Epidemiologists at the Big Data Institute have published research submitted to the Scientific Advisory Group on Emergencies (SAGE) modelling sub-committee (SPI-M) during the first wave of the COVID-19 pandemic in 2020. The research supported policy discussions on contact tracing, lockdowns, and the impact of reinfections following a loss of immunity.
Dr Thomas Crellen, a Sir Henry Wellcome Fellow at the Big Data Institute, produced one of the first analyses on the potential impact of waning immunity on COVID-19 dynamics in 2020. This study highlighted how recovered individuals becoming reinfected could increase COVID-19 transmission during a second wave in autumn/winter 2020 and that reaching a stable herd immunity threshold would only be possible with vaccination. Dr Crellen said: “Initially models assumed that recovery from COVID-19 infection led to lifelong immunity. We now know that reinfection does occur, which has implications for modelling the future trajectory of the epidemic and presents challenges for policy planning”.
Dr Emma Davis, a junior research fellow at Wolfson College and post-doctoral researcher at the Big Data Institute considered the impact of “test, trace and isolate” policies on COVID-19 transmission. The analysis considered that strict isolation policies could be counter-productive, as people would be less likely to self-report after possible exposure and subsequently a lower adherence would increase the chance of onward spread and outbreaks. Dr Davis said: “By considering more plausible behaviour in our modelling, we showed that longer isolation periods do not necessarily lead to reduced COVID-19 transmission. Ensuring that as many people as possible can adhere to isolation policies is a crucial factor in their success”. The work was co-led by Dr Timothy Lucas, who was a post-doctoral researcher in the Big Data Institute at the time.
Professor Deirdre Hollingsworth, a professor of infectious disease epidemiology at the Big Data Institute, contributed to a study which showed how “classic” insights from infectious disease models provide an understanding of the fundamentals of the COVID-19 pandemic. The epidemic is driven by the basic reproduction number “R0”; the expected number of secondary cases arising from a single infection. Interventions which reduce R0, such as social distancing or lockdown, impact on the final epidemic size and the peak prevalence which can be calculated using mathematical expressions. Professor Hollingsworth said: “At the beginning of the pandemic we had very limited data on COVID-19. Insights from simple models helped us make projections about the epidemic and relay these quickly to policy makers. They also provide a benchmark against results from more complex epidemiological simulations”.
These papers are part of a theme issue of Philosophical Transactions of the Royal Society B and were stimulated by collaborations formed by the Royal Society through the Rapid Assistance in Modelling the Pandemic Initiative (RAMP). This theme issue contains 20 articles detailing evidence that underpinned advice to the UK government between January 2020 and July 2020.