18. Weekly Update: Watch Out California

(5/21/20) We are going to change emphasis and focus on the U.S. and CA in particular in this update. The Northeast hotspots are recovering well, but other outbreaks are developing across the country and CA may be on the cusp of a huge outbreak.

We are going to dispense with our customary international and U.S. death rate plots to focus on key U.S. states. This is an extension of my recent post (5/19/20 – Daily Rumblings – CA the Next Hotspot), which you should read.

But first let me proudly say that my manuscript describing this model and its attributes has been published on the on-line site MedRxiv and can be found at:

https://www.medrxiv.org/content/10.1101/2020.05.16.20104430v1

You can preview or download a pdf of the full paper.

I may also start reducing the frequency of updates as these take time and I don’t have a lot of it. Further my model is now being closely replicated by the renowned UW IHME model that I have compared to (see below). My mission to provide state-of-the-art forecasts and to debunk the misleading data that proliferated (and still does) during the early days of this pandemic is less of a concern now.

So, below are death rate plots for the U.S., NY, and CA to show a range of different behavior most likely due to differing levels of diligence in practicing social distancing.

The symmetric Gaussian dotted line is for visualization to accentuate the varying deviations for NY vs. CA. You can see extreme Sunday under-counting for CA and for the U.S. overall but less evident. This quirk in counting deaths is also observed internationally as seen in previous blog posts.

New York was the deadliest place in the world due to Covid-19 and still has a high prevalence (active cases) but it is receding fast. CA is at a much lower level (about 6x lower by per capita), but deaths and hospitalizations are stubbornly flat and not receding (see Daily Rumblings 5/19/20 discussing LA and OC). In fact, CA looks to be going through a death rebound. Very sad; may downgrade it to red again. We forecast the 6x greater prevalence per capita for NY vs. CA (down from 20x three weeks ago) to fall below that in CA in about 3 weeks and this will make NY safer to ease social restrictions sooner than CA (though of course this has already started).

We now show our familiar table for forecasted total deaths, prevalence (current cases), and incidence (new cases) along with their values per capita (per million people) as well as dates we consider to be the earliest to begin a graduate easing of social distancing. We reiterate again that our model does not anticipate future changed behavior such as social relaxing, but rather past behavior, so we expect to see some upward movements that could be quite severe for certain populations.  We will be developing a revision to the model to explore various scenarios for relaxation of social restrictions.

Key observations include:

  • The U.S. trails the rest of the world: There just isn’t much else to say here. There are pockets of improvement particularly in the Northeast, but the rest of the country just doesn’t get it and there is just no leadership to follow. Wearing masks actually does help in many situations, but it also stands as a symbol of solidarity. Most of Americans get that, but the one person who needs to, doesn’t. OK zip it Jack!
  • NY and NJ: NY as stated above and last week is doing very well so far. NJ, which was showing good progress seems to be hitting a little plateau, which might reflect some regional outbreaks. We hope they can get that under control before the whole state goes to social easing. This is really like wild fires and you have to catch the small brush fires before they explode.
  • Social easing: I no longer pretend that we should wait as long as the ease dates in the above Table recommend, although that would keep the death rates down. I’ve said it before; we need to socially ease, but we have to be very smart about it and practice good hygiene and behavior. If we do these four things, we should be fine: keep distance where possible, wear a mask when close to people, don’t shake hands or hug, and wash hands and surfaces frequently.

I haven’t presented my favorite log-log plot of death rate vs. cumulative deaths in a while, but it is very informative in showing when recovery begins and also revealing when the recovery is floundering, as you can see below (see Figure caption). The European countries, except Sweden are recovering nicely. The U.S. is showing slow recovery and CA is relapsing badly.

Downward deviations from the linear slope indicate reduction from exponential growth and true recovery would be followed by near vertical recovery lines as seen for China and S. Korea.

Now for our comparison of forecasted total deaths to the venerable benchmark model from the Institute for Health Metrics and Evaluation (IHME) at the University of Washington (UW) (http://www.healthdata.org/covid/).

We’ve talked about these two models before. The IHME model to our knowledge has not yet been published to see what is under the hood. We of course have laid it out in our MedRxiv paper. However, two things are clear: (1) our models are similar as evidenced by the similar forecasts, (2) Whereas we already anticipated asymmetric recovery and built that into our models, they apparently did not and we see evidence of a scrambling to modify their models to account for this. This evidence is that they consistently underestimated total deaths relative to us, but now are almost totally overestimating, which may be right, but feels like tilting the pinball machine. Our forecasts, as measured statistically, still remains more stable regarding volatility (see Plot below) without any apparent penalty in forecasting accuracy (and precision).

Daily Rumblings

This post is a running account for shorter and more frequent updates to try to capture late breaking observations as events continue to change dramatically on a daily basis. I will continue regular posts (numbered) for weekly summaries and particularly poignant news.

5/19/20

The U.S. Continues to Lag the World

There is very little to cheer about in the U.S. even though it does look like the worst may be behind us. But the damage is done. The U.S. has less than 5% of the world’s population but 30% of its deaths and nearly 50% of the world’s active cases. The Northeast was devastated and is/was the deadliest place in the world. The following table shows that each of the four deadliest Northeast states exceed the per capita deaths of any country in the world.

Table of the deadliest U.S. states and foreign countries per capita as of 5/18/20.

There must also be a lot of nervousness in our nation’s capital as DC ranks as the 5th deadliest state (if it was a state).

California – The Next Hot Spot

CA has remained relatively unscathed in terms of infections and deaths (89 deaths per million compared to above table), but that is changing fast and unfortunately you cannot keep people at home when the weather is turning so nice. I witnessed in Newport Beach, crowds the size of Memorial Day crowds at the beach this weekend, with few masks and large congregations and parties and the news is it is happening everywhere. Signs of trouble are already in the statistics. Here are hospitalization rates in Orange County and LA. OC was trending down in early April, but has reversed course lately. LA is at a stubborn plateau.

These numbers are for Covid-19 confirmed and suspected and include all hospitalizations including ICU.

Here are the death rates for OC and LA. Deaths are not coming down very fast and looks like LA may get a rebound. We need to exercise extreme caution if we are to relax social restrictions. Oddly OC is at about 12% of the hospitalizations of LA but only at about 6% of the death rate of LA.

These numbers are daily death rates, but smoothed by a 7-day average to reduce large fluctuations.

As discussed before, deaths occur about 2.5 weeks after infections and hospitalizations somewhere in between. Interestingly the death statistics do seem to follow the hospitalization statistics by about 1-2 weeks. My gut is that we are going to see a rise in these statistics in the coming weeks, particularly in OC.

