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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\u2014phenomena 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\u2013exposed\u2013infectious\u2013removed model, by incorporating additional states that account for infections and\u00a0re-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\u00a0fixed period of time.<\/jats:p>","DOI":"10.1007\/s41109-022-00507-6","type":"journal-article","created":{"date-parts":[[2022,9,27]],"date-time":"2022-09-27T15:02:44Z","timestamp":1664290964000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Activity-driven network modeling and control of the spread of two concurrent epidemic strains"],"prefix":"10.1007","volume":"7","author":[{"given":"Daniel Alberto","family":"Burbano Lombana","sequence":"first","affiliation":[]},{"given":"Lorenzo","family":"Zino","sequence":"additional","affiliation":[]},{"given":"Sachit","family":"Butail","sequence":"additional","affiliation":[]},{"given":"Emanuele","family":"Caroppo","sequence":"additional","affiliation":[]},{"given":"Zhong-Ping","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Alessandro","family":"Rizzo","sequence":"additional","affiliation":[]},{"given":"Maurizio","family":"Porfiri","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,27]]},"reference":[{"issue":"9","key":"507_CR1","doi-asserted-by":"publisher","first-page":"964","DOI":"10.1038\/s41562-020-0931-9","volume":"4","author":"A Aleta","year":"2020","unstructured":"Aleta A, Martin-Corral D, Pastorey Piontti A, Ajelli M, Litvinova M, Chinazzi M, Dean NE, Halloran ME, Longini IM Jr, Merler S et al (2020) Modelling the impact of testing, contact tracing and household quarantine on second waves of COVID-19. 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