LSTM-Powered COVID-19 prediction in central Thailand incorporating meteorological and particulate matter data with a multi-feature selection approach
Issued Date
2024-05-15
Resource Type
ISSN
24058440
Scopus ID
2-s2.0-85191797338
Journal Title
Heliyon
Volume
10
Issue
9
Rights Holder(s)
SCOPUS
Bibliographic Citation
Heliyon Vol.10 No.9 (2024)
Suggested Citation
Winalai C., Anupong S., Modchang C., Chadsuthi S. LSTM-Powered COVID-19 prediction in central Thailand incorporating meteorological and particulate matter data with a multi-feature selection approach. Heliyon Vol.10 No.9 (2024). doi:10.1016/j.heliyon.2024.e30319 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/98266
Title
LSTM-Powered COVID-19 prediction in central Thailand incorporating meteorological and particulate matter data with a multi-feature selection approach
Author(s)
Corresponding Author(s)
Other Contributor(s)
Abstract
The COVID-19 pandemic has significantly impacted public health and necessitated urgent actions to mitigate its spread. Monitoring and predicting the outbreak's progression have become vital to devise effective strategies and allocate resources efficiently. This study presents a novel approach utilizing Multivariate Long Short-Term Memory (LSTM) to analyze and predict COVID-19 trends in Central Thailand, particularly emphasizing the multi-feature selection process. To consider a comprehensive view of the pandemic's dynamics, our research dataset encompasses epidemiological, meteorological, and particulate matter features, which were gathered from reliable sources. We propose a multi-feature selection technique to identify the most relevant and influential features that significantly impact the spread of COVID-19 in the region to enhance the model's performance. Our results highlight that relative humidity is the key factor driving COVID-19 transmission in Central Thailand. The proposed multi-feature selection technique significantly improves the model's accuracy, ensuring that only the most informative variables contribute to the predictions, avoiding the potential noise or redundancy from less relevant features. The proposed LSTM model demonstrates its capability to forecast COVID-19 cases, facilitating informed decision-making for public health authorities and policymakers.