Next, I ran a regression model. For those unfamiliar with academic statistical methods, regression – in this case linear regression – is a computerised mathematical technique that allows researchers to measure the influence of one variable on another with all of the other factors that might be relevant held constant. In this case, the variables for each state included in my model were: population, population density, median income, median age, diversity (measured as the percentage of minorities in a population), and the state’s Covid-19 response strategy (0 = lockdown, 1 = social distancing). The data set used to construct this model is available for anyone to request it.
The question the model set out to ask was whether lockdown states experience fewer Covid-19 cases and deaths than social-distancing states, adjusted for all of the above variables. The answer? No. The impact of state-response strategy on both my cases and deaths measures was utterly insignificant. The ‘p-value’ for the variable representing strategy was 0.94 when it was regressed against the deaths metric, which means there is a 94 per cent chance that any relationship between the different measures and Covid-19 deaths was the result of pure random chance.
The only variable to be statistically significant in terms of cases and deaths was population (p=0.006 and 0.021 respectively). Across the US states, each increase in the population of 100,000 correlated with 1,779 additional Covid-19 cases, even with multiple other factors adjusted for. Large, densely populated areas are more likely to struggle with Covid-19, no matter what response strategy they adopt – although erring on the side of caution might make sense for global megacities such as New York and Chicago.
Finally, I extended my analysis into the international arena. As has been widely reported, Sweden has opted not to lock down in the wake of Covid-19, and Swedes have instead followed similar social-distancing measures to those adopted in the seven US states I focused on.
Again, there is very little evidence that Sweden has become an unlivable Covid-19 hotbed. As of 17 April, Sweden’s Covid-19 statistics were: 13,216 total cases, 1,400 total deaths, 1,309 cases per million and 139 deaths per million. In terms of cases per million residents, Sweden ranks slightly ahead of its close neighbours, Denmark (1,221) and Norway (1,274). But in Europe as a whole, Sweden ranks 23rd in terms of cases per million and 10th in terms of deaths per million.
I am reluctant to compare European examples to the many East Asian countries which avoided significant shutdowns – particularly since these countries had significantly better early-response strategies and there can be larger cultural differences which are difficult to quantify. But essentially, the same pattern holds true. When I conducted my analysis, Japan had 9,231 total cases, 190 total deaths, 73 cases per million citizens, and two deaths per million. South Korea had 10,635 cases, 230 deaths, 207 cases per million and four deaths per million. Taiwan had a total of 395 cases and only six deaths, alongside 17 cases per million and 0.03 deaths per million.
Of course, no single analysis can provide a truly conclusive answer to questions as huge as those posed by Covid-19. Scholars and curious citizens reading this one might want to re-run my analysis with current active cases as a dependent variable rather than total cases or cases per million – although I doubt that would make much difference. It certainly might make sense to redo my regression with ‘date of first case’ thrown in as a variable. I kept the model limited to five independent variables due to the small number of state-level observations available, and left that one out because onset dates were fairly similar for most US states. However, including this information could theoretically produce different results. The more data, the better.
Overall, however, the fact that good-sized regions from Utah to Sweden to much of East Asia have avoided harsh lockdowns without being overrun by Covid-19 is notable.
The original response to Covid-19 was driven by an understandable fear of an unknown disease. The epidemiologist Neil Ferguson projected that 2.2million people could die in the US alone, and few world leaders were willing to risk being the one who would allow such grim reaping to occur.
However, as time has passed, new data have emerged. A top-quality team from Stanford University has pointed out that the infection rate for Covid-19 must logically be far higher than the official tested rate, and the fatality rate for the virus could thus be much closer to 0.1 per cent than the 2 to 4 per cent that was initially expected. And empirical analyses of national and regional response strategies, including this one, do not necessarily find that costly lockdowns work better against the virus than social distancing.
It should not be taboo to discuss these facts.
