April 29, 2020

Still Flying Blind: Can Meteorologists Help Epidemiologists with Coronavirus?

Things are not going well these days regarding predicting the future of coronavirus in the U.S., with the epidemiological community, including critical government agencies, not succeeding in these important areas:
  • They do not know the percentage of the U.S. population with active or past COVID-19 infections.
  • They do not have the ability to quality control and combine virus testing information into a coherent picture of the current situation.  This is a big-data problem.
  • The epidemiological simulation models used by U.S government agencies or American universities have a poor track record in their predictions, with their quantification of uncertainty unreliable.

But there is a group in the U.S. with deep experience and a highly successful track record in predicting complex environmental threats.  A group that is masterful in taking observations, combining them to create a good description of reality, building and testing predictive models, providing uncertainty information, and communicating the information to decision makers for critical life-threatening situations.

You know these people meteorologists involved in the large U.S. numerical weather prediction community.  And perhaps meteorologists can help epidemiologists and the U.S. government to get a handle on the coronavirus situation.

Now don't take this blog as one uppity weather guy trying to give advice "outside his lane."    A published paper in the Journal of Infectious Diseases (2016), said much of the same, with the authors noting the huge similarities in the work meteorologists and epidemiologists do and suggesting that the epidemiological community is roughly 40 years behind the numerical weather prediction enterprise.  They observed that both epidemiological and numerical weather prediction models are attempting to simulate complex systems with exponential error growth, and thus have great sensitivity to initial conditions.

So perhaps the experience of meteorologists, who spend much of their time thinking about how to improve weather forecasting, may be relevant to the current crisis.

The First Step in Prediction:  Describing the Initial State of the System

To predict the future you need to know what is happening now. The better you can describe the initial starting point of forecasts, the better the forecast.

Meteorologists have spent 3/4 of a century on such work, first with surface observations and balloon-launched radiosondes, and later with radars and satellite observations.  Billions have been invested in the weather observing system, which gives us a three-dimensional observational description of atmospheric structure.  Big data.  And we have learned how to quality control and combine the data with complex data assimilation techniques, with the resulting description of the atmosphere immensely improving our predictions.  This work is completed operationally by large, permanent groups such as NOAA and NASA, with large interactions with the research community.

Contrast this to the unfortunate state of epidemiologists predicting the future of the coronavirus.

They have very little data on what is happening now.  They don't know who in the population is currently infected or has been infected.  They don't even know the percentage of the current population that is infected.   Without such information, there is no way epidemiologists can realistically simulate the future of the pandemic.  They are trying, of course, but the results have been disappointing.

What they do have is death information and limited testing of those that are sick, but that information is insufficient to determine the state of current and past infection in the community, or essential parameters such as transmission rate and mortality rates.

Obviously,  the U.S. needs massive testing of the population to determine how the virus has invaded our communities and who is now immune.  The lack of such testing is a terrible failure of multiple levels of government.

But just as big a failure is the lack of random sampling of the population to determine the percentages of infection and how that varies around the nation.

We do have enough testing capability to do this (remember national political polls only use thousands of samples,  not millions).  Why is the epidemiological community and our political leaders not calling for such intelligent sampling of the population?   With random sampling we would KNOW what is going on and not act out of ignorance (as we currently are muddling by).   Why is the media not baying about this?

Quality control is another major problem faced by the epidemiological community, which deals with multiple types of tests of various quality that need to be brought together to produce an integrated picture of reality.  Death information is unreliable, because of non-reports or problems with determining the primary cause of death.  Quality control is a difficult task, faced by the meteorological community as well, one that we have dealt with in our data assimilation systems (e.g., observations weighted by their past quality and sophisticated consistency checks).

Simulation Models

Starting with an initial description of the system one is predicting (the 3-D atmospheric structure for meteorologists, the initial disease state of the population for epidemiologists), simulation models are used to predict the future.

Meteorologists use complex, full-physics models comprised of equations that predict the future  evolution of the atmosphere.  Then we apply statistical corrections to make the forecasts even better.

