Evidence map›Paper›PMID 34300045›Full record

ArticleInternational journal of environmental research and public health2021

Modeling of the Long-Term Epidemic Dynamics of COVID-19 in the United States.

Derek Huang, Huanyu Tao, Qilong Wu, Sheng-You Huang, Yi Xiao

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Modeling the Global Dynamic Contagion of COVID-19.Frontiers in public health · 2021
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Derek HuangWuhan Britain-China School, No.10 Gutian Rd., Qiaokou District, Wuhan 430022, China.
Huanyu TaoInstitute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan 430074, China.
Qilong WuInstitute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan 430074, China.
Sheng-You HuangInstitute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan 430074, China.ORCID 0000-0002-4209-4565
Yi XiaoInstitute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan 430074, China.ORCID 0000-0002-5023-3166

Funding

Statup of Huazhong University of Science and Technology 5003012075
6 · The paper itself

Abstract

Coronavirus 2019 (COVID-19) is causing a severe pandemic that has resulted in millions of confirmed cases and deaths around the world. In the absence of effective drugs for treatment, non-pharmaceutical interventions are the most effective approaches to control the disease. Although some countries have the pandemic under control, all countries around the world, including the United States (US), are still in the process of controlling COVID-19, which calls for an effective epidemic model to describe the transmission dynamics of COVID-19. Meeting this need, we have extensively investigated the transmission dynamics of COVID-19 from 22 January 2020 to 14 February 2021 for the 50 states of the United States, which revealed the general principles underlying the spread of the virus in terms of intervention measures and demographic properties. We further proposed a time-dependent epidemic model, named T-SIR, to model the long-term transmission dynamics of COVID-19 in the US. It was shown in this paper that our T-SIR model could effectively model the epidemic dynamics of COVID-19 for all 50 states, which provided insights into the transmission dynamics of COVID-19 in the US. The present study will be valuable to help understand the epidemic dynamics of COVID-19 and thus help governments determine and implement effective intervention measures or vaccine prioritization to control the pandemic.

Indexed as

COVID-19HumansPandemicsSARS-CoV-2United StatesCOVID-19epidemic modelepidemiologytransmissionvaccine prioritization

Identifiers

PMID34300045
PMCPMC8305610

What OpenQuestion holds

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Registered trials

None linked

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.