Quantification of Rotavirus Diarrheal Risk Due to Hydroclimatic Extremes Over South Asia: Prospects of Satellite‐Based Observations in Detecting Outbreaks

Abstract Rotavirus is the most common cause of diarrheal disease among children under 5. Especially in South Asia, rotavirus remains the leading cause of mortality in children due to diarrhea. As climatic extremes and safe water availability significantly influence diarrheal disease impacts in human...

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Main Authors: M. Alfi Hasan, Colleen Mouw, Antarpreet Jutla, Ali S. Akanda
Format: Article
Language:English
Published: American Geophysical Union (AGU) 2018-02-01
Series:GeoHealth
Subjects:
Online Access:https://doi.org/10.1002/2017GH000101
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spelling doaj-c4bf0f346b264a2c87b7cacd20e6defb2020-11-24T21:30:54ZengAmerican Geophysical Union (AGU)GeoHealth2471-14032018-02-0122708610.1002/2017GH000101Quantification of Rotavirus Diarrheal Risk Due to Hydroclimatic Extremes Over South Asia: Prospects of Satellite‐Based Observations in Detecting OutbreaksM. Alfi Hasan0Colleen Mouw1Antarpreet Jutla2Ali S. Akanda3Civil and Environmental Engineering University of Rhode Island Kingston RI USAGraduate School of Oceanography University of Rhode Island Narragansett RI USACivil and Environmental Engineering West Virginia University Morgantown WV USACivil and Environmental Engineering University of Rhode Island Kingston RI USAAbstract Rotavirus is the most common cause of diarrheal disease among children under 5. Especially in South Asia, rotavirus remains the leading cause of mortality in children due to diarrhea. As climatic extremes and safe water availability significantly influence diarrheal disease impacts in human populations, hydroclimatic information can be a potential tool for disease preparedness. In this study, we conducted a multivariate temporal and spatial assessment of 34 climate indices calculated from ground and satellite Earth observations to examine the role of temperature and rainfall extremes on the seasonality of rotavirus transmission in Bangladesh. We extracted rainfall data from the Global Precipitation Measurement and temperature data from the Moderate Resolution Imaging Spectroradiometer sensors to validate the analyses and explore the potential of a satellite‐based seasonal forecasting model. Our analyses found that the number of rainy days and nighttime temperature range from 16°C to 21°C are particularly influential on the winter transmission cycle of rotavirus. The lower number of wet days with suitable cold temperatures for an extended time accelerates the onset and intensity of the outbreaks. Temporal analysis over Dhaka also suggested that water logging during monsoon precipitation influences rotavirus outbreaks during a summer transmission cycle. The proposed model shows lag components, which allowed us to forecast the disease outbreaks 1 to 2 months in advance. The satellite data‐driven forecasts also effectively captured the increased vulnerability of dry‐cold regions of the country, compared to the wet‐warm regions.https://doi.org/10.1002/2017GH000101rotavirusclimatediarrheaextremesforecastingremote sensing
collection DOAJ
language English
format Article
sources DOAJ
author M. Alfi Hasan
Colleen Mouw
Antarpreet Jutla
Ali S. Akanda
spellingShingle M. Alfi Hasan
Colleen Mouw
Antarpreet Jutla
Ali S. Akanda
Quantification of Rotavirus Diarrheal Risk Due to Hydroclimatic Extremes Over South Asia: Prospects of Satellite‐Based Observations in Detecting Outbreaks
GeoHealth
rotavirus
climate
diarrhea
extremes
forecasting
remote sensing
author_facet M. Alfi Hasan
Colleen Mouw
Antarpreet Jutla
Ali S. Akanda
author_sort M. Alfi Hasan
title Quantification of Rotavirus Diarrheal Risk Due to Hydroclimatic Extremes Over South Asia: Prospects of Satellite‐Based Observations in Detecting Outbreaks
title_short Quantification of Rotavirus Diarrheal Risk Due to Hydroclimatic Extremes Over South Asia: Prospects of Satellite‐Based Observations in Detecting Outbreaks
title_full Quantification of Rotavirus Diarrheal Risk Due to Hydroclimatic Extremes Over South Asia: Prospects of Satellite‐Based Observations in Detecting Outbreaks
title_fullStr Quantification of Rotavirus Diarrheal Risk Due to Hydroclimatic Extremes Over South Asia: Prospects of Satellite‐Based Observations in Detecting Outbreaks
title_full_unstemmed Quantification of Rotavirus Diarrheal Risk Due to Hydroclimatic Extremes Over South Asia: Prospects of Satellite‐Based Observations in Detecting Outbreaks
title_sort quantification of rotavirus diarrheal risk due to hydroclimatic extremes over south asia: prospects of satellite‐based observations in detecting outbreaks
publisher American Geophysical Union (AGU)
series GeoHealth
issn 2471-1403
publishDate 2018-02-01
description Abstract Rotavirus is the most common cause of diarrheal disease among children under 5. Especially in South Asia, rotavirus remains the leading cause of mortality in children due to diarrhea. As climatic extremes and safe water availability significantly influence diarrheal disease impacts in human populations, hydroclimatic information can be a potential tool for disease preparedness. In this study, we conducted a multivariate temporal and spatial assessment of 34 climate indices calculated from ground and satellite Earth observations to examine the role of temperature and rainfall extremes on the seasonality of rotavirus transmission in Bangladesh. We extracted rainfall data from the Global Precipitation Measurement and temperature data from the Moderate Resolution Imaging Spectroradiometer sensors to validate the analyses and explore the potential of a satellite‐based seasonal forecasting model. Our analyses found that the number of rainy days and nighttime temperature range from 16°C to 21°C are particularly influential on the winter transmission cycle of rotavirus. The lower number of wet days with suitable cold temperatures for an extended time accelerates the onset and intensity of the outbreaks. Temporal analysis over Dhaka also suggested that water logging during monsoon precipitation influences rotavirus outbreaks during a summer transmission cycle. The proposed model shows lag components, which allowed us to forecast the disease outbreaks 1 to 2 months in advance. The satellite data‐driven forecasts also effectively captured the increased vulnerability of dry‐cold regions of the country, compared to the wet‐warm regions.
topic rotavirus
climate
diarrhea
extremes
forecasting
remote sensing
url https://doi.org/10.1002/2017GH000101
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