#004 Predicting the Future of COVID-19 Using Epidemiological Models — Dr James Hay (Computational Epidemiologist at Harvard School of Public Health)

Curious about how the UK made the decision to the lock the country down? It was made using epidemiological models. One research team has been particularly influential in their response — the Imperial College team led by Professor Neil Ferguson. Their model predicted that an unchecked COVID-19 epidemic would overwhelm the NHS and result in 500,000 UK deaths. They suggested that we may need to have some form of social distancing for 12 out of the next 18 months.

More recently, an Oxford University team led by Professor Sunetra Gupta published their own model. Any model on covid-19 has to make some assumptions — Imperial looked at the deaths we’ve had in the UK and assumed that COVID-19 hadn’t infected much of the UK, but had quite a high death rate. Oxford assumed the opposite — they constructed a model which assumed that COVID-19 had infected most of the population, but had a relatively low death rate. This was picked up in news outlets such as the Financial Times — “Coronavirus may have infected half of UK population“.

To find out how these types of COVID-19 models work, what their limitations are and how we should interpret them — I called up Dr James Hay — who’s a computational epidemiologist at the Harvard’s School of Public Health. For the record, this conversation was recorded on the 31st March 2020.

Imperial COVID-19 Model: Impact of non-pharmaceutical interventions (NPIs) to reduce COVID- 19 mortality and healthcare demand. https://www.imperial.ac.uk/media/imperial-college/medicine/sph/ide/gida-fellowships/Imperial-College-COVID19-NPI-modelling-16-03-2020.pdf

Oxford COVID-19 Model: Fundamental principles of epidemic spread highlight the immediate need for large-scale serological surveys to assess the stage of the SARS-CoV-2 epidemic. https://www.medrxiv.org/content/10.1101/2020.03.24.20042291v1

Financial Times article mentioning Oxford model: https://www.ft.com/content/5ff6469a-6dd8-11ea-89df-41bea055720b