Identifying the drivers underlying the spatial occurrence and spreading rate of COVID-19 can provide valuable information for their preventions and controls. Here, we examine how socio-economic and climate drivers affect the early growth rates of COVID-19 in China and the other countries, the former of which have consistently stricter quarantine during early epidemic and thus are used to enquire the influences of human interventions on trainsimissions. We find that the early growth rates of COVID-19 are higher in China than the other countries, which is consistent with previous reports. The global spread is mainly driven by the socio-economic factors such as GDP per capita, human movement and population density rather than climate. Among socio-economic factors, GDP per capita is most important showing negative relationships in China, while positive in the other countries. However, the predicability of early growth rates by socio-economic and climate variables is at least 1.6 times higher in China's provinces than the other countries, which is further supported by metapopulation network model. These findings collectively indicate that the stochasticity of transimission processes decrease upon strict quarantine measures such as travel restrictions.
Social-economic drivers overwhelm climate in underlying the COVID-19 early growth rate