The UK government yesterday announced a far-reaching package of social distancing measures to suppress the spread of COVID-19. These are based on some Imperial College London modelling work, published here. The model predicts that the UK approach to mitigate the impact of the UK epidemic would indeed reduce the overall number of people affected and those who die, but would still leave hundreds of thousands dead in an overwhelmed healthcare system. In contrast, a more intensive suppression approach would be effective in reversing the epidemic trend and keep the number of new cases to a low level – in the short term, at least.

The model tested and compared the impact of two approaches in the UK and US:

• Mitigation – an attempt to flatten the curve and reduce impact on healthcare systems; the aim is to reduce the reproduction number (the number of secondary cases of each case), but not to <1.
• Suppression – an attempt to reverse epidemic growth by reducing the reproduction number to <1.

Left unchecked, the model predicts that COVID-19 would infect 81% of the UK population and cause more than 0.5m deaths by the summer of 2020. Mitigation was found to halve deaths and reduce the total number of cases by two thirds. However, this would still mean hundreds of thousands of deaths and a healthcare system (particularly intensive care) overutilised many times over.

The outlook for suppression was more positive in a way, with the model predicting that the approach would achieve its aim of reversing epidemic growth and restricting the number of new cases to a low level. However, two major downsides: firstly, the minimum requirements for an effective suppression strategy is extensive social distancing for many months affecting the entire population, and home isolation of cases combined with quarantine of the entire household. Secondly, in the long term, the model predicts that once these drastic social distancing measures are relaxed or removed, we would quickly re-enter the epidemic phase. The model predicted that closing schools and universities would improve the effectiveness of a suppression strategy, but made the point that the consequences may outweigh the benefit of doing this (e.g. if schools closes, many healthcare workers cannot come to work, and outcomes for patients in hospitals could be less favourable).

The model predicted that stopping mass gatherings would make little impact on the shape of the epidemic, because the contact-time at these events is relatively small compared with time spent in homes, schools, work-places, and other community-based activities (e.g. pubs and theatres).

The parametisation of the model – crucial for the validity of its outcomes – seems reasonable. The only variable that caught my attention was that symptomatic individuals were 50% more likely to transmit to others and asymptomatic individuals; it’s not clear where this estimate came from. Vital to note that the model did not include any ethical or economic implications, which are really important considerations from a policy-maker viewpoint.

This model has clear implications for other parts of the world. Whilst the picture in China is looking considerably better than it was, will the suppression of new cases reverse once the stringent symptomatic management and social distancing is lifted? These models suggest that this is likely, unfortunately. And for the US, the same findings are true – only drastic social distancing will reverse the epidemic.

The proposed solution seems to be a “pulsing” on/off of the stringent social distancing measures until we reach a point that a vaccine is developed. The suggestion is to use the incidence of COVID-19 in the ICU as a trigger point for pulsing the social distancing measures on and off.

As we know, all models are wrong. Let’s hope that this one is one of the useful ones!