Multi-scale modelling of viral outbreak risks

What is it about?

A key question for public health policy makers is whether the introduction of a virus into a population is likely to lead to sustained transmission there. This is critical for understanding the epidemic and/or pandemic potential of a novel virus – notably, for example, following the first detected COVID-19 cases in Wuhan, China. Similarly, once a virus has become established within a country, understanding the risk of localised outbreaks occurring within sub-populations (e.g. in schools, workplaces and care homes) remains important for optimising ongoing public health measures. We developed a multi-scale mathematical modelling framework for estimating the outbreak risk (the probability that an introduced case leads to sustained transmission) for a viral pathogen. Specifically, we derived the outbreak risk analytically under a branching process model of transmission (the population-scale model), accounting for changes in viral shedding during each infection (characterised by an individual-scale model). To demonstrate our approach, we considered the risk of localised COVID-19 outbreaks, and explored the effectiveness for mitigating this risk of regular antigen testing of the entire local population (i.e. routine lateral flow testing, as was implemented in the UK in the acute phase of the COVID-19 pandemic). In our baseline analysis, we found that antigen testing can reduce the outbreak risk, but not prevent outbreaks entirely. However, the effectiveness of antigen testing depends on a range of factors, such as the extent of asymptomatic transmission, that are likely to differ between populations, time periods and SARS-CoV-2 variants (and different viruses more generally).

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Photo by Brett Jordan on Unsplash

Photo by Brett Jordan on Unsplash

Why is it important?

Individual-scale viral dynamics are crucial in determining the impact of numerous control interventions, for example mass antigen testing. A multi-scale modelling approach is therefore particularly useful for planning such interventions. Here, we found that regular antigen testing may reduce, but not entirely eliminate, the risk of localised COVID-19 outbreaks. While the main case study explored in this research was COVID-19, our multi-scale modelling framework can be used for guiding pre-emptive control interventions targeting a range of viruses going forwards.