History matching of a complex epidemiological model of human immunodeficiency virus transmission by using variance emulation
Complex stochastic models are commonplace in epidemiology, but their utility depends on their calibration to empirical data. History matching is a (pre)calibration method that has been applied successfully to complex deterministic models. In this work, we adapt history matching to stochastic models,...
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| Published in | Journal of the Royal Statistical Society Series C: Applied Statistics Vol. 66; no. 4; pp. 717 - 740 |
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| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
John Wiley & Sons Ltd
01.08.2017
Oxford University Press John Wiley and Sons Inc |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0035-9254 1467-9876 |
| DOI | 10.1111/rssc.12198 |
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| Abstract | Complex stochastic models are commonplace in epidemiology, but their utility depends on their calibration to empirical data. History matching is a (pre)calibration method that has been applied successfully to complex deterministic models. In this work, we adapt history matching to stochastic models, by emulating the variance in the model outputs, and therefore accounting for its dependence on the model's input values. The method proposed is applied to a real complex epidemiological model of human immunodeficiency virus in Uganda with 22 inputs and 18 outputs, and is found to increase the efficiency of history matching, requiring 70% of the time and 43% fewer simulator evaluations compared with a previous variant of the method. The insight gained into the structure of the human immunodeficiency virus model, and the constraints placed on it, are then discussed. |
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| AbstractList | Summary
Complex stochastic models are commonplace in epidemiology, but their utility depends on their calibration to empirical data. History matching is a (pre)calibration method that has been applied successfully to complex deterministic models. In this work, we adapt history matching to stochastic models, by emulating the variance in the model outputs, and therefore accounting for its dependence on the model's input values. The method proposed is applied to a real complex epidemiological model of human immunodeficiency virus in Uganda with 22 inputs and 18 outputs, and is found to increase the efficiency of history matching, requiring 70% of the time and 43% fewer simulator evaluations compared with a previous variant of the method. The insight gained into the structure of the human immunodeficiency virus model, and the constraints placed on it, are then discussed. Complex stochastic models are commonplace in epidemiology, but their utility depends on their calibration to empirical data. History matching is a (pre)calibration method that has been applied successfully to complex deterministic models. In this work, we adapt history matching to stochastic models, by emulating the variance in the model outputs, and therefore accounting for its dependence on the model's input values. The method proposed is applied to a real complex epidemiological model of human immunodeficiency virus in Uganda with 22 inputs and 18 outputs, and is found to increase the efficiency of history matching, requiring 70% of the time and 43% fewer simulator evaluations compared with a previous variant of the method. The insight gained into the structure of the human immunodeficiency virus model, and the constraints placed on it, are then discussed.Complex stochastic models are commonplace in epidemiology, but their utility depends on their calibration to empirical data. History matching is a (pre)calibration method that has been applied successfully to complex deterministic models. In this work, we adapt history matching to stochastic models, by emulating the variance in the model outputs, and therefore accounting for its dependence on the model's input values. The method proposed is applied to a real complex epidemiological model of human immunodeficiency virus in Uganda with 22 inputs and 18 outputs, and is found to increase the efficiency of history matching, requiring 70% of the time and 43% fewer simulator evaluations compared with a previous variant of the method. The insight gained into the structure of the human immunodeficiency virus model, and the constraints placed on it, are then discussed. Complex stochastic models are commonplace in epidemiology, but their utility depends on their calibration to empirical data. History matching is a (pre)calibration method that has been applied successfully