A sampling scheme for estimating the prevalence of a pandemic

TL;DR

This paper introduces a two-stage sampling scheme that leverages prior information on diagnosed COVID-19 cases and population distributions to estimate pandemic prevalence more efficiently, adaptable to complex distributions.

Contribution

It develops a novel sampling method combining global likelihood sampling with prior case data, improving estimation accuracy and practicality in pandemic prevalence studies.

Findings

The method outperforms traditional sampling in simulations.

It adapts well to complex distribution scenarios.

Practical implementation guidelines are provided.

Abstract

The spread of COVID-19 makes it essential to investigate its prevalence. In such investigation research, as far as we know, the widely-used sampling methods didn't use the information sufficiently about the numbers of the previously diagnosed cases, which provides a priori information about the true numbers of infections. This motivates us to develop a new, two-stage sampling method in this paper, which utilises the information about the distributions of both population and diagnosed cases, to investigate the prevalence more efficiently. The global likelihood sampling, a robust and efficient sampler to draw samples from any probability density function, is used in our sampling strategy, and thus, our new method can automatically adapt to the complicated distributions of population and cases. Moreover, the corresponding estimating method is simple, which facilitates the practical implementation. Some recommendations for practical implementation are given. Finally, several simulations and a practical example verified its efficiency.