An Update/Follow-on To My Last Post...

What Happened:

Yesterday, was day 24 of the great California re-closing of 2020, a.k.a. the systematic destruction of the California economy by government imposed starvation.  It was also the day Dr Fausti was forced to talk himself into an ever tightening circle while testifying in front of Congress (here).  He was asked, by Rep. Jim Jordan (R-OH), "Do protests increase the spread of the virus?"  Dr Fausti danced around the issue slightly by giving a more general answer of "Crowding together, particularly when you are not wearing a mask, contributes to the spread of the virus." As the congressman continued to prod the Doctor, he steadfastly refused to say the only scientifically honest answer to the original question, "Yes."  The weasel in Dr. Fausti, repeatedly refused to opine on whether the protests, as a specific example of "Crowding together", must have "contribute[d] to the spread of the virus".  This, despite the fact that  anyone with more than a few dozen active brain cells could easily see that there was absolutely no reason to not admit it.

The Conclusion:

When even Dr Fausti, if only inadvertently, admits that the protests must have contributed to the spread, we have clear and unassailable evidence that to intentionally ignore such events when a health officer (or their representative) is doing 'contact tracing' is a 'dereliction of their duty' (as described in my last post).  So, where are the charges of Malfeasance?  Why are they still being allowed to fill those positions?  Why hasn't anyone, other my lone voice, cried out for them to held accountable for their actions?

My Interpretation of Those Crying Out to 'Follow the Science':

We all know the answers to the above questions.  It isn't politically correct to suggest that those on the left are ever wrong.  If you do it will 'trigger' them.  They will 'cancel' you.  The media will descend and tear you apart like the ravening beasts are are.  Consequently, no one dares to challenge anyone in regards to their cloistered, safe-space, participation-trophy, snowflake existence.  This even though that group continuously call for us all to 'follow the science'.  Indeed, our Goober uses that phrase, but, as I have repeatedly demonstrated, has not in any way actually followed it.

Sadly, I have had to conclude that reality has no place in today's media or politics.   And it's not looking so good for tomorrow either.

Goober Newsom Looks More and More Like Just A Petty Tyrant

Statement for the record:

So there is no mistaking my intent, everything that follows is based on San Diego County, and may
or  may not be applicable to other locations in the state or country.

Bottom Line Up Front:

So here we are, three weeks into our Goober's lastest lockdown of San Diego, and the result is...

He deserves to be recalled.  Seriously, that's the kindest thing I can say.  Words like "tar and feathering" do come to mind, but I will try to stay reasonable and civilized (not that his side of the political spectrum worries about that).  His lockdown has accomplished what he really wanted.  Namely the economy is once again jerking spasmodically to a halt as more and more business close their doors forever and unemployment claims are again on the rise.  His actions are understandable from that point of view, but not from any other, and particularly not from the viewpoint of a sensible review of the data.  Now let me defend those statements.

Short review: 

I originally claimed (here) that the Goober was using metrics defined in terms of the original beliefs about the data, which are simply not relevant to today due to improvements in our understanding of the data.  I then joined our Goober in doubling down (here).  He insisted that we needed to further choke our businesses, while I insisted that that was somewhere between insane and criminal (oh, and I used the word 'malfeasance' in one context and I will further support that claim).

Update:

After one week I reported the score as "Goober: 0, Me: 1".  I've now got two more weeks in and I can say the score is now Goober: 0, Me: 3.  I can also add this new score as well San Diego County Health Officer: 0, Me: 1 (maybe 2).  So far it really hasn't been a close contest.  But let's look beyond the standings to see the box score, to show just how badly they've done.

So far my predictions (which are based on my admittedly ad hoc method to predict the deaths) were that the deaths would not see a major rise, and would remain well below the amount the Goober had shown himself to be willing to tolerate - due entirely to the case rise being of a completely different demographic distribution than the original case distribution.  That number is around 7 / week / 100,000 population, which is about 234 / week for San Diego County.  I will freely admit that I think that is bigger than I would want to tolerate, I'd be seriously concerned if we topped 150 / week, and would think some initial actions might be appropriate once you reach 120 / week (or about 1/2 his number).  However, my predictions for the total deaths for the weeks ending July 11, 18, and 25 were 66, 61, and 62, respectively (just under 2 / 100,000).  These are comfortably below the point where I'd start to worry (about 1/2 my point of concern).  In fact, the actual death totals were 35, 56, and 55.  Even smaller than I'd predicted, and way less than the kind of numbers that should cause our Goober to push for re-closing the re-opened business.

