Trends and Prediction in Daily New Cases and Deaths of COVID-19 in the United States: An Internet Search-Interest Based Model

Abstract

BACKGROUND AND OBJECTIVES The coronavirus disease 2019 (COVID-19) infected 586,000 patients in the U.S. However, the COVDI-19 daily incidence and deaths in the U.S. are poorly understood. Internet search interest was found highly correlated with COVID-19 daily incidence in China, but not yet applied to the U.S. Therefore, we examined the association of search interest with COVID-19 daily incidence and deaths in the U.S. METHODS We extracted the COVDI-19 daily incidence and death data in the U.S. from two population-based datasets. The search interest of COVID-19 related terms was obtained using Google Trends. Pearson correlation test and general linear model were used to examine correlations and predict future trends, respectively. RESULTS There were 555,245 new cases and 22,019 deaths of COVID-19 reported in the U.S. from March 1 to April 12, 2020. The search interest of COVID, COVID pneumonia, and COVID heart were correlated with COVDI-19 daily incidence with about 12-day of delay (Pearson r=0.978, 0.978 and 0.979, respectively) and deaths with 19-day of delay (Pearson r=0.963, 0.958 and 0.970, respectively). The COVID-19 daily incidence and deaths appeared to both peak on April 10. The 4-day follow-up with prospectively collected data showed moderate to good accuracies for predicting new cases (Pearson r=-0.641 to -0.833) and poor to good acuracies for daily new deaths (Pearson r=0.365 to 0.935). CONCLUSIONS Search terms related to COVID-19 are highly correlated with the trends in COVID-19 daily incidence and deaths in the U.S. The prediction-models based on the search interest trend reached moderate to good accuracies.