A modeling approach for estimating dynamic measles case detection rates

TL;DR

This paper presents a novel modeling approach that uses age data from reported measles cases to rapidly estimate undetected infections, enhancing infectious disease surveillance capabilities.

Contribution

It introduces a new method leveraging age data to estimate measles underreporting at a weekly time scale, applicable even with coarse age information.

Findings

Estimates of underreporting can be generated within two weeks.

The approach is demonstrated on UK data from 1948.

Potential modifications for modern contexts are discussed.

Abstract

The main idea in this paper is that the age associated with reported measles cases can be used to estimate the number of undetected measles infections. Somewhat surprisingly, even with age only to the nearest year, estimates of underreporting can be generated at the much faster, 2 week time-scale associated with measles transmission. I describe this idea by focusing on the well-studied, 60 city United Kingdom data set, which covers the transition to universal healthcare in 1948, and is, as a result, an interesting case study in infectious disease surveillance. Finally, at the end of the paper, I comment briefly on how the approach can be modified for application to modern contexts.