There are reports that water levels in both the Unit 1 and Unit 3 reactors at the wrecked Fukushima Nuclear Plant are falling as a result of additional damage from the earthquake a few weeks ago. (Nobody really knows what is going on in Unit 2, the sensors are offline.) This is Not Good(tm), since that water is essential for keeping the damaged reactors cool during the long decommissioning process, and it indicates further damage to the primary containment system. This is requiring Tokyo Electric Power Company (TEPCO, the owners of the reactors) to pump in more water to try to keep levels up. That means even more contaminated water coming out that has to be captured and stored or otherwise dealt with, and there just isn’t any place to put it. TEPCO received preliminary permission to slowly release the contaminated water offshore (to allow for dilution), but there is fierce opposition both by local fishermen and the international community and a final decision has not been made. The problem is, that decision might well be moot with this new damage: they will have to do controlled releases, because it’s about to get out of control. And, given the roughly 1.4 million gallons already stored, any additional quakes could result in a massive uncontrolled release. It’s a classic difficult decision: do you accept small harm over a long period of time from the slow releases, or risk massive catastrophe of an uncontrolled failure while you figure out something smarter to do.
In reading “news” stories lately, not to mention various comments in social media about topics ranging from politics to COVID vaccines, I was struck again by the power of binary thinking, as well as how perceptions are manipulated by asking (and answering) the wrong question. Another frequent related problem is making assertions that are perhaps true, but presented out of context in such a way as to create a false perception. This usually results in the two “sides” talking past one another and a shouting match ensues; there is no shared worldview to even begin a discussion.
Here’s a concrete example regarding vaccines: In skimming a discussion about mRNA vaccines it was said by one advocate that there is no evidence or “mechanism” they cause birth defects. The problem is, that’s “true” as far as it goes but also misleading. Pregnancy was a specifically excluded condition during the trails reported so far, and all of the documentation submitted to the FDA said it was not assessed. As for mechanism, there are in fact several potential mechanisms where something could go wrong, given the rapid and complex cell division that occurs during the early stages. Is it rare? Possible or impossible? Probable? Likely? We just don’t know – there is no evidence. Last time I looked at least 18 people had become pregnant during the trials and are being closely monitored, but that’s a very small sample size, and until the children are several years old, it can’t be said for sure that there were not problems. It was also said no long term side effects have been reported. That is true but highly misleading: the vaccines were only developed less than a year ago, so there hasn’t been enough time for any long term effects to develop or reach a statistical threshold. So therein lies the problem – saying “there is no evidence” when there have been very limited (or no) studies is absolutely not the same thing as saying “there have been detailed studies an no problem was found.” That’s a distinction that is lost on many people.
For the record on this subject, here is what CDC says as of 7 January 2021: Based on how mRNA vaccines work, experts believe they are unlikely to pose a specific risk for people who are pregnant. However, the actual risks of mRNA vaccines to the pregnant person and her fetus are unknown because these vaccines have not been studied in pregnant women. We know COVID19 presents risks to pregnant women, so if in a high risk group (like a health care provider) it might make sense to be vaccinated with an mRNA vaccine despite the unknowns. Work from home and sensible about social distancing, etc? Maybe best to wait. It’s not an easy call, based on an objective view of the available data.
Again, this isn’t to be anti-vaccine. There are rational risk-benefit arguments for some, and over time as more data is collected and if the early results hold up, increasingly large segments of the population to take these vaccines. What bothers me is that people present it as a binary, “no brainier” choice. It’s just not that straightforward and it is hubris to assert that it is.
Unfortunately there is no shortage of hubris, exaggeration, and binary thinking in order to sway opinions in our public dialogue these days. I could cite many examples, from election fraud (it probably didn’t impact the results, but that’s not the point: the US election system is broken, with deep structural flaws such that it doesn’t meet standards it imposes on other countries), to social debates like LGBTQ issues or abortion or climate change or …
In short, it takes objectivity and careful analysis to reach good conclusions. This is especially hard given the political parties benefit from a sharply divided electorate, advocates for various issues minimize or are even blind to potentially adverse consequences, and demand you “take a stand”, and of course the media industry profits from the noise and drama all that creates. Please don’t feed that process, and try to understand that many situations are not sound-byte simple.
