Transmission dynamics and baseline epidemiological parameter estimates of Coronavirus disease 2019 pre-vaccination: Davao City, Philippines

PLoS One. 2023 Apr 7;18(4):e0283068. doi: 10.1371/journal.pone.0283068. eCollection 2023.

Abstract

The Coronavirus disease 2019 (COVID-19) has exposed many systemic vulnerabilities in many countries' health system, disaster preparedness, and adequate response capabilities. With the early lack of data and information about the virus and the many differing local-specific factors contributing to its transmission, managing its spread had been challenging. The current work presents a modified Susceptible-Exposed-Infectious-Recovered compartmental model incorporating intervention protocols during different community quarantine periods. The COVID-19 reported cases before the vaccine rollout in Davao City, Philippines, are utilized to obtain baseline values for key epidemiologic model parameters. The probable secondary infections (i.e., time-varying reproduction number) among other epidemiological indicators were computed. Results show that the cases in Davao City were driven by the transmission rates, positivity proportion, latency period, and the number of severely symptomatic patients. This paper provides qualitative insights into the transmission dynamics of COVID-19 along with the government's implemented intervention protocols. Furthermore, this modeling framework could be used for decision support, policy making, and system development for the current and future pandemics.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • COVID-19* / epidemiology
  • COVID-19* / prevention & control
  • Humans
  • Philippines / epidemiology
  • Quarantine
  • SARS-CoV-2
  • Vaccination

Grants and funding

This research is funded by the Department of Science and Technology-Philippine Council for Health Research and Development through its Niche Center in the Regions for Research and Development, "Center for Applied Modeling, Data Analytics, and Bioinformatics for Decision Support Systems in Health".