Predicting Risks of a COVID-19 Outbreak by Using Outdoor Air Pollution Indicators and Population Flow with Queuing Theory

Yi Fang Chiang, Ka Ui Chu, Ling Jyh Chen, Yao Hua Ho*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


COVID-19 has been widespread in all countries since it was first discovered in December 2019. The high infectivity of COVID-19 is primarily transmitted between people via respiratory droplets on contact routes, which makes it more difficult to prevent it. Air quality has been considered to be highly correlated with respiratory diseases. In addition, population movement increases contact routes, which increases the risk of COVID-19 outbreaks. For epidemic prevention, the government’s strategies are also one of the factors that affect the risk of outbreaks, including whether it is mandatory to wear masks, stay-at-home orders, or vaccination. Wearing masks can reduce the risk of droplet infection, while stay-at-home orders can reduce contact between people. In this study, the number of COVID-19 confirmed cases and active cases of COVID-19 will be estimated according to the population movement, outdoor air pollution, and vaccination rates. Using the estimated results, the average recovery time will be predicted by Queuing Theory. The predicted average recovery time will be brought into risk analysis to estimate the possible high-risk periods. We compare the estimated high-risk periods with epidemic-prevention measures to provide a reference to evaluate the epidemic prevention plans enforced by relevant government agencies to achieve an improved control measure over the epidemic situation.

Original languageEnglish
Article number1727
Issue number10
Publication statusPublished - 2022 Oct


  • COVID-19
  • artificial (recurrent) neural network
  • coronavirus
  • machine learning
  • outbreak control
  • queuing theory
  • risk prediction

ASJC Scopus subject areas

  • Environmental Science (miscellaneous)


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