Artificial intelligence against the first wave of COVID-19: evidence from China

BACKGROUND: The COVID-19 pandemic unexpectedly broke out at the end of 2019. Due to the highly contagious, widespread, and risky nature of this disease, the pandemic prevention and control has been a tremendous challenge worldwide. One potentially powerful tool against the COVID-19 pandemic is artificial intelligence (AI). This study systematically assessed the effectiveness of AI in infection prevention and control during the first wave of COVID-19 in China. METHODS: To better evaluate the role of AI in a pandemic emergency, we focused on the first-wave COVID-19 in the period from the early December 2019 to the end of April 2020 across 304 cities in China. We employed three sets of dependent variables to capture various dimensions of the effect of AI: (1) the time to the peak of cumulative confirmed cases, (2) the case fatality rate and whether there were severe cases, and (3) the number of local policies for work and production resumption and the time span to having the first such policy. The main explanatory variable was the local AI development measured by the number of AI patents. To fit the features of different dependent variables, we employed a variety of estimation methods, including the OLS, Tobit, Probit, and Poisson estimations. We included a large set of control variables and added interaction terms to test the mechanisms through which AI took an effect. RESULTS: Our results showed that AI had highly significant effects on (1) screening and detecting the disease, and (2) monitoring and evaluating the epidemic evolution. Specifically, AI was useful to screen and detect the COVID-19 in cities with high cross-city mobility. Also, AI played an important role for production resumption in cities with high risk to reopen. However, there was limited evidence supporting the effectiveness of AI in the diagnosis and treatment of the disease. CONCLUSIONS: These results suggested that AI can play an important role against the pandemic. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12913-022-08146-4.