Sweden – The Experiment that Failed

Despite what is being reported, the experiment by Sweden to forego social restrictions and rely on the people’s good sense has failed. The per capita number of deaths now ranks as the 6th worst in the world and it is climbing faster than the ones above it as they are very much now into recovery. There is still a lot of denial on this, but the numbers don’t lie. The nation’s top epidemiologist, who advocated for this policy, to his credit, has pretty much been saying lately, “oops!”

Social Easing Considerations

Another way of looking at social easing is if the contagion rate was say R0 = 3 on average (this is not a constant, but dependent on environment, population density, culture, etc.), then to relax social restrictions, we would still need to reduce our typical contact rate by 2/3 to get to R0 =1 and no amplification. Maybe that is not too hard if we just practice better hygiene and behavior, e.g., keep distance where possible, wear a mask when close to people, don’t shake hands or hug, and wash hands and surfaces frequently.

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5/2/20

U.S. Still Lags the Rest of the World in Recovery

I try to keep personal opinions out of this blog as I strive for fact-based analysis, but I can’t avoid letting a little leak into these Daily Rumblings, having seen deaths to people close to me. Looking at the statistics every day it is depressing to see the U.S. continue to trail the rest of the world including much less advanced countries than ours. Other than the U.K. we were the last in the world to acknowledge the problem and then dawdled on how to respond such that even the U.K. is now ahead of us in recovery. Sweden oddly is in my opinion severely misguided in their voluntary approach to social isolation and they are now experiencing an epidemic that they seem to be denying. There will be plenty of time when this crisis passes to do a post mortem “lessons learned” analysis, but until then we have to continue to socially isolate and obey reasonable restrictions and ignore the babble that’s coming out of our administration and other states.

So here are one of my favorite plots, that I update weekly, for showing the course of recovery internationally and domestically. They mirror the conclusions from the death rate plots that I post in the Weekly Updates, but I think these plots below are visually clearer.

An indication of recovery is when the data turn downward from the positive linear slope. This indicates when the death rate slows down but not when it reaches a peak, which typically occurs a couple of weeks later (the death rate plots are better for seeing that). The points in the plots are weekly averages so each point is spaced by 1 week. The U.S. appears to have finally turned a corner this week, but we really need at least 2 weeks to confirm this trend. Countries like Italy, Spain, France, and the U.K. have turned this corner 3-5 weeks ago.

The situation in the U.S. states mirrors the lag in the overall U.S. statistics. Thankfully the NY death rate has taken a big drop, but again this is only a one-week data point. NJ is struggling as well as CA, though the latter is at about 1/20 the death rate per capita. WA is recovering and although MI and LA now show a two-week trend, the drop is not very big so the verdict is still out on their recoveries.

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4/23/20

U.S. Lags the Rest of the World in Recovery

The U.S. is behind all other major hotbed countries regarding signs of recovering from COVID-19. Even the U.K., the last of the countries to implement social distancing, is showing clear signs of getting over the death rate peak. One can look at the death rate plots in yesterday’s post “12. Weekly Update: All Populations are in Recovery” to see the progression from death rate acceleration to deceleration (getting over the peak) to see how much the U.S. is lagging. However, a very sensitive way to also see this is from the log-log plot of death rate vs. cumulative deaths below.

Deviations from the positive linear slope is an indication of progressing past the exponential growth rate. So, the point of departure is actually about half way up the death rate curve, which is about 2 weeks before the peak. The points in the plot above are weekly averages so each point is spaced by 1 week. This shows the U.S. is no less than 2 weeks behind the rest of the world in its progress toward recovery.

This situation in the U.S. states is also not encouraging and mirrors the lag in the overall U.S. statistics compared internationally. The log-log plot for U.S. states below shows encouraging signs for WA, and maybe for LA and MI. But for NY and NJ we may have prematurely called a peak in yesterday’s post. They do not look like they are recovering yet.

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4/18/20

A curve ball everyday

Let’s start internationally. Italy and Spain are clearly on the downward slope of the death rate curve and therefore also the prevalence (cases) curve. However, whereas Spain continues down a relatively symmetric path as the way up, Italy seems to have hit a plateau on the way down experience 500-600 deaths/day for the last 5 days (still down from a peak of 1000). If this trend continues up to our weekly update cutoff of Mondays then we will revise up the total forecasted deaths, but probably by no more than 20%. France and the U.K seem to have reached a peak, but we will not know that for sure until we see some downward motion.

Domestically, things are bleaker. NY continues to have a climbing death rate and our call on 4/14 that it had reached a peak appears to be premature. By comparison the UW IHME model claimed it was 4 days past it’s peak so that attests to how difficult it is to make a definitive claim. This will most likely push the total death count in NY to greater than 20,000. The CA death rate has risen to about 90 per day. Though a comparatively low number it is still climbing. Still we think it will be by a large margin a much less affected U.S. state when measured by per capita.

When deaths are not deaths: Not so accurate a measure

Our model premise is based on the assumption that there is no more accurate outcome measure than deaths. Well in the last couple of days we have learned that death counts have been understated for two key reasons: (i) The reported numbers are dependent on how diligent healthcare facilities report deaths and whereas hospitals are pretty reliable and also the primary location for deaths, it is now coming out that nursing homes, assisted living, and other less emergency based facilities have been slow to report this date. (ii) Reported deaths are for confirmed COVID-19 patients and those about to die in hospitals are usually diagnosed, however, it is now acknowledged that many deaths can be attributed to COVID-19 that were not diagnosed. This has caused corrections to data either officially as decreed by the CDC on April 15 or informally as has been done in France and China. In our next weekly update we will describe how we handle these one time surges in reported data.

Can current antiviral drugs work

Everyone is hoping for the magic bullet. Our President has harped on repurposed drugs as the savior and just around the corner. Dr. Fauci has presented a more measured and truthful response. Still there is some hope that a currently approved drug for other viral diseases might at least offer some reduction in death risk for the most afflicted patients. Of the promising candidates that are in expedited clinical trials, Gilead’s Remedivir, developed for Ebola virus, has reported unofficial results from the University of Chicago that suggests for this limited study a marked reduction in deaths in the most severe patients who were given the drug. There is not much more one can say until the statistics are better understood.

