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Routes of importation and spatial dynamics of SARS-CoV-2 variants during localized interventions in Chile

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dc.contributor.authorGutierrez, Bernardo
dc.contributor.authorL -H Tsui, Joseph
dc.contributor.authorPullano, Giulia
dc.contributor.authorMazzoli, Mattia
dc.contributor.authorInward, Rhys P D
dc.contributor.authorBajaj, Sumali
dc.contributor.authorEvans Pena, Rosario
dc.contributor.authorBusch-Moreno, Simon
dc.contributor.authorSuchard, Marc A.
dc.contributor.authorPybus, Oliver G.
dc.contributor.authorDunner, Alejandra
dc.contributor.authorPuentes, Rodrigo
dc.contributor.authorFernandez, Jorge
dc.contributor.authorAraos Bralic, Rafael Ignacio
dc.contributor.authorFerres, Leo
dc.contributor.authorColizza, Vittoria
dc.contributor.authorKraemer, Moritz U.G.
dc.date.accessioned2024-12-16T16:25:48Z
dc.date.available2024-12-16T16:25:48Z
dc.date.issued2024
dc.description.abstractHuman mobility is strongly associated with the spread of SARS-CoV-2 via air travel on an international scale and with population mixing and the number of people moving between locations on a local scale. However, these conclusions are drawn mostly from observations in the context of the global north where international and domestic connectivity is heavily influenced by the air travel network; scenarios where land-based mobility can also dominate viral spread remain understudied. Furthermore, research on the effects of nonpharmaceutical interventions (NPIs) has mostly focused on national- or regional-scale implementations, leaving gaps in our understanding of the potential benefits of implementing NPIs at higher granularity. Here, we use Chile as a model to explore the role of human mobility on disease spread within the global south; the country implemented a systematic genomic surveillance program and NPIs at a very high spatial granularity. We combine viral genomic data, anonymized human mobility data from mobile phones and official records of international travelers entering the country to characterize the routes of importation of different variants, the relative contributions of airport and land border importations, and the real-time impact of the country’s mobility network on the diffusion of SARS-CoV-2. The introduction of variants which are dominant in neighboring countries (and not detected through airport genomic surveillance) is predicted by land border crossings and not by air travelers, and the strength of connectivity between comunas (Chile’s lowest administrative divisions) predicts the time of arrival of imported lineages to new locations. A higher stringency of local NPIs was also associated with fewer domestic viral importations. Our analysis sheds light on the drivers of emerging respiratory infectious disease spread outside of air travel and on the consequences of disrupting regular movement patterns at lower spatial scales.
dc.description.versionVersión publicada
dc.format.extent16 p.
dc.identifier.citationBernardo Gutierrez, Joseph L -H Tsui, Giulia Pullano, Mattia Mazzoli, Karthik Gangavarapu, Rhys P D Inward, Sumali Bajaj, Rosario Evans Pena, Simon Busch-Moreno, Marc A Suchard, Oliver G Pybus, Alejandra Dunner, Rodrigo Puentes, Salvador Ayala, Jorge Fernandez, Rafael Araos, Leo Ferres, Vittoria Colizza, Moritz U G Kraemer, Routes of importation and spatial dynamics of SARS-CoV-2 variants during localized interventions in Chile, PNAS Nexus, Volume 3, Issue 11, November 2024, pgae483, https://doi.org/10.1093/pnasnexus/pgae483
dc.identifier.doihttps://doi.org/10.1093/pnasnexus/pgae483
dc.identifier.urihttps://hdl.handle.net/11447/9480
dc.language.isoen
dc.subjectGenomic epidemiology
dc.subjectViral importations
dc.subjectSpatial invasion
dc.titleRoutes of importation and spatial dynamics of SARS-CoV-2 variants during localized interventions in Chile
dc.typeArticle
dcterms.accessRightsAcceso abierto
dcterms.sourcePNAS Nexus
dspace.entity.typePublication
relation.isAuthorOfPublication69e40056-7a55-41b3-8a65-05aace0e07e3
relation.isAuthorOfPublicationefeee506-1489-4021-96bc-85968879a607
relation.isAuthorOfPublication.latestForDiscovery69e40056-7a55-41b3-8a65-05aace0e07e3

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