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Understanding risk factors of a new variant outburst through global analysis of Omicron transmissibility.
Djordjevic, Marko; Markovic, Sofija; Salom, Igor; Djordjevic, Magdalena.
  • Djordjevic M; Quantitative Biology Group, Institute of Physiology and Biochemistry, Faculty of Biology, University of Belgrade, Serbia. Electronic address: dmarko@bio.bg.ac.rs.
  • Markovic S; Quantitative Biology Group, Institute of Physiology and Biochemistry, Faculty of Biology, University of Belgrade, Serbia.
  • Salom I; Institute of Physics Belgrade, National Institute of the Republic of Serbia, University of Belgrade, Serbia.
  • Djordjevic M; Institute of Physics Belgrade, National Institute of the Republic of Serbia, University of Belgrade, Serbia.
Environ Res ; 216(Pt 1): 114446, 2023 01 01.
Article in English | MEDLINE | ID: covidwho-2061125
ABSTRACT
The 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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Observational study / Prognostic study / Randomized controlled trials Topics: Vaccines / Variants Limits: Humans Language: English Journal: Environ Res Year: 2023 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Observational study / Prognostic study / Randomized controlled trials Topics: Vaccines / Variants Limits: Humans Language: English Journal: Environ Res Year: 2023 Document Type: Article