Determine neighboring region spatial effect on dengue cases using ensemble ARIMA models

Abstract The state of Selangor, in Malaysia consist of urban and peri-urban centres with good transportation system, and suitable temperature levels with high precipitations and humidity which make the state ideal for high number of dengue cases, annually. This study investigates if districts within...

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Autores principales:	Loshini Thiruchelvam, Sarat Chandra Dass, Vijanth Sagayan Asirvadam, Hanita Daud, Balvinder Singh Gill
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7e84430baf354e1f8b3fb05e50fe6086
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spelling	oai:doaj.org-article:7e84430baf354e1f8b3fb05e50fe60862021-12-02T15:52:21ZDetermine neighboring region spatial effect on dengue cases using ensemble ARIMA models10.1038/s41598-021-84176-y2045-2322https://doaj.org/article/7e84430baf354e1f8b3fb05e50fe60862021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-84176-yhttps://doaj.org/toc/2045-2322Abstract The state of Selangor, in Malaysia consist of urban and peri-urban centres with good transportation system, and suitable temperature levels with high precipitations and humidity which make the state ideal for high number of dengue cases, annually. This study investigates if districts within the Selangor state do influence each other in determining pattern of dengue cases. Study compares two different models; the Autoregressive Integrated Moving Average (ARIMA) and Ensemble ARIMA models, using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) measurement to gauge their performance tools. ARIMA model is developed using the epidemiological data of dengue cases, whereas ensemble ARIMA incorporates the neighbouring regions’ dengue models as the exogenous variable (X), into traditional ARIMA model. Ensemble ARIMA models have better model fit compared to the basic ARIMA models by incorporating neighbuoring effects of seven districts which made of state of Selangor. The AIC and BIC values of ensemble ARIMA models to be smaller compared to traditional ARIMA counterpart models. Thus, study concludes that pattern of dengue cases for a district is subject to spatial effects of its neighbouring districts and number of dengue cases in the surrounding areas.Loshini ThiruchelvamSarat Chandra DassVijanth Sagayan AsirvadamHanita DaudBalvinder Singh GillNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Loshini Thiruchelvam Sarat Chandra Dass Vijanth Sagayan Asirvadam Hanita Daud Balvinder Singh Gill Determine neighboring region spatial effect on dengue cases using ensemble ARIMA models
description	Abstract The state of Selangor, in Malaysia consist of urban and peri-urban centres with good transportation system, and suitable temperature levels with high precipitations and humidity which make the state ideal for high number of dengue cases, annually. This study investigates if districts within the Selangor state do influence each other in determining pattern of dengue cases. Study compares two different models; the Autoregressive Integrated Moving Average (ARIMA) and Ensemble ARIMA models, using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) measurement to gauge their performance tools. ARIMA model is developed using the epidemiological data of dengue cases, whereas ensemble ARIMA incorporates the neighbouring regions’ dengue models as the exogenous variable (X), into traditional ARIMA model. Ensemble ARIMA models have better model fit compared to the basic ARIMA models by incorporating neighbuoring effects of seven districts which made of state of Selangor. The AIC and BIC values of ensemble ARIMA models to be smaller compared to traditional ARIMA counterpart models. Thus, study concludes that pattern of dengue cases for a district is subject to spatial effects of its neighbouring districts and number of dengue cases in the surrounding areas.
format	article
author	Loshini Thiruchelvam Sarat Chandra Dass Vijanth Sagayan Asirvadam Hanita Daud Balvinder Singh Gill
author_facet	Loshini Thiruchelvam Sarat Chandra Dass Vijanth Sagayan Asirvadam Hanita Daud Balvinder Singh Gill
author_sort	Loshini Thiruchelvam
title	Determine neighboring region spatial effect on dengue cases using ensemble ARIMA models
title_short	Determine neighboring region spatial effect on dengue cases using ensemble ARIMA models
title_full	Determine neighboring region spatial effect on dengue cases using ensemble ARIMA models
title_fullStr	Determine neighboring region spatial effect on dengue cases using ensemble ARIMA models
title_full_unstemmed	Determine neighboring region spatial effect on dengue cases using ensemble ARIMA models
title_sort	determine neighboring region spatial effect on dengue cases using ensemble arima models
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/7e84430baf354e1f8b3fb05e50fe6086
work_keys_str_mv	AT loshinithiruchelvam determineneighboringregionspatialeffectondenguecasesusingensemblearimamodels AT saratchandradass determineneighboringregionspatialeffectondenguecasesusingensemblearimamodels AT vijanthsagayanasirvadam determineneighboringregionspatialeffectondenguecasesusingensemblearimamodels AT hanitadaud determineneighboringregionspatialeffectondenguecasesusingensemblearimamodels AT balvindersinghgill determineneighboringregionspatialeffectondenguecasesusingensemblearimamodels
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Determine neighboring region spatial effect on dengue cases using ensemble ARIMA models

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