Evidence map›Paper›PMID 35729655›Full record

ArticleInfectious diseases of poverty2022

Compartmental structures used in modeling COVID-19: a scoping review.

Lingcai Kong, Mengwei Duan, Jin Shi, Jie Hong, Zhaorui Chang, Zhijie Zhang

Abstract readScoping Review
In one paragraph

Article in Infectious diseases of poverty, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 1 pooled it
–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

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

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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

6 authors.

Lingcai KongDepartment of Mathematics and Physics, North China Electric Power University, Baoding, 071003, China.
Mengwei DuanDepartment of Mathematics and Physics, North China Electric Power University, Baoding, 071003, China.
Jin ShiDepartment of Epidemiology and Health Statistics, Fudan University, Shanghai, 200032, China.
Jie HongDepartment of Epidemiology and Health Statistics, Fudan University, Shanghai, 200032, China.
Zhaorui ChangDivision of Infectious Disease, Key Laboratory of Surveillance and Early-Warning on Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, China.
Zhijie ZhangDepartment of Epidemiology and Health Statistics, Fudan University, Shanghai, 200032, China. epistat@gmail.com.ORCID http://orcid.org/0000-0001-5172-2896

Funding

13th Five-Year National Science and Technology Major Project for Infectious Diseases 2018ZX10725-509Fundamental Research Funds for the Central Universities 2021MS074Key projects of the PLA logistics Scientific research Program BHJ17J013Major Project of Scientific and Technical Winter Olympics from National Key Research and Development Program of China 2021YFF0306000Natural Science Foundation of China 81773487Natural Science Foundation of China 81973102Natural Science Funds of Hebei D2019502010Public Health Talents Training Program of Shanghai Municipality GWV-10.2-XD21Three-Side Innovation Projects for Aquaculture in Jiangsu Province GWV-10.1-XK16
6 · The paper itself

Abstract

backgroundThe coronavirus disease 2019 (COVID-19) epidemic, considered as the worst global public health event in nearly a century, has severely affected more than 200 countries and regions around the world. To effectively prevent and control the epidemic, researchers have widely employed dynamic models to predict and simulate the epidemic's development, understand the spread rule, evaluate the effects of intervention measures, inform vaccination strategies, and assist in the formulation of prevention and control measures. In this review, we aimed to sort out the compartmental structures used in COVID-19 dynamic models and provide reference for the dynamic modeling for COVID-19 and other infectious diseases in the future. MAIN TEXT: A scoping review on the compartmental structures used in modeling COVID-19 was conducted. In this scoping review, 241 research articles published before May 14, 2021 were analyzed to better understand the model types and compartmental structures used in modeling COVID-19. Three types of dynamics models were analyzed: compartment models expanded based on susceptible-exposed-infected-recovered (SEIR) model, meta-population models, and agent-based models. The expanded compartments based on SEIR model are mainly according to the COVID-19 transmission characteristics, public health interventions, and age structure. The meta-population models and the agent-based models, as a trade-off for more complex model structures, basic susceptible-exposed-infected-recovered or simply expanded compartmental structures were generally adopted.

conclusionThere has been a great deal of models to understand the spread of COVID-19, and to help prevention and control strategies. Researchers build compartments according to actual situation, research objectives and complexity of models used. As the COVID-19 epidemic remains uncertain and poses a major challenge to humans, researchers still need dynamic models as the main tool to predict dynamics, evaluate intervention effects, and provide scientific evidence for the development of prevention and control strategies. The compartmental structures reviewed in this study provide guidance for future modeling for COVID-19, and also offer recommendations for the dynamic modeling of other infectious diseases.

Indexed as

COVID-19EpidemicsDisease SusceptibilityForecastingHumansPublic HealthSARS-CoV-2CompartmentCOVID-19Dynamic modelEpidemic model

Identifiers

PMID35729655
PMCPMC9209832

What OpenQuestion holds

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Registered trials

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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.