COVID-19: Optimal Design of Serosurveys for Disease Burden Estimation

TL;DR

This paper develops a methodology for designing optimal serosurveys to estimate disease burden efficiently within budget constraints, applicable to various epidemiological contexts.

Contribution

It introduces a framework for optimal survey design considering costs, sensitivities, and specificities, including the existence of optimal designs in multiple settings.

Findings

Optimal designs exist under specified conditions.

Numerical examples demonstrate the methodology's practical utility.

Framework applicable to diverse epidemiological survey scenarios.

Abstract

We provide a methodology by which an epidemiologist may arrive at an optimal design for a survey whose goal is to estimate the disease burden in a population. For serosurveys with a given budget of $C$ rupees, a specified set of tests with costs, sensitivities, and specificities, we show the existence of optimal designs in four different contexts, including the well known c-optimal design. Usefulness of the results are illustrated via numerical examples. Our results are applicable to a wide range of epidemiological surveys under the assumptions that the estimate's Fisher-information matrix satisfies a uniform positive definite criterion.