Work

Predicting measles outbreak magnitude via modeled population susceptibility evidence from pre-vaccination data

Year: 2026

Type: article

Abstract: We characterized pre-vaccination measles dynamics using a discrete-time mechanistic model that integrates incidence time series and demographic data to estimate key epidemiological parameters and to quantify the relationship between the reconstructed population susceptibility and the observed epidemic magnitude. Weekly reported measles incidence data from England and Wales and its five largest cities during the pre-vaccination era were analyzed. A discrete-time age-of-infection model incorporating seasonality was fitted for each year. Several parameters were fixed using values from the literature, while others were estimated via maximum likelihood. Reconstructed susceptibility at the start of each year (S 0 ), inferred from surveillance and demographic data, was evaluated as a predictor of subsequent observed outbreak magnitude. The model accurately reproduced the temporal dynamics of measles across all regions. Reconstructed S 0 was strongly associated with observed attack rates in the following year, demonstrating its practical value as a surveillance-based predictor of epidemic size. The discrete-time age-of-infection model enables exploration of the mechanisms underlying observed incidence and epidemic dynamics. It captures the feedback between the susceptible pool and outbreak amplitude and supports one-year-ahead predictions of observed outbreak magnitude based on reconstructed susceptibility.

Source: Scientific Reports

DOI: 10.1038/s41598-026-61920-w

Authors: Ella Dor, Rami Yaari, Amit Huppert

Institutions: Tel Aviv University, Columbia University, Sheba Medical Center

Language: English

Topic: Mathematical and Theoretical Epidemiology and Ecology Models

Subfield: Public Health, Environmental and Occupational Health

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,690