Epidemiologists use three types of forecast models:

  • SEIR/SIR models is the most "traditional" approach, one in which the population is divided into different groups (susceptible, exposed, infected, recovered), using relatively simple equations to describe how folks move from one group to another, all of which have assumptions about how the disease is transmitted, the effects of social interactions and more. The UK Imperial Model is an example of this approach.
  • Statistical models that don't really simulate what is going on, but are really curve-fitting exercises, in which theoretical curves (often gaussians) are used to predict the future, adjusting the curves based on the evolution of disease in the past or at other locations.  There are many assumptions in this approach and they cannot properly consider the unique characteristics of the region in question. The UW IHME model is a well-known user of this approach.
  • Agent-based modeling actually try to simulate the community at an individual level and it is the most complex and computer intensive approach.   Although dependent on several assumptions (such as the transmission rates between individuals) this approach is the closest to the numerical weather prediction used by meteorologists. The GLEAM model from Northeastern University (and others) is an example of this.

The trouble is that none of these epidemiological models have proven particularly skillful and produce vastly different results, something noted in some of the media, social media,  and several new research papers.  The UW IHME model, often quoted by local and national political leaders, has been particularly problematic (this paper describes some of the issues), including the fact that its probability forecasts are highly uncalibrated.  The UK Imperial Model in mid-March predicted 1.1-1.2  million deaths in the U.S., even with mitigation (so far the U.S. death toll has been about 60,000).  Many of the coronavirus prediction efforts have evinced unstable forecasts, with great shifts as more data becomes available or the models are enhanced.

The poor performance of these models in predicting the coronavirus is not surprising:  the lack of testing and particularly the lack of rational random sampling of the population results in no viable description of what is happening now.  The favored IHME model is only based on death rates, not on the infection state of the community.   Can you imagine if meteorologists tried to predict weather only using data around active storms? Very quickly, the forecasts--even of storms--would become worthless.  The same happens with coronavirus.

You cannot skillfully predict the future if you don't have a realistic starting point.  Furthermore, some of the models are highly simplistic and not based on the fundamental dynamics of disease spread (like the curve-fitting IHME approach).

The U.S. has a permanent, large, well-funded governmental prediction enterprise for weather prediction, one that has improved dramatically over the past decades.  No such parallel effort exists in the government for epidemiological modeling.  Instead, University groups, such as UW IHME, have revved up ad-hoc efforts using research models.

The Bottom Line:

Our government and political leadership have been making extraordinary decisions to close down major sectors of the economy, promulgating stay-at-home orders, moving education online, and spending trillions of dollars. 

And they have done so with inadequate information.  Decision makers don't know how many people are infected or were infected. They don't know how many people are already immune or the percentage of infected that are asymptomatic.  They are using untested models that have not been shown to be reliable.  This is not science-based decision making, no matter how often this term has been used, and responsibility for this sorry state of affairs is found on both the Federal and state levels.

The meteorological community has a long and successful track record in an analogous enterprise, showing the importance of massive data collection to describe the environment you wish to predict, the value of sophisticated and well-tested models to make the prediction, and the necessity to maintain a dedicated governmental group that is responsible for state-of-science prediction.

Perhaps this approach should be considered by the infectious disease community. and the experience of the numerical weather prediction community might be useful.


  1. You are suggesting a technical solution to what is essentially, a political problem. Review the history of Covid testing in the U.S., starting when the Administration rejected the W.H.O. tests. Compare the composition of the C.D.C. today, versus what it was 4 years ago. You are whistling into the wind.

    You may also have an exaggerated sense of the interdisciplinary transferability of your skill set.

  2. As I've said before, most reactions from governments, subject matter experts, media outlets and individuals are based on their own fear and/or the manipulation of other groups fear. It's human nature and will never change.

    I agree with Cliff that a technical solution is part of our path forward. We can't fix it (whatever "it" is) if we don't know what's broken. Right now, we have no meaningful clue of what is actually happening. There are many layers of over and under exaggerated assumptions that are contributing to the problem.

