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Composite method for fast computation of individual level spatial epidemic models
Yirao Zhang, Rob Deardon, Lorna Deeth
https://arxiv.org/abs/2509.04660 https://

Composite method for fast computation of individual level spatial epidemic models
Individual-level models, also known as ILMs, are commonly used in epidemics modelling, as they can flexibly incorporate individual-level covariates that influence susceptibility and transmissibility upon infection. However, inference for ILMs is computationally intensive, especially as the total population size increases and additional covariates are incorporated. We propose a composite method, the composite ILM (C-ILM), that clusters the population into minimally-interfered subpopulations, wit…

Matching-Based Nonparametric Estimation of Group Average Treatment Effects
Peng Wu, Pengtao Zeng, Zhaoqing Tian, Shaojie Wei
https://arxiv.org/abs/2508.18157 https://

Matching-Based Nonparametric Estimation of Group Average Treatment Effects
Heterogeneous treatment effects, which vary according to individual covariates, are crucial in fields such as personalized medicine and tailored treatment strategies. In many applications, rather than considering the heterogeneity induced by all covariates, practitioners focus on a few key covariates to develop tailored treatment decisions. Based on this, we aim to estimate the group average treatment effects (GATEs), which represent heterogeneous treatment effects across subpopulations defined…