A New State-Space Epidemiological Model for Cost-Effectiveness Analysis of Non-Medical Interventions- A Study on COVID-19 in California and Florida

In the absence of sufficient testing capacity for COVID-19, a substantial number of infecteds are expected to remain undetected, and hence, are not quarantined. This, in turn, defeats the whole purpose of non-medical containment measures, like quarantine, lockdown, travel ban, physical distancingetc., by keeping the average reproduction rate above 1. To stress upon the importance of extensive random testing for breaking the chains of transmissions, we have formulated a detailed framework for carrying out cost-effectiveness analysis (CEA) of extensive random testing in comparison to targeted testing (the existing testing policy followed by most countries). This framework can be easily extended for CEA of any other non-medical or even medical interventions for containing epidemics.

We have developed a new version of the basic susceptible-infected-removed (SIR) compartmental model, called the susceptible-infected (quarantined/ free) - recovered-deceased [SI(Q/F)RD] model, to incorporate the impact of undetected cases on the transmission dynamics of the epidemic. Further, we have presented a Dirichlet-Beta state-space formulation of the SI(Q/F)RD model for the estimation of its parameters using posterior realizations from Gibbs sampling procedure. As an application, the proposed methodology is implemented to forecast the COVID-19 transmission in California and Florida, and further carry out CEA of extensive random testing over targeted testing.