VIPER: a new compartment model for prediction of infected and recovered patients in pandemics with case studies on COVID-19
摘要
We propose a compartment model called the Vaccine Immunization with Partial Effective and Restrictions (VIPER) model, which specifically accounts for two features in the recent transmission of COVID-19, namely partially effective vaccines and non-pharmaceutical interventions. Compared with existing compartment models, our proposed VIPER facilitates more complex transitions between states to improve its practical applicability. We conduct a comparative analysis using data from four countries, each from a different continent, demonstrating the improvement achieved by the VIPER model over a similar compartment model proposed in a recent study with the same compartments (SVEIHDR: Susceptible, Vaccinated, Exposed, Infected, Hospitalized, Death, Recovered) but simpler transition routes. We also compare against other common prediction approaches, including the long short-term memory (LSTM) and auto-regressive integrated moving average (ARIMA) models. In addition to predicting the number of recovered cases, as in most existing studies, we also focus on the infected cases, which are more variable and critical for medical resource planning and allocation purposes. Our results show that the VIPER significantly outperforms the original SVEIHDR model in predicting the numbers of both infected and recovered individuals, with the estimation error reduced by at least