It is well recognized that population heterogeneity plays an important role in the spread of epidemics. While individual variations in social activity are often assumed to be persistent, i.e. constant in time, here we discuss the consequences of dynamic heterogeneity. By integrating the stochastic dynamics of social activity into traditional epidemiological models we demonstrate the emergence of a new long timescale governing the epidemic, in broad agreement with empirical data. Our Stochastic Social Activity model captures multiple features of real-life epidemics such as COVID-19, including prolonged plateaus and multiple waves, which are transiently suppressed due to the dynamic nature of social activity. The existence of a long timescale due to the interplay between epidemic and social dynamics provides a unifying picture of how a fast-paced epidemic typically will transition to an endemic state.
All code needed to reproduce results of our Agent Based Model and fits of the epidemic dynamics in US regions is available on Github https://github.com/maslov-group/COVID-19-waves-and-plateaus
- Alexei V Tkachenko
- Sergei Maslov
- Tong Wang
- Ahmed Elbana
- George N Wong
- Nigel Goldenfeld
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
- Marc Lipsitch, Harvard TH Chan School of Public Health, United States
- Received: March 12, 2021
- Accepted: November 4, 2021
- Accepted Manuscript published: November 8, 2021 (version 1)
This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.