    I agree with Glenn that this is a political problem but, not with it being an essential part. Governments all over our little planet at every level have botched their response in some way. Some have had better luck than others and none have gotten it completely right.

    Once things calm down and people stop reacting to fear or letting others manipulate them and their fear, we'll be able to figure out what is actually going on and respond for the good of the whole human race.

  3. This whole thing is a hoax. Averaging 5 deaths/day for a week now and this justifies ruining our wonderful WA state economy. All because Jay Inslee has a political axe to grind against President Trump. Disgusting.

    1. Let's look at this hoax: 225k deaths reported worldwide, 61k deaths reported in the US, and approaching 800 reported in Washington. And the CDC is now indicating substantially under-reporting of COVID-19 deaths in the US. And this is no where near being over. Better look in the mirror to see the real hoax.

    2. Annually, up to 1/2 million people die worldwide from the seasonal flu.

    3. The worst flu season of the last 10 years in the United States was 2017-2018 when 61000 people died. COVID-19 surpassed that yesterday. Yesterday's death toll was just over 2400 people. Two weeks ago the daily death toll was just over 2800. I would expect we will average between 1500 and 2000 deaths per day nationwide for at least the next two weeks. This is a disease with a incident fatality rate at least 10 times the flu. That applies to all age groups: i.e. the 18-49 demographic has a flu IFR of around 0.01% (1 in 10000) for COVID-19 it is closer to 0.1% (1 in 1000), for those in the roughly 65 - 74 group seasonal flu IFR is 0.25% (1 in 400), for COVID-19 it is more like 2-3% (1 in 50). Data here: https://github.com/clauswilke/COVID19-IFR

      COVID-19 is not the flu. It is 10 times as deadly. It is more infectious. There is no vaccine. These are facts.

    4. Man, you're way behind on your trump lies. "Hoax" was so early February. Since then we've had
      - going to be zero cases in a couple of days
      - anybody that wants a test can get a test
      - declares national emergency (take that, hoax!)
      - this could be a hell of a bad 2 weeks - possibly 100K deaths
      - ingest disinfectants

      And if this is a political axe, Mike DeWine, republican governor of Ohio, owns the same axe as Jay Inslee - they shut down their states the same day.

    5. COVID-19 turning out to be huge hoax perpetrated by media:


      Infections are undercounted while deaths are difficult to estimate because of other conditions present. Some studies claim an incidence of fatality less than 0.5% like this one published in Nature:


      But I agree, this is not a flu, it's a cold virus.

    6. Missing@Random, the report by Ferguson et al that the statistics you mention is based on is old (16 March 2020). Those numbers are based on models that have turned out to be exaggerated and the predictions have never materialized:


    7. I won't bother with your link from the right-wing media. I also don't know why I'm arguing with somebody who credulously accepts that disinformation as valid and real, but for the sake of the audience here is a rebuttal.

      The Imperial College team is a pretty solid statistical shop, but yes, those estimate in the Github link are old. They did just release a really cool new model which estimates the reproduction rate, the effect of interventions, the total number infected (including asymptomatic infections) and the total number dead. It's here: https://mrc-ide.github.io/covid19estimates/

      If you download their data you'll see that they have an IFR of around 0.8 to 1%, it differs by country because demographics differ by country, and this disease has a different IFR for different age demographics. Your link to Nature is a news article and not the primary literature, but the 0.5% number comes from the Diamond Princess. It's not clear how representative the Diamond Princess population is when our scope of inference is to America at large. Iceland is another interesting case study given that they have their outbreak under control, they have one of the highest per capita test rates and most cases are closed, although the demographics of the infected population skews younger: https://www.covid.is/data The key thing is that Iceland test a lot of people of IFR for Iceland is 0.6%.

