PMC:7210464 / 4278-5601 JSONTXT

{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/7210464","sourcedb":"PMC","sourceid":"7210464","source_url":"https://www.ncbi.nlm.nih.gov/pmc/7210464","text":"In a linear equation of the current number of new cases on the number of new cases in the past, the unobserved determinants of new infections may be serially correlated for two reasons. First, the number of people infected by a disease usually first increases, reaches a peak, and then drops. Second, there are persistent, unobservable variables, such as clusters that generate large numbers of infections, people’s living habits, and government policies. Serial correlations in errors give rise to correlations between the lagged numbers of cases and the error term, rendering the ordinary least square (OLS) estimator biased. Combining insights in Adda (2016), the existing knowledge of the incubation period of COVID-19 (World Health Organization 2020b), and data on weather conditions that affect the transmission rates of COVID-19 (Lowen and Steel 2014; Wang et al. 2020b), we construct instrumental variables for the number of new COVID-19 cases during the preceding 2 weeks. Weather characteristics in the previous third and fourth weeks do not directly affect the 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 and reflect population transmission 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