I haven’t written about it much recently because I didn’t have much to say that added to the discussion. In Australia things are opening up again: most schools are open (but not ‘back to normal’), restaurants/cafes have started limited seating and generally people are out and about in shops and offices more. The running total number of cases are low (7 thousand) as are the total number of deaths (102). Luck and timing seem to have made a huge difference here but the big question is how big that reservoir of unobserved infections is. With more wintery weather here and social contact increasing, we’ll need to see if the infection rate starts zipping up again.
Meanwhile, nonsense continues from the usual suspects elsewhere. I’ve seen more than one attempt in conservative and global warming denial circles (but I repeat myself) trying to do a comparison of lockdown policies with infection rates and/or changes in infection rates. The idea is to roughly classify a nation’s response and then look at levels of infection and then (lo and behold) find no connection and declare that lockdowns did nothing.
The reasoning is fallacious. The fallacy is one we’ve seen before: looking at data but stripping out what we already know. Lockdown policies are connected to infection rates in multiple ways. Consider two different examples: Italy and New Zealand.
Italy suffered a massive early spike in infection rates relatively early for a European nation. With hospitals overwhelmed in the north, the country began quarantine measures at a point were relatively little was known and access to testing was limited. As a policy response it was pretty much an extreme emergency measure where authorities had very few options available to them. On the other hand (and in real time, not very long after) New Zealand adopted a strong lockdown policy precisely because the country had very FEW cases. NZ had a shot at an elimination strategy: close off people entering the country, shut down community spread, wait for a few weeks…and (maybe) the virus is gone.
So we have a range of policy measures that have been implemented as a response to quite different circumstances. Among the countries compared, most have significant measures in place of some kind but there is a lot of variety on specific ones (e.g. school closures is quite varied as a policy) and the impact of a government ‘recommending’ as a measure and enforcing a measure is also varied (i.e. in one nation a recommendation might have had a similar practical impact as a mandated policy).
In short, it’s a mess that will require more complex analysis than simply comparing ‘lockdown’ v ‘no lockdown’. The Our World In Data site has a decent overview of policy responses here https://ourworldindata.org/policy-responses-covid They also have a kind of aggregate index for a variety of countries (https://ourworldindata.org/policy-responses-covid#government-stringency-index ). How useful that is, I’m not sure. Again, simply comparing one date with total infections will produce gibberish e.g. currently NZ has a less severe score than Sweden but that hides that NZ had a VERY severe score ten days earlier, so you have not just different levels of severity but different PATTERNS of severity.
What happens next? Different regions have been impacted at different times. Obviously East Asian nations suffered the initial spike, followed by Western Europe and then North America. Cases continue to grow in the USA, although that growth is partly masked by falls in the parts of the US that were impacted first.
This graph shows some selected nations based on previous conversations or places that people have made comparisons between. Different choices of countries might lead to a very different perception of the trajectory. Also, as we’ve discussed before, the extent and efficacy of testing impacts these numbers.
Russia, parts of South America and the Gulf States also have rising numbers. The worldwide spread is far from over.