Section 2: How much was due to lockdowns?
What is not clear from data on measured deaths of people who are recorded as having the virus, on tested new cases of the infection and on excess deaths is the extent to which they have fallen because of (in many cases severe) restrictions on the population. There are at least three reasons why new infections and deaths could have fallen, perhaps sharply, even with much more limited government restrictions short of a lockdown: i. individuals would have altered their behaviour (washing hands more frequently, avoiding crowded spaces etc) with no legal restrictions on the ability to leave the home and with much more limited disruption to life; ii. a significant degree of immunity may have built up by the time severe restrictions were introduced because the infection may have spread quite widely and largely unnoticed with the asymptomatic a large fraction of the infected. iii. a substantial proportion of the population may have been effectively immune from the virus when lockdowns started not just because of recovery from past infections that conferred a degree of immunity but also because some proportion of the population was never susceptible. All three factors may have played a role, and all would mean that deaths and new infections would have slowed in the absence of severe government restrictions.
These three factors are not mutually exclusive and there is some (less than conclusive and often disputed) evidence that each of them may have played a role.
An Oxford University research team used death data to estimate the proportion of the population who might have built up some form of immunity before the UK lockdown was introduced in mid-March 2020. They put that fraction at around 60% (Lourenço et al (2020)) (3). Stedman et al (2020) (4) used data on differences in the spread of the infection across English regions to assess how many might have been infected and put that fraction at similarly high levels. Dimdore-Miles and Miles (2020) (5) fitted a SIR (Susceptible-Infected-Recovered) model to data on new cases of infections across several countries and estimated that the numbers who might have been infected with no (or few) symptoms were likely to be at least 10 times (and possibly as much as 200 times) as large as those who had symptoms and were more likely to have been tested up to late April 2020.
Wieland (2020) (6) modelled the spread of the infection across Germany and concluded that infections were past their peak and starting to decline ahead of the introduction of government restrictions there. The results were summarised thus: “ In a large majority of German counties, the epidemic curve has flattened before the social ban was established (March 23). In a minority of counties, the peak was already exceeded before school closures.”
Professors Karl Friston (of University College London) (7) and Michael Levitt (of Stanford University) (8) – experts in the application of statistical models to biological phenomena – have independently concluded that the numbers of people susceptible to the COVID-19 virus were  substantial before lockdowns were introduced and that the virus may have been burning itself out.
Despite these pieces of evidence, direct measures of how many people in the wider population have been infected by COVID-19, and the extent to which immunity from the virus has been built up by that route, are not high. Most estimates based on limited testing of a random sample of the population for antibodies put the level of those who have had the infection in European countries where the virus has spread most rapidly at 5-10%, though in some areas within countries it is still high enough to have had a significant impact on R.
It is nonetheless clear that the asymptomatic make up a high proportion of total infections – one of the reasons that some immunity has been built up without hospitals being swamped. It is possible that serology testing for past COVID-19 infection based on the presence of antibodies are not picking up cases where the infected had very few symptoms and not identifying others who are nonetheless not susceptible to the virus.