Seasonality and susceptibility from measles time series

TL;DR

This paper introduces mathematical tools to analyze seasonal variations and population susceptibility in measles transmission using publicly available case time series, aiding in targeted immunization strategies.

Contribution

The paper presents scalable methods to estimate measles seasonality and susceptibility from case data without demographic details, bridging empirical and complex disease models.

Findings

Effective characterization of measles epidemiology at global scale

Identification of optimal timings for supplementary immunizations

Validation against data-informed models shows essential dynamics are captured

Abstract

This paper develops mathematical tools to estimate seasonal changes in measles transmission rates and corresponding variation in population susceptibility. The tools are designed to leverage times series of cases in the absence of demographic data. In particular, we focus on publicly available suspected case reports from the World Health Organization (WHO), which routinely publishes country-level, monthly aggregated time series. With that as input, we show that measles epidemiologies can be characterized efficiently at global-scale, and we use our estimates to recommend context-specific, future supplementary immunization times. Throughout the paper, comparisons with more data-informed models illustrate that the approach captures the essential dynamics, and broadly speaking, the tools we describe represent a scalable intermediate between conventional empirical approaches and more intricate disease models.