In short, life is complex. Don’t fall into the trap of absolutes.
First, I would urge everyone not to focus on wobbles in the numbers. It really offends me how the media are saying things like “the US has more cases than any other country!” That is either gross ignorance and incompetence, or else misleading and irresponsible fear mongering (my bet is the former). For example, the US is reporting 216,722 cases, Italy 110,574. Leaving aside the difference in testing (who is tested, availability of testing, etc), the simple fact that Italy’s population is only around 60 million, vs. the US at over 330 million, comparing the two without adjusting for the fact the US is over five times the size of Italy is silly. On a per-capita basis, to exceed Italy the US would have to have over 600 thousand cases – assuming we are even measuring the same thing (and we are not). I started to do a plot of this and it was boring; you can barely even see the US points on a properly scaled plot yet.
Here’s the latest chart, which plots the mortality rate per 10,000 people so they are somewhat comparable. As previously discussed, these things move slowly, and day to day wobbles have to be viewed with caution. The good news it is does look like Spain is starting to “turn the corner” in the curve; Italy *might* have started to turn as well – we need another couple days of data to confirm. Any chart can be clicked to embiggen …
Lots of graphs are making the rounds, and in the White House briefing yesterday (31 March) there was a lot of talk about the Institute for Health Metrics and Evaluation (IHME) and other models of resource use or peak deaths. It seems there is some confusion about what the graphs are showing, here is a brief 😛 overview of the two most common you will see. The key difference is that they are trying to answer two different questions: how many total of something, and the peak rate of something.
Let’s take a detailed look at Spain mortality, since they are right at their peak, and it might be a good analog as to what New York is facing. Note that in all of my analyses I’m focusing on deaths. The reason is that the definition of a “case” or “hospitalization,” much less “infected,” is really fuzzy because to be blunt the testing is inconsistent and, well, a mess. But deaths are being tracked a bit better (at least outside China). It’s also important to recall that deaths lag “cases”, in this disease typically by about 15 or more days. So you will see the number of cases per day start to drop even though the death rate is still climbing or steady.
Here is a plot of the deaths in Spain, along with a statistical prediction (red dashed line), and the rate at which those deaths are happening (black line, exaggerated by a factor of 10 to better see it). In other words, the total deaths, and the deaths per day . You can clearly see the two curve shapes:
The peak death rate per day happens in the middle of the outbreak. In this case, Spain probably hit the peak sometime in the last couple of days. The model forecast 900 per day, 908 were reported Monday. Hopefully this forecast holds true, and Spain (and Italy) are starting to recover. Italy has reported a drop in new cases, the death rate should drop quickly as well. I tend to use the cumulative mortality plots since that is showing you the end point and where you are with respect to that end total. You can estimate the rates based on how steep the curve is. In technical terms, the total deaths to date is a cumulative function, and the rate of change is the first order derivative of that function (not for the faint of math heart).
All of my graphs other than the above example are normalized for population. What that means is that the denominator is fixed so we can compare. I really wish everyone would do this because otherwise you can’t see what is going on. For example, the population of Spain is 46.6 million, New York State around 20 million. So 100 deaths in Spain is the equivalent of 42 deaths in New York. So when you are looking at a graph, be very aware of what question it is trying to answer, and how it is scaled!
Here is the mortality data as of this morning for a variety of states and countries. This plot is the “cumulative deaths per 10,000 population plot,” which is trying to answer three questions, how do different regions compare, where are they in the overall progression of the outbreak, and how many total deaths can we expect:
As can be seen, New York is just entering the steepest part of the curve. If these trends and statistical projections pan out, New York can expect between five and six thousand deaths over the next two weeks, and across the US, between 75,000 and 125,000 by June. That is in keeping with what the more complex models are showing as well, in the case of my “in-house” model, around 130,000. Hopefully the mitigation measures will start to “bend” that curve, but remember what we are seeing now in deaths is the result of actions three to four weeks ago, so be patient and follow the mitigation guidelines so you or someone you care about won’t become a patient!
A lot of people who are playing with the numbers for COVID19 and coming up with huge death tolls, in the millions or even billions, are missing some key aspects of how infectious diseases and population growth works. Here is a bit more about the dark art of predicting how many people will fall ill from something like this.
