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Impacts of varying strengths of intervention measures on secondary outbreaks of COVID-19 in two different regions.

Jie YangSanyi TangRobert A Cheke
Published in: Nonlinear dynamics (2021)
By March 2020, China and Singapore had achieved remarkable results in the prevention and control of COVID-19, but in April Singapore's outbreak began to deteriorate, while China's remained controlled. Using detailed data from Tianjin, China, and Singapore, a stochastic discrete COVID-19 epidemic model was constructed to depict the impact of the epidemic. Parameter estimation and sensitivity analysis were developed to study the probability of imported cases inducing an outbreak in relation to different prevention and control efforts. Results show that the resumption of work and the re-opening of schools will not lead to an outbreak if the effective reproduction number is lower than 1 and approaches 0 and tracking quarantine measures are strengthened. Once an outbreak occurs, if close contacts can be tracked and quarantined in time, the outbreak will be contained. If work is resumed and schools are re-opened with the effective reproduction number greater than 1, then it is more likely that a secondary outbreak will be generated. Also, the greater the number of undetected foreign imported cases and the weaker the prevention and control measures, the more serious the epidemic. Therefore, the key to prevention of a second outbreak is to return to work and to re-open schools only after the effective reproduction number is less than 1 for a period, and when tracking quarantine measures have been strengthened. Our model provides a qualitative and quantitative basis for decision-making for the prevention and control of COVID-19 epidemics and the prediction, early warning and risk assessment of secondary outbreaks.
Keyphrases
  • coronavirus disease
  • sars cov
  • risk assessment
  • decision making
  • randomized controlled trial
  • machine learning
  • minimally invasive
  • heavy metals
  • climate change
  • artificial intelligence
  • electronic health record