Seq2Seq Surrogates of Epidemic Models to Facilitate Bayesian Inference

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Standard

Seq2Seq Surrogates of Epidemic Models to Facilitate Bayesian Inference. / Charles, Giovanni; Wolock, Timothy M.; Winskill, Peter; Ghani, Azra; Bhatt, Samir; Flaxman, Seth.

AAAI-23 Special Tracks. ed. / Brian Williams; Yiling Chen; Jennifer Neville. AAAI Press, 2023. p. 14170-14177 (Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023, Vol. 37).

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Harvard

Charles, G, Wolock, TM, Winskill, P, Ghani, A, Bhatt, S & Flaxman, S 2023, Seq2Seq Surrogates of Epidemic Models to Facilitate Bayesian Inference. in B Williams, Y Chen & J Neville (eds), AAAI-23 Special Tracks. AAAI Press, Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023, vol. 37, pp. 14170-14177, 37th AAAI Conference on Artificial Intelligence, AAAI 2023, Washington, United States, 07/02/2023.

APA

Charles, G., Wolock, T. M., Winskill, P., Ghani, A., Bhatt, S., & Flaxman, S. (2023). Seq2Seq Surrogates of Epidemic Models to Facilitate Bayesian Inference. In B. Williams, Y. Chen, & J. Neville (Eds.), AAAI-23 Special Tracks (pp. 14170-14177). AAAI Press. Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023 Vol. 37

Vancouver

Charles G, Wolock TM, Winskill P, Ghani A, Bhatt S, Flaxman S. Seq2Seq Surrogates of Epidemic Models to Facilitate Bayesian Inference. In Williams B, Chen Y, Neville J, editors, AAAI-23 Special Tracks. AAAI Press. 2023. p. 14170-14177. (Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023, Vol. 37).

Author

Charles, Giovanni ; Wolock, Timothy M. ; Winskill, Peter ; Ghani, Azra ; Bhatt, Samir ; Flaxman, Seth. / Seq2Seq Surrogates of Epidemic Models to Facilitate Bayesian Inference. AAAI-23 Special Tracks. editor / Brian Williams ; Yiling Chen ; Jennifer Neville. AAAI Press, 2023. pp. 14170-14177 (Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023, Vol. 37).

Bibtex

@inproceedings{c4455e587fb04c2fa8888432101eae1c,
title = "Seq2Seq Surrogates of Epidemic Models to Facilitate Bayesian Inference",
abstract = "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.",
author = "Giovanni Charles and Wolock, {Timothy M.} and Peter Winskill and Azra Ghani and Samir Bhatt and Seth Flaxman",
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 {\textcopyright} 2023, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.; 37th AAAI Conference on Artificial Intelligence, AAAI 2023 ; Conference date: 07-02-2023 Through 14-02-2023",
year = "2023",
language = "English",
series = "Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023",
pages = "14170--14177",
editor = "Brian Williams and Yiling Chen and Jennifer Neville",
booktitle = "AAAI-23 Special Tracks",
publisher = "AAAI Press",

}

RIS

TY - GEN

T1 - Seq2Seq Surrogates of Epidemic Models to Facilitate Bayesian Inference

AU - Charles, Giovanni

AU - Wolock, Timothy M.

AU - Winskill, Peter

AU - Ghani, Azra

AU - Bhatt, Samir

AU - Flaxman, Seth

N1 - 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.

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85167974762&partnerID=8YFLogxK

M3 - Article in proceedings

AN - SCOPUS:85167974762

T3 - Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023

SP - 14170

EP - 14177

BT - AAAI-23 Special Tracks

A2 - Williams, Brian

A2 - Chen, Yiling

A2 - Neville, Jennifer

PB - AAAI Press

T2 - 37th AAAI Conference on Artificial Intelligence, AAAI 2023

Y2 - 7 February 2023 through 14 February 2023

ER -

ID: 385515365