The Expected Value of Perfect Information of COVID-19 Treatment Using Discrete Event Simulation

Abstract

The Value of Perfect Information (EVPI) and also Sample Information (EVSI) are necessary for calculating the expected economic benefit of a research based on evidence about the cost and efficacy of novel therapies. The EVPI determines the maximum value resulting from soliciting data to decrease the uncertainties and the expected loss in case of providing ineffective treatment. In general, an inefficient decision will waste health resources that may be better spent elsewhere, thereby deteriorating health outcomes. In this article, the value of information resulting from reducing uncertainty will be applied in assessing two COVID-19 treatments, namely, the standard care and vaccines. A discrete event simulation model is introduced to expand the usage of EVPI calculations to medical applications with various sources of uncertainty as the case of COVID-19. Our simulation results show that further testing and vaccine validation will be of insignificant value if the response rate on vaccine is higher than 85%. The purpose of this study is to provide a step-by-step guide to the computation of the value pre-testing in the context of healthcare decision-making. Worked scenarios were presented for COVID-19 in UAE. The study can serve as a useful template for various decision-making problems in medical settings. © 2022 The Authors.