OK, before someone gets upset I’m not taking the current crisis seriously, don’t misunderstand: this is a serious situation. But there is no cause to lose our sense of humor or be grim. Yes, we must take action, but no, it’s not the end of the world (unless you’re a nurse or doctor, then it might feel like it for a couple of weeks).  I’ll crunch the numbers downthread and it’s not as bad as you might think if you keep perspective.  But do not doubt the sad fact that the US health care system can’t really keep up with a normal flu season; there is no way it can handle a rapid influx of respiratory patients. That is why COVID19 is so dangerous, and why everyone needs to take it seriously, following the CDC guidelines, limit interactions outside your immediate household (aka social distancing), keep strict hygiene protocols, and otherwise doing everything you can to try to slow down the rate of spread. It’s more than likely not about you. It’s about that 1% of so of the population who will get very sick, and may not get enough care because the system will be overloaded.

Here’s the latest analysis.  First, please, please, please, stop obsessing on every blip in the numbers!  They are not “skyrocketing” or whatever inflammatory phrase the media is using at the moment. Second, the absolute numbers don’t matter.  Yes, each and every one is a life, and a tragedy.  But what matters in terms of risk is the denominator: how many people are getting sick and passing away in terms of what size group?  Losing 100 people in Chatham County (pop about 290,000) is very different from losing 100 people in New York City (pop 8.6 million).  Don’t compare them.  It’s mortality per unit population that matters – and how fast that mortality happens.  Please stop feeding the beast by quoting and hyping how many deaths per day without context.  It’s not helpful, and causing people far more stress than is appropriate.

Time for some math: deaths from the virus are progressing along what is known as a logistic function.  This type of function was originally developed for use in population growth, but has found it’s way in to many other fields.  In biology, this is sometimes called a carrying capacity curve.  We are entering the scary part of that curve.  Here’s what the curve looks like with data for several areas as of 24 March 2020.  The black line is a theoretical curve that represents an estimate of how things might progress.  The grey line is for comparison, the 2017 H3N2 flu progression speeded up by a factor of 5.  The dots represent actual data as of the totals for yesterday as reported this morning.  Click to embiggen …

Of these, China (grey +) and Iran (blue o)  look weird.  I suspect those numbers are “munged.”  But Spain (red dots) and Italy (green dots), which are the farthest along of societies that might resemble the US, seem ok.  (South Korea is even further down the curve, but they took very early intervention, and have more hospital surge capacity than the US, so may not be a good analog).  New York is the cyan triangles that are hard to see because  it’s just starting to creep along the curve – about day 25 or so.  The next 20 days will be very scary – you can see that is the steepest part of the curve, and people will talk about doubling times, and extrapolation the daily rates far beyond the point where they will start to  settle down.

Where will it end?  The latest projections are that the US will see between 50 and 90 thousand deaths.  (2017 saw 61 thousand H3N2 influenza deaths – but over 6 months, not 6 weeks!).  New York will likely see upwards of 5 thousand (currently 200 or so).  Smaller communities will also see a rapid rise in deaths that, without context, will seem terrifying. Expect the health care system to be in crisis, and please do what you can to support the medical community.  This will be horrific for them.  Chatham County, Georgia hospitals, which serve about 400,000 people, will likely see nearly 1,000 respiratory cases, of which 100 may die, all in the next three weeks.  But again, by the end of April, most parts of the country should be at the upper end of the curve, with the deaths per day decreasing.

How soon will we know if that is our future, or something worse?  Italy should be at near their peak.  I expect that by early next week we will see a downward trend in their numbers, followed by Spain 4-5 days later.  If by the 1st of April Italy is still recording 700 or more per day, that will be a source of concern.  Will update the graph this weekend … meanwhile, don’t hoard TP like this guy.

OOPS: these are per 10,000, not 1000.  

