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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