S. Perrings, C. Philos Trans R Soc Lond B Biol Sci Articles Predicting the impact of natural disasters such as hurricanes on the transmission dynamics of infectious diseases poses significant challenges. In this paper, we put forward a simple modelling framework to investigate the impact of heavy rainfall events (HREs) on mosquito-borne disease transmission in temperate areas of the world such as the southern coastal areas of the USA. In particular, we explore the impact of the timing of HREs relative to the transmission season via analyses that test the sensitivity of HRE-induced epidemics to variation in the effects of rainfall on the dynamics of mosquito breeding capacity, and the intensity and temporal profile of human population displacement patterns. The recent Hurricane Harvey in Texas motivates the simulations reported. Overall, we find that the impact of vector-borne disease transmission is likely to be greater the earlier the HREs occur in the transmission season. Simulations based on data for Hurricane Harvey suggest that the limited impact it had on vector-borne disease transmission was in part because of when it occurred (late August) relative to the local transmission season, and in part because of the mitigating effect of the displacement of people. We also highlight key data gaps related to models of vector-borne disease transmission in the context of natural disasters. This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’. This issue is linked with the subsequent theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’. The Royal Society 2019-06-24 2019-05-06 /pmc/articles/PMC6553605/ /pubmed/31056044 http://dx.doi.org/10.1098/rstb.2018.0272 Text en © 2019 The Authors. http://creativecommons.org/licenses/by/4.0/ Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.
spellingShingle Articles
Chowell, G.
Mizumoto, K.
Banda, J. M.
Poccia, S.
Perrings, C.
Assessing the potential impact of vector-borne disease transmission following heavy rainfall events: a mathematical framework
title Assessing the potential impact of vector-borne disease transmission following heavy rainfall events: a mathematical framework
title_full Assessing the potential impact of vector-borne disease transmission following heavy rainfall events: a mathematical framework
title_fullStr Assessing the potential impact of vector-borne disease transmission following heavy rainfall events: a mathematical framework
title_full_unstemmed Assessing the potential impact of vector-borne disease transmission following heavy rainfall events: a mathematical framework
title_short Assessing the potential impact of vector-borne disease transmission following heavy rainfall events: a mathematical framework
title_sort assessing the potential impact of vector-borne disease transmission following heavy rainfall events: a mathematical framework
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6553605/
https://www.ncbi.nlm.nih.gov/pubmed/31056044
http://dx.doi.org/10.1098/rstb.2018.0272
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