Did COPVID-19 infections decline before UK lockdown?

My Facebook feed had a reference to an article in The Spectator which refered to a new study which claims that COVID-19 infections declined before the UK lockdown. This surprised me, so I thought I’d take a quick look. A web search found the source paper - an unreviewed publication by Professor Simon Wood, a statistician in the Mathematics department at Bristol University who has worked on the modelling of biological systems. Unreviewed papers can be poor, mistaken, and/or misleading, so I’m loath to sped time on them. On the other hand, the author seems to be respectable, I know a bit about mathematics, and I’ve spent some time looking at Covid-19 modelling and the impacts of lockdowns, so I felt like I should take a look.

I don’t feel confident to judge the details of the modelling techniques used in the paper, so I’ll leave that to the mathematical and epidemiological referees. But to summarise, he uses UK Office of National Statistics data on Covid-19 deaths in England and Wales (probably as good as it gets), together with his model of how deaths lag infections, to estimate the fatal infection rate. He concludes that the number of fatal infections peaked on the 18th March, 5 days before lockdown on the 23rd March. From that summary, one might conclude that there was no need for lockdown as things were already getting better.

However, although lockdown was declared on a particular day, I don't recall it being a  a sudden event - it seemed like part of a continuum. Several people I know had started taking lockdown-like precautions well prior to lockdown. For our (me and my wife) part, having flown in to the UK on March 15th, we left the house only once, to buy food, during the week prior to lockdown. Other people we know of, who'd seen the plague coming, took similar precautions. And we now know that some homes for the elderly took severe isolation measures a couple of weeks before lockdown. (Not only was this against the advice of the government, the authorities put great pressure on the home not to do this). So, anecdotally, things were locking down before "lockdown".

But we need more than anecodotes and it turns out that there is data which lets us assess the amount of effective lockdown in the run-up to lockdown. Apple and Google make available some data derived from mobile phone usage. For Apple it is routing queries, and for Google it is visits to types of location. The full datasets are available from Apple and Google, and they explain the data in more detail than I have. I’ve plotted UK data rather the paper's England and Wales data. 

For Apple Mobility Trends, 100 (%) is the activity level on 13th Jan 2020 and the other figures are percentages of that activity. For Google Mobility UK, 0 (%Δ) is the median value for the day of the week between Jan 3rd–Feb 6th 2020. This means the regular day-of-week fluctuations have been smoothed out in the Google data, but not in the Apple data.

I’ve added vertical lines showing lockdown (23rd March), the peak according to the paper (18/19th March), and the date at which a decline in activity sets in (8th March). The sole activity which increases after that date is the buying of groceries and pharmaceuticals (stocking up in anticipation of the lockdown). This is strong evidence that the expectation of lockdown brought about a decline in activity resulting in the the (projected) reduction in fatal infection rate from the 19th March.

Although it looks at cases rather than deaths, the work of Horace Dediu of Asymco.com, is interesting in this context. He plots daily Covid-19 cases against Apple mobility data for many countries and US states. They are worth a look (note that Horave’s graphs plot Apple’s data in the opposite sense to mine)  and lend credence to the hypothesis that strict lockdown leads to reduced cases.

To conclude, in the case of the UK, peak fatal infection rate preceding lockdown, can be explained by the behaviour of the population anticipating a formal lockdown.