AI Techniques for Forecasting Epidemic Dynamics: Theory and Practice

Aniruddha Adiga, Bryan Lewis, Simon Levin, Madhav V. Marathe, H. Vincent Poor, S. S. Ravi, Daniel J. Rosenkrantz, Richard E. Stearns, Srinivasan Venkatramanan, Anil Vullikanti, Lijing Wang

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationArtificial Intelligence in Covid-19
PublisherSpringer International Publishing
Number of pages36
ISBN (Electronic)9783031085062
ISBN (Print)9783031085055
StatePublished - Jan 1 2022

All Science Journal Classification (ASJC) codes

  • General Medicine
  • General Computer Science


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