I also complained about our County Health Officer (and everybody else) intentionally failing to ask during their contact tracing if the new cases had attended any of the 'protests', which were so vigorously defended by our Goober as protected free speach under the First Amendment.  I don't see that the Health Officers and contact tracers had the authority to not ask about attendance at such events.  I did a little web surfing and found this quote (which comes from the California Code, Health and Safety Code - HSC § 120175)

Each health officer knowing or having reason to believe that any case of the diseases made reportable by regulation of the department, or any other contagious, infectious or communicable disease exists, or has recently existed, within the territory under his or her jurisdiction, shall take measures as may be necessary to prevent the spread of the disease or occurrence of additional cases.

That seems very clear to me.  Now what is Malfeasance?  Here the first web search led me to this definition

Intentional conduct that is wrongful or unlawful, especially by officials or public employees. Malfeasance is at a higher level of wrongdoing than nonfeasance (failure to act where there was a duty to act) or misfeasance (conduct that is lawful but inappropriate).

Having intentionally not asked about attendance at the 'protests' is, as I read it, a clear intentional failure to do their official duty.  I further claim that as that intentional failure then led to them being unable to properly determine the source of the spread, and thereby allowed our Goober to order the re-closings, which then resulted in harm to San Diego County residents and businesses.  Hence, I claim it is a clear act of malfeasance.  Further, our County Health Officer has recently begun to include in their briefings, on the plot of daily new cases, an annotation showing the dates for the incubation period for bars and restaurants re-openings.  While I concede that they have shown this in technically accurate manner, it is also in what must be considered an intentionally misleading one in at least two ways.  First, it shows the incubation period for the initial reopening, but does not extend the incubation period for the entire time from when the restaurants and bars were reopened until the Goober re-closed them.  Secondly, it does not show the relevant incubation periods for the various protests.

I have reproduced my plot from the last post in Figure 1, but have made the change that the data are now 1 week averages, with the average assigned to the last day of the one week period. I also colored two points red, just to make orientation easy.  These two points are June 12 and July 4.  The other schematic lines are explained below.

Figure 1.  Plot of the 7 day running average of new cases for San Diego County along with schematic representations of the two cases.  Case 1 in green, Case 2 in yellow, see text for full descriptions of the two cases.  The two red points are for the dates July 4 and July 12 and are colored differently only to allow easier identification of various time periods. 

I can predict what should happen to such a plot based on the modeling experience I gained earlier.  Increased spread will not be seen in the case data for something like 7-14 days.  This is due to the increase having an induction period where the cases spread and then re-spread, this is then further delayed by the time lag inherent in diagnosing the cases, and finally another apparent delay due to the averaging reducing the response of the curve.  This very slow grow will be followed by a period of essentially exponential growth, which will then eventually roll over (typically showing the same kind of lag as the increase did).  We shall now consider in the two most likely cases.

Case 1: If the case rise is primarily due to the reopening of the restaurants and bars, the initial rise would be first visible some where around 7-14 days after the initial re-openings, which occurred May 21, so somewhere in the May 28-June 4 time period, maybe slightly later.  This would be followed by an essentially exponential period which would have no reason to roll over until the interaction was reduced (re-closing happened July 7).  The reduction would first be visible after a lag of over a week (for the same reasons we see the increases lag).  This means the rollover would only have started about the 14th (maybe slightly later), and be pretty flat starting about now.  This profile does not match the observed data.

Case 2: If the case rise is primarily due to the 'protests' (which occurred primarily from about May 29 - June 13, with a few smaller ones about a week later), then we'd expect to see an initial rise starting in the June 5-12 time frame, really picking up for a week or two, but then rapidly flattening due to the interaction being reduced to the previous level, but with a higher case number (and a concomitantly larger new case rate), which would appear as a flat zone starting around July 4. This is almost exactly what is observed in the data.

Anyways, like I said earlier, San Diego County Health Officer: 0, Me: 1.

