Work

Robust Inverse Reconstruction of Time-Varying Transmission Rates Across Model Structures and Incidence Forms

Year: 2026

Type: article

Abstract: Accurate, decision-ready estimates of time-varying transmission rates are critical, yet thought to be sensitive to model specification. We test this sensitivity by applying a continuous inverse method to weekly influenza and measles data, comparing reconstructions across eight common compartmental structures (SIS/SIR/SEIS/SEIR and vaccinated variants) and across five incidence forms (mass action vs. saturated). Timing and ordering of peaks and troughs in the transmission rates are highly consistent across influenza models, with amplitude shifts matching mechanistic expectations (attenuation with vaccination; smoothing with latent periods). For measles, we show that the transmission rates under saturated incidence preserve the rise-and-fall ordering observed under mass action and provide a sufficient condition ensuring matched monotonicity. These results indicate inverse transmission rate reconstructions are robust to typical structural and incidence choices, supporting their routine use for interpreting transmission dynamics, short-term forecasting, and intervention assessment.

Source: Bulletin of Mathematical Biology

DOI: 10.1007/s11538-026-01597-4

Authors: Xiunan Wang, Hao Wang

Institutions: University of Tennessee at Chattanooga, University of Alberta

Language: English

Topic: Mathematical and Theoretical Epidemiology and Ecology Models

Subfield: Public Health, Environmental and Occupational Health

Field: Medicine

Domain: Health Sciences

Open Access status: hybrid

Estimated APC paid: $3,090

Funders: National Science Foundation, Canada Research Chairs, Natural Sciences and Engineering Research Council of Canada