You’re seeing lots of graphs and tables on COVID 19.  In a disgusting display of fear mongering, networks are now keeping running counts of the cases and deaths on screen as if tracking stocks or something.  But all of that lacks context.  How does COVID19 compare with a bad influenza outbreak? We’re starting to get enough data to seriously answer that question. I’m using the 2017 H3N2 outbreak for reference, which was a bit worse than an average season, and the data from three areas that are relatively farther along in the process: Hubei, China, Daegu, ROK, and Lobmardy, Italy.  Influenza is in blue; Hubei is orange, Daegu yellow, and Italy in green.  We start the clock on our graphs at the first known case and plot cases per 10,000 population by week:

Oh.  That’s not very interesting.  The COVID19 cases barely show up!  What is going on?  Well, to start with, we are probably only detecting/reporting a fraction of cases.  Lets scale H3N2 flu to assume we are only detecting 5% of COVID-19 cases (which seems to be the range in the literature at the moment):

That’s a lot more interesting – and really illustrates how COVID19 is both different from and more stressful for the health care system, and not as bad for the general population, as influenza.  Notice how rapidly the cases explode for COVID19.  This is why the outbreak is so stressful for hospitals: the cases flood in over 3-4 weeks, as opposed to 20 weeks for a flu outbreak.

There is a lot to learn from these numbers and graphs.  Notice the sharp break in the Hubei China curve.  There are likely two reasons for that. First, they instituted rather draconian travel restrictions.  Second, they are likely not being entirely honest about their reporting, either internally or externally.  From the Korea curve. which is probably pretty reliable, it looks like COVID19 cases will level off between 1.5 and 2 cases per 10000.  H3N2 leveled off at over 60 per thousand, but if we scale it to the same detection rate we suspect we are seeing for COVID19, then 3 to 3.5 is the range, and therefore COVID will have maybe 2/3 the impact of a bad flu season in terms of total number of cases, and mortality.  Italy is in the steep part of the curve.  We should see their case rate slow over the next week and level off, probably in the 2.0 to 3.0 /10,000 range (a bit higher than Korea due to the older population and later implementation of control measures).

What about the US?  We are just entering the steep part of the curve.  It will be very scary as cases explode – but keep it all in context.  If these trends hold, the US can expect about 30 to 35 million people to be “symptomatic” (most mild), 400,000 to 500,000 need hospitalization, and 30-40 thousand deaths.  Compare to the 2017 influenza season: 45 million symptomatic, 810,000 hospitalized, 61,000 died.  HOWEVER, rather than coming over 20 weeks or so, those cases will come over maybe 4 weeks -five times faster.  The US health care system can’t really keep up with a normal flu season; there is no way it can handle this flood.  That is why COVID19 is so dangerous, and why everyone needs to take this seriously, following the CDC guidelines, exercising social distancing and hygiene protocols, and otherwise doing everything you can to try to slow down the spread.  It’s more than likely not about you.  It’s about that 1% of so of the population who will get very sick, and may not get enough care because the system will be overloaded.  The state of the US system is a disgrace, and its inability to handle this outbreak is the result of health care policy decisions going back decades.  That will likely be the subject of an upcoming vehement rant …

Data sources:
Influenza Hospitalization Surveillance Network (FluSurv-NET), US Centers for Disease Control.
2019 Novel Coronavirus COVID-19 (2019-nCoV) Data Repository by Johns Hopkins CSSE

Between the sheer magnitude of the misreporting going on in the major news media, and the monumental incompetence and feckless positioning to take advantage or avoid blame for the COVID19 crisis among US political leaders, it’s hard to know where to start.  The second biggest thing that bugs me is the assumption that people are stupid and have to be manipulated.  The biggest thing that bugs me is that the people doing the manipulating (media, leaders) think they are knowledgeable and smart enough to know what is best and that manipulation is required.  So for today a “short” post pointing out one small element of the madness.