Further Considerations For Predicting the Expected Deaths:

I could write many many pages about what I've done with the data I now have (three weeks of day by day deaths, broken down into age groups of 10 years, and almost five months of daily new case data broken down the same way).  I will not inflict that on you dear reader.  Instead I will simply state the best way I found to predict the deaths is to use each age group separately and for each of these to use the new cases over one week, to predict the deaths in the next week.  The current best fits are by using the death rates as given in Table 1.  I will note that using older case numbers like 2 or 3 weeks  previous work OK, but not quite as well.  If, however, you try to use the aggregate cases, without regard to the age grouping, the predictions are essentially worthless.

If I go back in time to the beginning of my tirades, and re-predict the deaths you get the data shown in Table 2.  These are clearly better results than my original ad hoc predictions, but the actual conclusions are completely unaffected, and the score remains Goober: 0, Me: 3, with my next prediction being that one week from today we will have seen 53 new deaths in San Diego County, and the Goober's re-closing will be further shown to be an exercise in over-reach.  The only possible conclusions are that the Goober is a complete idiot (which I don't really believe) or that he is happy to shut the economy down for no good reason, other than the political gain he believes he will have.
__________________

Table 1.  Best fits for death rates for each age group using cases from the weeks ending 6/27, 7/04, 7/11, and 7/18/2020 to predict the deaths for the weeks ending  7/04, 7/11, 7/18, and 7/25, respectively.  *- There have been exactly 0 deaths for these time periods for these groups, even though there have been 3 and 4 deaths total for these two age groups over the entire CoViD-19 outbreak.

Age group	Death rate
0-10	0
10-20	0
20-30	0*
30-40	0*
40-50	0.0055
50-60	0.0138
60-70	0.0444
70-80	0.117
80+	0.269

Table 2.  Predictions, both the previous ad hoc ones and the new ones based on better analysis,and the observed deaths. 

Week Ending	Old Prediction	New Prediction	Observed
7/04/2020	33	54	27
7/11/2020	66	49	35
7/18/2020	61	50	56
7/25/2020	62	51	55
8/01/2020	-	53	??

California's Goober Doubles Down... I Do, Too.

Background and Bottom Line

Our pitiful excuse for a Goober doubled down on his recent 'rollbacks' due to increases in CoViD-19 cases (actually on his twitter feed he wrote "spread at alarming rates"). I made my case last week (here) for why I thought it was a mistake when he rolled back San Diego last week, and now he's doubling down. Well, two can play at that game.

I have a lot to say, but for those of you who don't want to read the whole thing, let me give the most important data right up front. I predicted the death totals for three consecutive weeks. Let's call them weeks 0, 1, and 2, since those were the numbers of weeks into the future the prediction was for. OK, predicting "0" weeks into the future isn't really a prediction, but it does help to validate the basis for the other predictions. So how have I done?

1) For week 0 (week ending 7/4/2020): I predicted, 33 deaths, and there were 27 observed deaths. Not bad. The prediction was 20% on the high side. This could be due to using data over the entire historical time to predict what is happening "Now". If the treatments were improving, or if there are simply far more tests finding a greater fraction of the cases (better/more complete contact tracing), then you have to expect an over estimation.

 2) For week 1 (week ending 7/11/2020) I predicted 66 deaths. If we believe the 20% over estimation, maybe it should be 53. In any case the actual number of observed deaths for that week was 35. So actually it begins to look like I may be significantly OVER estimating the deaths. So when I claimed the Goober was imposing reopening rollbacks based on faulty metrics, I appear (at least so far) to be correct. Now he's increasing the rollbacks! Yet the raw prediction for the total for week 2 is still 61 (perhaps 49, maybe less), and we will see how that comes out.

Without bothering to 'show my work', I will state that I can now make a prediction for week 3 (week ending 7/25). It is 62.13, which I will round to 62. So there is still no prediction for a major rise in deaths and the Goober is reacting to a completely misleading metric (at least for San Diego County). 

So far the score is: Goober: 0, Me: 1.

A More Thorough Analysis of the Prediction Methodology and Assumptions 

If you go through my long post from last week you will see that I based my predictions on rates determined by dividing the deaths by the number of cases two weeks previously. That was of course a completely ad hoc assumption based on the reports that many deaths take 'weeks'. During this past week, I began to wonder, is that the right delay? Can I actually find an answer in the data?

KT was kind enough in the comments to the post last week to provide a pointer to Gummi Bear's Twitter thread where they talked about some observations they had made of CoViD data. In this there were lots of numbers and more than a few plots. The plot that struck me was a side by side comparison of deaths over time for New York City and Spain. Even though I had seen that plot for NYC before, the fact that the plots looked nearly identical triggered something.

