An Improved Inverse Method for Estimating Disease Transmission Rates in Low-Prevalence Epidemics
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908714342023168 |
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| author | Jing, Shuanglin Huang, Yuting Huo, Hai-Feng |
| author_facet | Jing, Shuanglin Huang, Yuting Huo, Hai-Feng |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2512_13759 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An Improved Inverse Method for Estimating Disease Transmission Rates in Low-Prevalence Epidemics Jing, Shuanglin Huang, Yuting Huo, Hai-Feng Populations and Evolution 92B99 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. |
| title | An Improved Inverse Method for Estimating Disease Transmission Rates in Low-Prevalence Epidemics |
| topic | Populations and Evolution 92B99 |
| url | https://arxiv.org/abs/2512.13759 |