Imperial College Modelling Falsely Assumes No Seasonality to COVID-19
by Glen Bishop Is this the source of Neil Ferguson's model assumptions? (Credit: Miriam Elia) How likely is it, that there is going to be a summer surge in SARS-CoV-2, a surge where hospitals have similar numbers of coronavirus admissions to what they saw in January? Pretty unlikely, I would have guessed. But doing the rounds in the news was a paper by the Imperial College modelling team projecting just that. It predicts, as a mid-range scenario, 130,000 more deaths, with a catastrophic summer surge even if restrictions are lifted at the current snail's pace and the vaccination programme goes well, at two million doses per week from February. As a maths student at Nottingham University, I have been reading through some of the modelling done for SAGE recently. Bad modelling at the beginning of a pandemic, particularly with a communist country withholding or obscuring crucial data, is understandable. What I did not understand was how predictions of scenarios worse than have occurred anywhere in the world keep being predicted for the UK. On reading the paper, I realised they are assuming there is absolutely no seasonality to SARS-CoV-2. This is fundamentally wrong. They are attributing all the reduction in R-value last spring to non-pharmaceutical interventions (NPIs) and none to seasonality, leading to assumptions that the base R-value in July ...
