Developing a forecasting model for cholera incidence in Dhaka megacity through time series climate data.

Daisy, SS; Saiful Islam, AKM; Akanda, AS; Faruque, ASG; Amin, N; Jensen, PKM

Developing a forecasting model for cholera incidence in Dhaka megacity through time series climate data.

Daisy, SS Saiful Islam, AKM Akanda, AS Faruque, ASG Amin, N Jensen, PKM

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Publisher:: IWA PUBLISHING
Publication Type:: Journal Article
Citation:: J Water Health, 2020, 18, (2), pp. 207-223
Issue Date:: 2020-04

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Field	Value	Language
dc.contributor.author	Daisy, SS
dc.contributor.author	Saiful Islam, AKM
dc.contributor.author	Akanda, AS
dc.contributor.author	Faruque, ASG
dc.contributor.author	Amin, N
dc.contributor.author	Jensen, PKM
dc.date.accessioned	2022-03-14T07:00:44Z
dc.date.available	2022-03-14T07:00:44Z
dc.date.issued	2020-04
dc.identifier.citation	J Water Health, 2020, 18, (2), pp. 207-223
dc.identifier.issn	1477-8920
dc.identifier.issn	1996-7829
dc.identifier.uri	http://hdl.handle.net/10453/155215
dc.description.abstract	Cholera, an acute diarrheal disease spread by lack of hygiene and contaminated water, is a major public health risk in many countries. As cholera is triggered by environmental conditions influenced by climatic variables, establishing a correlation between cholera incidence and climatic variables would provide an opportunity to develop a cholera forecasting model. Considering the auto-regressive nature and the seasonal behavioral patterns of cholera, a seasonal-auto-regressive-integrated-moving-average (SARIMA) model was used for time-series analysis during 2000-2013. As both rainfall (r = 0.43) and maximum temperature (r = 0.56) have the strongest influence on the occurrence of cholera incidence, single-variable (SVMs) and multi-variable SARIMA models (MVMs) were developed, compared and tested for evaluating their relationship with cholera incidence. A low relationship was found with relative humidity (r = 0.28), ENSO (r = 0.21) and SOI (r = -0.23). Using SVM for a 1 °C increase in maximum temperature at one-month lead time showed a 7% increase of cholera incidence (p < 0.001). However, MVM (AIC = 15, BIC = 36) showed better performance than SVM (AIC = 21, BIC = 39). An MVM using rainfall and monthly mean daily maximum temperature with a one-month lead time showed a better fit (RMSE = 14.7, MAE = 11) than the MVM with no lead time (RMSE = 16.2, MAE = 13.2) in forecasting. This result will assist in predicting cholera risks and better preparedness for public health management in the future.
dc.format	Print
dc.language	eng
dc.publisher	IWA PUBLISHING
dc.relation.ispartof	J Water Health
dc.relation.isbasedon	10.2166/wh.2020.133
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	Microbiology
dc.subject.mesh	Bangladesh
dc.subject.mesh	Cholera
dc.subject.mesh	Cities
dc.subject.mesh	Climate
dc.subject.mesh	Forecasting
dc.subject.mesh	Humans
dc.subject.mesh	Incidence
dc.subject.mesh	Models, Theoretical
dc.subject.mesh	Seasons
dc.subject.mesh	Humans
dc.subject.mesh	Cholera
dc.subject.mesh	Incidence
dc.subject.mesh	Cities
dc.subject.mesh	Climate
dc.subject.mesh	Seasons
dc.subject.mesh	Models, Theoretical
dc.subject.mesh	Forecasting
dc.subject.mesh	Bangladesh
dc.title	Developing a forecasting model for cholera incidence in Dhaka megacity through time series climate data.
dc.type	Journal Article
utslib.citation.volume	18
utslib.location.activity	England
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (Research)
pubs.organisational-group	/University of Technology Sydney/DVC (Research)/Institute For Sustainable Futures
utslib.copyright.status	open_access	*
dc.date.updated	2022-03-14T07:00:43Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	18
utslib.citation.issue	2

Abstract:

Cholera, an acute diarrheal disease spread by lack of hygiene and contaminated water, is a major public health risk in many countries. As cholera is triggered by environmental conditions influenced by climatic variables, establishing a correlation between cholera incidence and climatic variables would provide an opportunity to develop a cholera forecasting model. Considering the auto-regressive nature and the seasonal behavioral patterns of cholera, a seasonal-auto-regressive-integrated-moving-average (SARIMA) model was used for time-series analysis during 2000-2013. As both rainfall (r = 0.43) and maximum temperature (r = 0.56) have the strongest influence on the occurrence of cholera incidence, single-variable (SVMs) and multi-variable SARIMA models (MVMs) were developed, compared and tested for evaluating their relationship with cholera incidence. A low relationship was found with relative humidity (r = 0.28), ENSO (r = 0.21) and SOI (r = -0.23). Using SVM for a 1 °C increase in maximum temperature at one-month lead time showed a 7% increase of cholera incidence (p < 0.001). However, MVM (AIC = 15, BIC = 36) showed better performance than SVM (AIC = 21, BIC = 39). An MVM using rainfall and monthly mean daily maximum temperature with a one-month lead time showed a better fit (RMSE = 14.7, MAE = 11) than the MVM with no lead time (RMSE = 16.2, MAE = 13.2) in forecasting. This result will assist in predicting cholera risks and better preparedness for public health management in the future.

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http://hdl.handle.net/10453/155215