These places are associated with increased transmission rates and superspreader events.īut perhaps surprisingly, stay-at-home orders appear to have had only a modest effect in slowing transmission. Limiting gatherings, closing businesses with high exposure rates, and closing schools and universities effectively reduced the spread and limited deaths. The different dots represent different ways of modelling the effects of control measures – showing that how you choose to measure impact does make a difference. They also point out that different actions achieved different things in different countries.Įstimates of the effects of different control measures on the number of new COVID infections during the first epidemic wave. While accepting that no method of assessment is perfect, most published studies have found evidence to support lockdown measures being effective. Imperfect assessments, clear resultsĭespite these difficulties, a lot of researchers have worked to try to analyse the impact of lockdowns. And for other countries, like the UK, its even more difficult to find good pairings for comparison. While often similar, no two states are directly comparable. A common approach has been to compare data from US states that differed in the stringency, timing and extent of their control measures but otherwise share many characteristics.īut of course, this runs into our initial problem. For example, the “difference-in-difference” method compares outcomes from countries or regions that are similar in all respects except the implemented control measures. Their outputs depend on making assumptions and estimates, including about human behaviour, which is notoriously difficult to capture accurately.Īn alternative is to use a data-driven method that doesn’t rely so strongly on the assumptions of models. However, they’ve also been heavily criticised for their pessimistic predictions about the impact of the virus. Such models are very useful and have been used throughout the pandemic.
Mathematical models can be used to produce “what if” scenarios, where applying various different COVID control measures is simulated to estimate what would work best, essentially comparing the value of lockdown to other measures – or doing nothing at all. Instead, researchers must resort to other methods to try to measure lockdowns’ effects. There are no directly equivalent parts of a country or the world that can act as true test and control groups, and so no possibility of a controlled trial.
Unfortunately, this isn’t possible for lockdowns. This is why medical trials have a control group, whose members are given a placebo and whose characteristics match the testing group as closely as possible. Properly evaluating the effectiveness of any health-oriented treatment, be it a new medicine, vaccine or lockdown measure, involves comparing its introduction with a counterfactual situation where everything is the same except for what’s being tested. Since the UK first entered lockdown on March 23 2020, little in the pandemic has attracted so much attention and controversy as this decision to grind social and economic life to a halt.
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