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COVID-19 vaccination strategies depend on the underlying network of social interactions.
Saunders, Helena A; Schwartz, Jean-Marc.
  • Saunders HA; Department of Analytical Chemistry, University of Vienna, Vienna, Austria.
  • Schwartz JM; School of Biological Sciences, University of Manchester, Manchester, UK. jean-marc.schwartz@manchester.ac.uk.
Sci Rep ; 11(1): 24051, 2021 12 15.
Article in English | MEDLINE | ID: covidwho-1585803
ABSTRACT
Since the onset of the coronavirus disease 2019 (COVID-19) pandemic, different mitigation and management strategies limiting economic and social activities have been implemented across many countries. Despite these strategies, the virus continues to spread and mutate. As a result, vaccinations are now administered to suppress the pandemic. Current COVID-19 epidemic models need to be expanded to account for the change in behaviour of new strains, such as an increased virulence and higher transmission rate. Furthermore, models need to account for an increasingly vaccinated population. We present a network model of COVID-19 transmission accounting for different immunity and vaccination scenarios. We conduct a parameter sensitivity analysis and find the average immunity length after an infection to be one of the most critical parameters that define the spread of the disease. Furthermore, we simulate different vaccination strategies and show that vaccinating highly connected individuals first is the quickest strategy for controlling the disease.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Mass Vaccination / COVID-19 Vaccines / COVID-19 Topics: Vaccines Limits: Humans Language: English Journal: Sci Rep Year: 2021 Document Type: Article Affiliation country: S41598-021-03167-1

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Mass Vaccination / COVID-19 Vaccines / COVID-19 Topics: Vaccines Limits: Humans Language: English Journal: Sci Rep Year: 2021 Document Type: Article Affiliation country: S41598-021-03167-1