Jessica T. Davis, Matteo Chinazzi, Nicola Perra, Kunpeng Mu, Ana Pastore y Piontti, Marco Ajelli, Natalie E. Dean, Corrado Gioannini, Maria Litvinova, Stefano Merler, Luca Rossi, Kaiyuan Sun, Xinyue Xiong, Ira M. Longini Jr, M. Elizabeth Halloran, Cécile Viboud & Alessandro Vespignani
Nature (2021)

Considerable uncertainty surrounds the timeline of introductions and onsets of local transmission of SARS-CoV-2 globally1–7. Although a limited number of SARS-CoV-2 introductions were reported in January and February 20208,9, the narrowness of the initial testing criteria, combined with a slow growth in testing capacity and porous travel screening10, left many countries vulnerable to unmitigated, cryptic transmission. Here we use a global metapopulation epidemic model to provide a mechanistic understanding of the early dispersal of infections, and the temporal windows of the introduction and onset of SARS-CoV-2 local transmission in Europe and the United States. We find that community transmission of SARS-CoV-2 was likely in several areas of Europe and the United States by January 2020, and estimate that by early March, only 1 to 3 in 100 SARS-CoV-2 infections were detected by surveillance systems. The modelling results highlight international travel as the key driver of the introduction of SARS-CoV-2 with possible introductions and transmission events as early as December 2019–January 2020. We find a heterogeneous, geographic distribution of cumulative infection attack rates by 4 July 2020, ranging from 0.78%–15.2% across US states and 0.19%–13.2% in European countries. Our approach complements phylogenetic analyses and other surveillance approaches and provides insights that can be used to design innovative, model-driven surveillance systems that guide enhanced testing and response strategies.

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Alexander T. J. Barron, Johan Bollen

Mass communication over social media can drive rapid changes in our sense of collective identity. Hashtags in particular have acted as powerful social coordinators[1], playing a key role in organizing social movements like the Gezi park protests, Occupy Wall Street, #metoo, and #blacklivesmatter[2, 3, 4]. Here we quantify collective identity from the use of hashtags as self-labels in over 85,000 actively-maintained Twitter user profiles spanning 2017-2019. Collective identities emerge from a graph model of individuals’ overlapping self-labels, producing a hierarchy of graph clusters. Each cluster is bound together and characterized semantically by specific hashtags key to its formation. We define and apply two information-theoretic measures to quantify the strength of identities in the hierarchy. First we measure collective identity coherence to determine how integrated any identity is from local to global scales. Second, we consider the conspicuousness of any identity given its vocabulary versus the global identity map. Our work reveals a rich landscape of online identity emerging from the hierarchical alignment of uncoordinated self-labeling actions.

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