Reported confirmed cases way undercounting

In just the last few days we have had the following reports:

  • China has restated the number of deaths in Wuhan from 2,579 to 3,869 attributed to unaccounted for deaths at home. That this increase is precisely 50.0% raises suspicions about the truthfulness of this report. Based on demands for funeral homes and cremations, some accounts estimate the death count to be closer to 40,000. Regardless of the absolute numbers, I believe they have significantly reduced the death rate so that the curve if not the amplitude is somewhat believable.
  • At home deaths in NY are also now reported to be significantly undercounted and the official count was adjusted by nearly 4,000 deaths on 4/17/20 to correct for this and this now accounts for about 25% of the total death count in NY.
  • France made a similar one-day correction on 4/4/20 add about 1,500 to its previous count of 4,500 (now at 19,000, 4/18/20).

Accuracy of COVID-19 diagnostic tests

There have been reports of patients who have had the virus who get re-infected, which has baffled the medical profession. But there may be another explanation, that they never had it in the first place.

The accuracy of a diagnostic test is typically measured by sensitivity, which is the percentage of positive samples the register as positive (true positives and the misses are called false negatives) and specificity, which is the percentage of negative samples that register as positive (false positives vs. true negatives). No test is 100%, but the standard reverse transcription polymerase chain reaction (RT-PCR) is reported to have sensitivity near 100% and specificity of about 96%. This sounds pretty good and it is, however, here is how it plays out in real life. Let’s say you take 100 random people and that 5 have the virus. Then the PCR test will probably detect all 5 positives. However, of the 95 negative patients and a 4% false positive rate there is likely to be 4 negative patients who are diagnosed as positive. Well that is now 9 positive readings of which only 5 are real. So one has to suspect that a significant number of confirmed cases are not true positives.

Now based on current protocols of testing only those most suspected to be infected in which case about 25% register positive, the false positive contribution will be about 16% (4%/25%). However, as we expand testing to more of the general public, this ratio will change dramatically and we need to be prepared for how to handle that situation. I have no suggestions to offer at this time other than to be aware of this error when this data is used for real purposes.

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4/11/20

Response time is everything!

U.S. just surpassed Italy today and Spain three days ago in deaths becoming the world’s most afflicted country and with a death rate still growing at an alarming rate.

I try not to get on my soap box, but quick recognition of a problem and immediate implementation of solutions is absolutely crucial to controlling an epidemic. I have shown previously (Post #7: Weekly Update: Grim News), and I’m not by any means alone in this assessment, that every week of dithering will cost a factor of 2-4 in total deaths. That is the perniciousness of exponential growth.

There is now strong evidence (post-crisis analyses should make this irrefutable evidence) that quick action to implement intervention such as social distancing and extensive testing can considerably curb the outbreak and death rate (China, S. Korea, Taiwan, Singapore). Conversely the evidence shows that countries that delay a response suffer costly multiplications of death rate. Examples of late comers are the U.S., France, and U.K, each first nationally sounding the alarm on 3/16, 3/16 and 3/28, respectively, and they are posed to be the three deadliest countries in the world. The U.S. alone, however, didn’t implement any federal actions to recluse, that was left to the states. And again, a good example of the necessity for quick action. CA enacted a stay-at-home edict on 3/18, NY not until 3/23. CA currently has 14 deaths per million people, NY has over 400! So the U.S., the most advanced nation in the world, will be ignominiously remembered for its lack of responsiveness and preparedness costing tens of thousands of American lives. Even worse is that this was willful. This country has 5% of the world’s population and 20% of the COVID-19 deaths.

As far as detecting a rolling over to a peak in infections (new cases or incidence), hospitalization rates are a good indicator, but whereas death rates lag incidence by about 2.5 weeks, hospitalization probably lags by 1-2 weeks since the virus will be coursing through someone before they are serious enough to require hospitalization. So, I still think the gold standard for knowing when you hit a peak is deaths.

The question when is it safe to relax social distancing? That will be addressed in a new blog in a few days. But suffice it to say that life can never return to normal until we have a vaccine or when upwards of 50% of the population has gotten the disease and has developed immunizing antibodies. Otherwise we will be just back to where we started.

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4/4/20

Italy and Spain reaching peak deaths. U.S. still accelerating.

Below are latest death rate plots for our coverage of hot-spot countries. There appears to be a real rolling over in the rate for Italy, that we perceived a couple of days ago, which allowed us to rank it yellow (red, yellow, green ratings) and now looks to be reaching a peak. If so our death projections in Post #8 may come down by a factor of 2. Spain also may be further up the death rate curve than thought two days ago. Unfortunately the U.S. France, and U.K are still in exponential growth so still low on the death rate curve.

Death rate plots with our ranking of seriousness. There is an apparent rollover for Italy and Spain over the last few days

The U.S. is lagging most of the rest of the world perhaps due to being late to declare COVID-19 a national emergency. New York and California are still in exponential growth, though per capita NY is still about 20x higher in total deaths and death rate. Washington state is appearing to be reaching a peak.

That’s it for today. Serious social isolation needs to be practiced.

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3/28/20

U.S. News

We are tracking NY, CA, and WA, but may add to that soon. Below are plots of the daily death counts for these states and the following are observations:

  • It appeared for a few days that WA was containing the contagion but on 3/27 there was a surge.
  • NY and CA also look to be leveling off, but we will need at least a couple of more days to see if the death rate is slowing; however, they are not going down yet either.

Another way to look at the data is per capita. The table below reveals how horrendous NY is at this time especially compared to CA that is also considered a hot spot, but not nearly so when viewed per population.

  • Per capital NY has 15x more deaths and 25x greater daily death rate than CA. Washington has a very high cumulative deaths per capita but its deaths per day is subsiding.
  • Not shown in the plots or tables, we estimate the number of true cases today are about 1 in 240 people in NY, 1 in 4000 in CA and 1 in 1300 in WA. The numbers will be less if social distancing has been working since inception about 1-2 weeks ago.
  • On the whole the U.S. cumulative deaths now exceed 1,500 though flattening out at about 200-250 deaths per day! We’ll be anticipating a reduction in the next few days due to social distancing (fingers crossed!).

Regarding other states:

  • Louisiana has catapulted to #3 in the nation for total deaths (119 yesterday and 137 today and counting) and 1st in death rate (as of yesterday), but NJ has now just overtaken them today to #3 (140 deaths and counting) and is now comparable to the top death rates (about 25% increase per day). Louisiana’s sudden emergence may be due to a surge in cases arising from Mardi Gras that is now evolving to either recovery or death.
  • Other new trouble spots are MI (111 deaths), FL (54 deaths) and IL (47 deaths).