      So lets take 0.5 to 0.6% as the IFR for COVID-19. These are pretty optimistic, but I'm an optimist so lets roll with it. Importantly, realize that these number represent the IFR averaged over all age groups and includes asymptomatic cases (lots of testing which caught asymptomatic cases). If we want to continue with the flu comparison (which is a bad comparison because many at-risk populations are vaccinated against the flu and because it is less infectious,but you seem obsessed with it), then to make an apples-to-apples comparison we need to consider the IFR of the flu including asymptomatic cases. The commonly cited IFR for the flu of 0.1% is really the case fatality rate for *symptomatic* cases (it says so right there on the CDCs website). The CDC also estimates that something like 40% of flu infections are asymptomatic. So if we include those numbers that drops the IFR of the flu to something more like 0.06-0.07%. That number is about 10 times less than the IFR you just cited for COVID-19. So COVID-19 is just shy of 10 times as deadly as the flu. The other points remain, namely it is more infectious and there is no vaccine (the flu infects between 5 - 20% of Americans every year, that includes asymptomatic cases and accounts for vaccinations - roughly two-thirds of Americans 65+ get the flu vaccine).

      It also means that 1 in 200 people infected with COVID-19 will die. Do you know more than 200 people?

      Suppose only 10% of Americans get COVID-19 with a (very optimistic) IFR of 0.5% - that's 165,000 deaths. Let's split the difference between the Imperial College estimates and the Diamond Princess IFR (0.75%) and suppose 20% of Americans get COVID-19 - that's just shy of 500,000 deaths. Now suppose Imperial College is right and suppose also that the experts are right who say we won't be out of the woods until something like two-thirds of Americans have immunity which the only way to get right now is to get infected (maybe, we don't know whether people can get reinfected with COVID-19): 328 million * 2/3 * 0.01 = 2.2 million dead.

      Stay home. Wash your hands. Don't watch Fox News.

    8. Missing@Random - The last thing we need is your scare-mongering with bogus made up 'facts'. The whole thing is a total hoax and a joke.

    9. Sweden's death rate has been 22 every 100,000 (not 1 in 200). We need to stop believing these bogus predictions that are causing the world economy to be destroyed.

    10. I dunno if im reading it wrong or what, but on the CDC website, it says 37,000 have died as of may 1 from covid19.

  4. Dear Uppity Weather Man, Thank you for being so brazen as to think outside of the box. Some may suggest that you should stay in your lane, don't try to push the envelope; but, I would not bother reading your blog if I thought you were some dull-witted yes man!

  5. The Epidemiologists are more than 150 years behind the NWS - once the telegraph systems were set up, real time weather observations were possible over wide areas, and then it took until the WW1 era to begin to develop anything close to what would be considered accurate visualizations of what storms were and how they developed. It then was a giant political issue to get both the world to share data and then in the US to provide certain types of forecasts, like tornado watches and severe weather forecasts, and the technology to improve forecasts has been evolving but there is still an element of uncertainty. And we deal with weather forecasts occuring every day, versus maintaining a similar capacity for pandemics that are once in a decade to once in a century occurrences. It's really hard to translate one system to the other, tho I wish we could.

  6. Great notion, Cliff. As others have eluded (and given credence to), the science may not matter at this point. Its being drowned out by the usual incessant noise that drowns EVERYTHING out.


    The pandemic has already been hopelessly woven into the vitriolic fabric of the dysfunctional political discourse that is our nation, and there is no turning back. Models and testing won't matter, because even this crisis has failed to bridge our differences and convince the nation that maybe we should work together and check the (political) baggage at the door. Which makes sense. No model can take into account the random chaos that is Humanity, and the USA is more chaotic than most places.

    The logical progression from here is opening up most everything by mid-summer, more than likely without the data from testing. Which might just fall by the wayside at this point. The testing phase might be declared met by decree, and not by actual testing. Schools will need to open for a summer session (if anything) to get working parents back to their jobs. Schools are babysitters primarily and sometimes dispense education as a secondary benefit. The trick will be convincing consumers that their civic duty lies with spending money and participation in the broader economy, even if it means a greater chance of illness and/or death.

