October 16: Increase continues.


Daily Status, October 16

Reminder:  any sections that are unchanged since yesterday are grayed out.  

As some of you know, situations in my life has changed.  8 years a go battled metastatic kidney cancer, and with a few surgeries, "won".  Well, I won two battles. Over the last few months I had not been feeling well.  This week, we found out that I am entering round 3.  Round three will be a tough fight.  The treatment goal is not curative, but rather quality and longevity of life.  This is why there have been some missed days this week.  I am not sorry -- only sorry that I could not explain why.

I am both horrified and honored that so many people rely on this blog.  Horrified, because it reflects the low quality of main-stream scientific reporting such that people need information not supplied, and honored that you turn to me.

I am not sure how I will be able to manage this going forward.  I hope to be able to update it several times a week at least.  And I am probably going to streamline the workflow to minimize my effort which may make it harder to read.  Alternatively, if someone is willing to help with the formatting, I would be grateful for the help.   I want this to be easy to read.  

The one thing to remember is the day-to-day changes are minor. Any trend takes several days to identify, and I will be looking at the data daily (that is easy), but may not update the blog.

Situational Awareness

Since my last update, the VDH has reported 2514 new cases of COVID-19.  The data is was reported by VDH on October 16, 2020 (based on data collected on October 15th by 5 PM) There were 1331 cases on reported on Thursday  and 1183 today (Friday), which, when averaged (1257) and correct for day of week bias (Thur and Friday typically see more cases than average) is less than one standard deviation above the three week average bias-adjusted average of 937 cases (correcting for the bias does not impact the average cases, but reduces the variance/standard deviations). I am now confident that the downward trend for September was either a fluke or is over.     Viewed over the last seven days, Virginia reported 7475 cases, or 1068 cases per day on which works out to 13.5 cases/100K people.  While the number seems high, it is inflated because of a data glitch last week; without it we would be showing 12.79 cases/100K/day.   The latter is a small increase from the 12 cases/100K in August, but well above the 9 cases per 100K from late September .  It is worth noting that there was a dip in hospitalizations that coincided with the September dip, leading me to believe it was real.  Furthermore, if we subtract the effect of the college outbreaks, there was a constant decrease from just before August 1 to Sept 25.  By October 3, there was a measurable increase.  The new increasing trend is real.

The trends over the last three weeks, now are showing a modest increase, at 2.2%/per day or  16% per week.  

By combining our current regional trends with the typical reporting for the day of the week, I expect 1360 cases tomorrow (Thursday) with a 90% chance of the numbers falling between  921 - 2016 cases.  If tomorrow's numbers differer from that, we are having a very rare event.  This is useful to consider if I am unable to report tomorrow, as you can compare the state reported numbers with this.

Regionally, all districts showing positive case growth. The testing numbers now show the percent positive to below the 5% metric over the last week (4.88%) that is often used to indicate sufficient testing (e.g., is is safe to reopen schools).    Virginia is testing about 2.8% of the population every two weeks, which is about 1/4th the number of tests desired for random sampling, but most of the Commonwealth's testing is driven by medical needs and not surveillance. Surveillance would ideally test at least 5% of the population per week but the testing is somewhat invasive.

On one other front, over the last week, we well above 10 cases per 100,000 to enter states like NY without quarantine -- we are at nearly 13 cases per 100K.  As expected NY added us back on the quarantine list. 

Outside of a multiple smaller cities/towns, the state is doing better than it has since the Hampton Roads surge except we are seeing more localized outbreaks; the hot spots are isolated and probably associated with facility outbreaks.  Also, looking at the chance of being exposed in a group of 50 (e.g, eating at a restaurant), while almost all of the state is blue or clear, the teal to yellow regions (dangerous) are increasing.


