Systematic review of predictive mathematical models of COVID-19 epidemic

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

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.

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.

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.