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

Yun Qiu; Xi Chen; Wei Shi

For infectious diseases, the number of infected people usually increases first before reaching a peak and then drops. This pattern implies that for a linear equation of new cases on the number of cases in the past, the unobserved determinants of new infections are serially correlated. The unobserved determinants of new cases may be serially correlated, also because they measure persistent factors, such as people's habit and government policy. Serial correlations in errors give rise to correlations between the lagged number of cases and the error term, and ordinary least square (OLS) estimator may be biased. Combining insights in Adda (2016) and the existing knowledge of the incubation period of COVID-19, we construct instrumental variables for the number of new COVID-19 cases in the preceding two weeks. Weather characteristics in the previous third and fourth weeks do not directly affect today's number of new COVID-19 cases after controlling for the number of new COVID-19 cases and weather conditions in the preceding first and second weeks. Therefore, our estimated impacts have causal interpretations. We use the Lasso method to select instrumental variables among eleven weather characteristics that have the highest predictive power for the average number of new confirmed cases during each of the past two weeks. Furthermore, we examine the moderator effects of socioeconomic factors on the transmission of COVID-19 in China, which include population flow out of Wuhan, the distance between cities, GDP per capita, number of doctors, and contemporaneous weather characteristics. We focus on the effect of population flows from the origin of the COVID-19 outbreak, because data on real time travel between cities have recently become available and we examine whether it can explain the disease spread, and that Wuhan is a major city and a transportation hub with significant population movements.