In the spring, COVID-19 in Virginia was primarily a concern in the DC suburbs.  Over a 3-4 weeks, in late may to early June, NOVA recovered, and for about a month, the disease was under control, to the point where the restrictions were eased.  Unfortunately, in eastern VA/Hampton Roads, the easing of restrictions results in a surge in cases,  which peaked just before August 1 (and resulted in stricter restrictions in that area).  Since then, the the exception of growth on college campus, the disease has been stable, except for the rural parts of the state, where safeguards (social distancing and masks) are largely ignored.  Starting in October, our weekly case count has been increasing, particularly in NOVA, NWVA and SWVA. 

But, looking at the weekly numbers, it appears that the rural areas, whose caseloads were already increasing, surged with the the influx of a carefree demographic (college students).  I think we area so seeing some impact of the White House outbreak, as many employees live  in northern VA.  This is mostly showing up in the Upper Middle Class suburbs. 

Looking at the weekly case count, we see that the case numbers are more in line where we were at the beginning of September.  We have now seen two consecutive weeks of increasing cases.

Regional growth rates are (in fraction per day) continue to to show improvement.  Over three .  It is worth noting that it is easier to show a decline when the prior numbers increased.

NOVA: 1.024-- GMU

Central VA: 1.024 --  VCU

Hampton Roads/Eastern VA:  1.016-- W&M, CNU & ODU

SW VA:  1.029-- VT & Radford

NW VA:  1.011 -- JMU & UVA

The state as a whole is is increasing with Rt=1.0215

The following table shows the number per 100k for each region. Again, NOVA and Eastern VA are doing the best, and the mountainous regions in NW & SW continue having more cases.   The concerning aspect is in all regions, the numbers for last week are significantly higher than the prior 3 weeks.  What is most concerning is SW &NW VA, where it is hitting very high numbers, inspite of VT and Radford doing better.

Daily Cases/100,000 


Last month

Last week
















(replace the numbers above the the numbers below)

NOVA,  7.6, 10.1
Eastern,  8.1,  9.5
Central, 10.7, 12.6
NW, 10.5, 18.8
SW, 16.0, 22.1

The following charts are for the 5 regions. in the chart that shows all regions,  The individual line charts show the unfiltered data per day, coupled with the trend lines.  The trend lines show the different periods of growth.    Early in the pandemic, the different parts of Virginia were functioning largely independently, with NOVA mimicking the northern states, and Hampton Roads mimicking the southern states. But, since September 1, the regions have trended with each other.  Starting in late Sept., NoVA and SWVA diverged from the rest of the state.  The increase in NoVA amounts to a total of about 750 extra cases, which may be too many to attribute to the 9/26 White House event.

Note that the effects at both ends of the chart are probably an artifact of the 7-day polynomial filtering I use.  The filter is called a Savitzky-Golay filter, and basically is a moving window polynomial filter.  At the edge (first and last days of the time series), the filter will over compensate for the trend.

Local/Northern VA:

After the early peak in May (~1000 cases per day), NOVA saw a sharp drop in all COVID metrics, reaching a broad valley in mid June(~200 cases/dat), which lasted until around August 1, when by Sept 1, we increased back to around three hundred. September showed notable caseload drops to about 150 by late in the month. Since then, it has increased to 250+ per day.

Currently, most jurisdictions are increasing, but very slowly.

Fairfax Co.: 1.025
Arlington Co.: 1.036
City of Alexandria: 0.993
Prince William Co.: 1.012
Loudoun Co.: 1.032
The number above is Rt:  Rt is an exponential time constant, where the number of cases in a time segment is approximately, n=Ao Rt ^ t, where Ao is the number of cases at the start of the segment, Rt is the exponential growth rate, and t is the number of days since the start of the segment.  So, if Rt is greater than 1, it is growing exponentially, if it is less than one, it is decreasing each day.  \

Another way to look at it, todays number are approximately the growth rate times yesterday's numbers.  This is the exponential time constant.  Fortunately, the time constants are below 1 and our rate of cases is about 6/100,000 per day.  Ideally, we would be 0, but 6 is much better than our peak in which was around 30/100000K. 

Looking at the trends, the strong downward trend in daily case count we observed since around Sept. 1, has ended.  We now see significant jump in cases in every jurisdiction. The cause of that is unknown, but may relate to the cooler weather.  