The exponential growth phase of any predator (the SARS-COV-2 virus) moving into a new environment is limited by the food source in terms of both the raw supply and behavior of that food supply (the food supply, in this case, is us). If you want to learn more about that, here is a nice article that describes how this works. The bottom line is that the period of time of exponential growth for a virus is limited both in terms of the total population, immunity (either existing or developed) in that population, and changes in the behavior of that population (for humans, things like “social distancing”). So the “curve” always ends up being “S” shaped following what is called a logistic function or logistic curve. At some point, the thing just runs out of food …
In modeling viral outbreaks, the simplest models just try to figure out the three parameters that describe that curve (the midpoint, the peak rate or shape, and the maximum). In the early days of the outbreak, you can collect data such as mortality, and “fit” that data to the curve to try to estimate the ultimate variable of interest, usually the end total population mortality. More advanced models simulate things like transportation networks, interaction between people, infection rates, development of immunity, etc. These kinds of models are really useful to figure out what is the most effective way of dealing with an outbreak. But the neat thing is that these advanced models usually end up generating a logistic curve. There are theoretical reasons why this works that I won’t bore you with here (sort of like how the central limit theorem and probability bell curves work).
If we look at the data as of this morning (30 March 2020), we can fit the various data sets to logistic functions and see what the future might hold, and how things are progressing in various locations. One of my real pet peeves is when people put raw numbers on a graph that are not “normalized” for population. For example, even though a US State is similar in geography and size to a European country, comparing New York (19.5 million people) and Italy (over 60 million) to Georgia (10 Million) directly doesn’t work unless you scale it for the population. The most common way of doing that is in deaths per 10,000 people. That way you can compare them more directly. I’m not showing the “whole US” numbers, because the US is a very disparate place, with multiple “epicenters”.
Here I’m running a simple model on Italy and Spain, as they are far enough along to see how things are going, and comparing to US States. Here’s today’s plot of the data (points), with several projections (lines). As always, click to embiggen:
The solid black line is based on data from around the world as of the first week of March. At that point, we had the China data, but didn’t really trust all of it. We also had limited data from the Diamond Princess. The solid light grey line is a line derived from the H3N2 outbreak in 2017, but assumes the day of maximum rate occurred 5 times sooner (in other words, the progression of the outbreak happened five times faster). This line is interesting since it provides context in terms of the final outcome, but also to reinforce the fact that COVID19 is dangerous because it moves so fast. Of course now we have nearly 20 days more data, and Italy and Spain are much further “down the curve.” If we fit these lines, we end up with two additional estimates of our three parameters. The end point for Italy would seem to be about 2.9 deaths per 10,000 people. For Spain, it is on track to be higher, 3.1 per 10,000. Spain may be a bit high, due to two separate “epicenters” of their outbreak, but let’s stick with what the data says as a boundary. For reference, the end mortality rate for the unvaccinated population of H3N2 was 2.96 per 10,000. Netherlands and France are along similar tracks. Elsewhere, I think we can say that China and Iran are not really reasonable. South Korea is a special case – fast action, prepared health care system.
As you can see from the US state points, we’ve got a variety of things going on. Washington State, after being the initial epicenter, has done well in limiting the spread. NOLA scared everyone but is now below the projections – but I suspect that is a reporting artifact and will “jump” back up to the rest of the pack. NY and NJ are right on track. I’m having a really hard time believing the Georgia reports. I suspect munging.
So what does that mean for the US? The US is a big place with weeks separating the exposure times across the country. Some areas will be hit harder than others based on urbanization, how soon and how proactive the measures were taken, how patient folks are in sticking with them. Here are the end values using each of these four estimates, along with an estimate from a complex biological warfare model:
- Early March COVID Model: 72,820
- H3N2 Analog: 97,976
- Italy Curve: 95,990
- Spain Curve: 102,610
- TAOS(tm) Eir: 133,215
Dr. Anthony Fauci on CNN’s “State of the Union” Sunday talked a bit about this and CDC’s internal models:
Whenever the models come in, they give a worst-case scenario and a best-case scenario. Generally, the reality is somewhere in the middle. I’ve never seen a model of the diseases that I’ve dealt with where the worst case actually came out … They always overshoot. I mean, looking at what we’re seeing now, you know, I would say between 100 and 200,000 (deaths). But I don’t want to be held to that.