to complex deterministic models. In this work, we adapt history matching to stochastic models, by emulating the variance in the model outputs, and therefore accounting for its dependence on the model's input values. The method proposed is applied to a real complex epidemiological model of human immunodeficiency virus in Uganda with 22 inputs and 18 outputs, and is found to increase the efficiency of history matching, requiring 70% of the time and 43% fewer simulator evaluations compared with a previous variant of the method. The insight gained into the structure of the human immunodeficiency virus model, and the constraints placed on it, are then discussed. Summary Complex stochastic models are commonplace in epidemiology, but their utility depends on their calibration to empirical data. History matching is a (pre)calibration method that has been applied successfully to complex deterministic models. In this work, we adapt history matching to stochastic models, by emulating the variance in the model outputs, and therefore accounting for its dependence on the model's input values. The method proposed is applied to a real complex epidemiological model of human immunodeficiency virus in Uganda with 22 inputs and 18 outputs, and is found to increase the efficiency of history matching, requiring 70% of the time and 43% fewer simulator evaluations compared with a previous variant of the method. The insight gained into the structure of the human immunodeficiency virus model, and the constraints placed on it, are then discussed. |
| Author | Andrianakis, I. McKinley, T. J. McCreesh, N. Vernon, I. White, R. G. Goldstein, M. Oakley, J. E. Nsubuga, R. N. |
| AuthorAffiliation | 4 University of Sheffield UK 2 Durham University UK 5 Medical Research Council Uganda Kampala Uganda 1 London School of Hygiene and Tropical Medicine UK 3 University of Exeter UK |
| AuthorAffiliation_xml | – name: 2 Durham University UK – name: 5 Medical Research Council Uganda Kampala Uganda – name: 3 University of Exeter UK – name: 4 University of Sheffield UK – name: 1 London School of Hygiene and Tropical Medicine UK |
| Author_xml | – sequence: 1 givenname: I. surname: Andrianakis fullname: Andrianakis, I. – sequence: 2 givenname: I. surname: Vernon fullname: Vernon, I. – sequence: 3 givenname: N. surname: McCreesh fullname: McCreesh, N. – sequence: 4 givenname: T. J. surname: McKinley fullname: McKinley, T. J. – sequence: 5 givenname: J. E. surname: Oakley fullname: Oakley, J. E. – sequence: 6 givenname: R. N. surname: Nsubuga fullname: Nsubuga, R. N. – sequence: 7 givenname: M. surname: Goldstein fullname: Goldstein, M. – sequence: 8 givenname: R. G. surname: White fullname: White, R. G. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/28781386$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1007/978-1-4612-2290-3_2 10.1126/science.1094442 10.1111/j.1467-9868.2009.00736.x 10.1080/00401706.2012.715835 10.1007/s00382-013-1896-4 10.1111/1467-9868.00294 10.1097/00002030-199401000-00013 10.1080/09540129108253064 10.1016/j.jspi.2009.08.006 10.1016/j.jspi.2008.07.019 10.1007/978-1-4612-0703-0 10.1080/00401706.2012.707580 10.1016/S2352-3018(15)00056-9 10.1198/jasa.2009.0005 10.1111/1467-985X.00125 10.1016/j.ecolmodel.2006.04.023 10.1098/rsif.2008.0172 10.1198/TECH.2009.08040 10.1016/j.csda.2013.09.024 10.1097/OLQ.0b013e318254c84a 10.1093/imammb/15.1.19 10.1016/S0140-6736(08)61697-9 10.1198/TECH.2009.08019 10.1080/00031305.1994.10476030 10.1198/106186008X384032 10.2202/1557-4679.1171 10.1016/S0140-6736(12)61960-6 10.1287/opre.1090.0754 10.1016/S0140-6736(11)60842-8 10.1016/S0140-6736(94)90133-3 10.1214/12-STS412 |
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| Snippet | Complex stochastic models are commonplace in epidemiology, but their utility depends on their calibration to empirical data. History matching is a... Summary Complex stochastic models are commonplace in epidemiology, but their utility depends on their calibration to empirical data. History matching is a... Summary Complex stochastic models are commonplace in epidemiology, but their utility depends on their calibration to empirical data. History matching is a... |
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| SubjectTerms | Antiretroviral drugs Calibration Computer simulation Constraint modelling Epidemiology Gaussian processes HIV Human immunodeficiency virus Immune system Individual‐based models Inverse problems Matching Original Probability theory Stochastic models Stochastic simulators Variance Viruses |
| Title | History matching of a complex epidemiological model of human immunodeficiency virus transmission by using variance emulation |
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