International News

Below are plots of daily death counts for several countries that we are tracking and the following are observations:

  • Spain has nearly overtaken Italy for number of deaths/day although both countries are still experiencing alarming death rates.
  • The press sometimes reports news based on two-day trends. This leads to false conclusions, which is dangerous misinformation and what I try to avoid here. The reasons why short term trends can be false include:
    • Official numbers are often miscounted when counting live and it sometimes takes a couple of days for the numbers to settle down. I see this in the gold standard daily reports published by the WHO. For some reason they lag quite a bit on U.S. deaths.
    • Often there are two sets of daily numbers, those collected on Greenwich Mean Time (GMT) and others collected in local time zones. For example, a GMT reaches midnight when say California is at 4 pm. That means by the GMT standard, the subsequent CA count will be included in the next GMT day. I will update with whatever data is most current, but I then reconcile to the WHO GMT data when it comes out the next day.
  • The U.S. appears to be trending toward a flattening out of the death rate. Let’s follow this for a couple of more days to see if it persists.
  • Italy looked to be flattening too but then saw a surge on 3/27. Too soon to tell what the trend is.
  • Iran seems to have flattened their death rate. Let’s now hope to see a reduction in daily rates that trends toward near zero
  • The U.K. also gave a false hope for a few days, but then surged upward on 3/26 and 3/27.

I like the following representation of cumulative deaths plotted on a log scale. I have presented similar plots, but this puts it on a scale starting at 10 deaths for each country so the days since do not occur on the same calendar days, but is a good way to see whether a country’s death rate presages a previous country’s agony. For example, although Spain lags Italy in deaths because its contagion started about 11 days later, the death rate appears to be greater than it was in Italy for the same time after inception. Spain is in Italy-scale serious trouble.

We still do not see too many bright spots around the world, but most countries are still within 2 weeks of initiating strict measures, so if these are effective we may not see them in the death statistics for another week or so.

17. Weekly Update: Kudos to NY and NJ

(5/14/20) Most of the world and U.S. states are improving in key statistics but agonizingly slow with some exceptions that we highlight. But fortunately, the two biggest hot-spots in the world, NY and NJ, appear to be recovering well. Every other statistic for the U.S., however, lags the rest of the world and underscores the serious consequences of our nation’s delayed and unprepared response to COVID-19.

The plots below show the familiar death rate curves for hotbed countries and U.S. states. We retain Iran for one more week and plan to show Sweden next week as an example of a lackadaisical approach to social containment.

There were no new upgrades in our 3-color ranking system Internationally and Spain is on the verge of a downgrade for its stubbornly persistent death rate. Domestically we gave NY and NJ well-deserved upgrades but WA is on the brink of a downgrade. The NY and NJ death rate decline is faster than most other populations as you can see from the plots below. This can turn at any point, and NJ still shows signs of new outbreaks, so hopefully they do not relax social restrictions too aggressively and start another firestorm. In fact, the whole COVID-19 situation around the world feels like a huge forest fire that we may believe we are just about to contain, but a sudden change in weather could cause another uncontrollable outbreak. With the social, economic, and political pressure to increase social easing, this is bound to happen. Two states that were early leaders in taming the outbreak, WA and CA, are now having a tough time reducing deaths and active cases as evidenced by the plots below. (You can read about the specifics of CA and Orange County in just released Post 16. Can Orange County, CA Begin Opening this Week?)

We continue to plot a symmetric Gaussian but for visualization only. Our analyses now use asymmetric functional fits that we will detail in a separate post in the near future.

Next is our familiar table for forecasted total deaths, prevalence (current cases), and incidence (new cases) along with their values per capita (per million people) as well as dates we consider to be the earliest to begin a graduate easing of social distancing. These results fully incorporate our asymmetric Gaussian model, introduced last week and to be described in a future post and publication. We remind readers that these forecasts do not account for future premature social easing that could set off new outbursts. The forecasts do, however, represent the extent of social distancing to date as they are reflected in the actual death data.

The threshold prevalence for the easing date was raised this week from 100 to 200 active cases per million population for no better reason than I think I was being too stringent. This number really depends on the tolerable death rate, which is a subject we will treat in a future post.

Key observations include:

  • The U.S. trails the rest of the world: It is hard to criticize our country, but we can’t ignore tough lessons not just for the next pandemic, but for this one if our administration makes yet another mistake and sends premature messages on social easing and digs us into a deeper death trap. By every statistical measure the U.S. lags the rest of the world in handling COVID-19; (i) Next to last to declare an emergency; the U.K. was last, (ii) the last to reach the death rate peak, (iii) last to implement testing and protective gear, and all still at inadequate levels per capita,  (iv) has 5% of the world’s population but 30% of the deaths and nearly half of the prevalence (active cases), (v) will be last to be safe to ease social restrictions (but we will not be the last to implement it), (vi) has seen the most upward forecasts of death by major models of any country (see plot below) meaning our social distancing is not being rigorously practiced.
  • Jack’s rant: I have resisted taking any political views in this forum and have just reported the facts like a good impartial scientist hoping that policy makers will respond appropriately to these facts. However, our nation continues to mismanage this pandemic and is now further sidelining and ostracizing well-meaning medical experts from reporting the truth in order to push a politically motivated agenda to revive the economy. I am all for revitalizing the economy, but at what cost? I will have more to say on this in a future post. But we all have to start speaking out as this callous behavior is needlessly costing tens of thousands of American lives!
  • NY and NJ: These states have made excellent progress in reducing the death rate; however, because they started at such a high level, they still have the largest per capita death rates in the world being, respectively, 69 and 130 deaths per week per million population vs. the world’s worst of 48, 45, and 34, respectively, for the U.K, Sweden, and the U.S.
  • Social easing: It is understandable that we must give great consideration to the economy, but we will be worse off if we socially ease prematurely. Easing as little as 2 weeks too soon could lead to epidemic growth again and require another 2 months of social distancing. That is an atrocious tradeoff.

The table below compares our total death forecasts to the benchmark model from the Institute for Health Metrics and Evaluation (IHME) at the University of Washington (UW) (http://www.healthdata.org/covid/).

The IHME model dropped the reporting of ‘days from peak’, but they do report the peak date so we can calculate that above.

