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A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
Published on: December 9, 2015
Effects of individual variation and seasonal vaccination on disease risks
William S Hart1, Jina Amin2, Hyeongki Park3,4
1Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, UK. william.hart@maths.ox.ac.uk.
Abstract:
Estimates of the risk of a large outbreak resulting from pathogen introduction into a population are valuable for planning interventions. Two key factors affecting outbreak risks are variation in transmission between individuals (e.g., superspreading individuals) and change over time (e.g., through seasonality or changing population immunity due to vaccination). Here, we develop an outbreak risk estimation framework that accounts for both features simultaneously. To demonstrate the real-world application of our framework, we consider the design of annual COVID-19 booster vaccination campaigns, using a multi-scale approach incorporating an individual-level model of vaccine-induced antibody dynamics. Near the start of annual vaccine distribution, when population immunity is low, a high outbreak risk is possible; this can be mitigated by distributing vaccines over a longer period. We show that longer distribution periods are particularly beneficial if vaccine coverage and/or effectiveness is high, and if seasonality in transmission is limited.
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