Activity-driven network modeling and control of the spread of two concurrent epidemic strains

The emergency generated by the current COVID-19 pandemic has claimed millions of lives worldwide. There have been multiple waves across the globe that emerged as a result of new variants, due to arising from unavoidable mutations. The existing network toolbox to study epidemic spreading cannot be re...

Full description

Saved in:
Bibliographic Details
Published in:Applied network science 2022-12, Vol.7 (1), p.66-27, Article 66
Main Authors: Burbano Lombana, Daniel Alberto, Zino, Lorenzo, Butail, Sachit, Caroppo, Emanuele, Jiang, Zhong-Ping, Rizzo, Alessandro, Porfiri, Maurizio
Format: Article
Language:English
Subjects:
Bi-virus
Complex networks
Complexity
Computer Appl. in Social and Behavioral Sciences
Computer Science
Control
Coronaviruses
COVID-19
Disease transmission
Epidemics
Mathematical models
Mutation
Simulation and Modeling
Temporal network
Viral diseases
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The emergency generated by the current COVID-19 pandemic has claimed millions of lives worldwide. There have been multiple waves across the globe that emerged as a result of new variants, due to arising from unavoidable mutations. The existing network toolbox to study epidemic spreading cannot be readily adapted to the study of multiple, coexisting strains. In this context, particularly lacking are models that could elucidate re-infection with the same strain or a different strain—phenomena that we are seeing experiencing more and more with COVID-19. Here, we establish a novel mathematical model to study the simultaneous spreading of two strains over a class of temporal networks. We build on the classical susceptible–exposed–infectious–removed model, by incorporating additional states that account for infections and re-infections with multiple strains. The temporal network is based on the activity-driven network paradigm, which has emerged as a model of choice to study dynamic processes that unfold at a time scale comparable to the network evolution. We draw analytical insight from the dynamics of the stochastic network systems through a mean-field approach, which allows for characterizing the onset of different behavioral phenotypes (non-epidemic, epidemic, and endemic). To demonstrate the practical use of the model, we examine an intermittent stay-at-home containment strategy, in which a fraction of the population is randomly required to isolate for a fixed period of time.
ISSN:2364-8228
2364-8228
DOI:10.1007/s41109-022-00507-6