The two comparative models give similar results (plots below) suggesting a similar algorithm, e.g., strong dependence on death statistics. By some measures we may be performing better in terms of week-to-week volatility and quickness to detect new trends as can be visually see in the plots below. To compare volatility, we calculated the sum of squares for error (SSE) for variability relative to the latest forecast values. By this SSE measure the IHME model forecasts have varied greater from week to week than the present model for all but one of the cases (France). If averaged for the international and U.S. states, respectively, that we track the SSE’s are: 26% and 26% for our model vs. 41% and 46% for the IHME model (lower means less variability). At present we do not see a penalty to the present model’s relative stability, but time will tell. It also appears that they are about a week behind the trends that we are forecasting as evidenced by their weekly adjustments tending to values we forecasted the previous week. On the other hand, they have made a brazen call on doubling the U.S. forecasted total deaths (not helping their volatility factor), a trend we also see but not to the same magnitude. We hope they are wrong for our country’s sake!

16. Can Orange County, CA Begin Opening this Week?

(5/13/20) I am grateful to have been invited to write a feature on COVID-19 in California and Orange County (OC) in the Orange County Business Journal for the prestigious back page OC Leader Board. There is a lot going on in OC and like everywhere strong sentiment to begin easing social restrictions. Gov. Gavin Newsom is implementing plans to relax restriction, but contingent on meeting certain metrics regarding rates of decreases of active cases and minimum testing requirements. Nearly all counties do not meet them yet, but of course we are seeing open rebellion, Tesla being the most covered example.

California has been doing very well compared to other states in the union and OC better than most CA counties. However, the numbers, e.g., death rate, cases, and most particularly hospitalization rates are not declining as expected and this vulnerable population is still prone to a major outbreak, particularly as LA is reaching epidemic proportions. Rather than repeat what I have written, please check out the article. Here is the article and a link to download:

15. Weekly Update: Recovery at Hand but Slower than Expected

(5/6/20) Recovery is everywhere but stubbornly slow and gives pause to suggested safe easing dates. CA and NJ are significantly revised out in time due to persistently high daily deaths and hospitalization counts. We also highlight an interesting “Sunday Effect”. This week we implemented our asymmetric Gaussian model to account for the slow downslope and compare further to the UW IHME model.

You know the drill so we will launch into it.

The plots below show the familiar death rate curves for hotbed countries and U.S. states. We dropped China and Korea last week as now being “uninteresting.” Next week we will drop Iran and add Sweden to highlight a country that is paying the price for a lackadaisical approach to social containment.

Internationally there are new upgrades (U.S. marginally, France), but also two down-grades (Italy, Iran) on our 3-color ranking. Domestically MI got an upgrade.

We continue to plot a symmetric Gaussian but for visualization only. Our analyses now use asymmetric functional fits that we will detail in a separate post in the near future.

We make the following observations:

  • All of our tracked hotbed countries and U.S. states are on the downside of the death peak and therefore the prevalence (case) peak. However, it just doesn’t feel solid with several sudden surges most likely due to reporting fluctuations, but we worry about hidden deaths and small outbreaks that can grow quickly into big ones.
  • Do Fewer People Die on Sunday? We have noticed fluctuations in the death rates, but now we see that it is repeatable and is found in many countries and states. We have adjusted our plot grid lines to lie on Sundays and you can readily see the strong dip in reported deaths. These are particularly noticeable in the U.S. (NJ, LA, CA), but also in Europe (U.K. and very much Sweden not shown this week). We attribute this to a data recording quirk.
  • The symmetric Gaussian model is breaking down on the downside of the death rate curve as we expected and we have implemented an asymmetric function that has different sigma values for the rise and fall sides of the rate curve. We will discuss the details in an imminent post.

Next is our familiar table for forecasted total deaths, prevalence (current cases), and incidence (new cases) along with their values per capita (per million people) as well as dates we consider to be the earliest to begin a graduate easing of social distancing. We will continue to call this an easing date and not a safe date to dampen excessive hopefulness.

We have lowered the mortality factors for Italy and Spain from 2% to 1.5% and for NY and WA from 1.5% to 1.0% as the healthcare system in these populations are becoming less overwhelmed in treating patients.

Last week we implemented an asymmetry factor to adjust our values until we could come up with a rigorous functional form. This has now been implemented but still needs to be “burned in” and tested more but we felt it was important to apply it here. Most of our forecasts have gone up only moderately, but some rather significantly (US, NJ, CA). This has also pushed out the so-called easing date for most populations and significantly, a month or greater, for the latter ones cited. As these dates are untenable in the current social, economic, and political climate very careful limitations need to be placed on any phased easing of social restrictions that should occur before these dates. We will also give further consideration to whether our threshold of 100 active cases per million people for safe easing is too stringent and whether we could recommend more modest easing at earlier dates.

But we mustn’t lose sight that we are at great risk of prematurely easing, which as Dr. Fauci has said “could backfire.” We will need to observe outcomes in Europe and U.S. states where social easing is already being implemented. I’d be interested in your thoughts on my posting: “11. Recommended Guidelines for Easing of Social Distancing,” which proposes a three-phased easing with check-gates at each step.

We now wrap up by comparing our results to that of the Institute for Health Metrics and Evaluation (IHME) at the University of Washington (UW), which is now the most highly cited and quoted model for informing our nation on the state of COVID-19 (http://www.healthdata.org/covid/).

The IHME model seems to have dropped the statistic for ‘days from peak’ that we found to be an interesting comparison.

It is clear that we have similar components to our models with an apparent heavy emphasis on death rate. This is a very nice, visual, and professionally developed model (funded by Bill Gates) that we can’t compete with in its full glory. The IHME model has also moderately to significantly increased their total death forecasts, particularly for the U.S. to the point where it feels like they are putting a little “tilt” into it in response to latest administration and media hype. We shall see. The two models differ on Sweden in which IHME is projecting more than twice as many deaths as us. They may be factoring in a social distancing component to their model that amplifies this number. We believe that the death rate curve embodies all of these effects.

Although the two comparative models give generally similar results, by some measures we may be performing better in terms of week-to-week volatility and quickness to detect new trends (see post 14. Benchmarking COVID-19 Forecasting Models). Our model also provides calculations of current and forecasts of future prevalence (active cases) and incidences (new cases) that are notoriously difficult to measure because of lack of adequate COVID-19 testing making the reported confirmed values almost meaningless.

14. Benchmarking COVID-19 Forecasting Models

(5/2/20) There are probably as many different opinions on models as there are models themselves. None can forecast with total accuracy and if one could you wouldn’t know it until it is too late. Still we need some forecasting tools to guide policy and responses.

As evident throughout this blog, we have been developing a model based primarily on the most reliable data available, deaths. From death rate and cumulative deaths and other measurable variables, we can compute number of cases (prevalence) and new cases (incidence) as well as forecast these values as well as deaths into the future. A useful outcome of these tools is the ability to forecast dates when it is safe to resume some level of work and social activity.