    If a second wave hits, do not be surprised if the USA, and maybe the world, decide they might just take their chances, accept the collateral damage, and tough it out by staying open. Which means basically ignoring the science and leaving it up to fate to decide until there is a vaccine (which people have to be convinced to take).

    1. "do not be surprised if the USA, and maybe the world, decide they might just take their chances, accept the collateral damage, and tough it out by staying open"

      You would hope so. But governments have savored how easy it is to keep people under control by instilling fear about some virus. 25% of all deaths have been in senior homes that could have been easily isolated early on!

    2. The precedents from the past did not offer much in the way of conclusive evidence that the virus was going to make victims of only the elderly and infirm. The 1918 flu was very indiscriminate. Perfectly healthy, younger adults dropped dead from that outbreak.

      Plus, we are talking about deaths. The challenges levied upon the medical system are treating those that will more than likely live, but still require ICU resources. That can occur at any age. Hospitals still need to be able to respond to other emergencies as well. The stay at home orders were geared toward not having our very lean, "Just in time" model of medical delivery collapse. Even with the lock downs, there probably were coin toss incidents where two equally sick people needed a ventilator, but there was only one available.

      Its doubtful the virus could have been contained to just old folks homes at the onset of all this. Who knows how long it was circulating before the alarm bells finally went off. So it was too late to just state that locking up the old and the infirm was going to be all required, with the intent to maintain the booming economy. Something that certain politicians are basing their entire legitimacy upon.

      Bottom line here is there were so many unknowns when this started and plenty of denial as well. Now at this stage, plenty of lip service is being paid to the fact that millions are so far not dying, and that number of deaths is too low a price paid thus far for the reciprocal damage afflicted to the economy. Which suggests that having millions actually die would be a fair trade for the obvious recession we are now in. Since anecdotal evidence suggests those who die are not productive members of the economy to start with, those millions dying would not matter much in terms of productivity or consumer demand.

      Do we really want to go there? Make a cost/benefit analysis of it all?

      The recession would probably happen anyway, if the death toll were that extreme. People would go into lock down without being ordered to for the sake of their own safety. The virus can make its victims potentially very sick and with millions dead, most everyone would know a friend or loved one who has perished to it. That sorrow and fear will drag consumer confidence down significantly. Its easy to be brave and combative online when the crisis doesn't directly effect you, but when it strikes close to home, most people will change their tune.

      Plus the rest of the world has their own approaches. The United States can't order other nations to stay open just to preserve international supply chains, manufacturing and business travel. This isn't just a United States problem, but as a nation of mostly self centered, self absorbed materialistic citizens enthralled with toxic political infighting, we of course made it all about ourselves.

      So what is done is done. Its pretty easy to hop on the internet and state that the experts are wrong, the government is wrong or that there is a price in lives that has to be met before the damage to the economy is justified. All of it is at best conjecture and supposition. Lets not even get into all the conspiracy theories and other silliness floating around. The challenges are here, they are real and the problems need to be worked. The energy spent on blame games and shouldawouldacoulda's needs to be focused more positively.

    3. I agree that "what is done is done". But we can prevent that more damage is done to the economy by lifting the stay home order right now and going back to normal. Sweden never went into lockdown and the disease there has had the same course. Switzerland opened this week all shops including barbershops, is re-opening schools on May 11 and they have a clear plan in place how to get everything 100% back up and all restrictions lifted by August. Washington state should do the same!

    4. There isn't a one size fits all solution for all nations. Also, opening up won't guarantee business will come roaring back to where it was prior to the outbreak. Probably far from it. As long as the virus lurks, people will still weigh if its even worth it to go shopping as leisure. Everything people NEED is open right now. The rest are WANTS such as dinners out etc. Going to a restaurant doesn't even seem like a fun experience right now. Personally, I want to just wait until the full casual dining experience is back to normal. Some sparsely set room full of uneasy customers and masked wait staff who probably want to be somewhere else doesn't seem like good times. The owner is probably debating if its even worth it to be losing money just to be able to say "Yes, we have sit down dining now".

