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

Wang WC 1 , Lin TY 1 , Chiu SY 2 , Chen CN 3 , Sarakarn P 4 , Ibrahim M 5 Show all authors , Chen SL 6 , Chen HH 1 , Yeh YP 7

Affiliations 

  • 1 Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
  • 2 Department of Health Care Management and Healthy Aging Research Center, Chang Gung University, Taoyuan, Taiwan
  • 3 Center for Functional Image and Interventional Therapy, National Taiwan University, Taipei, Taiwan; Department of Surgery, National Taiwan University Hospital, Taipei, Taiwan
  • 4 Epidemiology and Biostatistics Department, Faculty of Public Health, Khon Kaen University, Khon Kaen, Thailand
  • 5 Department of Community and Family Medicine, University Malaysia Sabah, Malaysia
  • 6 School of Oral Hygiene, College of Oral Medicine, Taipei Medical University, Taipei, Taiwan
  • 7 Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan; Changhua Public Health Bureau, Changhua, Taiwan. Electronic address: [email protected]
J Formos Med Assoc, 2021 Jun;120 Suppl 1:S26-S37.
PMID: 34083090 DOI: 10.1016/j.jfma.2021.05.010

Abstract

BACKGROUND: As Coronavirus disease 2019 (COVID-19) pandemic led to the unprecedent large-scale 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 

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.

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