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

TL;DR

This paper introduces an improved inverse method utilizing exponential B-spline interpolation to accurately estimate time-varying transmission rates in low-prevalence epidemics, enhancing robustness and applicability across various infectious disease models.

Contribution

The paper presents a novel inverse estimation technique that ensures non-negative, smooth transmission rate estimates in low-prevalence settings, addressing limitations of traditional methods.

Findings

Accurately estimates transmission rates in low-prevalence epidemics.

Demonstrates robustness across multiple infectious disease models.

Applicable to real-world data from China.

Abstract

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.