Trends and prediction in daily incidence and deaths of COVID-19 in the United States: a search-interest based model.

BACKGROUND AND OBJECTIVES

The coronavirus disease 2019 (COVID-19) infected more than 586,000 patients in the U.S. However, its daily incidence and deaths in the U.S. are poorly understood. Internet search interest was found correlated with COVID-19 daily incidence in China, but not yet applied to the U.S. Therefore, we examined the association of internet 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 ~12-day of delay (Pearson's r=0.978, 0.978 and 0.979, respectively) and deaths with 19-day of delay (Pearson's 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's r=-0.641 to -0.833) and poor to good accuracies for daily new deaths (Pearson's 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.