Sampling designs for epidemic prevalence estimation

TL;DR

This paper explores adaptive network tracing as an efficient sampling method for estimating epidemic prevalence, leveraging contact tracing to improve case detection over random sampling.

Contribution

It introduces and evaluates adaptive network tracing designs that combine contact tracing with prevalence estimation, enhancing sampling efficiency during epidemics.

Findings

Adaptive network tracing increases case yield compared to random sampling.

The designs are effective for both cross-sectional and change estimation.

Unified tracing and sampling approaches can improve epidemic monitoring.

Abstract

Intuitively, sampling is likely to be more efficient for prevalence estimation, if the cases (or positives) have a relatively higher representation in the sample than in the population. In case the virus is transmitted via personal contacts, contact tracing of the observed cases (but not noncases), to be referred to as \emph{adaptive network tracing}, can generate a higher yield of cases than random sampling from the population. The efficacy of relevant designs for cross-sectional and change estimation is investigated. The availability of these designs allows one unite tracing for combating the epidemic and sampling for estimating the prevalence in a single endeavour.