Usage of Compartmental Models in Predicting COVID-19 Outbreaks

Abstract

Accurately predicting the spread of the SARS-CoV-2, the cause of the COVID-19 pandemic, is of great value for global regulatory authorities to overcome a number of challenges including medication shortage, outcome of vaccination, and control strategies planning. Modeling methods that are used to simulate and predict the spread of COVID-19 include compartmental model, structured metapopulations, agent-based networks, deep learning, and complex network, with compartmental modeling as one of the most widely used methods. Compartmental model has two noteworthy features, a flexible framework that allows users to easily customize the model structure and its high adaptivity that allows well-matured approaches (e.g., Bayesian inference and mixed-effects modeling) to improve parameter estimation. We retrospectively evaluated the prediction performances of the compartmental models on the CDC COVID-19 Mathematical Modeling webpage based on data collected between August 2020 and February 2021, and subsequently discussed in detail their corresponding model enhancement. Finally, we presented examples using the compartmental models to assist policymaking. By evaluating all models in parallel, we systemically evaluated the performance and evolution of using compartmental models for COVID-19 pandemic prediction. In summary, as a 100-year-old epidemic approach, the compartmental model presents a powerful tool that is extremely adaptive and can be readily customized and implemented to address new data or emerging needs during a pandemic. Graphical Abstract

Multivariate time series prediction of Covid-19 daily new cases in Indonesia based on Deep Learning: Unlocking the role of effective reproduction number (Rt)

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Forecasting the effects of vaccination on the COVID-19 pandemic in Malaysia using SEIRV compartmental models

“…Compartmental models serve as effective tools for determining the impacts of vaccination, thanks to their flexible frameworks that allow for the inclusion of state variables representing vaccination effects within the model structure [ 36 ]. In our study, a strategy for accurately modeling the effects of vaccination involved considering the weighted average vaccine efficacy.…”

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“…On the other hand, compartmental models [9][10][11] and stochastic transmission models 12 are analytic approaches specifically tailored to reproduce the evolution of an infection in a population and in the presence of variants [13][14][15][16] . These models incorporate factors such as population demographics, rates of infection, recovery, and mortality, providing a mathematical representation of epidemic progression 17,18 .…”