However, how do we know if a model is performing well? That usually means running it on historical data. Even though, like trying to predict the stock market, past performance is no guarantee of future results. We are here in the middle of an epidemic and so we can gain some indication based on the most recent historical performance. We can also compare ourselves to other models. However, all models are different and give different results. Still we have been benchmarking to what is emerging as the gold standard model and is now clearly the most widely followed model by the press and government agencies. This is the model by the University of Washington’s (UW) Institute for Health Metrics and Evaluation (IHME). The details of this model have not yet been published and are not well known. We believe we have many similarities such as depending strongly on death statistics.

The IHME and our model also make updates to forecasts based on new data. This would seem to be a sensible approach, but many “expert” modelers and epidemiologists consider this strategy a shortcoming because the forecasts can bounce around. Well I began my model for the very reason that these so-called experts were pontificating things that seemed far fetched and their models seemed to be too complicated with many variables that are hard to measure or define. So, these models may be good for ultimately understanding pandemics better (after the fact), but we are in the middle of a pandemic and need some real-time guidance and forecasts so I believe the strategy that I, and apparently IHME, are taking are valuable. There is a need for many models as they all make different assumptions or are trying to measure or forecast different properties.

So, to cut to the chase the plots below show the forecasted total final deaths for the various hotbed countries and U.S. states that my model is following alongside the IHME model forecasts over the same time period.

The following are key observations:

  • The two models would appear to be more similar than dissimilar based on the relative qualitative agreement on death forecasts, e.g., the order of severity for different countries and states.
  • Both models fluctuate, but not excessively relative to an epidemiological model that is calculating based on first principles.
  • Both models seem to be on a slight rise in forecasting deaths. We think that is because both models a priori assume that the rise and fall behavior are symmetric, but evidence is now showing the decline is slower. We know how to modify our model to do this, but will not be able to do so before the pandemic mostly plays out. Instead we are applying an asymmetry factor to the downside to compensate. We do not know if IHME is doing anything similar.
  • Our latest forecasts are all above those of IHME, but not greatly making us think that the difference is due to our including the asymmetry factor for the first time in the last update.
  • Not shown in these plots but evident in the Weekly Update postings is that we tend to read the death rate data such that we believe countries and states are not as far past the peak as IHME indicates. We think this is due to our using a Gaussian function to monitor peaking, whereas they use an error function (integral of a Gaussian) to do so, which we believe is less sensitive to detecting changes, e.g., peaking. We both apparently use the error function to forecast total deaths but from different parts of the curve depending on how we read displacement from the peak.

13. Weekly Update: Further Worldwide Recovery; U.S. May Have Reached its Peak

(4/29/20) The world continues its recovery and the U.S., including the hot-bed states, appear to have reached their peak with the possible exception of NJ. Our advisory for the earliest date to implement easing of social restrictions range from 5/14/20 for WA to 6/2/20 for NJ.

Be sure to read Post 11. Recommended Guidelines for Easing of Social Distancing

We maintain the same format as our previous weekly updates We’ll continue our routine of presenting the death rates internationally and domestically and from that and other conditions and assumptions detailed in our Model from previous postings we forecast total deaths, prevalence and incidence of cases and easing dates. We also compare our forecasts with that of the heralded UW IHME model.

The plots below show the familiar death rate curves for hot-bed countries and for U.S. states. We dropped China and Korea as now being “uninteresting.” But we may add Sweden as a country that has flouted government mandated restrictions allowing voluntary behavioral changes. Though they claim it to be a success they are in my estimation in trouble with a rising death rate and already a cumulative death count of 222/million, which exceeds the U.S. at 172/million and is closing in on Italy, Spain, France, and the U.K. who range from 311/million (U.K.) to 503/million (Spain).

We did not upgrade any countries (3-color ranking) though the U.S., France and the U.K are on the cusp of one and we anticipate if trends continue, we will do so next week. We did upgrade NY from red to orange and MI and LA should get an upgrade next week.

We make the following comments:

  • Most countries and states have clearly advanced past the peak of the death rate curve. NJ appeared to reach that summit, but today had its greatest daily death count at 398.
  • We have noticed that most U.S. states and some European countries oscillate in their daily reported deaths with low numbers on the weekends and high numbers on Mondays and Tuesdays. So, we now see that some of the daily fluctuations may be due to reporting or data processing diligence.
  • The symmetric Gaussian model is starting to break down on the downside of the death rate curve progressing slower than anticipated. Of course, nothing is really anticipated as pandemics and how the population responds are not predictable. Consequently, we are planning to build in an asymmetry factor.

Next is our familiar table for forecasted total deaths, prevalence (current cases), and incidence (new cases) along with their values per capita (per million people) as well as dates we consider to be the earliest to begin a graduate easing of social distancing. We will continue to call this an easing date and not a safe date to dampen excessive hopefulness.

These values assume an asymmetry factor such that a slower than modeled decline in the death rate can be adjusted. We will look for better functional forms than a Gaussian in the future (after this all blows over), but for now we simply scale the weeks from peak proportionately back by where the last death points lie on the symmetric Gaussian. The true weeks from peak, however, are shown in the table.

We repeat from last week that as a rough rule of thumb the easing date cannot be before the point when the prevalence count drops to less than what it was when the death rate took off. Roughly the easing date should be about 4-5 weeks after the death peak, the range depending on how severe the outbreak was for a particular population. Our date is based on when the total active case count (prevalence) is forecasted to drop to 100/million. This should be adjusted for population density as this number for sparce populations is probably safe, but for dense populations, e.g., NY, it would be prudent to add a few days.

Continued public and political pressure will surely lead to premature easing, which as Fauci has said “could backfire.” So please read my posting: “11. Recommended Guidelines for Easing of Social Distancing” proposing a three-phased easing with check-gates at each step. And of course, please give me your thoughts.

We now close by comparing our results to that of the Institute for Health Metrics and Evaluation (IHME) at the University of Washington (UW), which has emerged as perhaps the leading model for informing our nation on the state of COVID-19 (http://www.healthdata.org/covid/).

We continue to track closely to the IHME model indicating that there must be components of each model that are similar. We tend to forecast a little earlier from peak rates and as a result we forecast somewhat higher total deaths. Whereas the accuracy of forecasts should improve as we traverse the peak in the death rate curve, we are learning that the downward side of the curve is not symmetric but stretched out in time, which will elevate the actual statistics relative to a symmetric forecast. As noted above we have added an asymmetry factor to account for this effect.