All I can say is I’m with Dr. Fauci: I don’t want to be held to any of this either 😛
What does all this mean to you personally? To repeat: take this seriously, follow the CDC guidelines, limit interactions outside your immediate household (aka social distancing), keep strict hygiene protocols, and otherwise do 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. Don’t focus on the numbers, just take care of yourself, your family, and your neighbors, and in three or four weeks the worst should be over.
Don’t be scared by the numbers or media terms like “skyrocketing” and the heartbreaking individual stories. As you can see from the curves, that’s a natural part of the process. I understand the sensitivity around comparing COVID to influenza because it is moving so much faster, but as horrible as this is going to get for our health care professionals, the “good” news is from a whole population mortality rate it’s not all that different. The 2017 flu season probably killed 61,000 (1.87/10,000 whole pop, 2.96/10k unvaccinated). In the late 1990s, several years had rates well above 3/10k (1998 was 3.46, or with today’s population, 115 thousand deaths). As I have said, it’s not that we are taking COVID19 too seriously, it’s we don’t take influenza seriously enough most of the time.
Nothing much has changed since Wednesday. Here is the same plot I ran the 25th, updated with three more days of data. The black line is a theoretical curve that represents a generic model of how COVID19 mortality should behave. The grey line is the 2017 H3N2 flu progression sped up by a factor of 5 for comparison purposes (in other words, the 24 weeks of the heart of the normal flu season compressed into 4-5 weeks). The dots represent actual data as of the totals as reported this morning. I added a few more regions like France and the states of Georgia and Louisiana, as well as South Korea. Click to embiggen …
Of these, China (grey +) and Iran (grey 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 to be following the expected progression. South Korea is a clear outlier, but they jumped on this very early, have a very aggressive testing and containment approach, and have FOUR TIMES more hospital beds than the US. New York is the cyan triangles that are hard to see because it’s just starting to creep along the curve – about day 30 or so, as is Louisiana. 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. But the curve will almost certainty, and quickly, stabilize.
Where will it end? Not too different from the mid-week estimates. The latest projections are that the US will see between 50 and 80 thousand deaths. That sounds like lot, but the 2017 influenza season saw 61 thousand H3N2 influenza deaths, albeit 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 – even if the risk for you personally is low if you do not have pre-existing health problems. 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 really is our future, or something worse? Italy should be passing their peak number of deaths per day. 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.
Short Version: yes, the numbers without context are scary. The media is shifting attention from one “center” (like NYC) to another (NOLA) depending on which gives the bigger headline. It is obscene. The big picture is nothing has changed. Take this pandemic seriously but not to panic, 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.
And STOP focusing on the “death race” numbers. It’s just not healthy. Check the news maybe once a day to see if local guidance has changed, and otherwise take care of yourself and your family. PS – don’t binge watch ST:Picard, even Sir Patrick couldn’t save it 😛
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.
As of the final totals from yesterday, 22 March 2020, there have been 5476 deaths from SARS-COV-2 in Italy. To put that in perspective, in the 2013/14 influenza season, there were 7027 excess deaths due to influenza recorded. In 2014/15, a 20,259 deaths were attributed to that outbreak, while in the worse recent year, 2016/17, 24,981 died from influenza. (from Rosano et al, Int. J. Infections Diseases, Vol 88, Nov 2019, pp 127-134).
Yes, COVID19 is different in how fast cases are coming, but not in whole population mortality. The speed of progression seems to be about 4 and 6 times that of influenza, and that is producing a HUGE strain on the system. But the outcomes have yet to approach a bad influenza outbreak. The present rate of the last three days of 690/day will have to continue for another 28 days to reach the 2016/17 flu season toll. I’d be very surprised if the rates don’t start to drop soon. If they haven’t dropped in Italy in two weeks, maybe then it’s time to worry, but for now, things seem on track for this to be a “flu season in 6 weeks” virus. Catastrophic for the health care system, but not a big deal in whole population terms. In economic terms, that’s a whole different question …
To repeat from yesterday: 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. That is why COVID19 is so dangerous, and why everyone needs to take it seriously, following the CDC guidelines, exercising social distancing and 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. Fixating on every up or down tick in the numbers, and chasing down every wild number or wild theory making the rounds is just not sensible or conducive to sanity. My advice is to be careful, keep watch over those around you, take advantage of the time off as you can, check the news maybe once a day to see if anything has really changed as to what you should do, but don’t drive yourself crazy hitting refresh; this is a slow motion disaster. April will be the cruelest month – but by the last week things should be looking up.
