Measurement of contagion spatial spread probability in public places: A case study on COVID-19

Lu Chen, Xiuyan Liu, Tao Hu, Shuming Bao, Xinyue Ye, Ning Ma, Xiaoxue Zhou

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


The scale and scope of the COVID-19 epidemic have highlighted the need for timely control of viral transmission. This paper proposed a new spatial probability model of epidemic infection using an improved Wasserstein distance algorithm and Monte Carlo simulation. This method identifies the public places in which COVID-19 spreads and grows easily. The Wasserstein Distance algorithm is used to calculate the distribution similarity between COVID-19 cases and the public places. Further, we used hypothesis tests and Monte Carlo simulation to estimate the spatial spread probability of COVID-19 in different public places. We used Snow's data to test the stability and accuracy of this measurement. This verification proved that our method is reliable and robust. We applied our method to the detailed geographic data of COVID-19 cases and public places in Wuhan. We found that, rather than financial service institutions and markets, public buildings such as restaurants and hospitals in Wuhan are 95 percent more likely to be the public places of COVID-19 spread.

Original languageEnglish (US)
Article number102700
JournalApplied Geography
StatePublished - Jun 2022
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Forestry
  • Geography, Planning and Development
  • General Environmental Science
  • Tourism, Leisure and Hospitality Management


  • Covid-19
  • Public places
  • Spatial analysis
  • Spread probability
  • Wasserstein distance


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