An extended Weight Kernel Density Estimation model forecasts COVID-19 onset risk and identifies spatiotemporal variations of lockdown effects in China

Commun Biol. 2021 Jan 25;4(1):126. doi: 10.1038/s42003-021-01677-2.

Abstract

It is important to forecast the risk of COVID-19 symptom onset and thereby evaluate how effectively the city lockdown measure could reduce this risk. This study is a first comprehensive, high-resolution investigation of spatiotemporal heterogeneities on the effect of the Wuhan lockdown on the risk of COVID-19 symptom onset in all 347 Chinese cities. An extended Weight Kernel Density Estimation model was developed to predict the COVID-19 onset risk under two scenarios (i.e., with and without the Wuhan lockdown). The Wuhan lockdown, compared with the scenario without lockdown implementation, in general, delayed the arrival of the COVID-19 onset risk peak for 1-2 days and lowered risk peak values among all cities. The decrease of the onset risk attributed to the lockdown was more than 8% in over 40% of Chinese cities, and up to 21.3% in some cities. Lockdown was the most effective in areas with medium risk before lockdown.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • COVID-19 / epidemiology*
  • COVID-19 / prevention & control*
  • COVID-19 / virology
  • China / epidemiology
  • Cities / epidemiology
  • Data Accuracy
  • Forecasting / methods
  • Humans
  • Models, Statistical*
  • Pandemics / prevention & control*
  • Prognosis
  • Quarantine / methods*
  • Risk Factors
  • SARS-CoV-2*
  • Spatial Analysis*
  • Transients and Migrants / statistics & numerical data