      Those who have money will sit on it. Those without, well they are not worried about buying things they do not immediately want (but don't need) as it is. Getting the outdoor activities back on line was one of the best actions that could be done RIGHT NOW to lift people's spirits a bit and allow folks to stretch their legs and not feel like they are prisoners.

      Just as an aside, Yakima now has the greatest infection rate on the entire West Coast. The factual reasons as to why are not know to me as of this post, but speculation is that rural areas are not taking this seriously. Mostly due to political ideology and not for a lack of being informed. Understandable, but still very unfortunate. We could be talking almost about self inflicted wounds.

    5. It's totally fine with me if people who are concerned want to stay home a bit longer. The rest of us should be allowed to go out and resume activities.

  7. The Stanford & USC infection rate studies were supposed to get a better handle on data, but both were very flawed. The Stanford study got volunteers from Facebook. The USC supposedly was representative sampling. Even the antibody tests weren't FDA approved.

  8. Cliff,

    My hero--challenging hearts and minds! Good work.

    1. If epidemiologists were given the advantage of the infrastructure that weather systems have, we would be far along. Public health has been stripped of funding for the last 50 years: no wonder we're 40 years behind. If you're still begging for money to maintain your weather balloons, you're not going to get a tool that requires a rocket to put into orbit.

  9. Posted came comment at WUWT.

    On Monday, April 20 (I missed it at the time) there was an opinion by Andy Kessler in the Wall Street Journal. I made it mid-way in the first of 4 columns and started to laugh.
    The title is "Upgrade Our 8-Track Government."

    Do your best to find and read this.
    - - - -

    We live in Washington State about 100 miles east of Seattle. The county has a large area and a small scattered population – students are gone from the local university, and so on.
    As of today the county has 14 confirmed cases with maybe 650 tested. Number of deaths = Zero.
    This is like living in a rain shadow in a state noted for rain and mist. Meteorologists might be able to help with such issues.

  10. The naive Cliff Mass does not seem to comprehend that game-theoretic decision-making plays a role in epidemiology models, unlike the weather that humans have no control over.

  11. There are met folk looking at the Covid problem; see https://bskiesresearch.wordpress.com/2020/04/14/model-calibration-nowcasting-and-operational-prediction-of-the-covid-19-pandemic/

  12. I do not trust ANY weather model beyond 96hrs.
    So, how can the WHO trust any beyond 1wk.?

  13. This is the inevitable course we as irrational humans will take. Cliff doesn't seem to understand that 99% of the population aren't scientists, or even engineers, and lead their lives by "gut feel" and by the unspoken mantra "it's all about me and my survival, and the survival of my bank account". That is except perhaps for a few very short examples of larger thinking, such as America during WWII, but these episodes quickly pass and things soon return to normal.

  14. Genuine research question: with mass testing, assuming a critical mass of people has already had the disease then the testing will provide us a macro view of potential (though lagging) people that still "need" to get the disease to reach societal immunity (we're not cattle). the concern is that testing is used as a method of entry, which is to say (using baseball as example) that MLB will open their season and test all players before the season/games...but if i test negative today, i could possibly test positive tomorrow and then testing fails as an "entry metric" - help me understand mass testing and uses of mass testing?

  15. Perhaps focus on areas of success. Here in Moses lake, population 25,000, we had no new cases (again) and remain stuck at 33 cases. Closing down the city makes absolutely zero sense. Am I wrong?

    1. One rationale I read for not opening up areas with low infection rates is that people from still closed high infection rate areas will flock to those areas seeking Recreation, camping and a sit-down Burger.

      Flow force from high pressure to low pressure.

