Study on the Risk of Transmission of COVID-19 Based on Population Migration

Abstract

Since the emergence of COVID-19, migration of people has transferred the virus to new locations, causing the epidemic to expand, and local governments have put in place control measures to prevent the virus from spreading further. As of January 24, 2020, we calculated the population immigration from Wuhan to the rest of mainland China using migration statistics from the Gaode Map. In addition, we utilized machine learning methods to simulate the curve of the COVID-19 epidemic in different regions and over different time periods. Furthermore, we used machine learning methods to simulate the COVID-19 epidemic curve in various regions and over various time periods. Based on the Wuhan exodus, we built a migration transmission risk model. From January 24 to February 19, 2020, we predicted the location, severity, and timing of epidemics in various parts of mainland China. We showed how discrepancies in model predictions might be utilized to measure transmission load in different parts of the country. Higher transmission risk indices suggest more community transmission in the region. According to the study, states with lower transmission risk indices but fewer cases than expected may have taken highly effective public health measures.