12. Weekly Update: All Populations are in Recovery

(4/22/20) The world continues to recover with all hot-bed countries showing declining death rates. The U.S. has turned the corner, but appears about 2 weeks behind the rest of the world, with the U.K. being the other laggard. Our estimates for when gradual easing of social restrictions has pushed out a few days since last weeks forecast.

Remember to check my post called “Daily Rumblings” for late breaking updates.

We’ll continue our routine of presenting the death rates internationally and domestically and from that and other conditions and assumptions detailed in our Model from previous postings we forecast total deaths, prevalence and incidence of cases and easing dates. We also compare our forecasts with that of the heralded UW IHME model.

Before we go on, please see the previous posting (just posted): “11. Recommended Guidelines for Easing of Social Distancing.”

The plots below show the familiar death rate curves for hot-bed countries (we may drop S. Korea and China in the future) and for U.S. states. We have upgraded the severity scale (3-color ranking) for some of these. The U.S. remains the only country still ranked as serious with a red spot. Several states are still in that category as well.

The qualitative red, yellow, and green rankings reflect accelerated, rolling over to a peak, and well on the decrease death rates, respectively.

We make the following comments:

  • Most countries and states have advanced past the peak of the death rate curve. Some that we have called at the top still need more data to strengthen that assessment
  • The Gaussian model is holding up reasonably well, but we might expect a slower decline than rise as new, but lower density outbreaks are triggered. We will look at final data before adjusting the model.

Next is our familiar table for forecasted total deaths, prevalence (current cases), and incidence (new cases) along with their values per capita (per million people) as well as dates we consider to be the earliest to begin a graduate easing of social distancing. We will continue to call this an easing date and not a safe date to dampen excessive hopefulness.

Assumptions: Mortality factor is estimated as 1.0% for most favorable populations (not yet strained health care system) and up to 2.0% for least favorable populations (strained healthcare system).

We repeat from last week that as a rough rule of thumb the easing date cannot be before the point when the prevalence count drops to less than what it was when the death rate took off. This is because we don’t have a vaccine nor is there sufficient herd immunity (those who have had the disease and developed antibodies) to change the vulnerability to new outbreaks. Roughly the easing date should be about 4-5 weeks after the death peak, the range depending on how severe the outbreak was for a particular population.

Now that the momentum to ease restrictions is gaining momentum and we are sure to initiate this prematurely, we must have a phased approach. The Administration has proposed something that includes many common sense recommendations, e.g., continue to practice good hygiene and advising sick people to stay at home. However, the three-phased approach is lacking in specifics, e.g., “bars may operate with diminished standing-room occupancy,” without defining density or distance requirements. So please read my posting: “11. Recommended Guidelines for Easing of Social Distancing” and please give me your thoughts.

Finally, we provide a new update on the comparison of our forecast of critical values to that of the Institute for Health Metrics and Evaluation (IHME) at the University of Washington (UW), which has emerged as perhaps the leading model for informing our nation on the state of COVID-19 (http://www.healthdata.org/covid/).

We appear to be tracking very closely indicating that there must be components of each model that are similar. We tend to forecast a little earlier from peak rates. As the death rate curve flattens the accuracy of forecasts improve greatly because it is more evident where in the rise and fall cycle a given population is. This is evident as our forecast Total deaths are starting to converge.

11. Recommended Guidelines for Easing of Social Distancing

(4/21/20) Relaxation of social restrictions will happen and probably prematurely. Given that political reality, it is imperative to put into place a phased approach where each next phase is based on metrics to minimize new outbreaks.

Remember to check my post called “Daily Rumblings” for late breaking updates.

The U.S. Administration released guidelines for relaxing social distancing. There are many common sense recommendations, e.g., continue to practice good hygiene and advising sick people to stay at home. However, the three phased approach is lacking in specifics, e.g., “bars may operate with diminished standing-room occupancy,” without defining density or distance requirements.

The following suggested guidelines are a phased approach to relaxing social restrictions. In all cases it is still mandatory that people who are sick or feel sick should stay home. These recommendations are for healthy individuals only. I post this in hopes that it gets noticed and if found reasonable contributes to a collective set of guidelines that can be followed, monitored, and allow maximum freedom and economic opportunity without exposing the public to excessive COVID-19 risk.

Phase I – At recommended easy datePhase II – 3 weeks later if no negative trendsPhase III – 3 weeks later if no negative trends
Work– Employees work at office 2 days a week on two different cycles, e.g., Mon/Wed, Tue/Thu
– Maximum density of employees is 1 per 100 square feet
– Mandate use of hand sanitizer entering and exiting work
– Place hand sanitizers in many locations (at least 1 per 1,000 square feet) and every bathroom or breakout room
– Minimize contact and exercise caution
-Employees may return to work full time
– No other changes
– No other changes
Schools– Healthy students may return to classes
– Maintain 6-foot distance in class rooms or lecture halls
– Mandate use of hand sanitizer entering and exiting class rooms
– Minimize contact and exercise caution
– No other changes– 6 foot distance restriction may be relaxed
– No other changes
Restaurants– Open with every other table unoccupied
– Bar area closed to customers
– Mandate use of hand sanitizer entering and exiting establishment
– Hand sanitizer at every table and bathroom
– Servers must wear masks and gloves
– All tables may be served
– Bar area open only for sitting and stools spaced 6 feet apart
– Hand sanitizers on bar every 12 ft apart
– No other changes
– Remove all restrictions, but:
– Maintain signs to minimize contact
– Maintain hand sanitizers
Retail stores (e.g., groceries, mall, etc.)– Open all stores except those requiring contact (e.g., hair dressers, manicure, etc.)
– Place signs stating rules against congregations
– Place hand sanitizers in many locations (at least 1 per 1,000 square feet) and every bathroom or breakout room
– Assign security to resolve unlawful congregations or rule violations
– Employees must wear masks and gloves
– Open all stores
– No other changes
– Remove all restrictions, but:
– Maintain signs to minimize contact
– Maintain hand sanitizers
Stadiums (sports concerts)– Not allowed– Allow ¼ capacity and space seating for different groups
– Mandate use of hand sanitizer entering and exiting
– Hand sanitizers in every bathroom
– Cone off every other parking space (this may be difficult), at the very least place signage to forbid adjacent parking.
– Remove all restrictions, but:
– Maintain signs to minimize contact
Beaches– Open beaches and parking lots.
– Cone off every other parking space (this may be difficult), at the very least place signage to forbid adjacent parking.
– Place signs stating rules against congregations
– Station police officers every half mile to resolve unlawful congregations or rule violations
All parking acceptableNo other changes– Remove all restrictions, but:
– Maintain signs to minimize contact
Gyms– Aerobics or cycling classes forbidden
– Limit density of attendees to 1 per 100 square feet
– Mandate use of hand sanitizer entering and exiting gym
– Place hand sanitizers in many locations (at least 1 per 1,000 square feet) and every bathroom or breakout room
– Minimize contact, and exercise caution
– Aerobics and cycling classes allowed, but with 6-foot distancing
– No other changes
– No other changes
Other– All service people must still wear masks and gloves
– In general, when out avoid handshakes and any touching with strangers
– No other changes– Service people may dispense with masks and gloves
– No other changes

Ironically, the safest time for an individual to socially ease is just before it becomes practiced by all as cases will surely go up at that point.

