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• W4220698328 abstract "With vaccination against COVID-19 stalled in some countries, increasing vaccine accessibility and distribution could help keep transmission under control. Here, we study the impact of reactive vaccination targeting schools and workplaces where cases are detected, with an agent-based model accounting for COVID-19 natural history, vaccine characteristics, demographics, behavioural changes and social distancing. In most scenarios, reactive vaccination leads to a higher reduction in cases compared with non-reactive strategies using the same number of doses. The reactive strategy could however be less effective than a moderate/high pace mass vaccination program if initial vaccination coverage is high or disease incidence is low, because few people would be vaccinated around each case. In case of flare-ups, reactive vaccination could better mitigate spread if it is implemented quickly, is supported by enhanced test-trace-isolate and triggers an increased vaccine uptake. These results provide key information to plan an adaptive vaccination rollout." @default.
• W4220698328 created "2022-04-03" @default.
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• W4220698328 date "2022-03-17" @default.
• W4220698328 modified "2023-10-17" @default.
• W4220698328 title "Agent-based modelling of reactive vaccination of workplaces and schools against COVID-19" @default.
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• W4220698328 doi "https://doi.org/10.1038/s41467-022-29015-y" @default.
• W4220698328 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35301289" @default.
• W4220698328 hasPublicationYear "2022" @default.
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