Comparison of Public Responses to Containment Measures During the Initial Outbreak and Resurgence of COVID-19 in China: Infodemiology Study (Preprint)

Abstract

BACKGROUND Monitoring public responses is crucial to inform policy measures to prepare for COVID-19 resurgence around the world. OBJECTIVE To assess and compare public responses towards containment measures during the initial outbreak and resurgence of COVID-19 epidemic in China. METHODS We collected all COVID-19 related posts from Sina Weibo (China’s Twitter) during the initial outbreak in China and resurgence in Beijing. With a Python script, we constructed subsets of Weibo posts focusing on three containment measures: lockdown, test-trace-isolate, and suspension of gatherings. Baidu’s open source sentiment analysis model, and Latent Dirichlet Allocation model, a widely-used machine learning algorithm, were used to assess public’s attention, sentiment, and frequently discussed topics on each containment measure. RESULTS During the initial outbreak, the amount of Weibo posts escalated dramatically following Wuhan lockdown, a high proportion of posts showed negative sentiment towards all three containment measures, and discrimination-related topics arose. Compared to the initial outbreak, the public expressed less attention and less negative sentiment on containment measures, and were more supportive towards containment measures during the COVID-19 resurgence. The public shifted their focus to the impacts of containment measures on their daily lives or work. CONCLUSIONS As China’s response strategy switched from lockdown to a targeted response in the COVID-19 resurgence, public responses became more rational. When COVID-19 resurges, more targeted test-trace-isolate strategies for high-risk population should be promoted to balance epidemic control and its impacts on daily lives and the economy.