An Improved Inverse Method for Estimating Disease Transmission Rates in Low-Prevalence Epidemics

The accurate estimation of time-varying transmission rates is fundamental for understanding infectious disease dynamics and implementing effective public health interventions. To this end, we propose an improved inverse method for estimating time-varying transmission rates in low-prevalence settings, where conventional data preprocessing approaches often fail due to sparse case observations. To overcome this difficulty, we introduce an exponential B-spline interpolation approach that integrates both continuous and discrete inverse methods. This method ensures that transmission rate estimates remain non-negative and smooth, even when the observed data exhibit low cases. We apply this approach to several infectious disease models using real-world data from China, including a scarlet fever model, a multi-strain influenza model, and an age-structured influenza model. The results show that our method provides accurate transmission rate estimates, particularly in low-prevalence infectious diseases and multi-group epidemic models, demonstrating its robustness and applicability across various epidemiological contexts. The improved inverse method offers a new perspective for epidemiological modeling and provides reliable technical support for related theoretical exploration and public health decision-making.

• Publication year

  2025

• Venue

  Unknown venue

• Publication date

  2025-12-15

• Fields of study

  Biology, Medicine, Mathematics, Environmental Science

• Identifiers
  arXiv 2512.13759
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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