Chen Lu, Liu Xiuyan, Hu Tao, Bao Shuming, Ye Xinyue, Ma Ning, Zhou Xiaoxue
School of Management and Economics, Southeast University, Nanjing, 211189, China.
National School of Development and Policy, Southeast University, Nanjing, 211189, China.
Appl Geogr. 2022 Jun;143:102700. doi: 10.1016/j.apgeog.2022.102700. Epub 2022 Apr 7.
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.
新冠疫情的规模和范围凸显了及时控制病毒传播的必要性。本文提出了一种新的疫情感染空间概率模型，该模型使用了改进的瓦瑟斯坦距离算法和蒙特卡罗模拟。此方法可识别出新冠病毒易于传播和扩散的公共场所。瓦瑟斯坦距离算法用于计算新冠病例与公共场所之间的分布相似度。此外，我们使用假设检验和蒙特卡罗模拟来估计新冠病毒在不同公共场所的空间传播概率。我们使用斯诺的数据来检验这种测量方法的稳定性和准确性。这种验证证明了我们的方法可靠且稳健。我们将该方法应用于武汉新冠病例和公共场所的详细地理数据。我们发现，与金融服务机构和市场相比，武汉的餐馆和医院等公共建筑成为新冠病毒传播公共场所的可能性要高出95%。
