How modelling Covid has changed the way we think about epidemics | Coronavirus

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Think back on some of the things you learned about Covid-19 in 2020: information such as “fatality risk” and “incubation period”; the potential for “super-spreading events” , and the fact that transmission can happen before symptoms appear. There were the suggestions in mid-January that the Covid-19 outbreak in Wuhan was much larger than initial reports suggested, and we learned how Wuhan’s subsequent lockdown led to a reduction in transmission. What links these early insights? All of them involved epidemic modelling, which would become a prominent part of the Covid-19 response.

In essence, a model is a structured way of thinking about the dynamics of an epidemic. It allows us to take the knowledge we have, make some plausible assumptions based on that knowledge, then look at the logical implications of those assumptions. We can then compare our results with available datasets, to understand what might be driving the patterns we see. Models can help us make sense of patchy early data and explore possible outcomes – such as future epidemic waves – that haven’t happened yet.

During prior disease epidemics, such as swine flu in 2009 and Ebola in 2014–15, the public rarely got to see modelling insights until they were later published in scientific papers. In contrast, Covid-19 researchers have routinely built online dashboards so people can track transmission levels and compare possible scenarios, while also making pre-print reports rapidly available. In their efforts to understand the new coronavirus variants detected in the UK and South Africa, researchers have shared real-time modelling analysis of genetic data and case trends, with platforms such as Nextstrain making it possible to see how these variants are spreading globally.

Despite these developments, the pandemic has shown there is still more to do. Outbreak research should ideally be fast, reliable and publicly available. But the pressures of real-time Covid-19 analysis – which many academics have done in their spare time without dedicated funding – can force difficult choices. Should researchers prioritise updating scenarios for governments and health agencies, writing detailed papers describing their methods, or helping others adapt the models to answer different questions? These are not new problems, but the pandemic gave them new urgency. In the US, for example, the most comprehensive Covid-19 databases have been run by volunteers. The pandemic has flagged inefficient and unsustainable features of modelling and outbreak analysis, and illustrated that there is a clear need for change.

Alongside coverage of specific modelling studies, mathematical concepts have also become part of everyday discussions. Whether talking about reproduction numbers, lags in data, or how vaccines might protect the non-vaccinated through “herd immunity”, journalists have started to think more deeply about epidemic dynamics. Prior to the outbreak, I never thought I’d end up…

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