Some time ago I had noticed (on the very excellent NYC CoViD site) that the curves for cases, hospitalizations, and deaths all had the same shape, but with the deaths clearly lagging the other two by about a week (don't just believe me: go to the site and scroll down to the "Daily Counts", it comes up on the Cases data, click on the Hospitalizations tab, see that it changes only modestly, then click on the Deaths tab, except for shifting to the right, it also doesn't really change). Anyways, that flash of memory suggested to me that the proper lag might be only 1 week, despite the stories of folks lingering.

Just trying to plot the raw daily data, with all the inherent noise seemed a fool's errand, but I decided that if I averaged the cases over 7 days, and the deaths over the same 7 days, I ought to be able to then plot the deaths vs cases, where if I delayed the cases by an optimal number of days I should be able to make a reasonable straight line. So off I went. To 'cut to the case', the optimal number, was, to my great surprise, four (4) days (see the plot below).

Plot of  7-day averages for deaths vs cases (delayed by 4 days) for New York City.  The quality of the correlation is obvious.

Now, I concede that the data, particularly back in time, may well be less than directly comparable to today (that is, of course, exactly what I claim our Goober is doing, so I must be careful not to fall into the same trap). How might it be easily different and in a way that would effect the apparent lag time? One very obvious way (well obvious to some - Thanks, KT!) is that back then in NYC, the place was suffering from massive numbers of cases, and insufficient testing availability. If that meant that people were not getting tested as soon as they would today (likely!), then it would reduce the apparent lag versus what we'd observe today. So really I need to use the latest and best data I can get my hands on. For me that's the San Diego County data. While I've been saving lots of case data for the county for almost 4 months, I did not start to collect the daily demographics for deaths until this month. So I won't have sufficient data to say anything more definite for another week or two. I promise to do a follow on once I do.

Fool Me Once, Shame On You. Fool Me Twice, Shame On Me 

In the previous post, and so far in this one, I have focused on only one metric. But let's take a quick look at all the metrics that San Diego County monitors, where we have gone into "alert" mode. The first is the same idiotic metric that our Goober is apoplectic about, namely that the number of cases is more than 100/100,000. The other two county metrics are "Case Investigations" and "Community Outbreaks".

The Case Investigations metric is indicating that the county isn't getting the contact tracing started fast enough. That's purely a function of the number of people doing the tracing and the number of cases. Clearly this is a reasonable thing to watch, and it is important to trace as fast as possible (to try to get out in front of the spread), but it isn't really contributing to our reclosing. The other metric, Community Outbreaks, is much like the case numbers in that the county is not comparing apples to apples.  Whether the metric as formed (more than 7 outbreaks in 7 days) is reasonable, depends. For example they are defining an outbreak as 3 or more people, who are from different households, that test positive, and are associated to a specific location/event. A week ago the county reported 21 such outbreaks, 15 of which were associated to "restaurants/bars". Yesterday they reported 17 outbreaks, with 7 associated with restaurants/bars. So far, so good. A reasonable person should ask two questions: 1) Are they truly investigating ALL the possible sources of interactions? and 2) What would we expect from random chance of people who were already infected being in the same place at 'the same time'? I really want to tackle the second in some detail, but that distracts from the far more significant first question. Consequently, I have moved the entire discussion of the second question to the bottom of this post under the heading "Real Effect or Random Correlation?".

But we still have the first question I posed above to deal with: Are they truly investigating ALL the possible sources of interactions? Here I can give an absolute answer. NO. Actually, Hell No! Here the county (and state) have completely lost their moral compass and intentionally failed to perform their duty. They should all be be charged with malfeasance and fired.

But what could cause me to be so sure and so angry? Simple. They refused to even ask if any of the people testing positive attended any of the protests (a.k.a. the riots). As we know these were also attended by predominately the same age groups as were also frequenting restaurants and bars. However, due to the intentional malfeasance of the county health officials, we can never know for certain if these played a role in the spread.  We can however demonstrate a smoking gun...

I can say definitively that the jump in cases is observed as having occurred during the 10 day period June 14 - 24. You can convince yourself of the same thing by merely looking at the plot below of positive test cases vs date reported.

