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Forecasting HFMD cases using weather variables and Google search queries in Sabah, Malaysia

HFMD is a viral-mediated infectious illness of increasing public health importance. This study aimed to develop a forecasting tool utilizing climatic predictors and internet search queries for informing preventive strategies in Sabah, Malaysia. HFMD case data from the Sabah State Health Department,...

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Main Authors: Jayaraj, Vivek Jason, Hoe Abdullah, Victor Chee Wai
Format: Article
Published: MDPI 2022
Subjects:
GE Environmental Sciences
RA0421 Public health. Hygiene. Preventive Medicine
Online Access:http://eprints.um.edu.my/40288/
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spelling my.um.eprints.402882023-11-24T03:47:27Z http://eprints.um.edu.my/40288/ Forecasting HFMD cases using weather variables and Google search queries in Sabah, Malaysia Jayaraj, Vivek Jason Hoe Abdullah, Victor Chee Wai GE Environmental Sciences RA0421 Public health. Hygiene. Preventive Medicine HFMD is a viral-mediated infectious illness of increasing public health importance. This study aimed to develop a forecasting tool utilizing climatic predictors and internet search queries for informing preventive strategies in Sabah, Malaysia. HFMD case data from the Sabah State Health Department, climatic predictors from the Malaysia Meteorological Department, and Google search trends from the Google trends platform between the years 2010-2018 were utilized. Cross-correlations were estimated in building a seasonal auto-regressive moving average (SARIMA) model with external regressors, directed by measuring the model fit. The selected variables were then validated using test data utilizing validation metrics such as the mean average percentage error (MAPE). Google search trends evinced moderate positive correlations to the HFMD cases (r(0-6weeks): 0.47-0.56), with temperature revealing weaker positive correlations (r(0-3weeks): 0.17-0.22), with the association being most intense at 0-1 weeks. The SARIMA model, with regressors of mean temperature at lag 0 and Google search trends at lag 1, was the best-performing model. It provided the most stable predictions across the four-week period and produced the most accurate predictions two weeks in advance (RMSE = 18.77, MAPE = 0.242). Trajectorial forecasting oscillations of the model are stable up to four weeks in advance, with accuracy being the highest two weeks prior, suggesting its possible usefulness in outbreak preparedness. MDPI 2022-12 Article PeerReviewed Jayaraj, Vivek Jason and Hoe Abdullah, Victor Chee Wai (2022) Forecasting HFMD cases using weather variables and Google search queries in Sabah, Malaysia. International Journal of Environmental Research and Public Health, 19 (24). ISSN 1660-4601, DOI https://doi.org/10.3390/ijerph192416880 <https://doi.org/10.3390/ijerph192416880>. 10.3390/ijerph192416880
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic GE Environmental Sciences
RA0421 Public health. Hygiene. Preventive Medicine
spellingShingle GE Environmental Sciences
RA0421 Public health. Hygiene. Preventive Medicine
Jayaraj, Vivek Jason
Hoe Abdullah, Victor Chee Wai
Forecasting HFMD cases using weather variables and Google search queries in Sabah, Malaysia
description HFMD is a viral-mediated infectious illness of increasing public health importance. This study aimed to develop a forecasting tool utilizing climatic predictors and internet search queries for informing preventive strategies in Sabah, Malaysia. HFMD case data from the Sabah State Health Department, climatic predictors from the Malaysia Meteorological Department, and Google search trends from the Google trends platform between the years 2010-2018 were utilized. Cross-correlations were estimated in building a seasonal auto-regressive moving average (SARIMA) model with external regressors, directed by measuring the model fit. The selected variables were then validated using test data utilizing validation metrics such as the mean average percentage error (MAPE). Google search trends evinced moderate positive correlations to the HFMD cases (r(0-6weeks): 0.47-0.56), with temperature revealing weaker positive correlations (r(0-3weeks): 0.17-0.22), with the association being most intense at 0-1 weeks. The SARIMA model, with regressors of mean temperature at lag 0 and Google search trends at lag 1, was the best-performing model. It provided the most stable predictions across the four-week period and produced the most accurate predictions two weeks in advance (RMSE = 18.77, MAPE = 0.242). Trajectorial forecasting oscillations of the model are stable up to four weeks in advance, with accuracy being the highest two weeks prior, suggesting its possible usefulness in outbreak preparedness.
format Article
author Jayaraj, Vivek Jason
Hoe Abdullah, Victor Chee Wai
author_facet Jayaraj, Vivek Jason
Hoe Abdullah, Victor Chee Wai
author_sort Jayaraj, Vivek Jason
title Forecasting HFMD cases using weather variables and Google search queries in Sabah, Malaysia
title_short Forecasting HFMD cases using weather variables and Google search queries in Sabah, Malaysia
title_full Forecasting HFMD cases using weather variables and Google search queries in Sabah, Malaysia
title_fullStr Forecasting HFMD cases using weather variables and Google search queries in Sabah, Malaysia
title_full_unstemmed Forecasting HFMD cases using weather variables and Google search queries in Sabah, Malaysia
title_sort forecasting hfmd cases using weather variables and google search queries in sabah, malaysia
publisher MDPI
publishDate 2022
url http://eprints.um.edu.my/40288/
_version_ 1783876698560790528
score 13.211869

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