Imperial College, London, released a report on modeling the potential impact of the virus.  It’s an interesting study, properly interpreted it’s good, valuable work.  But that’s certainly not what is happening.  One example is the New York Times article on the study.  They plucked out a graphic showing 2.2 Million US deaths, labeling it in the fine print as showing the number of deaths “in the absence of actions.”  What they don’t say is what the report itself said: that this scenario was unlikely.  That upper bound assumes that people would do nothing even as others around them fell sick.  Worse, this scenario is now in fact impossible: actions already taken will have a profound reduction on this total, even if the assumptions in the model (which are very bad case assumptions) are correct.  There are also reasons to believe that many of the assumptions in the paper are extreme, based on data that has become available later – this is a rapidly moving situation, and it is hard for peer reviewed research to keep up.  But it is valuable document showing the potential impact of various mitigation actions.

As I have ranted previously, there are always a huge range and variation of scenarios for any disaster.  Which scenario you use for a given purpose varies depending on the application.  There is no “right” number.  But there are a lot of wrong numbers, especially for what is most likely to happen.  I think most people understand that you sometimes have to plan and take action based on what could happen, even if though what is likely to happen is something more benign.  The problem is, by always emphasizing what might happen (much less the most extreme version of that) just causes a lot of fear, anxiety, and panic.  I just don’t buy it you have to scare people: most people, most of the time, will do the right thing if you just take the time to explain it to them.

As I have been saying all along, your best bet as to what to do is still the CDC COVID-19 web site.  For other preparation tips, try the DHS/FEMA site.  By now you should know the drill: wash your hands, don’t touch your face, social distancing as needed, stay home if you don’t feel good.  Help those around you as needed.  And try not to stress too much over the numbers, the drama, and the politics.  Most of the numbers (and the rhetorical extremes) are bogus anyway.

Before the numbers, a question: Is the COVID19 pandemic a crisis, or not?  In the US how you answer that seems to break along political lines.  But the answer isn’t really a simple “yes” or “no,” and that is a source of conflict and confusion in a society that demands simple, sound bite style answers that hopefully break down along party lines.  In many instances, the question “is or is not” (“to be or not to be” as some English dude wrote) is the wrong question.  The Buddhists call this “dualistic thinking”.  In the COVID crisis, the answer to the question above is a firm “it depends.”

The short version (and you know the longer version is below!) is that this is not a crisis for the vast majority of people.  Even if you get it, it’s not as bad as many of the influenza strains going around.  HOWEVER: if you are in a vulnerable population, it is as much as 10 times deadlier than the flu.  In addition, because of the rapid progression, lack of immunity, and severe respiratory distress of patients who present for treatment, it is a potential catastrophe for our health care system, which does not have the capacity to deal with this.  Thus the contradiction and conflict: for most people it’s not a big deal, but for the health care system it risks overload, and for vulnerable populations, it’s deadly.  So how do you balance all that, prepare, but not scare people and wreck the economy?  Clearly, not how we’re doing things at the moment …

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The US is now well in to the mode where state and local leaders are having to take key decisions on COVID-19 with respect  to closures.  It’s a difficult situation.  The dilemma is that the available data indicates this has no more implications for the general population than a bad influenza strain. That’s not to minimize COVID19; the problem is we don’t take influenza as seriously as we probably should. The 2017 H3N2 strain hospitalized 810,000 people and killed about 61,000.  The current modeling indicates that COVID19 will hospitalize 378 thousand, and kill about 20,000.  Big numbers, but smaller than the flu.  However …

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One of my major frustrations with the “news” media is how they treat numbers. In a situation like we are facing with the COVID-19 outbreak, the data is very fuzzy, and we are apparently seeing wild swings in the numbers.  Headlines like this one in the New York Times are facing folks as they wake up this morning: Cases and deaths surge in Italy as its north is locked down.  So we should panic, right?  Maybe not.  

As previously noted, I’m using data from the nCoV-2019 Data Working Group data base, Epidemiological Data from the nCoV-2019 Outbreak: Early Descriptions from Publicly Available Data as well as the current (9 March 2020) World Health Organization database. I’m also indirectly using other sources of information to cross check these data.

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