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ArticleExploratory research and hypothesis in medicine2020

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

Xiaoling Yuan, Jie Xu, Sabiha Hussain, He Wang, Nan Gao, Lanjing Zhang

Abstract read
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Article in Exploratory research and hypothesis in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 1 of them a synthesis that pooled it.

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46citing papers in PubMed, 1 pooled it
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

46 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  7. Forecasting and Surveillance of COVID-19 Spread Using Google Trends: Literature Review.International journal of environmental research and public health · 2022
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Xiaoling YuanDepartment of Infectious Disease, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Jie XuDepartment of Infectious Disease, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Sabiha HussainDepartment of Medicine, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA.
He WangDepartment of Pathology, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA.
Nan GaoDepartment of Biological Sciences, Rutgers University Newark, NJ, USA.
Lanjing ZhangDepartment of Biological Sciences, Rutgers University Newark, NJ, USA.

Funding

Mucosal modulation by LGG and R. gnavus specific tryptophan metabolitesR01AT010243 · NCCIH · RUTGERS THE STATE UNIV OF NJ NEWARK · PI FERRARIS, RONALDO PARAOAN, GAO, NAN · 2018 to 2021
$1.6M
6 · The paper itself

Abstract

BACKGROUND AND

objectivesThe 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.

methodsWe 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.

resultsThere 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

conclusionsSearch terms related to COVID-19 are highly correlated with the COVID-19 daily new cases and deaths in the USA.

Indexed as

COVID-19IncidenceModelPandemicSearch interestTrendUSA

Identifiers

PMID32348380
PMCPMC7176069

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.