Impacts of social and economic factors on the transmission of coronavirus disease (COVID-19) in China

Yun Qiu; Xi Chen; Wei Shi

Our main conclusions still hold with these mediating factors included. The effects of past infections are much smaller in the second half sample, suggesting that the virus outbreak has been contained. For the between-city transmissions, population flow from Wuhan poses a risk for new infections in other cities, with one week lag in the first half sample, and with two week lag in the second half sample. The effects are robust with the inclusion of other measures of proximity, such as those based on geography and economic similarity. Table 8 shows variables that affect transmissions within cities and whose coefficients are statistically significant at 0.1. In order to help compare across variables, we compute the changes in the variables needed in order for the coefficient of past infections on current cases to be reduced by 1. The effects of the environmental factors and population density are mixed. We also observe that the transmission rate is lower in cities with more doctors. Cities with higher per capita GDP have higher transmission rates, which may be due to more social interactions as a result of more economic activity 17 . However, until clearer mechanisms are identified and tested, this should be interpreted with caution.