Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2021 Jul;77(Suppl 2):S385-S392.
doi: 10.1016/j.mjafi.2021.05.005. Epub 2021 Jul 26.

Systematic review of predictive mathematical models of COVID-19 epidemic

Subramanian Shankar  1 , Sourya Sourabh Mohakuda  2 , Ankit Kumar  3 , P S Nazneen  3 , Arun Kumar Yadav  4 , Kaushik Chatterjee  5 , Kaustuv Chatterjee  6
Affiliations

Systematic review of predictive mathematical models of COVID-19 epidemic

Subramanian Shankar et al. Med J Armed Forces India. 2021 Jul.

Abstract

Background: Various mathematical models were published to predict the epidemiological consequences of the COVID-19 pandemic. This systematic review has studied the initial epidemiological models.

Methods: Articles published from January to June 2020 were extracted from databases using search strings and those peer-reviewed with full text in English were included in the study. They were analysed as to whether they made definite predictions in terms of time and numbers, or contained only mathematical assumptions and open-ended predictions. Factors such as early vs. late prediction models, long-term vs. curve-fitting models and comparisons based on modelling techniques were analysed in detail.

Results: Among 56,922 hits in 05 databases, screening yielded 434 abstracts, of which 72 articles were included. Predictive models comprised over 70% (51/72) of the articles, with susceptible, exposed, infectious and recovered (SEIR) being the commonest type (mean duration of prediction being 3 months). Common predictions were regarding cumulative cases (44/72, 61.1%), time to reach total numbers (41/72, 56.9%), peak numbers (22/72, 30.5%), time to peak (24/72, 33.3%), hospital utilisation (7/72, 9.7%) and effect of lockdown and NPIs (50/72, 69.4%). The commonest countries for which models were predicted were China followed by USA, South Korea, Japan and India. Models were published by various professionals including Engineers (12.5%), Mathematicians (9.7%), Epidemiologists (11.1%) and Physicians (9.7%) with a third (32.9%) being the result of collaborative efforts between two or more professions.

Conclusion: There was a wide diversity in the type of models, duration of prediction and the variable that they predicted, with SEIR model being the commonest type.

Keywords: COVID-19; Mathematical models; Nonpharmaceutical interventions; Systematic review.

PubMed Disclaimer

Conflict of interest statement

Four of the authors have previously authored a mathematical model for the COVID epidemic.

Figures

Fig. 1
Fig. 1
PRISMA: Selection of articles.
Fig. 2
Fig. 2
Characteristics of included mathematical models.
Fig. 3
Fig. 3
Geographical origin of included mathematical models.
See this image and copyright information in PMC

References

    1. Bryne J. Live Science; 2020 Mar 14. 1st known case of coronavirus traced back to November in China. Editor-in-Chief.
    1. Coronavirus Disease (COVID-19) - events as they happen [Internet]. [cited 2020 Jun 19]. Available from: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/events-a....
    1. Coronavirus Update (Live): 8,649,131 Cases and 458,122 Deaths from COVID-19 Virus Pandemic - Worldometer [Internet]. [cited 2020 Jun 19]. Available from: https://www.worldometers.info/coronavirus/.
    1. Kermack W.O., McKendrick A.G., Walker G.T. A contribution to the mathematical theory of epidemics. Proc R Soc Lond - Ser A Contain Pap a Math Phys Character. 1927 Aug 1;115(772):700–721.
    1. Brainard J. Scientists are drowning in COVID-19 papers. Can new tools keep them afloat? Science. May. 13, 2020 https://www.sciencemag.org/news/2020/05/scientists-are-drowning-covid-19... [Internet] AAAS. 2020 [cited 2020 Jun 20]. Available from:

LinkOut - more resources