Plot of the 3 day running average of the number of new cases versus date for San Diego Couny.  The two red points represent the data for July 15 and July 25, in between which the entire rise of case numbers from about 120 to about 470 occurs.

The protests occurred mostly during the period June 1 - 13 (I did a web search for "protests San Diego" and just noting the dates of the reports). If we assume these were significant vectors for spread, we would have seen a rise that was most pronounced in precisely the date range we see it (in the plot above, the two red points are for 6/15/2020 and 6/25/2020). If the rise were due to restaurants/bars the rise would have a coincident rise point, but it would not have leveled off so abruptly, while an abrupt level off is completely reasonable for the end of the protests. In fact, had the cause truly been the restaurants and bars, the earliest we would have expected it to level off, assuming the Goober's reclosing of the bars and restaurants was the primary factor, would be about a week after it took effect, which would work out to... today, the 14th of July.

I see no way to argue anything other than: 1) the 'protests' were the primary driver of the case increase in San Diego County, 2) our Goober and County officials are punishing business that had, at most, a minor effect on the case rise, 3) to increase the closures, in San Diego County, due to disease "spread at alarming rates", is unjustifiable, alarmist, and borderline criminal, and 4) every government official who encouraged, or even merely acquiesced to, not asking about attendance at the 'protests' should be fired immediately for the damage to the people and business of San Diego County they caused by not properly doing their jobs.

Real Effect or Random Correlation?

I have tried to gather the best possible data to answer this question. So here goes, and please bear with me. After doing a bunch of web searches I have concluded that there were well over 180 restaurants before the first lockdown. That is the number of members in the San Diego County Restaurant Association (listed as "180+"). I do not know if a restaurant group (all owned by one person/entity) count as 1 or many. Clearly the number might be bigger, possibly way bigger. I'm also ignoring the number of non-restaurant bars (and that's a bunch too, but I can't really get a good handle on the number). I also don't know how close to capacity the restaurants have been during the reopened period (the news stories I've seen suggest that they've been near their capacity). I also can't say what the true capacity would be. I also don't know how close in time the people needed to be to count as 'at the same time' in the contact tracing. But let's take some educated guesses.

I know, from personal observation, that most of the restaurants I used to frequent would have signs listing their capacity. Those listed capacities were always over 100, usually more like 120. During the reopen period there were requirements to maintain social distancing, so the density was clearly reduced. I also know from the local media that many restaurants had been using outside spaces to add some of the capacity back. So let's take a swag at something like 50 being a reasonable estimate of the average capacity (ASIDE: This is a really important number as the more people there are, the higher the likelihood of random clusters, i.e., groups of 3).

We know that restaurants generally don't seat every table, then later clear out all tables and then reseat every seat and repeat. It is much more continuous than that. Let's guess that during the lunchtime, something like 100 people can be there close enough in time to be counted as 'at the same time'. I'd guess that dinnertime you'd see something larger, maybe two groups of 100. We also know, from the plot above, that the county has had a variable, but consistent case count over the last two weeks of between 400 and 500 (the daily average over the last two weeks is actually 471). We could just use this, however, when we start talking about the number that might be going out to restaurants it is likely to be too big. That '471' is all age demographics summed up. But we know that it is almost entirely the under 40 crowd, and truthfully almost entirely the 'over 20 and under 40' crowd that are going out to the restaurants. So let's just count the cases for those aged 20 - 40. The numbers are that over the last two weeks there were 3,296 cases in this age bracket, and the county population is (for the same age group) about 1,046,000. If we use these data to determine the number of active cases on any given day (by multiplying by an average duration of 10 days and then dividing by 2 to remove the identified cases - who presumably stop going out) we get 1,177 cases (infected but not identified) in a population of 1,046,000, or an average infection rate of essentially 0.11%.

Now we can calculate the expectation of finding at least 3 random people in a 'seating' of 100 as 0.0013. If we then take 3 'seatings' per day per restaurant and 180 restaurants we expect to see about 0.61 'outbreaks'/day (or 4 per week). Note these are 'outbreaks' where the association is not that they spread the disease while at the restaurant/bar, but rather that they just happened to randomly be in the 'same place at the same time'. I strongly believe that this estimate is a severe underestimation. I believe this because I expect that the cases will be concentrated in the fraction of the population that are going out (for example, I have 3 sons in this age group and NONE of them, their wives, and certainly not their children, are going out). I would think that I could easily be a factor of 2 or more low on the expected number of random 'clusters'. If so, then ALL the observed restaurant/bars 'outbreaks' of last week are actually just non-causal clusters. If the factor of 2 is correct, then there may be essentially no true Community Outbreaks, and even if 3 are real 'outbreaks', then it gets closer and closer to having the actual number of outbreaks to be below the metric. Anyways, yet again misuse of a metric is so easy to see, if you bother to look...

