science

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


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.

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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 fielding media requests to discuss a statistical parameter such as “K”, which quantifies the potential for super-spreading.

Unfortunately, there have been challenges with coverage too. Some modelling results – particularly in the early stages of the pandemic – were widely misinterpreted, like the headlines in March suggesting half of the UK might have already been infected. Throughout summer and autumn, research groups also had to contend with media critics who misled the public with claims that the pandemic was over, dismissing warnings about the potential for a large second wave.

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Given the European epidemic waves to date, there can be little doubt that in the absence of control measures, Covid-19 would have been catastrophic for our health systems. Across the world, populations altered their behaviour in response to growing epidemics, but the extent of this unprecedented shift – and its effect on spread – was extremely hard to predict at the start of last year. Although infections such as Ebola and Sars have previously spurred behaviour change, Covid-19 triggered shutdowns of society on a scale unseen since the 1918 influenza pandemic.

As well as modelling the spread of disease, researchers have had to track the dynamics of social behaviour. Because of modern digital footprints, they have been able to do this in more detail than ever, providing unique insights into how individuals and communities respond to outbreaks. These behavioural changes, whether driven by explicit government policies or local awareness of infection risk, have in turn had complex social, economic and health impacts. Untangling such effects will no doubt be the subject of research far into the future.

Covid-19 has cemented a growing trend for research teams that work across multiple aspects of disease dynamics, from modelling and epidemiology to immunology and human behaviour. In the UK, researchers involved in modelling the disease have set up studies of social interactions and infection levels within communities, with these datasets then feeding back into new models.

As well as interdisciplinary links, there have also been new international connections. Political responses to the pandemic have been country-specific, but throughout 2020, scientific insights – including datasets, modelling results and code – have been shared and built upon by teams across multiple continents. Past epidemics have brought mathematical tools to new audiences, but the scale of Covid-19 has resulted in epidemiological ideas being exchanged across disciplines and borders as never before. If sustained, such collaborations and networks could be hugely valuable in tackling other global epidemic challenges in future.

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The events of last year have altered the dynamics of many diseases, beyond Covid-19, as seen in the disappearance of certain seasonal infections or the disruption of vaccination programmes. Had the pandemic not happened, I would have spent much of 2020 abroad, setting up studies of influenza, Zika and dengue. When these projects eventually resume, will we see smaller outbreaks than before, or belated large epidemics? The pandemic has created a tragic “natural experiment”, a once-in-a-century jolt to disease ecosystems that could produce unexpected insights into immunity, social behaviour, seasonal effects and evolution. We’ve learned a lot about Covid-19 in the past 12 months, but there’s much more that modelling will help us discover in the coming years.



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