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

doi:10.1016/j.apgeog.2022.102700

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

Appl Geogr. 2022 Jun:143:102700. doi: 10.1016/j.apgeog.2022.102700. Epub 2022 Apr 7.

Authors

Lu Chen¹, Xiuyan Liu^{1

2}, Tao Hu³, Shuming Bao^{4

5}, Xinyue Ye⁶, Ning Ma⁷, Xiaoxue Zhou⁸

Affiliations

¹ School of Management and Economics, Southeast University, Nanjing, 211189, China.
² National School of Development and Policy, Southeast University, Nanjing, 211189, China.
³ Department of Geography, Oklahoma State University, Stillwater, OK, 74075, USA.
⁴ Center for Geographic Analysis, Harvard University, Cambridge, MA, 02138, USA.
⁵ Geo-computation Center for Social Science, Wuhan University, Hubei, 430079, China.
⁶ Department of Landscape Architecture and Urban Planning, Texas A&M University, College Station, TX, 77840, USA.
⁷ School of Law, Southeast University, Nanjing, 211189, China.
⁸ Jiangsu Institute of Industrial Development, Nanjing University of Finance and Economics, Nanjing, 210023, China.

Abstract

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.

Keywords: Covid-19; Public places; Spatial analysis; Spread probability; Wasserstein distance.