Goodfellow Lucy, Pearson Carl A B, Procter Simon R
Department of Infectious Disease Epidemiology and Dynamics, London School of Hygiene and Tropical Medicine, London, United Kingdom.
Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina, United States of America.
PLoS Comput Biol. 2025 Sep 8;21(9):e1013420. doi: 10.1371/journal.pcbi.1013420. eCollection 2025 Sep.
Compartmental infectious disease models are used to calculate disease transmission, estimate underlying rates, forecast future burden, and compare benefits across intervention scenarios. These models aggregate individuals into compartments, often stratified by characteristics to represent groups that might be intervention targets or otherwise of particular concern. Ideally, model calculation could occur at the most demanding resolution for the overall analysis, but this may be infeasible due to availability of computational resources or empirical data. Instead, detailed population age structure might be consolidated into broad categories such as children, working-age adults, and seniors. Researchers must then discretise key epidemic parameters, like the infection-fatality ratio, for these lower resolution groups. After estimating outcomes for those crude groups, follow-on analyses, such as calculating years of life lost (YLLs), may need to distribute or weight those low-resolution outcomes back to the high resolution. The specific calculation for these aggregation and disaggregation steps can substantially influence outcomes. To assist researchers with these tasks, we developed paramix, an R package which simplifies the transformations between high and low resolution. We demonstrate applying paramix to a common discretisation analysis: using age structured models for health economic calculations comparing YLLs. We compare how estimates vary between paramix and several alternatives for an archetypal model, including comparison to a high resolution benchmark. We consistently found that paramix yielded the most similar estimates to the high-resolution model, for the same computational burden of low-resolution models. In our illustrative analysis, the non-paramix methods estimated up to twice as many YLLs averted as the paramix approach, which would likely lead to a similarly large impact on incremental cost-effectiveness ratios used in economic evaluations.
compartmental传染病模型用于计算疾病传播、估计潜在发病率、预测未来负担以及比较不同干预方案的效益。这些模型将个体汇总到不同的类别中，通常按特征分层，以代表可能是干预目标或其他特别关注的群体。理想情况下，模型计算可以在整体分析所需的最高分辨率下进行，但由于计算资源或实证数据的可用性，这可能不可行。相反，详细的人口年龄结构可能会被合并为宽泛的类别，如儿童、工作年龄成年人和老年人。然后，研究人员必须为这些较低分辨率的群体离散化关键的流行参数，如感染致死率。在估计这些粗略群体的结果后，后续分析，如计算生命年损失（YLLs），可能需要将这些低分辨率结果分配或加权回高分辨率。这些汇总和分解步骤的具体计算会对结果产生重大影响。为了帮助研究人员完成这些任务，我们开发了paramix，这是一个R包，它简化了高分辨率和低分辨率之间的转换。我们展示了将paramix应用于一个常见的离散化分析：使用年龄结构模型进行健康经济计算，比较YLLs。我们比较了paramix与一个原型模型的几种替代方法之间的估计差异，包括与高分辨率基准的比较。我们一致发现，对于低分辨率模型相同的计算负担，paramix产生的估计与高分辨率模型最相似。在我们的示例分析中，非paramix方法估计避免的YLLs是paramix方法的两倍之多，这可能会对经济评估中使用的增量成本效益比产生类似的重大影响。
