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.