Public Risk Perception Explains the Mitigation of COVID-19

Abstract Background Public awareness of self-protection (PASP) is of vital importance in predicting the spread of infectious diseases. It can change the way people travel and socialize, thereby curbing the spread of the infectious diseases and mitigating its impact. The objective of this study is to explore the impact of PASP on the transmission of COVID-19 and to predict its epidemic trend. Methods Based on large-scale Weibo and Twitter datasets, we analyzes the temporal patterns of PASP for COVID-19 and develop improved models integrating PASP to predict the spread of COVID-19 in both China and UK. Additionally, we implement the models to evaluate non-pharmaceutical intervention strategies such as travel restrictions. Results During the first two months of local outbreaks with mitigation actions, the rate of online users with PASP in China and UK increased by 53% and 26%, respectively. And the integrated models yield an improved\({R^2}\)of 96.57% and 95.12% for predicting outbreaks in China and UK. Conclusions This study presents a new attempt to quantify PASP and extend it to predict the epidemic trend with massive online social media data. And we demonstrate that measuring public response had instructional significance in epidemiological models and is important in infectious disease prevention and control.