The contrast in the Northern Virginia map is increasing. There are small scale variations, but the relationships seem to be 1) more related to locations with more shared living arrangements than anything else, or 2) locations not too far from DC, but relatively wealthy.  Another way to say this is, while social distancing is hard on all of us, for the lower income people, it may be impossible.

All of localities in Northern VA are showing significant case increases.  Though, the surge seen in Vienna and McLean has abated, probably the result of of the White House outbreak running its course.  The increase in Arlington is possibly related to relaxation of social distancing restrictions.


Last month

Last week

Growth rate (%/day)

Fairfax Co









 So. Alexandria








 Annandale/Fall Church









 No. Arlington




 So. Arlington








 Vienna,  5.2,  6.8,  4.5
 McLean,  5.7,  6.3,  1.4
 So. Alexandria,  9.6, 10.3, -1.2
 Reston/Herndon,  7.9, 10.8,  2.2
 Annadale/Fall Church,  9.0, 12.3,  4.0
 Fairfaix,  6.4,  9.6,  8.8
 No. Arlington,  6.7,  9.5,  7.7
 So. Arlington,  9.8, 12.6,  6.6
 Alexandria, 11.7, 11.9, -1.6

During most of the last 2 months, Vienna and McLean tracked each other, as did Reston and Fairfax.  Except, for Labor Day to about 1 week ago where Vienna increased its case load.  Now, though, it appears Vienna and McLean are again tracking each other.  Fairfax has dropped (and Vienna/McLean have increased) so the three localities all very close.

It is worth noting that almost every region is seeing small upticks in the last few days.  Focusing in on the regions of N. Arlington, Falls Church McLean and Vienna, we see a significant uptick 1 week after the White House event.  Now, this may be recovering cases that were not reported in September, or they may be people affected with the White House (e.g., Secret Service, staff, etc).

Local Safety/Risk

I am attempting a different means of discussing risk.  Risk is a very personal item.  Different people will respond to COVID differently.  However, we know how many people have died (and how many cases) for each age group in Virginia.  So, we can get a case fatality rate for age groups.  Also, based on serology studies, we know the case numbers are low by a factor of 2.4, so we can get a infection fatality rate (IFR).  We also know how co-morbities play in. 

So in the following tables, I have combined the current probability of person being infected around Vienna to compute the probability that a person in a specific crown is infected.  

Next, assuming one interacts with everyone in the crowd (big assumption), I assume if you interact with an infected person, you have a 50% chance of getting infected.  I do not know what that rate is:  it will be a function of how long you interact, how close, and if masks were used, etc.  This is the big unknown.

That gives me a probability of being infected based on the number of interactions.  I then combine that with the IFR to estimate the risk of dying by age and number of interactions for people with and without co-morbidities.  

For comparison, the risk of dying in a car accident in a given day is about 3 in 1 million. (3.1e-6).  That is a baseline as I know few people that will not get in a car because of the risk of dying.

No Co-morbidities
#exposure        0-9      10-19      20-29      30-39      40-49      50-59      60-69      70-79        80+ 
         1   0.00e+00   3.16e-08   2.85e-07   1.08e-06   2.85e-06   7.50e-06   1.70e-05   2.74e-05   5.15e-05 
         5   0.00e+00   1.58e-07   1.42e-06   5.37e-06   1.42e-05   3.74e-05   8.49e-05   1.37e-04   2.57e-04 
        15   0.00e+00   4.71e-07   4.24e-06   1.60e-05   4.24e-05   1.12e-04   2.53e-04   4.08e-04   7.66e-04 
        50   0.00e+00   1.54e-06   1.39e-05   5.23e-05   1.39e-04   3.65e-04   8.28e-04   1.33e-03   2.51e-03 
       100   0.00e+00   3.00e-06   2.70e-05   1.02e-04   2.70e-04   7.10e-04   1.61e-03   2.60e-03   4.88e-03 
       500   0.00e+00   1.22e-05   1.10e-04   4.14e-04   1.10e-03   2.89e-03   6.55e-03   1.06e-02   1.98e-02 
      1000   0.00e+00   1.92e-05   1.73e-04   6.53e-04   1.73e-03   4.55e-03   1.03e-02   1.67e-02   3.13e-02 
      2000   0.00e+00   2.56e-05   2.31e-04   8.71e-04   2.31e-03   6.07e-03   1.38e-02   2.22e-02   4.17e-02 

