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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2509.04660 |
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| _version_ | 1866911138841624576 |
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| author | Zhang, Yirao Deardon, Rob Deeth, Lorna |
| author_facet | Zhang, Yirao Deardon, Rob Deeth, Lorna |
| contents | 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, with between-cluster infections enabled through a ``spark function.'' This approach allows for parallel computation of subsets before aggregation. Focusing on C-ILM, we consider four ``spark functions'', and introduce a Dirichlet process mixture modelling (DPMM) algorithm for clustering. Simulation results indicate that, in addition to faster computation, C-ILM performs well in parameter estimation and posterior predictions. Furthermore, within C-ILM framework, DPMM algorithm demonstrates superior performance compared to the conventional $K$-means algorithm. We apply the methods to data from the 2001 UK foot-and-mouth disease outbreak. The results provide evidence that C-ILM is not only computationally efficient but also achieves a better model fit compared to the basic spatial ILM. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_04660 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Composite method for fast computation of individual level spatial epidemic models Zhang, Yirao Deardon, Rob Deeth, Lorna Methodology 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, with between-cluster infections enabled through a ``spark function.'' This approach allows for parallel computation of subsets before aggregation. Focusing on C-ILM, we consider four ``spark functions'', and introduce a Dirichlet process mixture modelling (DPMM) algorithm for clustering. Simulation results indicate that, in addition to faster computation, C-ILM performs well in parameter estimation and posterior predictions. Furthermore, within C-ILM framework, DPMM algorithm demonstrates superior performance compared to the conventional $K$-means algorithm. We apply the methods to data from the 2001 UK foot-and-mouth disease outbreak. The results provide evidence that C-ILM is not only computationally efficient but also achieves a better model fit compared to the basic spatial ILM. |
| title | Composite method for fast computation of individual level spatial epidemic models |
| topic | Methodology |
| url | https://arxiv.org/abs/2509.04660 |