Accurate Calibration of Agent-based Epidemiological Models with Neural Network Surrogates

Rushil Anirudh, Jayaraman J. Thiagarajan, Peer-Timo Bremer, Timothy Germann, Sara Del Valle, Frederick Streitz
Proceedings of the 1st Workshop on Healthcare AI and COVID-19, ICML 2022, PMLR 184:54-62, 2022.

Abstract

Calibrating complex epidemiological models to observed data is a crucial step to provide both insights into the current disease dynamics, i.e. by estimating a reproductive number, as well as to provide reliable forecasts and scenario explorations. Here we present a new approach to calibrate an agent-based model – Epicast – using a large set of simulation ensembles for different major metropolitan areas of the United States. In particular, we propose: a new neural network based surrogate model able to simultaneously emulate all different locations; and a novel posterior estimation that provides not only more accurate posterior estimates of all parameters but enables the joint fitting of global parameters across regions.

Cite this Paper

BibTeX
@InProceedings{pmlr-v184-anirudh22a, title = {Accurate Calibration of Agent-based Epidemiological Models with Neural Network Surrogates}, author = {Anirudh, Rushil and Thiagarajan, Jayaraman J. and Bremer, Peer-Timo and Germann, Timothy and Del Valle, Sara and Streitz, Frederick}, booktitle = {Proceedings of the 1st Workshop on Healthcare AI and COVID-19, ICML 2022}, pages = {54--62}, year = {2022}, editor = {Xu, Peng and Zhu, Tingting and Zhu, Pengkai and Clifton, David A. and Belgrave, Danielle and Zhang, Yuanting}, volume = {184}, series = {Proceedings of Machine Learning Research}, month = {22 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v184/anirudh22a/anirudh22a.pdf}, url = {https://proceedings.mlr.press/v184/anirudh22a.html}, abstract = {Calibrating complex epidemiological models to observed data is a crucial step to provide both insights into the current disease dynamics, i.e. by estimating a reproductive number, as well as to provide reliable forecasts and scenario explorations. Here we present a new approach to calibrate an agent-based model – Epicast – using a large set of simulation ensembles for different major metropolitan areas of the United States. In particular, we propose: a new neural network based surrogate model able to simultaneously emulate all different locations; and a novel posterior estimation that provides not only more accurate posterior estimates of all parameters but enables the joint fitting of global parameters across regions.} }
Endnote
%0 Conference Paper %T Accurate Calibration of Agent-based Epidemiological Models with Neural Network Surrogates %A Rushil Anirudh %A Jayaraman J. Thiagarajan %A Peer-Timo Bremer %A Timothy Germann %A Sara Del Valle %A Frederick Streitz %B Proceedings of the 1st Workshop on Healthcare AI and COVID-19, ICML 2022 %C Proceedings of Machine Learning Research %D 2022 %E Peng Xu %E Tingting Zhu %E Pengkai Zhu %E David A. Clifton %E Danielle Belgrave %E Yuanting Zhang %F pmlr-v184-anirudh22a %I PMLR %P 54--62 %U https://proceedings.mlr.press/v184/anirudh22a.html %V 184 %X Calibrating complex epidemiological models to observed data is a crucial step to provide both insights into the current disease dynamics, i.e. by estimating a reproductive number, as well as to provide reliable forecasts and scenario explorations. Here we present a new approach to calibrate an agent-based model – Epicast – using a large set of simulation ensembles for different major metropolitan areas of the United States. In particular, we propose: a new neural network based surrogate model able to simultaneously emulate all different locations; and a novel posterior estimation that provides not only more accurate posterior estimates of all parameters but enables the joint fitting of global parameters across regions.
APA
Anirudh, R., Thiagarajan, J.J., Bremer, P., Germann, T., Del Valle, S. & Streitz, F.. (2022). Accurate Calibration of Agent-based Epidemiological Models with Neural Network Surrogates. Proceedings of the 1st Workshop on Healthcare AI and COVID-19, ICML 2022, in Proceedings of Machine Learning Research 184:54-62 Available from https://proceedings.mlr.press/v184/anirudh22a.html.

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