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The effect of climate change on cholera disease: The road ahead using artificial neural network

Climate change has been described to raise outbreaks of water-born infectious diseases and increases public health concerns. This study aimed at finding out these impacts on cholera infections by using Artificial Neural Networks (ANNs) from 2021 to 2050. Daily data for cholera infection cases in Qom...

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Autores principales: Asadgol, Zahra, Mohammadi, Hamed, Kermani, Majid, Badirzadeh, Alireza, Gholami, Mitra
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6834266/
https://www.ncbi.nlm.nih.gov/pubmed/31693708
http://dx.doi.org/10.1371/journal.pone.0224813
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author Asadgol, Zahra
Mohammadi, Hamed
Kermani, Majid
Badirzadeh, Alireza
Gholami, Mitra
author_facet Asadgol, Zahra
Mohammadi, Hamed
Kermani, Majid
Badirzadeh, Alireza
Gholami, Mitra
author_sort Asadgol, Zahra
collection PubMed
description Climate change has been described to raise outbreaks of water-born infectious diseases and increases public health concerns. This study aimed at finding out these impacts on cholera infections by using Artificial Neural Networks (ANNs) from 2021 to 2050. Daily data for cholera infection cases in Qom city, which is located in the center of Iran, were analyzed from 1998 to 2016. To determine the best lag time and combination of inputs, Gamma Test (GT) was applied. General circulation model outputs were utilized to project future climate pattern under two scenarios of Representative Concentration Pathway (RCP2.6 and RCP8.5). Statistical downscaling was done to produce high-resolution synthetic time series weather dataset. ANNs were applied for simulating the impact of climate change on cholera. The observed climate variables including maximum and minimum temperatures and precipitation were tagged as predictors in ANNs. Cholera cases were considered as the target outcome variable. Projected future (2020–2050) climate in previous step was carried out to assess future cholera incidence. A seasonal trend in cholera infection was seen. Our results elucidated that the best lag time was 21 days. According to the results of downscaling tool, future climate in the study area by 2050 will be warmer and wetter. Simulation of cholera cases indicated that there is a clear trend of increasing cholera cases under the worst scenario (RCP8.5) by the year 2050 and the highest cholera cases observe in warmer months. The precipitation was recognized as the most effective input variable by sensitivity analysis. We observed a significant correlation between low precipitation and cholera infection. There is a strong evidence to show that cholera disease is correlated with environment variables, as low precipitation and high temperatures in warmer months could provide the swifter bacterial replication. These conditions in Iran, especially in the central parts, may raise the cholera infection rates. Furthermore, ANNs is an executive tool to simulate the impact of climate change on cholera to estimate the future trend of cholera incidence for adopting protective measures in endemic areas.
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spelling pubmed-68342662019-11-14 The effect of climate change on cholera disease: The road ahead using artificial neural network Asadgol, Zahra Mohammadi, Hamed Kermani, Majid Badirzadeh, Alireza Gholami, Mitra PLoS One Research Article Climate change has been described to raise outbreaks of water-born infectious diseases and increases public health concerns. This study aimed at finding out these impacts on cholera infections by using Artificial Neural Networks (ANNs) from 2021 to 2050. Daily data for cholera infection cases in Qom city, which is located in the center of Iran, were analyzed from 1998 to 2016. To determine the best lag time and combination of inputs, Gamma Test (GT) was applied. General circulation model outputs were utilized to project future climate pattern under two scenarios of Representative Concentration Pathway (RCP2.6 and RCP8.5). Statistical downscaling was done to produce high-resolution synthetic time series weather dataset. ANNs were applied for simulating the impact of climate change on cholera. The observed climate variables including maximum and minimum temperatures and precipitation were tagged as predictors in ANNs. Cholera cases were considered as the target outcome variable. Projected future (2020–2050) climate in previous step was carried out to assess future cholera incidence. A seasonal trend in cholera infection was seen. Our results elucidated that the best lag time was 21 days. According to the results of downscaling tool, future climate in the study area by 2050 will be warmer and wetter. Simulation of cholera cases indicated that there is a clear trend of increasing cholera cases under the worst scenario (RCP8.5) by the year 2050 and the highest cholera cases observe in warmer months. The precipitation was recognized as the most effective input variable by sensitivity analysis. We observed a significant correlation between low precipitation and cholera infection. There is a strong evidence to show that cholera disease is correlated with environment variables, as low precipitation and high temperatures in warmer months could provide the swifter bacterial replication. These conditions in Iran, especially in the central parts, may raise the cholera infection rates. Furthermore, ANNs is an executive tool to simulate the impact of climate change on cholera to estimate the future trend of cholera incidence for adopting protective measures in endemic areas. Public Library of Science 2019-11-06 /pmc/articles/PMC6834266/ /pubmed/31693708 http://dx.doi.org/10.1371/journal.pone.0224813 Text en © 2019 Asadgol et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Asadgol, Zahra
Mohammadi, Hamed
Kermani, Majid
Badirzadeh, Alireza
Gholami, Mitra
The effect of climate change on cholera disease: The road ahead using artificial neural network
title The effect of climate change on cholera disease: The road ahead using artificial neural network
title_full The effect of climate change on cholera disease: The road ahead using artificial neural network
title_fullStr The effect of climate change on cholera disease: The road ahead using artificial neural network
title_full_unstemmed The effect of climate change on cholera disease: The road ahead using artificial neural network
title_short The effect of climate change on cholera disease: The road ahead using artificial neural network
title_sort effect of climate change on cholera disease: the road ahead using artificial neural network
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6834266/
https://www.ncbi.nlm.nih.gov/pubmed/31693708
http://dx.doi.org/10.1371/journal.pone.0224813
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