Figures Don't Lie, But Liars Can Figure

Updated on 7/8/2020 - see below

And sadly I must report that the San Diego County Health Officials (and the CA State folks, up to and including Goober Newsom) have been 'figuring' (or perhaps just showing their ignorance).

First a bit of background.  When Goober Newsom allowed the various counties around CA to 'open up' (albeit slowly), he put some 'metrics' in place to monitor the counties behavior/results.  Now at first blush this seems a reasonable and responsible action.  However, the metrics (set in mid April) have not been modified to account for the changing reality, and as a result are now being applied, I believe improperly, to shut businesses down in San Diego County (and I must wonder if in other counties as well).  I have been saving lots of the historical CoViD data for San Diego County and I dug into it to determine if they are comparing apples to oranges.  And the result is (drum roll)...

They are comparing apples to squirrels.  The data today simply can not be compared in a straight forward way to metrics based on data from back then for a myriad of reasons.  I will now attempt to convince you, dear reader, that I know what I'm talking about.

The metric which has caused us to go on the Goober's 'watchlist' is that we have exceeded the allowable average daily count of new cases (a 14 day running average which is also divided by the county's population - in 100,000's, for San Diego that means it is divided by about 33).  Now if that is unclear, please excuse me, but that's the metric.  When the metric exceeds 100 for 3 consecutive days, the state starts to 'watch' and if it stays above 100 for 3 more days, then the state orders all 'indoor' business to close for at least 3 weeks.  But why?  If we are aiming to someday reach herd immunity (and so long as we lack a vaccine, that's the only path available to a general return to normalcy) then having the cases rise speeds the process.  Of course I would prefer we do this as safely as is reasonably possible, so it is reasonable and prudent to try to keep cases, hospitalizations, and ICU admissions down, but really we should be striving to keep deaths down.  You will note that the metric simply does not take this into account.  Additionally, the metric does not take into account the tremendous rise in testing since those early days.  Perhaps they were simply trying to get a surrogate for deaths, not after people had died, but in metric that would predict deaths.  At first blush cases seem a reasonable choice.

As of the day they set the metric, the death rate was running at about 6% in San Diego County^*.  At the time the highest number of deaths in a week was 40.  If we divide that by 33 to get it per 100,000, we get 1.2 deaths per 100,000 per week, at the maximum.  But the cases were running at about 570 per week, and that's about 17 per week per 100,000.  So let's work with the assumption that they were prepared to accept the deaths that would be produced if the case rate rose to the 'magic' 100 per week per 100,000.  That works out to about 1.2*100/17 or about 7 per day per 100,000.  So far, while the metric is convoluted, and only marginally predictive, it would be not insensible.

But how does that apply to today?  Somewhere between poorly and utterly and completely irrelevant.

Why?  Because the demographics of those getting the disease has changed markedly, the testing capacity has seen a major growth, and, I suspect, the treatments are better.  Let's agree to ignore the last possibility and focus on the first two.  As to the number of tests, back in mid-April we were running around 1,100 tests per day in San Diego County.  Today that average is more like 6,700.  If the actual fraction of people infected stays the same, and the number of cases is substantially larger than the number confirmed, then the total testing positive would be a fairly constant ratio of the tests, so we'd expect to see something like 6 times the number of cases (and it is  a factor of 4.5, not too far off). Additionally, as contact tracing has improved we'd expect to see even more of the asymptomatic cases be identified, so just using case numbers is nonsense! And we haven't even started on the issue of the demographic change.

The demographics of the cases for San Diego County in mid-April consistently showed that the three most susceptible age groups, 60-70, 70-80, and 80+ accounted for 25% of the cases, and nearly 90% of the deaths (with the under 40 demographics accounting for about 35% of the cases, but only 2% of the deaths).  So if we really want to keep the deaths down, we need to worry about the 60 and over crowd (and be somewhat concerned as to those 40-60).

