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A household-structured approach to modelling non-pharmaceutical interventions

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If you have a question about this talk, please contact Dr Ciara Dangerfield.

The response to the COVID 19 pandemic has been characterised by the use of non-pharmaceutical interventions such as stay-at-home orders and physical distancing restrictions, often on a dramatic scale and in jurisdictions where these interventions have never before been implemented. These control measures carry the potential for highly disruptive social and economic side-effects, and projections of their impact provide a key piece of evidence in the case for justifying these policies. Here we present a mathematical model of COVID 19 dynamics which includes both age/risk structure and household structure, specifically designed to simulate a range of non-pharmaceutical interventions. The presence of age structure in our model grants it the potential to capture both age-structured contact behaviours and age-stratified physiological variations which affect susceptibility to infection, morbidity, and mortality. By structuring our model population on household lines we are also able to capture the impact of family structure on infection, and to create detailed models of interventions which modify the behaviour of individuals on the between- and within-household level. Our model uses a Markovian “self-consistent equations” structure which offers an advantage in terms of mathematical tractability over network or agent-based approaches, and which can be implemented computationally through high performance and parallel computing. To demonstrate the capabilities of our model we present the results of analyses covering several different aspects of the UK’s COVID 19 response, including “support bubble” exemptions to lockdown orders, out-of-household isolation of detected cases, and within-household controls on transmission through ventilation and hygiene.

This talk is part of the Worms and Bugs series.

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