The effect of large-scale anti-contagion policies on the COVID-19 pandemic
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Source: Nature Magazine
Governments around the world are responding to the novel coronavirus (COVID-19) pandemic with unprecedented policies designed to slow the growth rate of infections. Many actions, such as closing schools and restricting populations to their homes, impose large and visible costs on society, but their benefits cannot be directly observed and are currently understood only through process-based simulations.
Here, we compile new data on 1,717 local, regional, and national non-pharmaceutical interventions deployed in the ongoing pandemic across localities in China, South Korea, Italy, Iran, France, and the United States (US). We then apply reduced-form econometric methods, commonly used to measure the effect of policies on economic growth, to empirically evaluate the effect that these anti-contagion policies have had on the growth rate of infections. In the absence of policy actions, we estimate that early infections of COVID-19 exhibit exponential growth rates of roughly 38% per day.
We find that anti-contagion policies have significantly and substantially slowed this growth. Some policies have different impacts on different populations, but we obtain consistent evidence that the policy packages now deployed are achieving large, beneficial, and measurable health outcomes. We estimate that across these six countries, interventions prevented or delayed on the order of 62 million confirmed cases, corresponding to averting roughly 530 million total infections. These findings may help inform whether or when these policies should be deployed, intensified, or lifted, and they can support decision-making in the other 180+ countries where COVID-19 has been reported.
Read more: https://www.nature.com/articles/s41586-020-2404-8_reference.pdf
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There's something off with the numbers, which only makes sense--this isn't a robust data set that's being analyzed in a dozen different ways. That means wide error bars. The more tests you run on a dataset, the less the usual calculated error actually holds.
If you look at the extended data for the US, you'll see that the effect of school closures, no gatherings, religious closure are quantified. Depending on what you think the lag is, those have a greater or lesser positive effect on COVID. (Yes, the simplest take is that if you close schools, there's greater rate of infection; if you ban meetings, it reduces the rate of infection.) You can play with the lag time to get the contribution of school closures to infections reduced--but when you do that, you start eating into the helpful effect of banning gatherings. At least one tried-and-true way of reducing spread, the analysis says, leads to increased transmission. The effects vary significantly (and by that I mean "greatly"
from country to country.
Introduce more variables, and errors increase in step. Add fuzziness--so the finding, not in this article, that Americans started the lockdown a week or so before the official start date by reducing travel and interactions, can't help the analysis.
Instead of taking this as sacred writ, it's an early contribution, and a valuable one. But first and early contributions are often not right.
With the various openings happening in different places and different ways, comparing data will be a bit harder--but provide a wealth of data to be crunched so maybe we *can* isolate those factors that actually contribute to reducing the transmission rate from those that don't. Hotspot in Podunkton, ZY? Fine, maybe the policy makers could look at a good set of quantified effects and say that they can reach their target by requiring masks, work-at-home, and halving the capacity of mass transit--instead of closing the economy down wholesale. Or maybe just masks and WAH. Same problem in NoWheresburg, YZ? Maybe no work-at-home because it's all factory and retail, but increasing leave policies and implementing other measures can get the same reduction.
Analysis - the forum hosts agreed this triggers the “analysis” exception to the SOP for breaking news, so belongs in GD instead if you’d like to repost there. It’s an informative piece 