Please use this identifier to cite or link to this item: https://biore.bio.bg.ac.rs/handle/123456789/6755
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dc.contributor.authorĐorđević, Markoen_US
dc.contributor.authorMarković, Sofijaen_US
dc.contributor.authorSalom, Igoren_US
dc.contributor.authorDjordjevic, Magdalenaen_US
dc.date.accessioned2023-11-28T08:48:22Z-
dc.date.available2023-11-28T08:48:22Z-
dc.date.issued2023-01-01-
dc.identifier.issn00139351-
dc.identifier.urihttps://biore.bio.bg.ac.rs/handle/123456789/6755-
dc.description.abstractThe emergence of a new virus variant is generally recognized by its usually sudden and rapid spread (outburst) in a certain world region. Due to the near-exponential rate of initial expansion, the new strain may not be detected at its true geographical origin but in the area with the most favorable conditions leading to the fastest exponential growth. Therefore, it is crucial to understand better the factors that promote such outbursts, which we address in the example of analyzing global Omicron transmissibility during its global emergence/outburst in November 2021-February 2022. As predictors, we assemble a number of potentially relevant factors: vaccinations (both full and boosters), different measures of population mobility (provided by Google), estimated stringency of measures, the prevalence of chronic diseases, population age, the timing of the outburst, and several other socio-demographic variables. As a proxy for natural immunity (prevalence of prior infections in population), we use cumulative numbers of COVID-19 deaths. As a response variable (transmissibility measure), we use the estimated effective reproduction number (Re) averaged in the vicinity of the outburst maxima. To select significant predictors of Re, we use machine learning regressions that employ feature selection, including methods based on ensembles of decision trees (Random Forest and Gradient Boosting). We identify the young population, earlier infection onset, higher mobility, low natural immunity, and low booster prevalence as likely direct risk factors. Interestingly, we find that all these risk factors were significantly higher for Africa, though curiously somewhat lower in Southern African countries (where the outburst emerged) compared to other African countries. Therefore, while the risk factors related to the virus transmissibility clearly promote the outburst of a new virus variant, specific regions/countries where the outburst actually happens may be related to less evident factors, possibly random in nature.en_US
dc.language.isoenen_US
dc.publisherAcademic Pressen_US
dc.relation.ispartofEnvironmental researchen_US
dc.subjectEcological regressionsen_US
dc.subjectEffective reproduction numberen_US
dc.subjectMachine learningen_US
dc.subjectOmicron transmissibilityen_US
dc.subjectOutburst risk factorsen_US
dc.titleUnderstanding risk factors of a new variant outburst through global analysis of Omicron transmissibilityen_US
dc.typeJournal Articleen_US
dc.identifier.doi10.1016/j.envres.2022.114446-
dc.identifier.pmid36208783-
dc.identifier.scopus2-s2.0-85139727115-
dc.identifier.urlhttps://api.elsevier.com/content/abstract/scopus_id/85139727115-
dc.description.rankM21aen_US
dc.description.impact8.431en_US
dc.description.startpage114446en_US
dc.relation.issn0013-9351en_US
dc.description.volume216en_US
dc.description.issuePt 1en_US
item.languageiso639-1en-
item.cerifentitytypePublications-
item.openairetypeJournal Article-
item.fulltextNo Fulltext-
item.grantfulltextnone-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
crisitem.author.deptChair of General Physiology and Biophysics-
crisitem.author.deptChair of General Physiology and Biophysics-
crisitem.author.orcid0000-0002-2903-3119-
crisitem.author.orcid0000-0001-7506-500X-
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