#exposure        0-9      10-19      20-29      30-39      40-49      50-59      60-69      70-79        80+ 
         1   0.00e+00   3.16e-07   2.85e-06   1.08e-05   2.28e-05   3.75e-05   5.10e-05   5.48e-05   1.03e-04 
         5   0.00e+00   1.58e-06   1.42e-05   5.37e-05   1.14e-04   1.87e-04   2.55e-04   2.74e-04   5.14e-04 
        15   0.00e+00   4.71e-06   4.24e-05   1.60e-04   3.39e-04   5.58e-04   7.60e-04   8.16e-04   1.53e-03 
        50   0.00e+00   1.54e-05   1.39e-04   5.23e-04   1.11e-03   1.82e-03   2.48e-03   2.67e-03   5.01e-03 
       100   0.00e+00   3.00e-05   2.70e-04   1.02e-03   2.16e-03   3.55e-03   4.84e-03   5.20e-03   9.76e-03 
       500   0.00e+00   1.22e-04   1.10e-03   4.14e-03   8.77e-03   1.44e-02   1.97e-02   2.11e-02   3.96e-02 
      1000   0.00e+00   1.92e-04   1.73e-03   6.53e-03   1.38e-02   2.28e-02   3.10e-02   3.33e-02   6.25e-02 
      2000   0.00e+00   2.56e-04   2.31e-03   8.71e-03   1.84e-02   3.04e-02   4.13e-02   4.44e-02   8.34e-02 

Age Distribution: 

As expected, there was an increase in cases among Middle Ages (and young adults) delayed from when the JMU students returned home.  There are about about 150 cases from the JMU students. We are now back to the baseline number for that age group.  The JMU students infected their parents, resulting in an increase of The concern is that the JMU students will infect their families, and we saw an uptick in the middle aged population, which has since recovered; about 50-100 extra people were infected, which was less than I expected.

Note, I will talk about the age distribution in college communities under "college communities"


Overview:  Things are improving at most colleges.  I have added the Lexington VA colleges (VMI/Washington and Lee) to the discussion.  My data mining by zip code requires I group them together, because they share a zip code.  Two schools have moved to good to watch:  CNU and ODU.

My process combines the VA Department of Health data and what is reported by the colleges.  The report is as of 11:00 ET.   I need to point out that the VDH cases may include cases not affiliated with the university as I am using geographic surveillance.  It is also worth noting that all assume that students feeling ill are going to health service; I have heard anecdotal reports of people not doing that because they did not want to quarantine.  The colleges usually update the dashboards after this post, or on Monday/Tuesdays.  Except for Radford, all weekly updates are complete at this time.  And the numbers are encouraging (except for UVA).

As the process has evolved, it was clear that I needed to show active cases (e.g., last 10 days) in addition to cumulative cases.

Also, I am assuming the colleges are promptly reporting there numbers to the Virginia Department of Health.  As it turns out, W&M is reporting the cases approximately 2 days after updating the dashboard.  It is probably a result of the phasing of the process.

Note: I have been tuning the following table to improve the estimated number of cases (the order of operations has changed a bit).  Also, I am adjusting the pre-student case olad:  to estimate the number of cases from students, I subtract out the average number of cases prior to the student arrivals from the reported cases.  If the numbers are large (UVA, VT, JMU & Radford), the impact is minimal.  This makes it harder, though, to identify cases in more urban settings, like ODU, VCU and CNU.

