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.