TLDR: The 27 March 2020 forecast all population symptomatic case fatality rate based on the Diamond Princess cruse ship data was 1.71%. The current (20 January 2021) US symptomatic case fatality rate is 1.67%. So while the public perceptions and actions have been shifting, the bottom line hasn’t changed that much, and while there was a lot of uncertainty back then, the early work wasn’t bad. Here’s some more background, including a rant about people who think people aren’t dying from this, and a look back at the mortality forecasts made early last year.
Our first solid look at COVID19 in a controlled environment was the cruse ship “Diamond Princess”. Almost exactly one year ago, on January 20, 2020, the ship departed Yokohama on a three hour tour, um, on a round trip tour of Southeast Asia timed to coincide with the Lunar New Year celebrations. A single passenger from China brought with him a hitchhiker: the SARS-COV-2 virus. Over the ensuing weeks (which included some dumb measures by the Japanese Government that made things worse), something like over a third of the passengers and crew are thought to have contracted the virus. Around 400 of the passengers and crew became sick enough to be classified as symptomatic, nearly 200 of those were hospitalized, and about 10 or 12 had a primary cause of death being from the disease which is now called COVID19.
So where do things stand? I again rant that real time death counters seen on “news” outlets are disgusting displays of much that is wrong with American “Journalism.” Let’s look at the National Center for Health Statistics data, which is probably the most authoritative/reliable source. These statistics are updated weekly; those of you with wonky tendencies may want to read the technical notes, but the bottom line is that mortality statistics take time to compile in the US. Nationally, only 60% of death records are submitted to NCHS within 10 DAYS of death! Lets ltake a look at the week of December 26th. As of the 14th of January (two weeks after), the number of reported deaths were 41,796. As of yesterday that number is up to 72,710 … now, that’s a worse than usual example due to the holidays, but it shows the dangers of relying on the “real time” data. The Johns Hopkins data sets that many in the media are using are quite good, but they are not definitive, and the sensationalist abuse of this data is not helping. Again, pandemics are “slow motion” disasters. They rarely evolve from hour to hour or day to day, it’s more of a week to week process, with the decisions of millions of individuals influencing the course of the outbreak. The hype is stressful, distracting, and given the politics, divisive, as it encourages people who think they are being railroaded to believe there isn’t a problem.
But there is. I’ve posted similar graphics before, but here it is again, updated through yesterday. The blue line is shows total reported deaths. The orange line is expected deaths based on the US population at the time. The yellow line is “non-COVID related deaths” as classified by the ICD–10 codes. The “expected” line is wavy because more people die in winter than summer – usually due to Influenza and Pneumonia. You can very clearly see that the 2017-18 influenza season was bad, and that 2018-19 was mild. The 2019-2020 season was pretty normal – until in early February something drastic happened. That “something” was the SARS-COV-2 virus. Even if the only data you had was the blue line – reported deaths – you’d know that there was a new disease stalking the land. Look carefully at the orange line – non-COVID deaths – and how high it is during the summer. Contrary to what some with an ax to grind are saying, it seems that we were more than likely under-counting COVID deaths rather than over counting them, although it is possible that some people died who would not have otherwise because they did not seek medical care out of fear, or some other indirectly related causes. In any event, reported deaths are obviously well above normal, there is obviously some new disease running through the population, and anyone who is saying otherwise is simply wrong.
Looking back at the early mortality forecasts, my own forecasts were off by a factor of two – the estimates from late March/Early April were on the order of 200,000 by now, but reported US fatalities recently topped 400,000. I had assumed average people would demonstrate more common sense than they did (yeah, you can rarely go wrong assuming people are idiots but despite being on doomwatch I try to be optimistic 😛 ). I also thought the initial reaction would be stronger nationally, and widespread masking start earlier, than how it played out. I never really thought the vaccines would be ready by now – and I’m still rather pessimistic it will have the impacts the vaccine chorus is singing. First, this is a corona virus: other beasties of this type are responsible for about 20-30% common cold cases, and they mutate so rapidly the immune system can’t keep up; it’s unrealistic to expect vaccines to keep up, although it’s also wrong to discount them, as like with influenza vaccines, they can provide some protection even against unrelated strains. Second, despite the PR deluge, the efficacy hasn’t really been statistically demonstrated to the usual standards, and the quality control and massive rollout have created problems that have harmed the process. Cheerleading and glossing over things like adverse reaction rates isn’t really great way to build confidence. As I have said, I am absolutely in favor of vaccines – but I’m also in favor of a careful, “first do no harm” approach to public policy that a rushed “do something NOW” process rarely allows. Appearances do matter, but the data matters more.
When I teach emergency management, the very first thing I try to get decision makers to understand is that no matter what they do, they are going to kill people. There is almost never such a thing as “erring on the side of caution” because all actions have consequences – and as I often point out, economic harm also causes physical harm, a fact that is often overlooked. Ultimately the trick is to figure out policies that will cause the least harm in the long run.
As the COVID19 pandemic shows, that’s a very difficult thing to do.