RED means there is clear evidence for community spread
YELLOW means there may be community spread; still ambiguous
GREEN means no evidence of community spread
BLACK means they went online.


% Positive


Last 10 days

Dashboard Cases

% that had COVID***

VDH Cases*



VDH Cases*



Va Tech
























































































The following are for the columns in order except for dashboard. Dashboard was updated.  I may updated it later.
    Va Tech,   4.7,    1646,  4802,     201,   264,  14.1 
        GMU,   2.4,      86,    90,      29,    33,   0.3 
        UVA,   5.6,     804,  1841,      90,   194,   7.7 
        ODU,  13.8,      84,   174,      36,   117,   0.7 
        JMU,  10.1,    1699,  7490,     137,   280,  35.3 
        CNU,  12.8,      23,   107,      20,    74,   2.1 
        UMW,   4.1,       0,     3,       0,     0,   0.1 
    Radford,  12.6,     590,  2819,      33,    87,  35.6 
        VCU,   4.4,     179,   336,      18,    25,   1.1 
     WL/VMI,   8.9,     131,   261,      56,   143,   6.8 
        W&M,   1.5,      21,    21,       2,     3,   0.2 

* estimated from the number of cases in the zip codes associated with the university removing the pre-student arrival case rate

** estimated number of cases is an attempt to normalize for testing limitations. Specifically, I assume at 5% positive, 100% of the cases would be caught. so I normalize it to that value.  If the % positive is very high (>40%) I am likely overestimating the numbers.
*** Dashboard cases are only counted if I can find the dashboard.  In some cases, it is difficult to distinguish positive tests from cases (1 case may have multiple positive tests; that is mostly at VT).  I include active cases if reported, otherwise, I use total cases.
****% population uses the total reported number of students rather than just those on campus; it may be off when the percent positive is above >40%.
***** Old data, not updated for today.
****** Active cases, not total cases

Large Scale Community Spread:

JMUJMU returned to in-person class 10/5; here is their plan. So far so good with the return. The case count at JMU to rise, but slowly.  JMU has been fairly transparent with the situation, but could not get ahead of it initially.  If the cases do not increase  in the next week or so, I will declare the outbreak over.

Virginia Tech: Virginia Tech is finally getting a handle on COVID.  with on a few new cases a day.  Virginia Tech is more than a College...it is a football team.  And the football team has been decimated by COVID-19.  The real test is when VT can field a full team.  

W&L / VMIThere is clearly an outbreak occurring at these schools.  The numbers are low because the schools are small.  My surveillance approach is based on ZIPCodes, and they are in the same ZIPCode, so they are combined into a single entry.  This is just an artifact of how things are tracked, and not a judgement on either school. 

Watch List:

CNU -- I am not really sure what is happening at CNU, because they are in a populated area, and their dashboard is not sufficient.  They are showing a significant increase in caseload.  I will be monitoring them more closely now.

ODU -- ODU saw a significant (71%) jump in cases week over week.  But, the numbers remain relatively low for the size of the university.  It is difficult to track ODU because it is in a populated area and they only update the dashboard weekly.   
UVAWith the students return, there has been a marked increase in cases.  Week over week.  It appears, that with the much stricter rule UVA imposed two weeks ago ago, the case load is abating.  As a Hokie, nothing saddens me more than having to move UVA on the watch list (down) before I do VT.  Oh well.  They still messed up COVID.  There is that.

Other schools:

William & Mary -- There is at least a small scale outbreak in the athletic department.  W&M tested all athletes, and stood down on athletics.  They had a total of 20 positives within the athletic program.  Most of the active cases are among athletes. It is worth noting that W&M is quick to update the dashboard; they update prior to reporting the VDH.  W&M tested all students prior to arrival in town. With the current outbreak (albeit small), the college probably is a bigger threat to the town than the other way around, which is a reversal of the last several weeks

VCU seems to have the virus under control -- They have beaten back an outbreak, the % positive is good, and there are few new cases. It was promoted from RED to YELLOW. and now Green.  The numbers have shown no significant increase in the last several days; quarantine and isolation space is becoming more prevalent.   Being in an urban setting the zip-code and regional surveillance that works well at some of the other schools is not particularly helpful here.  So, I have to rely on the dashboard.   

