Classification of community-acquired outbreaks for the global transmission of COVID-19: Machine learning and statistical model analysis

Wang, Wei-Chun and Lin, Ting-Yu and Chiu, Sherry Yueh-Hsia and Chen, Chiung-Nien and Pongdech Sarakarn and Mohd Yusof Hj Ibrahim and Chen, Sam Li-Sheng and Chen, Hsiu-Hsi and Yeh, Yen-Po (2021) Classification of community-acquired outbreaks for the global transmission of COVID-19: Machine learning and statistical model analysis. Journal of the Formosan Medical Association, 120. S26-S37. ISSN 0929-6646

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URL: https://www.sciencedirect.com/science/article/pii/...

Abstract

Background: As Coronavirus disease 2019 (COVID-19) pandemic led to the unprecedent largescale repeated surges of epidemics worldwide since the end of 2019, data-driven analysis to look into the duration and case load of each episode of outbreak worldwide has been motivated. Methods: Using open data repository with daily infected, recovered and death cases in the period between March 2020 and April 2021, a descriptive analysis was performed. The susceptible-exposed-infected-recovery model was used to estimate the effective productive number (Rt). The duration taken from Rt > 1 to Rt < 1 and case load were first modelled by using the compound Poisson method. Machine learning analysis using the K-means clustering method was further adopted to classify patterns of community-acquired outbreaks worldwide. Results: The global estimated Rt declined after the first surge of COVID-19 pandemic but there were still two major surges of epidemics occurring in September 2020 and March 2021, respectively, and numerous episodes due to various extents of Nonpharmaceutical Interventions (NPIs). Unsupervised machine learning identified five patterns as “controlled epidemic”, “mutant propagated epidemic”, “propagated epidemic”, “persistent epidemic” and “long persistent epidemic” with the corresponding duration and the logarithm of case load from the lowest (18.6 ± 11.7; 3.4 ± 1.8)) to the highest (258.2 ± 31.9; 11.9 ± 2.4). Countries like Taiwan outside five clusters were classified as no community-acquired outbreak. Conclusion: Data-driven models for the new classification of community-acquired outbreaks are useful for global surveillance of uninterrupted COVID-19 pandemic and provide a timely decision support for the distribution of vaccine and the optimal NPIs from global to local community.

Item Type:	Article
Keyword:	Covid-19 , Machine learning , Cluster analysis , Community-acquired outbreak
Subjects:	H Social Sciences > HB Economic theory. Demography > HB1-3840 Economic theory. Demography > HB131-147 Methodology > HB135-147 Mathematical economics. Quantitative methods Including econometrics, input-output analysis, game theory R Medicine > RA Public aspects of medicine > RA1-1270 Public aspects of medicine
Department:	FACULTY > Faculty of Medicine and Health Sciences
Depositing User:	SAFRUDIN BIN DARUN -
Date Deposited:	08 Jun 2022 12:10
Last Modified:	08 Jun 2022 12:10
URI:	https://eprints.ums.edu.my/id/eprint/32677

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