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Neural parameter calibration and uncertainty quantification for epidemic forecasting

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  • Thomas Gaskin
  • Tim Conrad
  • Grigorios A Pavliotis
  • Christof Schütte

Abstract

The recent COVID-19 pandemic has thrown the importance of accurately forecasting contagion dynamics and learning infection parameters into sharp focus. At the same time, effective policy-making requires knowledge of the uncertainty on such predictions, in order, for instance, to be able to ready hospitals and intensive care units for a worst-case scenario without needlessly wasting resources. In this work, we apply a novel and powerful computational method to the problem of learning probability densities on contagion parameters and providing uncertainty quantification for pandemic projections. Using a neural network, we calibrate an ODE model to data of the spread of COVID-19 in Berlin in 2020, achieving both a significantly more accurate calibration and prediction than Markov-Chain Monte Carlo (MCMC)-based sampling schemes. The uncertainties on our predictions provide meaningful confidence intervals e.g. on infection figures and hospitalisation rates, while training and running the neural scheme takes minutes where MCMC takes hours. We show convergence of our method to the true posterior on a simplified SIR model of epidemics, and also demonstrate our method’s learning capabilities on a reduced dataset, where a complex model is learned from a small number of compartments for which data is available.

Suggested Citation

  • Thomas Gaskin & Tim Conrad & Grigorios A Pavliotis & Christof Schütte, 2024. "Neural parameter calibration and uncertainty quantification for epidemic forecasting," PLOS ONE, Public Library of Science, vol. 19(10), pages 1-16, October.
  • Handle: RePEc:plo:pone00:0306704
    DOI: 10.1371/journal.pone.0306704
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    References listed on IDEAS

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    1. Sebastian A Müller & Michael Balmer & William Charlton & Ricardo Ewert & Andreas Neumann & Christian Rakow & Tilmann Schlenther & Kai Nagel, 2021. "Predicting the effects of COVID-19 related interventions in urban settings by combining activity-based modelling, agent-based simulation, and mobile phone data," PLOS ONE, Public Library of Science, vol. 16(10), pages 1-32, October.
    2. Tornatore, Elisabetta & Maria Buccellato, Stefania & Vetro, Pasquale, 2005. "Stability of a stochastic SIR system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 354(C), pages 111-126.
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