Why is it difficult to accurately predict the COVID-19 epidemic?

By W. Roda, M. Varughese, Donglin Han, Michael Y. Li. Infectious Disease Modelling, 2020

597 citations recorded by the source corpus.

Since the COVID-19 outbreak in Wuhan City in December of 2019, numerous model predictions on the COVID-19 epidemics in Wuhan and other parts of China have been reported. These model predictions have shown a wide range of variations. In our study, we demonstrate that nonidentifiability in model calibrations using the confirmed-case data is the main reason for such wide variations. Using the Akaike Information Criterion (AIC) for model selection, we show that an SIR model performs much better than an SEIR model in representing the information contained in the confirmed-case data. This indicates that predictions using more complex models may not be more reliable compared to using a simpler model. We present our model predictions for the COVID-19 epidemic in Wuhan after the lockdown and quarantine of the city on January 23, 2020. We also report our results of modeling the impacts of the strict quarantine measures undertaken in the city after February 7 on the time course of the epidemic, and modeling the potential of a second outbreak after the return-to-work in the city.

Subfields: Economics and Econometrics, Epidemiology, Modeling and Simulation

View Paper: https://www.semanticscholar.org/paper/b6c9b49739911ab91cf60298995fce2f7ad14dce

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Citations in the corpus

To mask or not to mask: Modeling the potential for face mask use by the general public to curtail the COVID-19 pandemic (2020, unknown): “, [46] on identifiability problems).”
How simulation modelling can help reduce the impact of COVID-19 (2020, background): “Simulation models are often based on input data that are still very uncertain or missing and in the case of a fast-moving pandemic, inputs may be based only on con ﬁ rmed cases (Roda et al., 2020).”