      Chris H.
      Heli-free North Cascades

  16. How much are we spending on weather forecasting, and how much were we spending on pandemic forecasting over these last 5 years? The US was getting hints of a problem the last few days of December. Limited action was taken in late January, but no decisive action until mid March. Testing capability is still not up to our needs. Cliff is right that a fairly massive system should be built out. But with viruses some of the building out needs to be done anew for each virus. As I understand it the UW IHME model was mostly designed for hospital planning over the next few/several days. It was better than nothing, but still obviously not close to what is needed.

  17. Cliff,

    We need statisticians, not meteorologists. This is missing data problem and an estimation problem. The SEIR models, to the extent they're mechanistic like many numerical weather models, actually do a pretty good job of describing this disease. The difficulty is that we don't know what the parameters are. With 3.4 million cases and 240,000 deaths worldwide, we have a lot of data, but it's definitely incomplete (i.e. there are lot of unidentified cases, wide disparities in testing, etc). The Imperial College team is doing as fine a job as anyone. They've just released a new model which importantly estimates the impacts of non-medical interventions. They show that only "lockdowns" meaning something like Inslee's stay-at-home order or stricter are effective in reducing R-naught below 1, but that they need to be maintained for quite sometime. See here: https://mrc-ide.github.io/covid19estimates/

    Second, I don't know why you're saying their March 16th paper is any sense "wrong" or has poor performance. For their "mitigation" scenarios to which the 1.1 million dead number applies, they did not look at the effects of any more strict non-pharmaceutical interventions then self-isolation of suspected case, home quarantine of known cases, closing schools, and isolation of the elderly. They did not provide a total deaths number
    for their "suppression" scenarios, but suppression is thought to entail population-wide social distancing like the stay-at-home order. We decided, more or less, in this country to (briefly) adopt the suppression strategy. The exact effect of the suppression strategy (at least in Europe) on the reproduction rate is looked at a bit more in their more recent paper.

    Even so, take a look at Figure A1 in the PDF you linked to. The green line estimates critical care beds occupied under the strictest set of interventions they consider (i.e. population wide social distancing). That line peaks out in mid-May at something like 3.5 critical beds occupied for per 100,000 population. According to Worldmeters (https://www.worldometers.info/coronavirus/#countries) as of 5 PM PDT on May 1st, there are 16,500 critical cases right now. That is roughly 5 per 100,000 population. So we're actually about where they said we would be under suppression. But now it seems like many states want to move to a mitigation-type strategy. Consider that Figure A is the closest they get to depicting that for the United States (really that is the do nothing scenario, under mitigation the curve would be shifted to the right). It's really hard to see with the scale of the axes, but in Figure A the deaths per day for the United States was predicted to be something like 1 per 100,000 in early May. We're at 0.6 per 100,000 right now. We're under that curve but not by much and politically moving in the mitigation direction. These observations do not make me think that the Imperial College paper is poor perfoming.

    Third, the IHME model definitely has problems. It is way too optimistic on the backside of the curve because it assumes that case will follow a symmetric distribution curve. Talk to Carl Bergstrom there at UW, he has good sense of the shortcomings in that model and can probably point you to better models.

    Lastly, I just want to point out these lines from the end Imperial College's March 16th paper: "We therefore conclude that epidemic suppression is the only viable strategy at the current time. The social and economic effects of the measures which are needed to achieve this policy goal will be profound. ... However, we emphasise that it is not at all certain that suppression will succeed long term; no public health intervention with such disruptive effects on society has been previously attempted for such a long duration of time. How populations and societies will respond remains unclear."

    It is becoming more clear that we could only put up with for about a month and a half.

  18. You can. Personal responsibility is just that. Decisions and consequences. THERE IS ABSOLUTELY NOTHING ELSE to say.

  19. Typical panic and overreaction by government and media. The risk in no way justifies shutting down the entire economy and scaring the populace. But it's too late, the sheeple are all cowering with masks as if this was some kind of hemorrhagic fever with imminent death. It makes sense to quarantine the sick and elderly, the rest should go about their business and restore the economy. If you are one of the many who's livelihood has been destroyed, vote accordingly in November.


Please make sure your comments are civil. Name calling and personal attacks are not appropriate.

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