Doing the math on 22M new unemployment claims and 45,000 deaths we come up with a ratio of about 500 unemployed per death. So, what is the ratio that can be tolerated? Would doubling the deaths to halve the unemployment be worth it? That would be then sacrificing a life for 250 jobs. Then we get into moral issues such as what if it is an elderly death. Should the figure of merit be how many ‘years’ of life do we sacrifice? That’s a question that someone must be answering somewhere.

I would say whatever date one considers safe for social easing add 2 weeks just to be safer and less risky of an infection rebound; as Fauci says if we relax too soon it “will backfire”. Further our testing capacity is still woefully inadequate to test for small outbreaks that could lead to large outbreaks. However, the country will succumb to economic pressures to re-open so we need a thoughtful prudent plan.

10. Weekly Update: The World is Recovering

(4/15/20) Nearly all hot-spot countries and U.S. states are near or past the peak for death rate. That means the number of active cases (prevalence) is on the decline and some amount of social easing can be reasonably considered. However, until our population is largely immunized by having had the disease or by vaccination, social interactions cannot return to normal.

Remember to check my post called “Daily Rumblings” for late breaking updates.

Our model has been described in previous posts so we can spare that detail for now! Let’s put it to further practice and use it to help inform us on when we can start to relax social distancing and to what extent. We begin by showing the latest death rate plots for hot-spot countries and U.S. states below. Now that these curves have developed toward and past their peaks we superimpose a Gaussian function to visualize the progress made by these populations.  Note that we have de-rated the severity of several of these populations as represented by the colored circles. Major recovery is evident, and as predicted, about 3 weeks after serious social distancing was implemented.

The qualitative red, yellow, and green rankings reflect accelerated, rolling over to a peak, and well on the decrease death rates, respectively.

We make the following comments:

  • The Gaussian dependence in most cases represents well the reported death rate data (even while fixing the width at half max to 4 weeks). This dependence, and the symmetry assumed by the Gaussian, is expected to get distorted due to interventions to reduce the rate, e.g., social distancing. The breakdown in the fit is evident for those populations who were most aggressive, such as China and S. Korea, the latter never reaching exponential growth and the death rate instead being more constant.
  • Once a country reaches the peak in death rate, it is about half way to its final death count.
  • Italy and Spain are making remarkable progress and we (perhaps prematurely) have upgraded their situation to green (out of trouble). How they manage their retreat from social isolation will determine how successful they will be in the long term. This is the topic for below.
  • The U.S. is lagging these other countries in reaching a peak; however, it does appear that the peak is imminent.
  • Regarding the U.S. states: Washington is past its death rate peak and has been upgraded to a green. California never saw high death counts, just high death rate that appears to be peaking so we upgrade that to yellow (warning).
  • We have not followed MI, NJ, and LA from the beginning so we have limited data to fit to the Gaussian, but each of these states is making progress and if the current trends continue over the next week, they will all be upgraded as well.

We now present our familiar table for forecasted total deaths, prevalence (current cases), and incidence (new cases) along with their values per capita (per million people). We also add a new column for the date we consider to be the earliest each population base can begin relaxing social distancing. We will tentatively call this an easing date and not a safe date so as not to conjure up excessive hopefulness.

Assumptions: Mortality factor is estimated as 1.0% for most favorable populations (not yet strained health care system) and up to 2.0% for least favorable populations (strained healthcare system).

A rough rule of thumb is that the easing date cannot be before the point when the prevalence count drops to less than what it was when the death rate took off. If that was about 4-6 weeks before the death rate peak, then one might think it should be about 4-6 weeks after the peak since the rise and the fall is approximately symmetric. However, as I showed in the previous Post #9, the incidence and prevalence curves precede the death rate curve by about 2.5 and 1.25 weeks, respectively, and that results in the prevalence count coming down to its say minus 5-week mark at about 4 weeks after the death rate peak (this time accounts for decreasing incidence and recovery from the disease). Now this date would not be safe because it corresponds to a prevalence that previously set off the exponential growth in death. However, if we exercise some precautions then sometime soon after 4 weeks may be considered safe.

For our purposes, we assume that as an absolute minimum condition to consider some social relaxation, that the prevalence must drop below 100 active cases per million (i.e., 1/10,000 people). One would still have to ensure that close contact with strangers is minimized and voluminous testing must be continued among other moderation. Frankly, we can never return to normal until some high percentage of a population is immunized either by having had the disease or by vaccine. A ballpark figure is about 50%, but no population yet has had more than 10% infected (Projected by 6/1/20: Italy ~ 4%, Spain ~ 5%, U.S. ~ 2%, NY ~ 10%).

Main comments are:

  • Without making a pretense about safety, our forecasts for when it is reasonable to consider social relaxation are given in the right most column in the above Table.
  • China and S. Korea are already relaxing social isolation so the rest of the world has many weeks to observe the prudence of their approach and decide on some combination of emulating and modifying.
  • Iran would appear to be the next country to drop below the 100 per million prevalence threshold (4/29/20). Italy and Spain follow next in the beginning of May and the U.S. and U.K. not until mid May.
  • Regarding the U.S. states it is not prudent to consider social relaxation for any of the hot-spot states before mid May except for CA and WA for which early May may suffice. NY will not reach an easing date until about 5/19/20.

Finally, we provide an update on the comparison of our forecast of critical values to that of the Institute for Health Metrics and Evaluation (IHME) at the University of Washington (UW), which has emerged as perhaps the leading model for informing our nation on the state of COVID-19 (http://www.healthdata.org/covid/).

We appear to be tracking very closely indicating that there must be components of each model that are similar. We tend to forecast a little earlier from peak rates. As the death rate curve flattens the accuracy of forecasts improve greatly because it is more evident where in the rise and fall cycle a given population is. This also accounts for the better agreement between the two models relative to previous weeks.