Modelling wellbeing during public health interventions for respiratory epidemics
DOI:
https://doi.org/10.31273/reinvention.v17i2.1488Keywords:
Mathematical epidemiology, infectious diseases, COVID-19, wellbeing modelling, mixing patterns, time-use surveysAbstract
Throughout the COVID-19 pandemic, the use of non-pharmaceutical interventions (NPIs), including lockdown measures, by governments around the world has been informed by mathematical modelling. Broadly, these models look to gauge how well NPIs control disease transmission. Here we present a model that not only forecasts the effectiveness of NPIs in restricting contacts but also assesses their influence on the mental wellbeing of affected populations. Our model is informed by data from the United Kingdom Time Use Survey, 2014-2015. This survey recorded the time participants spent in different social settings as well as self-reported enjoyment in these settings, allowing us to augment a quantitative model of social contact behaviour with associated wellbeing estimates. We use this model to assess the effectiveness of NPIs aimed at reducing social contacts in different settings and estimate their impact on population-level wellbeing. Our findings indicate that workplace closures represent the most effective intervention for slowing disease spread, while NPIs targeting other contact locations have comparatively limited impacts on transmission. Our model suggests that workplace closures not only effectively reduce infections but also have a relatively modest effect on population wellbeing levels.
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Copyright (c) 2024 Joe Brooks
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