Well we have the data, so what's the death rate today and what do we expect to see for the next 2 weeks (based on today's cases). As of the last three days we have seen a total of 0 deaths.  OK, even I don't believe that.  It's probably something of an artifact, likely caused by the holiday weekend.  Let's look instead at the deaths for the 7 days prior.  Total deaths: 27.  After we divide by 33 (to get in per 100,000) the total is less than 1 (it is just under 0.79).  Not even as high as the totals we'd seen before, let alone jumping up towards 7.  How about the projection for two weeks out (based on the fact that there were 3392 cases reported in the week 6/28-07/04)?  Well we need to break these down into their demographic groups.  Fortunately, I have that data (it is in Table 1 below for those who want to check my math).  There it is, the projected deaths per week per 100,000 in two weeks is likely to be less than 2.  Again, no where near the 7 they seemed to be willing to tolerate.  Given that, the closure of businesses is completely unwarranted.

             
UPDATE: Note added as proof.

It strikes me that if my method of predicting deaths from cases has any validity I should be able to take the case data from two weeks ago and predict the deaths as of today.  I can't believe I didn't think to do this yesterday.  Anyways the data are shown in Table 2 below.  The prediction is a total of 32.74 deaths for the week ending July 4.  The observation was 27.  Anyways that seems close enough to me.  It also suggests that prediction for 2 weeks from now is likely to be high by about 20% (this could be due to lots of things, but I'd bet on most of it being due to more cases that are asymptomatic being found through contact tracing, which would have simply gone as undetected previously, increasing the case count, but not contributing to the death total).  I won't be surprised to see deaths for two weeks from now to come in at around 50 (or about 1.5 per 100,000).  I also added Table 3, which is the prediction for the deaths for the week ending 7/11/2020.  (Again, I can't see why I didn't think to do this earlier.)  So we'll see how this works. Oh, and that's for data ending before San Diego got on our Goober's Watchlist and it is slightly higher than the prediction for deaths for the week that cased us to go on the list, but still essentially 2 / 100,000.

             

* - this number is what I get by taking an average number of new cases a little before the date the metric was set and an average number of deaths over the time period 14 days after that.  It's crude, but it isn't unreasonable.

             

TABLE 1: The death rate for each age group is calculated by taking the number of deaths (as of 7/4/2020) dividing those by the number of cases two weeks previous to that (as of 6/20/2020).  The new case totals are for the week ending 7/4/2020, so the death prediction is for the week ending 7/18/2020. The total number of deaths would be expected at about 61 for that week (compared to about 40 above).  That's still less than 2 / 100,000.

Age group	New cases	Death rate	Expected Deaths
0-10	110	0/254	0
10-20	267	0/558	0
20-30	1109	3/2152	1.55
30-40	674	4/2000	1.35
40-50	446	12/1633	3.28
50-60	381	29/1810	6.10
60-70	235	60/1129	12.49
70-80	93	93/645	13.41
80+	74	186/604	22.79
Total Expected deaths	60.97 ≅ 61

             

TABLE 2: The death rate for each age group is as in Table 1.  The total cases by age group is as of the 7 days ending 6/20/2020.  The expected deaths are for the week ending 7/4/2020.  The total number of deaths would have been expected at about 33 for the week (compared to 27 observed).

Age group	New cases	Death rate	Expected Deaths
0-10	48	0/254	0
10-20	122	0/558	0
20-30	360	3/2152	0.50
30-40	221	4/2000	0.44
40-50	173	12/1633	1.27
50-60	207	29/1810	3.32
60-70	125	60/1129	6.64
70-80	53	93/645	7.64
80+	125	186/604	12.93
Total Expected deaths	32.74 ≅ 33
Total Observed deaths	27

             

TABLE 3: The death rate for each age group is as in Table 1.  The total cases by age group is as of the 7 days ending 6/27/2020.  The expected deaths are for the week ending 7/11/2020. 

Age group	New cases	Death rate	Expected Deaths
0-10	83	0/254	0
10-20	210	0/558	0
20-30	734	3/2152	1.02
30-40	483	4/2000	0.97
40-50	341	12/1633	2.51
50-60	269	29/1810	4.31
60-70	201	60/1129	10.68
70-80	116	93/645	16.73
80+	98	186/604	30.18
Total Expected deaths	66.40 ≅ 66