RADFORDRadford was moved to the watch list, as the percent positive has improved.  Radford updates the dashboard once a week, which is not sufficient in my opinion, but fortunately they report to VDH regularly.  As such, the data on Radford's dashboard is now 7 days old.  While it is not how many students at Radford contracted COVID-19.  It would not surprise me to see enough that the community, when isolated effectively has herd immunity (meaning over 60-80% contracted COVID-19).  The only way to know would be antibody studies.  Hopefully, VDH will look into that.  But, currently, it seems that Radford has a manageable number of cases.  While only 57% of the cases in the last two months are attributed to students, there were so few cases prior to the students return, this means either the dashboard is undercounting, or there were a lot of non-students infected by students. 

UMW -- Nothing noteworthy.  A few cases but they just returned.  I am concerned because they did not test all students.

GMU -- Nothing noteworthy.  GMU tested all students.

College Communities:

When I started talking about communities, the  focus was on the safety for incoming students.  Unfortunately, that concept as changed.  Now, we are seeing the colleges impacting the surrounding communities.  If we look at the age distribution of cases in the communities of JMU, Radford and VT (New River, and Central Shenandoah health districts), we see that, starting about 4 week ago, number of cases for non-college age citizens is increasing -- about two weeks behind the college age curve.  This suggests the disease is infecting the general population.  So far, it looks like about an extra 372 middle aged and seniors have been infected were infected; we have gone from 12 cases to 24 cases per day in those groups.  Furthermore, in the 5 months from the beginning of the epidemic to early Sept, there was an average of 10 deaths per month; in the last 7 weeks there have been 26 deaths.  This is the concern.  This trend has been observed elsewhere in the country, where the college students infect the more vulnerable populations.


Since Donald Trump has been infected, or more accurately, with the infection of Trump, we have learned:
  1. People can test negative and still spread the disease.
  2. Even if everyone who interacts with you is tested immediately prior to the interaction, you can still get sick (see 1).
  3. If you have an event with hundreds of people, there is potential for it to be a super-spreader event, even if you test everyone at the event.
  4. If you do this frequently enough, you will get unlucky.
  5. A non-spaced, but outside event is still dangerous.
This has two impacts for us:  first, some of the attendees and support people live in our community. I have no information as to the impact this has had.   Second, we now know that any crowded situation is a potential spreading event, even if outside.  This includes sporting events, concerts, and worship.  Please think about this before planning activities.

There are safety concern with athletics, but those can be mitigated.  I am more concerned about the fans in the stands.

I have reason to believe about 100 or more of the last week's Virginia cases are a direct result of the combination of the Newport News Trump rally and the Whitehouse outbreak.  If we are placed back on the NY quarantine list, it is because of these political activities.  


1) You can repost / share in the entirety by forwarding the link, 2) If you want share partial content, you must receive my permission – I need to make sure you understand what I am saying. If anyone sees this work being used without attribution, please let me know as soon as possible. I am willing to have an informed discussion / debate on my approach, but I want to make sure the proper context is captured.


Source data is from the Virginia Dept of Health COVID Site

Why I did this:  About the blog
I have thoughts on the spread in college communities which can be seen here.  This is the same link at the top of the write-up.

Other Sites:  John's Hopkins

Kids can pass covid to parents: Pediatric SARS-CoV-2: Clinical Presentation, Infectivity, and Immune Responses

A fun video showing masks work, guy style:

How to wear a mask.

Politics:  Some lie.

Donald Trump yesterday said that without blue states, the death rate would be much better.  He needs to stop using alternate facts.  Red and blue are defined by who the state voted for in 2016.  47% of the deaths come from red states, which is about his percent of the popular vote.  But, more importantly, after June first, when states could use science to mitigate the virus, 65% of the deaths are from red states.  Facts matter.


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