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

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<b>Research article published in</b>: <br>Explor Res Hypothesis Med; 2020 Apr 18 ; 5 (2) 1-6. DOI: 
http://doi.org/10.14218/ERHM.2020.00023

<h3><u>Abstract</u></h3>
<b>Background and objectives</b><br>
The daily incidence and deaths of coronavirus disease 2019 (COVID-19) in the USA are poorly understood. Internet search interest was found to be correlated with COVID-19 daily incidence in China, but has not yet been applied to the USA. Therefore, we examined the association of internet search-interest with COVID-19 daily incidence and deaths in the USA.
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<b>Methods</b><br>
We extracted COVID-19 daily new cases and deaths in the USA from two population-based datasets, namely 1-point-3-acres.com and the Johns Hopkins COVID-19 data repository. The internet search-interest of COVID-19-related terms was obtained using Google Trends. The Pearson correlation test and general linear model were used to examine correlations and predict trends, respectively.
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<b>Results</b><br>
There were 636,282 new cases and,325 deaths of COVID-19 in the USA from March 1 to April 15, 2020, with a crude mortality of 4.45%. The daily new cases peaked at 35,098 cases on April 10, 2020 and the daily deaths peaked at 2,494 on April 15, 2020. The search interest of COVID, "COVID pneumonia" and "COVID heart" were correlated with COVID-19 daily incidence, with 12 or 14 days of delay (Pearson's r = 0.978, 0.978 and 0.979, respectively) and deaths with 19 days of delay (Pearson's r = 0.963, 0.958 and 0.970, respectively). The 7-day follow-up with prospectively collected data showed no significant correlations of the observed data with the predicted daily new cases or daily deaths, using search interest of COVID, COVID heart, and COVID pneumonia.
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<b>Conclusions</b><br>
Search terms related to COVID-19 are highly correlated with the COVID-19 daily new cases and deaths in the USA.

<h3><u>License</u></h3>
This is an Open Access article distributed under the terms of the Creative Commons Attribution-Noncommercial 4.0 International License (CC BY-NC 4.0), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.