A major frustration with how the COVID19 cases and deaths are being presented is that they are without context. Raw case or death numbers, or mortality rates based off of them, just aren’t useful. Lets take a closer look at the State of New York, since we are starting to get some data there. Sorry, there will be math.
Nobody likes to think about death. The fact is people die every day from a variety of causes. Death is part of life. Where diseases or accidents are concerned, what we worry about is “excess mortality” – in other words, how many people die that, all things equal, probably shouldn’t have? Death rates change during the year – higher in winter, lower in summer. Daily rates are very “noisy”, so it’s probably best to use weekly aggregations. For weeks 10 and 11 (mid March, where we are now), between 2015 and 2019 there were between 2000 and 2300 deaths per week in week 11 in New York (about half in the City). Pneumonia and influenza – “P&I” deaths – are the cause in about 10% of those cases this time of year, or ~190 per week.
So, this week we will probably see 60-70 deaths from COVID19 in New York (60 as of this morning). That’s significant – but in perspective, only a 3% or so increase in average mortality. Assuming the rates are hitting the steep part of the curve, we can probably expect to see on the order of 350 a week for the peak week, or 15% higher than a “bad” influenza week, and that rate sustained over a couple of scary weeks until it drops. The media will go berserk – and the strain on hospitals and health care workers will be horrific – but the big picture is that, for the major of people, it’s not a catastrophe. You can do a similar exercise on the Italy numbers. Yes, they are scary. And not to take away from the individual tragedies of each death, or the toll on the health care system, the increases we are seeing in overall population mortality are not catastrophic in whole population terms. If we start to see the numbers go above 500 in a week in New York, or the rates stay above 300 for more than three or four weeks, there may be something else going on.
For what it’s worth, my viewpoint hasn’t really changed: We are just entering the steep part of the curve. It will be very scary as cases numbers seem to jump up from day to day – 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, and given the testing problem, many will never know it), 400,000 to 600,000 need hospitalization, and 35-60 thousand deaths. The economic damage from this is going to be far greater than I anticipated a month ago ($85 Billion), largely because I overestimated the advance planning being done by western governments (who screwed this up big time), and therefore underestimated the overall reaction later. I think we’re in for a rough ride this year – probably on the order of 10 times that in direct impacts, or $850 Billion to a Trillion (!) dollars (out of a roughly $20 Trillion economy). Some of that are “paper” losses, but for small businesses I fear it will be all too real and many won’t make it.
As noted before, for perspective compare to the 2017 influenza season: 45 million symptomatic, 810,000 hospitalized, 61,000 died, probably in the ball park of $80 Billion in economic impacts. HOWEVER, rather than coming over 20 weeks or so, the bulk of the COVID19 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 influx. That is why COVID19 is so dangerous, and why everyone needs to take it 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.
At this point, there are way too many people doing way too much speculation based on way too little accurate data. I don’t want to contribute to that. Fixating on every up or down tick in the numbers, and chasing down every wild number or wild theory making the rounds is just not sensible or conducive to sanity. So unless something drastic changes, and it’s relevant to my areas of research, this will be my last COVID19 post for a while. My advice is to be careful, keep watch over those around you, take advantage of the time off as you can, check the news maybe once a day to see if anything has really changed as to what you should do, but don’t drive yourself crazy hitting refresh; this is a slow motion disaster. April will be the cruelest month – but by the last week things should be looking up.
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 …
Influenza Hospitalization Surveillance Network (FluSurv-NET), US Centers for Disease Control.
2019 Novel Coronavirus COVID-19 (2019-nCoV) Data Repository by Johns Hopkins CSSE