Seq2Seq Surrogates of Epidemic Models to Facilitate Bayesian Inference
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Epidemic models are powerful tools in understanding infectious disease. However, as they increase in size and complexity, they can quickly become computationally intractable. Recent progress in modelling methodology has shown that surrogate models can be used to emulate complex epidemic models with a high-dimensional parameter space. We show that deep sequence-to-sequence (seq2seq) models can serve as accurate surrogates for complex epidemic models with sequence based model parameters, effectively replicating seasonal and long-term transmission dynamics. Once trained, our surrogate can predict scenarios a several thousand times faster than the original model, making them ideal for policy exploration. We demonstrate that replacing a traditional epidemic model with a learned simulator facilitates robust Bayesian inference.
| Original language | English |
|---|---|
| Title of host publication | AAAI-23 Special Tracks |
| Editors | Brian Williams, Yiling Chen, Jennifer Neville |
| Number of pages | 8 |
| Publisher | AAAI Press |
| Publication date | 2023 |
| Pages | 14170-14177 |
| ISBN (Electronic) | 9781577358800 |
| Publication status | Published - 2023 |
| Event | 37th AAAI Conference on Artificial Intelligence, AAAI 2023 - Washington, United States Duration: 7 Feb 2023 → 14 Feb 2023 |
Conference
| Conference | 37th AAAI Conference on Artificial Intelligence, AAAI 2023 |
|---|---|
| Land | United States |
| By | Washington |
| Periode | 07/02/2023 → 14/02/2023 |
| Sponsor | Association for the Advancement of Artificial Intelligence |
| Series | Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023 |
|---|---|
| Volume | 37 |
Bibliographical note
Funding Information:
This research was funded in whole, or in part, by the Wellcome Trust [Grant number 220900/Z/20/Z]. For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.
Funding Information:
G.C, T.M.W., P.W., A.G. and S.B. acknowledge support from the MRC Centre for Global Infectious Disease Analysis (MR/R015600/1), jointly funded by the UK Medical Research Council (MRC) and the UK Foreign, Commonwealth & Development Office (FCDO), under the MR-C/FCDO Concordat agreement, and also part of the ED-CTP2 programme supported by the European Union. SB is funded by the National Institute for Health Research (NIHR) Health Protection Research Unit in Modelling and Health Economics, a partnership between the UK Health Security Agency, Imperial College London and LSHTM (grant code NIHR200908). Disclaimer: “The views expressed are those of the author(s) and not necessarily those of the NIHR, UK Health Security Agency or the Department of Health and Social Care.” S.B. acknowledges support from the Novo Nordisk Foundation via The Novo Nordisk Young Investigator Award (NNF20OC0059309). SB acknowledges support from the Danish National Research Foundation via a chair grant. S.B. acknowledges support from The Eric and Wendy Schmidt Fund For Strategic Innovation via the Schmidt Polymath Award (G-22-63345) which also supports GDC. SF acknowledges the EPSRC (EP/V002910/2). T.M.W. was supported by the Bill and Melinda Gates Foundation (INV-647002606). P.W. is funded by the Bill and Melinda Gates Foundation (INV-043624).
Publisher Copyright:
Copyright © 2023, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
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