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http://bura.brunel.ac.uk/handle/2438/24231| Title: | The impact of uncertainty on predictions of the CovidSim epidemiological code |
| Authors: | Edeling, W Arabnejad, H Sinclair, R Suleimenova, D Gopalakrishnan, K Bosak, B Groen, D Mahmood, I Crommelin, D Coveney, PV |
| Keywords: | computational models;infectious diseases;SARS-CoV-2 |
| Issue Date: | 22-Feb-2021 |
| Publisher: | Springer Nature |
| Citation: | Edeling, W. et al. (2021) 'The impact of uncertainty on predictions of the CovidSim epidemiological code', Nature Computational Science, 1 (2), pp. 128 - 135. doi: 10.1038/s43588-021-00028-9. |
| Abstract: | Copyright © 2022 The Autor(s). Epidemiological modelling has assisted in identifying interventions that reduce the impact of COVID-19. The UK government relied, in part, on the CovidSim model to guide its policy to contain the rapid spread of the COVID-19 pandemic during March and April 2020; however, CovidSim contains several sources of uncertainty that affect the quality of its predictions: parametric uncertainty, model structure uncertainty and scenario uncertainty. Here we report on parametric sensitivity analysis and uncertainty quantification of the code. From the 940 parameters used as input into CovidSim, we find a subset of 19 to which the code output is most sensitive—imperfect knowledge of these inputs is magnified in the outputs by up to 300%. The model displays substantial bias with respect to observed data, failing to describe validation data well. Quantifying parametric input uncertainty is therefore not sufficient: the effect of model structure and scenario uncertainty must also be properly understood. |
| Description: | Data availability Figure 1a,b displays publicly available cumulative death count data for the UK, which were obtained from ref. 22. Source Data are available with this paper. Furthermore, the parameter list—with all input parameters, a description, their default values and reasons for inclusion or exclusion from the Imperial College London CovidSim team—is available as Supplementary Data. Supplementary information: https://static-content.springer.com/esm/art%3A10.1038%2Fs43588-021-00028-9/MediaObjects/43588_2021_28_MOESM1_ESM.pdf Supplementary discussion, Figs. 1–7 and Table 1. Supplementary Data 1: https://static-content.springer.com/esm/art%3A10.1038%2Fs43588-021-00028-9/MediaObjects/43588_2021_28_MOESM2_ESM.xlsx All input parameters, a description, their default values and reasons for inclusion or exclusion from the Imperial College London CovidSim team. Source data Source Data Fig. 1: https://static-content.springer.com/esm/art%3A10.1038%2Fs43588-021-00028-9/MediaObjects/43588_2021_28_MOESM3_ESM.xlsx The computed cumulative death results for Fig. 1 and the observed death count data. Source Data Fig. 2: https://static-content.springer.com/esm/art%3A10.1038%2Fs43588-021-00028-9/MediaObjects/43588_2021_28_MOESM4_ESM.xlsx The Sobol indices of Fig. 2. |
| URI: | https://bura.brunel.ac.uk/handle/2438/24231 |
| DOI: | https://doi.org/10.1038/s43588-021-00028-9 |
| Appears in Collections: | Dept of Computer Science Research Papers |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| FullText.pdf | This is a pre-copyedited, author-produced version of an article accepted for publication in Nature Computational Science following peer review. The final authenticated version is available online at https://doi.org/10.1038/s43588-021-00028-9. | 871.27 kB | Adobe PDF | View/Open |
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