Difference between revisions of "Section 1: Introduction (from DOI:10.14218/ERHM.2020.00023)"
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| − | <b>From publication:</b> "Trends | + | <b>From publication:</b> "Current Trends in Diagnostics of Viral Infections of Unknown Etiology" published as Viruses; 2020 Feb 14 ; 12 (2); DOI: https://doi.org/10.3390/v12020211 <br> |
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<h3><u>Section 1: Introduction</u></h3> | <h3><u>Section 1: Introduction</u></h3> | ||
<p>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.</p> | <p>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.</p> | ||
Latest revision as of 15:41, 23 June 2020
From publication: "Current Trends in Diagnostics of Viral Infections of Unknown Etiology" published as Viruses; 2020 Feb 14 ; 12 (2); DOI: https://doi.org/10.3390/v12020211
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.
