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AI Techniques for Forecasting Epidemic Dynamics: Theory and Practice

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Artificial Intelligence in Covid-19

Abstract

Forecasting epidemic dynamics has been an active area of research for at least two decades. The importance of the topic is evident: policy makers, citizens, and scientists would all like to get accurate and timely forecasts. In contrast to physical systems, the co-evolution of epidemics, individual and collective behavior, viral dynamics, and public policies make epidemic forecasting a problematic task. The situation is even more challenging during a pandemic as has become amply clear during the ongoing COVID-19 pandemic. Researchers worldwide have put in extraordinary efforts to try to forecast the time-varying evolution of the pandemic; despite their best efforts, it is fair to say that the results have been mixed. Several teams have done well on average but failed to forecast upsurges in the cases.

In this chapter, we describe the state-of-the-art in epidemic forecasting, with a particular emphasis on forecasting during an ongoing pandemic. We describe a range of methods that have been developed and discuss the experience of our team in this context. We also summarize several challenges in producing accurate and timely forecasts.

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Notes

  1. 1.

    https://www.cdc.gov/flu/weekly/flusight/index.html.

  2. 2.

    The unit of time (which may be a day, a week, etc.) depends on the epidemic that is being modeled.

  3. 3.

    This is analogous to the transmission rate β introduced under the compartmental model above.

  4. 4.

    With minor modifications, our theoretical results discussed in section “Theoretical Foundations for Forecasting in Network Models” can be shown to hold even when the infectious period for each node is any constant number of time units.

  5. 5.

    A Boolean formula in conjunctive normal form [117] consists of clauses which are connected together by the AND operator. Each clause itself is the OR of negated or unnegated forms of a subset of variables.

  6. 6.

    A matching M of a bipartite graph H(V1, V2, E) is a subset of edges so that no two edges are incident on the same node. When ∣V1 ∣  =  ∣ V2 ∣  = n, a perfect matching of H is a matching of size n.

  7. 7.

    A Boolean formula in disjunctive normal form consists of product terms which are connected together by the OR operator. Each product term is the AND of a subset of negated and/or unnegated variables.

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Acknowledgments

We thank members of the Biocomplexity COVID-19 Response Team and the Network Systems Science and Advanced Computing (NSSAC) Division of the University of Virginia for their thoughtful comments and suggestions related to epidemic modeling and response support. This work has been partially supported by DTRA (Contract HDTRA1-19-D-0007), University of Virginia Strategic Investment Fund award number SIF160, VDH Grant PV-BII VDH COVID-19 Modeling Program VDH-21-501-0135, NSF XSEDE Grant TG-BIO210084, National Institutes of Health (NIH) Grants 1R01GM109718, 2R01GM109718, The James S. McDonnell Foundation twenty-first Century Science Initiative Collaborative Award in Understanding Dynamic and Multi-scale Systems, the C3.ai Digital Transformation Institute and Microsoft Corporation, Gift from Google, LLC, and the National Science Foundation (NSF) grants IIS-1633028 (BIG DATA), CMMI-1745207 (EAGER), OAC-1916805 (CINES), CCF-1918656 (Expeditions), CNS-2028004 (RAPID), OAC-2027541 (RAPID), CCF-2142997 (RAPID), CNS-2041952 (PREPARE), IIS-1908530, IIS-1955797, IIS-2027848, CNS-2027908 and CCF1917819. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon.

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Authors and Affiliations

  1. Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA

    Aniruddha Adiga, Bryan Lewis, Madhav V. Marathe, S. S. Ravi, Daniel J. Rosenkrantz, Richard E. Stearns, Srinivasan Venkatramanan & Anil Vullikanti

  2. Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA

    Simon Levin

  3. Department of Computer Science, University of Virginia, Charlottesville, VA, USA

    Madhav V. Marathe & Anil Vullikanti

  4. Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ, USA

    H. Vincent Poor

  5. Department of Computer Science, University at Albany—State University of New York, Albany, NY, USA

    S. S. Ravi, Daniel J. Rosenkrantz & Richard E. Stearns

  6. Boston Children’s Hospital and Harvard Medical School, Boston, MA, USA

    Lijing Wang

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  1. Aniruddha Adiga
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Correspondence to H. Vincent Poor .

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  1. Karolinska Institute, Stockholm, Sweden

    Niklas Lidströmer

  2. Weizmann Institute of Science, Rehovot, Israel

    Yonina C. Eldar

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Adiga, A. et al. (2022). AI Techniques for Forecasting Epidemic Dynamics: Theory and Practice. In: Lidströmer, N., Eldar, Y.C. (eds) Artificial Intelligence in Covid-19. Springer, Cham. https://doi.org/10.1007/978-3-031-08506-2_9

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