Evidence map›Paper›PMID 32098289›Full record

ArticleJournal of clinical medicine2020

Short-term Forecasts of the COVID-19 Epidemic in Guangdong and Zhejiang, China: February 13-23, 2020.

Kimberlyn Roosa, Yiseul Lee, Ruiyan Luo, Alexander Kirpich, Richard Rothenberg, James M Hyman, Ping Yan, Gerardo Chowell

Abstract read
In one paragraph

Article in Journal of clinical medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 84 papers.

0numbers the graph read from it
0cells of the map it votes in
84citing 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

84 citing papers in PubMed.

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  10. A reliable emergency logistics network for COVID-19 considering the uncertain time-varying demands.Transportation research. Part E, Logistics and transportation review · 2023
    Article
  11. Article
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  15. Nonlinear time-series forecasts for decision support: short-term demand for ICU beds in Santiago, Chile, during the 2021 COVID-19 pandemic.International transactions in operational research : a journal of The International Federation of Operational Research Societies · 2022
    Article
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  20. Empirical Modeling of COVID-19 Evolution with High/Direct Impact on Public Health and Risk Assessment.International journal of environmental research and public health · 2022
    Article

24 more citing papers are in PubMed but not listed here.

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

8 authors.

Kimberlyn RoosaDepartment of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA 30302, USA.
Yiseul LeeDepartment of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA 30302, USA.
Ruiyan LuoDepartment of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA 30302, USA.
Alexander KirpichDepartment of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA 30302, USA.
Richard RothenbergDepartment of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA 30302, USA.
James M HymanDepartment of Mathematics, Center for Computational Science, Tulane University, New Orleans, LA 70118, USA.ORCID 0000-0001-5247-5794
Ping YanInfectious Disease Prevention and Control Branch, Public Health Agency of Canada, Ottawa, ON K1A 0K9, Canada.
Gerardo ChowellDepartment of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA 30302, USA.

Funding

National Science Foundation 1610429National Science Foundation 1633381
6 · The paper itself

Abstract

The ongoing COVID-19 epidemic continues to spread within and outside of China, despite several social distancing measures implemented by the Chinese government. Limited epidemiological data are available, and recent changes in case definition and reporting further complicate our understanding of the impact of the epidemic, particularly in the epidemic's epicenter. Here we use previously validated phenomenological models to generate short-term forecasts of cumulative reported cases in Guangdong and Zhejiang, China. Using daily reported cumulative case data up until 13 February 2020 from the National Health Commission of China, we report 5- and 10-day ahead forecasts of cumulative case reports. Specifically, we generate forecasts using a generalized logistic growth model, the Richards growth model, and a sub-epidemic wave model, which have each been previously used to forecast outbreaks due to different infectious diseases. Forecasts from each of the models suggest the outbreaks may be nearing extinction in both Guangdong and Zhejiang; however, the sub-epidemic model predictions also include the potential for further sustained transmission, particularly in Zhejiang. Our 10-day forecasts across the three models predict an additional 65-81 cases (upper bounds: 169-507) in Guangdong and an additional 44-354 (upper bounds: 141-875) cases in Zhejiang by February 23, 2020. In the best-case scenario, current data suggest that transmission in both provinces is slowing down.

Indexed as

ChinacoronavirusCOVID-19phenomenological modelsreal-time forecastssub-epidemic model

Identifiers

PMID32098289
PMCPMC7073898

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.