Evidence map›Paper›PMID 33758464›Full record

ArticleNonlinear dynamics2021

Mechanisms of recurrent outbreak of COVID-19: a model-based study.

Chuanliang Han, Meijia Li, Naem Haihambo, Pius Babuna, Qingfang Liu, Xixi Zhao, Carlo Jaeger, Ying Li, Saini Yang

Open access · bronzeAbstract read
In one paragraph

Article in Nonlinear dynamics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
3.6field-weighted citation impact, top 6% of its field
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

19 citing papers in PubMed, 47 citations in OpenAlex.

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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

9 authors at 6 institutions in 5 countries.

Chuanliang HanState Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875 China.
Meijia LiFaculty of Psychology and Center for Neuroscience, Vrije Universiteit Brussel, 1050 Brussels, Belgium.
Naem HaihamboFaculty of Psychology and Center for Neuroscience, Vrije Universiteit Brussel, 1050 Brussels, Belgium.
Pius BabunaSchool of Environment, Beijing Normal University, Beijing, 100875 China.
Qingfang LiuDepartment of Psychology, The Ohio State University, Columbus, OH 43210 USA.
Xixi ZhaoBeijing Anding Hospital, Capital Medical University, Beijing, 100088 China.
Carlo JaegerGlobal Climate Forum, 10178 Berlin, Germany.
Ying LiAcademy of Disaster Reduction and Emergency Management, Ministry of Emergency Management and Ministry of Education, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875 China.
Saini YangAcademy of Disaster Reduction and Emergency Management, Ministry of Emergency Management and Ministry of Education, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875 China.
Beijing Normal University · CNVrije Universiteit Brussel · BECapital Medical University · CNChinese Institute for Brain Research · CNGlobal Climate Forum · DEThe Ohio State University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recurrent outbreaks of the coronavirus disease 2019 (COVID-19) have occurred in many countries around the world. We developed a twofold framework in this study, which is composed by one novel descriptive model to depict the recurrent global outbreaks of COVID-19 and one dynamic model to understand the intrinsic mechanisms of recurrent outbreaks. We used publicly available data of cumulative infected cases from 1 January 2020 to 2 January 2021 in 30 provinces in China and 43 other countries around the world for model validation and further analyses. These time series data could be well fitted by the new descriptive model. Through this quantitative approach, we discovered two main mechanisms that strongly correlate with the extent of the recurrent outbreak: the sudden increase in cases imported from overseas and the relaxation of local government epidemic prevention policies. The compartmental dynamical model (Susceptible, Exposed, Infectious, Dead and Recovered (SEIDR) Model) could reproduce the obvious recurrent outbreak of the epidemics and showed that both imported infected cases and the relaxation of government policies have a causal effect on the emergence of a new wave of outbreak, along with variations in the temperature index. Meanwhile, recurrent outbreaks affect consumer confidence and have a significant influence on GDP. These results support the necessity of policies such as travel bans, testing of people upon entry, and consistency of government prevention and control policies in avoiding future waves of epidemics and protecting economy.

Indexed as

COVID-19Government policyLogistic modelRecurrent outbreakSEIDR model

Identifiers

PMID33758464
PMCPMC7972336
OpenAlexW3139083774

What OpenQuestion holds

Textmetadata
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.