Section 1: Introduction (from DOI:10.14218/ERHM.2020.00023)

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Article	Trends and Prediction in Daily New Cases and Deaths of COVID-19 in the United States: An Internet Search-Interest Based Model
Sections in this Publication
Section	Section 1: Introduction (from DOI:10.14218/ERHM.2020.00023)
Section	Section 2: Methods (from DOI:10.14218/ERHM.2020.00023)
Section	Section 3: Results (from DOI:10.14218/ERHM.2020.00023)
Section	Section 4: Discussion (from DOI:10.14218/ERHM.2020.00023)
Section	Section 5: Future directions (from DOI:10.14218/ERHM.2020.00023)
Section	Section 6: Conclusions (from DOI:10.14218/ERHM.2020.00023)
Section	References (from DOI:10.14218/ERHM.2020.00023)
Named Entities in this Section
Entity	COVID-19 (disease - MeSH supplementary concept)
Entity	Cardiac Death (disease - MeSH descriptor)
Dataset	Pubtator Central BioC-JSON formatted article files

From publication: "Trends and Prediction in Daily New Cases and Deaths of COVID-19 in the United States: An Internet Search-Interest Based Model" published as Explor Res Hypothesis Med; 2020 Apr 18 ; 5 (2) 1-6. DOI: https://doi.org/10.14218/ERHM.2020.00023

Section 1: Introduction

Coronavirus disease 2019 (COVID-19) has been pandemic in the world.1–4 It has now affected more than 560,000 Americans.3,5 Several attempts were successfully made to model COVID-19 daily incidence in China.1,6 However, the trends of daily incidence and deaths of COVID-19 in the USA are still poorly understood. Recently, internet search-interest was found to be correlated with daily incidence of COVID-19 in China, with the lag time of 8 to 10 days.7 Google search-interest was also used to track or model COVID-19 trends in Europe, Iran, and Taiwan.8–10 Indeed, internet search-interest has been used for modelling and detecting influenza epidemics in the USA and Australia.11,12 We, therefore, aimed to examine the association of search-interest with daily incidence/new cases and deaths of COVID-19 in the USA, using population-based data and a semiparametric model.