Investigating Complex HPV Dynamics Using Emulation and History Matching
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913483968217088 |
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| author | Iskauskas, Andrew Cohen, Jamie A. Scarponi, Danny Vernon, Ian Goldstein, Michael Klein, Daniel White, Richard G. McCreesh, Nicky |
| author_facet | Iskauskas, Andrew Cohen, Jamie A. Scarponi, Danny Vernon, Ian Goldstein, Michael Klein, Daniel White, Richard G. McCreesh, Nicky |
| contents | The study of transmission and progression of human papillomavirus (HPV) is crucial for understanding the incidence of cervical cancers, and has been identified as a priority worldwide. The complexity of the disease necessitates a detailed model of HPV transmission and its progression to cancer; to infer properties of the above we require a careful process that can match to imperfect or incomplete observational data. In this paper, we describe the HPVsim simulator to satisfy the former requirement; to satisfy the latter we couple this stochastic simulator to a process of emulation and history matching using the R package hmer. With these tools, we are able to obtain a comprehensive collection of parameter combinations that could give rise to observed cancer data, and explore the implications of the variability of these parameter sets as it relates to future health interventions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_15805 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Investigating Complex HPV Dynamics Using Emulation and History Matching Iskauskas, Andrew Cohen, Jamie A. Scarponi, Danny Vernon, Ian Goldstein, Michael Klein, Daniel White, Richard G. McCreesh, Nicky Applications Computation The study of transmission and progression of human papillomavirus (HPV) is crucial for understanding the incidence of cervical cancers, and has been identified as a priority worldwide. The complexity of the disease necessitates a detailed model of HPV transmission and its progression to cancer; to infer properties of the above we require a careful process that can match to imperfect or incomplete observational data. In this paper, we describe the HPVsim simulator to satisfy the former requirement; to satisfy the latter we couple this stochastic simulator to a process of emulation and history matching using the R package hmer. With these tools, we are able to obtain a comprehensive collection of parameter combinations that could give rise to observed cancer data, and explore the implications of the variability of these parameter sets as it relates to future health interventions. |
| title | Investigating Complex HPV Dynamics Using Emulation and History Matching |
| topic | Applications Computation |
| url | https://arxiv.org/abs/2408.15805 |