Forecasting Influenza Incidence in Public Health Region 8 Udonthani, Thailand by SARIMA model

Influenza can be easily spread among humans by coughs or sneezes. It is one of the major public health problems caused by viruses. An influenza epidemic occurs in Thailand every year and produces social burdens. Public health forecasts show societal information in advance and can point to the future...

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Main Authors: Masronee Arwaekaji, Jutatip Sillabutra, Chukiat Viwatwongkasem, Pichitpong Soontornpipit
Other Authors: Mahidol University
Format: Article
Published: 2022
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Online Access:https://repository.li.mahidol.ac.th/handle/123456789/73117
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spelling th-mahidol.731172022-08-04T11:04:21Z Forecasting Influenza Incidence in Public Health Region 8 Udonthani, Thailand by SARIMA model Masronee Arwaekaji Jutatip Sillabutra Chukiat Viwatwongkasem Pichitpong Soontornpipit Mahidol University Agricultural and Biological Sciences Biochemistry, Genetics and Molecular Biology Environmental Science Influenza can be easily spread among humans by coughs or sneezes. It is one of the major public health problems caused by viruses. An influenza epidemic occurs in Thailand every year and produces social burdens. Public health forecasts show societal information in advance and can point to the future magnitude of various public health issues. Therefore, this study was to perform the model in order to explain and predict influenza incidence using a seasonal autoregressive moving average model with Box-Jenkins (SARIMA). The monthly influenza virus infection cases in Public Health Region 8, Udonthani, Thailand from January 2016 to December 2018 were used to develop the model. The best fit model was determined by Akaike’s Information Criteria (AIC), Bayesian Information Criteria (BIC) and Root Mean Square Error (RMSE). The results showed that SARIMA (1,0,1)(1,0,0)12 was the best model for forecasting influenza incidence. This model had the lowest AIC (59.24), BIC (67.16) and RMSE (0.4574). Based on the comparison of actual and forecast values, the mean absolute percentage error (MAPE) was 24.15%. It shows that the model could be used to predict and demonstrate the influenza incidence. 2022-08-04T03:36:37Z 2022-08-04T03:36:37Z 2022-01-01 Article Current Applied Science and Technology. Vol.22, No.4 (2022) 10.55003/cast.2022.04.22.015 25869396 2-s2.0-85133883180 https://repository.li.mahidol.ac.th/handle/123456789/73117 Mahidol University SCOPUS https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85133883180&origin=inward
institution Mahidol University
building Mahidol University Library
continent Asia
country Thailand
Thailand
content_provider Mahidol University Library
collection Mahidol University Institutional Repository
topic Agricultural and Biological Sciences
Biochemistry, Genetics and Molecular Biology
Environmental Science
spellingShingle Agricultural and Biological Sciences
Biochemistry, Genetics and Molecular Biology
Environmental Science
Masronee Arwaekaji
Jutatip Sillabutra
Chukiat Viwatwongkasem
Pichitpong Soontornpipit
Forecasting Influenza Incidence in Public Health Region 8 Udonthani, Thailand by SARIMA model
description Influenza can be easily spread among humans by coughs or sneezes. It is one of the major public health problems caused by viruses. An influenza epidemic occurs in Thailand every year and produces social burdens. Public health forecasts show societal information in advance and can point to the future magnitude of various public health issues. Therefore, this study was to perform the model in order to explain and predict influenza incidence using a seasonal autoregressive moving average model with Box-Jenkins (SARIMA). The monthly influenza virus infection cases in Public Health Region 8, Udonthani, Thailand from January 2016 to December 2018 were used to develop the model. The best fit model was determined by Akaike’s Information Criteria (AIC), Bayesian Information Criteria (BIC) and Root Mean Square Error (RMSE). The results showed that SARIMA (1,0,1)(1,0,0)12 was the best model for forecasting influenza incidence. This model had the lowest AIC (59.24), BIC (67.16) and RMSE (0.4574). Based on the comparison of actual and forecast values, the mean absolute percentage error (MAPE) was 24.15%. It shows that the model could be used to predict and demonstrate the influenza incidence.
author2 Mahidol University
author_facet Mahidol University
Masronee Arwaekaji
Jutatip Sillabutra
Chukiat Viwatwongkasem
Pichitpong Soontornpipit
format Article
author Masronee Arwaekaji
Jutatip Sillabutra
Chukiat Viwatwongkasem
Pichitpong Soontornpipit
author_sort Masronee Arwaekaji
title Forecasting Influenza Incidence in Public Health Region 8 Udonthani, Thailand by SARIMA model
title_short Forecasting Influenza Incidence in Public Health Region 8 Udonthani, Thailand by SARIMA model
title_full Forecasting Influenza Incidence in Public Health Region 8 Udonthani, Thailand by SARIMA model
title_fullStr Forecasting Influenza Incidence in Public Health Region 8 Udonthani, Thailand by SARIMA model
title_full_unstemmed Forecasting Influenza Incidence in Public Health Region 8 Udonthani, Thailand by SARIMA model
title_sort forecasting influenza incidence in public health region 8 udonthani, thailand by sarima model
publishDate 2022
url https://repository.li.mahidol.ac.th/handle/123456789/73117
_version_ 1763496620135022592