2024/05/15 by Niket Thakkar, Thakkar, Niket, Sonia Jindal +3
Mathematics · Medicine · #COVID-19 epidemiological studies #FOS: Biological sciences #Populations and Evolution (q-bio.PE) #Virology and Viral Diseases
paper · pdf · doi:10.48550/arxiv.2405.09664
openalex publication_date 2024/05/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.