{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T12:39:06Z","timestamp":1726231146360},"reference-count":61,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2020,12,1]],"date-time":"2020-12-01T00:00:00Z","timestamp":1606780800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Executive Agency for Higher Education, Research, Development and Innovation Funding","award":["PN-III-P1-1.1-TE-2016-0362"]},{"name":"The Slovenian Research Agency","award":["J1-2457","J1-9112","P1-0403"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,3,7]]},"abstract":"Abstract<\/jats:title>\n Human mobility networks are crucial for a better understanding and controlling the spread of epidemics. Here, we study the impact of human mobility networks on the COVID-19 onset in 203 different countries. We use exponential random graph models to perform an analysis of the country-to-country global spread of COVID-19. We find that most countries had similar levels of virus spreading, with only a few acting as the main global transmitters. Our evidence suggests that migration and tourism inflows increase the probability of COVID-19 case importations while controlling for contiguity, continent co-location and sharing a language. Moreover, we find that air flights were the dominant mode of transportation while male and returning travellers were the main carriers. In conclusion, a mix of mobility and geography factors predicts the COVID-19 global transmission from one country to another. These findings have implications for non-pharmaceutical public health interventions and the management of transborder human circulation.<\/jats:p>","DOI":"10.1093\/comnet\/cnaa041","type":"journal-article","created":{"date-parts":[[2020,10,15]],"date-time":"2020-10-15T03:17:19Z","timestamp":1602731839000},"source":"Crossref","is-referenced-by-count":37,"title":["The impact of human mobility networks on the global spread of COVID-19"],"prefix":"10.1093","volume":"8","author":[{"given":"Marian-Gabriel","family":"H\u00e2ncean","sequence":"first","affiliation":[{"name":"Department of Sociology, University of Bucharest, Bucharest, Romania"}]},{"given":"Mitja","family":"Slavinec","sequence":"additional","affiliation":[{"name":"Faculty of Natural Sciences and Mathematics, University of Maribor, Maribor, Slovenia"}]},{"given":"Matja\u017e","family":"Perc","sequence":"additional","affiliation":[{"name":"Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan & Complexity Science Hub Vienna, Vienna, Austria"}]}],"member":"286","published-online":{"date-parts":[[2021,3,7]]},"reference":[{"key":"2021031105082928600_B1","article-title":"Coronavirus disease (COVID-19)","year":"2020","journal-title":"Weekly Epidemiological Update"},{"key":"2021031105082928600_B2","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1126\/science.aba9757","article-title":"The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak","volume":"368","author":"Chinazzi,","year":"2020","journal-title":"Science"},{"key":"2021031105082928600_B3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1093\/jtm\/taaa022","article-title":"Quantifying the association between domestic travel and the exportation of novel coronavirus (2019-nCoV) cases from Wuhan, China in 2020: a correlational analysis","volume":"27","author":"Zhao,","year":"2020","journal-title":"J. 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