A Markov multiple state model for epidemic and insurance modelling

Abstract With recent epidemics such as COVID-19, H1N1 and SARS causing devastating financial loss to the economy, it is important that insurance companies plan for financial costs of epidemics. This article proposes a new methodology for epidemic and insurance modelling by combining the existing deterministic compartmental models and the Markov multiple state models to facilitate actuarial computations to design new health insurance plans that cover epidemics. Our method is inspired by the seminal paper (Feng and Garrido (2011) North American Actuarial Journal , 15 , 112–136.) of Feng and Garrido and complements the work of Hillairet and Lopez et al. in Hillairet and Lopez ((2021) Scandinavian Actuarial Journal , 2021 (8), 671–694.) and Hillairet et al. ((2022) Insurance: Mathematics and Economics , 107 , 88–101.) In this work, we use the deterministic SIR model and the Eyam epidemic data set to provide numerical illustrations for our method.