{"author":[{"last_name":"Gómez","first_name":"Arturo","full_name":"Gómez, Arturo"},{"last_name":"Oliveira","first_name":"Goncalo","id":"58abbde8-f455-11eb-a497-98c8fd71b905","full_name":"Oliveira, Goncalo"}],"has_accepted_license":"1","publication":"Scientific Reports","doi":"10.1038/s41598-022-19827-9","external_id":{"isi":["001003345000051"]},"publication_identifier":{"eissn":["2045-2322"]},"citation":{"mla":"Gómez, Arturo, and Goncalo Oliveira. “New Approaches to Epidemic Modeling on Networks.” <i>Scientific Reports</i>, vol. 13, 468, Springer Nature, 2023, doi:<a href=\"https://doi.org/10.1038/s41598-022-19827-9\">10.1038/s41598-022-19827-9</a>.","ieee":"A. Gómez and G. Oliveira, “New approaches to epidemic modeling on networks,” <i>Scientific Reports</i>, vol. 13. Springer Nature, 2023.","ista":"Gómez A, Oliveira G. 2023. New approaches to epidemic modeling on networks. Scientific Reports. 13, 468.","chicago":"Gómez, Arturo, and Goncalo Oliveira. “New Approaches to Epidemic Modeling on Networks.” <i>Scientific Reports</i>. Springer Nature, 2023. <a href=\"https://doi.org/10.1038/s41598-022-19827-9\">https://doi.org/10.1038/s41598-022-19827-9</a>.","apa":"Gómez, A., &#38; Oliveira, G. (2023). New approaches to epidemic modeling on networks. <i>Scientific Reports</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s41598-022-19827-9\">https://doi.org/10.1038/s41598-022-19827-9</a>","ama":"Gómez A, Oliveira G. New approaches to epidemic modeling on networks. <i>Scientific Reports</i>. 2023;13. doi:<a href=\"https://doi.org/10.1038/s41598-022-19827-9\">10.1038/s41598-022-19827-9</a>","short":"A. Gómez, G. Oliveira, Scientific Reports 13 (2023)."},"article_type":"original","article_number":"468","_id":"12329","language":[{"iso":"eng"}],"intvolume":"        13","volume":13,"year":"2023","file_date_updated":"2023-01-23T07:53:23Z","isi":1,"acknowledgement":"Gonçalo Oliveira is supported by the NOMIS Foundation, Fundação Serrapilheira 1812-27395, by CNPq grants 428959/2018-0 and 307475/2018-2, and by FAPERJ through the grant Jovem Cientista do Nosso Estado E-26/202.793/2019.","ddc":["510"],"type":"journal_article","file":[{"date_updated":"2023-01-23T07:53:23Z","success":1,"file_id":"12336","date_created":"2023-01-23T07:53:23Z","file_size":2167792,"checksum":"a8b83739f4a951e83e0b2a778f03b327","relation":"main_file","content_type":"application/pdf","access_level":"open_access","creator":"dernst","file_name":"2023_ScientificReports_Gomez.pdf"}],"day":"10","month":"01","article_processing_charge":"No","date_updated":"2023-08-01T12:31:40Z","tmp":{"image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"date_published":"2023-01-10T00:00:00Z","publication_status":"published","department":[{"_id":"TaHa"}],"abstract":[{"lang":"eng","text":"In this article, we develop two independent and new approaches to model epidemic spread in a network. Contrary to the most studied models, those developed here allow for contacts with different probabilities of transmitting the disease (transmissibilities). We then examine each of these models using some mean field type approximations. The first model looks at the late-stage effects of an epidemic outbreak and allows for the computation of the probability that a given vertex was infected. This computation is based on a mean field approximation and only depends on the number of contacts and their transmissibilities. This approach shares many similarities with percolation models in networks. The second model we develop is a dynamic model which we analyze using a mean field approximation which highly reduces the dimensionality of the system. In particular, the original system which individually analyses each vertex of the network is reduced to one with as many equations as different transmissibilities. Perhaps the greatest contribution of this article is the observation that, in both these models, the existence and size of an epidemic outbreak are linked to the properties of a matrix which we call the R-matrix. This is a generalization of the basic reproduction number which more precisely characterizes the main routes of infection."}],"oa_version":"Published Version","oa":1,"date_created":"2023-01-22T23:00:55Z","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","publisher":"Springer Nature","scopus_import":"1","title":"New approaches to epidemic modeling on networks","quality_controlled":"1","status":"public"}