paramix: An R package for parameter discretisation in compartmental models, with application to calculating years of life lostLucy Goodfellow, Carl A B Pearson and Simon R Procter PLOS Computational Biology, 2025, vol. 21, issue 9, 1-13 Abstract: 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.Author summary: Researchers use infectious disease models to understand trends in disease spread, including predicting future infections under different interventions. Constraints like data availability and numerical complexity drive researchers to group individuals into broad categories; for example, all working age adults might be represented as a single set of model compartments. Key epidemic parameters can vary widely across such groups. Additionally, model outcomes calculated using these broad categories often need to be disaggregated to a high resolution, for example a precise age at death for calculating years life lost, a key measure when estimating the cost-effectiveness of interventions. To satisfy these needs, we present a software package, paramix, which provides tools to move between high and low resolution data. In this paper, we demonstrate the capabilities of paramix by comparing various methods of calculating deaths and years of life lost across broad age groups. For an analysis of an archetypal model, we find that paramix best matches a high-resolution model, while the alternatives are substantially different. Date: 2025 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1013420 DOI: 10.1371/journal.pcbi.1013420 Access Statistics for this article More articles in PLOS Computational Biology from Public Library of Science |
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