Inefficiency of SIR models in forecasting COVID-19 epidemic: a case study of Isfahan

The multifaceted destructions caused by COVID-19 have been compared to that of World War II. What makes the situation even more complicated is the ambiguity about the duration and ultimate spread of the pandemic. It is especially critical for the governments, healthcare systems, and economic sectors to have an estimate of the future of this disaster. By using different mathematical approaches, including the classical susceptible-infected-recovered (SIR) model and its derivatives, many investigators have tried to predict the outbreak of COVID-19. In this study, we simulated the epidemic in Isfahan province of Iran for the period from Feb 14th to April 11th and also forecasted the remaining course with three scenarios that differed in terms of the stringency level of social distancing. Despite the prediction of disease course in short-term intervals, the constructed SIR model was unable to forecast the actual spread and pattern of epidemic in the long term. Remarkably, most of the published SIR models developed to predict COVID-19 for other communities, suffered from the same inconformity. The SIR models are based on assumptions that seem not to be true in the case of the COVID-19 epidemic. Hence, more sophisticated modeling strategies and detailed knowledge of the biomedical and epidemiological aspects of the disease are needed to forecast the pandemic.

Automated summary

The multifaceted destructions caused by COVID-19 have been compared to World War II, creating ambiguity about the duration and spread of the pandemic. Governments, healthcare systems, and economic sectors are crucial to estimate the future of this disaster. While many investigators have used mathematical approaches like the SIR model to predict the outbreak of COVID-19, these models have limitations in accurately forecasting the long-term spread and pattern of the epidemic. More sophisticated modeling strategies and detailed knowledge of the disease are needed to predict the pandemic more accurately.