Early Warning and Predicting of COVID-19 Using Zero-inflated Negative Binomial Regression Model and Negative Binomial Regression Model

Abstract Background It is difficult to detect the outbreak of emergency infectious disease based on the exiting surveillance system. Here we investigate the utility of the Baidu Search Index (BSI) in the early warning and predicting the epidemic trend of COVID-19. Methods The daily number of cases and the Baidu Search Index of 8 keywords (weighted by population) from December 1, 2019 to March 15, 2020 were collected and analyzed with times series and Spearman correlation with different time lag. Zero-inflated negative binomial regression and negative binomial regression model was used to predict the epidemic trend. Results The Baidu Search Index of all keywords in Wuhan was significantly higher than Hubei (excluded Wuhan) and China (excluded Hubei). Before the causative pathogen was identified, the search volume of “Influenza” and “Pneumonia” in Wuhan increased with the number of new onset cases, their correlation coefficient was 0.69 and 0.59, respectively. After the pathogen was public but before COVID-19 was classified as a notifiable disease, the search volume of “SARS”, “Pneumonia”, “Coronavirus” in all study areas increased with the number of new onset cases with the correlation coefficient was 0.69~0.89, while “Influenza” changed to negative correlated (rs: -0.56 ~ -0.64). After COVID-19 was closely monitoring, the BSI of “COVID-19”, “Pneumonia”, “Coronavirus”, “SARS” and “Mask” could predict the epidemic trend with 15 days, 5 days and 6 days lead time, respectively in Wuhan, Hubei (excluded Wuhan) and China (excluded Hubei). The number of cases would increase 1.84 and 4.81 folds, respectively in Wuhan and Hubei (excluded Wuhan) from 21 January to 9 February if without the implementation of comprehensive intervention measures. Conclusion The BSI could be used in the early warning and predicting the epidemic trend of COVID-19, but the search keywords changed in different period. Considering the time lag from onset to diagnosis, especially in the areas with medical resources shortage, internet search data can be a highly effective supplement of the existing surveillance system.