Status for Nov 10: trends continue. We are looking at the daily caseload doubling by the end of the year if the trends continue.

 Daily Status, Nov 10

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


Please be kind.  I have been putting out a daily update since March.  As you may have noticed, I had not put one in 10 days.  I am trying to get this one out, but my energy level is low.  I spent half of the last 10 days in the hospital, and had multiple procedures to fix the acute issues (bile duct blockage) so I can address the longer term issues of fighting off the cancer which is growing inside of my body.    I had started working with an editor to improve the quality of the writing, but I am going to forgo that, as the process takes time, and I can not be assured that I will have the energy to complete the writing at that time.  

Also, I have fundamentally changed the reporting on colleges.  Instead of detailed analysis of each school, I am looking only at the overall trends.  The individual school dashboards (with a few exceptions) now give very good information.

Situational Awareness

Big picture: We are well into the fall surge in COVID-19 Cases.  I do not know how bad it will get, but it is getting worse.  


Local: The caseload in Vienna doubled in the last month, but leveled off.  Because the case numbers are low, random variations can make it more difficult to assess the trends.  But they are increasing.

Colleges:  Colleges are all doing as well as could be expected, given the environment (large-scale communal living).


Yesterday, VA reported 1,432 new cases of COVID. The data were reported by VDH on Nov 10, 2020 were just above (within 1 standard deviation) of the three-week bias-adjusted average of 1,350 cases. 


Viewed over the last seven days, VA reported 10,233 cases, or 1,462 cases per day, which works out to 17.5 cases/100K people. This is the highest verified weekly number during the pandemic, though it is likely the May numbers were significantly under-reported by a factor of four. The best way to demonstrate that is through hospitalizations which peaked at 1,600 in early May but are currently at 1080 (but increasing).  Since hospitalizations should not be impacted by testing levels that provides a strong indication that the caseload was probably higher in May, but underreported.


The trends over the last three weeks now are showing a minimal increase at 1.75% per day or 13% per week.  


By combining our current regional trends with the typical reporting for the day of the week, I expect about 1267 cases tomorrow (Wed) with a 90% chance of the numbers falling between 1019 to 1577 cases. If tomorrow's numbers differ from that, we will have a very rare event. This is useful to consider if I am unable to report tomorrow since you can compare the state reported numbers with this.

The testing numbers now show the percent positive to be above the the 5% metric over the last week (6.4%) which is often used to indicate sufficient testing. VA is testing about 2.8% of the population every two weeks .  

When we look at the local ZIP code data, we see that the observed increases are almost universal across VA. I am comparing the current estimated % positive to that of one month ago. Note that almost all is a warmer color (further from blue and closer to yellow). This is an indication of the uniformity of the increase. As a practical matter, it means to be safe and careful, no matter where in VA you live, there is risk, and it is increasing.


Oct 1:


In the spring, COVID-19 in VA was primarily a concern in the DC suburbs. Over three to four weeks, (from late May to early June), NOVA recovered and for about a month the disease was under control to the point that restrictions were eased. Unfortunately, in eastern VA/Hampton Roads, the easing of restrictions resulted in a surge in cases which peaked just before August 1st resulting in stricter restrictions in that area. Since then, with the exception of growth on college campuses, the disease has been stable, excluding 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 throughout the Commonwealth, particularly in NOVA and SWVA. 



Looking at the weekly case count, we see that the numbers are higher than at any other point in the pandemic.


Regional growth rates (in fraction per day) continue to show degradation over the past three weeks. Note: It is easier to show a decline when the prior numbers increased. The current growth rates for the different regions are shown below.


NOVA:                                            1.026 --GMU

Central VA:                                     1.074 --VCU

Hampton Roads/Eastern VA:         1.033 --W&M, CNU & ODU

SW VA:                                           1.023--VA TECH & Radford

NW VA:                                           1.036-- JMU & UVA


The entire state is increasing with Rt=1.028


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 that in all regions the numbers for last week are significantly higher than the preceding three weeks. What is most concerning is that SW & NW VA are hitting significantly higher caseloads.


Daily Cases/100,000 


Last month

Last week
















The following charts show all five regions of the Commonwealth over time.

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 VA were functioning largely independently, with NOVA mimicking the northern states, and Hampton Roads mimicking the southern states. Since September 1, the regions have trended together.  


Currently, all regions are growing at significant rates. Interestingly,  SWVA has the greatest number of cases even though they have half the population of NOVA.  

Note that the effects at both ends of the chart are probably artifacts of the (seven-day polynomial filtering I use for averaging); the filter is poorly constrained in the first and last few days of the time history.  For those technically inclined, the filter is called a Savitzky-Golay filter, basically a moving window polynomial filter. At the edge (first and last days of the time series), the filter will over-compensate for the trend as it is unconstrained. I recommend the Wikipedia article if anyone is interested in more information, or contact me.


Local/Northern VA:


After the early peak in May (~1,000 cases per day), NOVA saw a sharp drop in all COVID metrics, reaching a broad valley in mid-June (~200 cases/day), which lasted until around August 1st.  By Sept 1, NOVA increased to 300 but the caseload dropped to about 150 by late in the month. Since then we have had a steady increase averaging about 360+ per day, which is significantly higher than our recent low of 150/day in late Sept.


Fairfax Co.


Arlington Co.


City of Alexandria


Prince William Co.


Loudoun Co.


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 September 1st has ended.  We now see significant jump in cases in every jurisdiction, starting about 4 weeks ago. The cause of that is unknown but may relate to the cooler weather.  I expect the trend to continue for the foreseeable future.

The difference in the colors (contrast) in the NOVA map is increasing. In addition, the NOVA map is warming (as is happening throughout the Commonwealth). At this point, it seems likely that this is related to the fall surge others had predicted.



Oct 29


Most localities in NOVA have case counts near or above 10/100K/day.  In Vienna, for example, we were under five in late September but are now at 10.3/100K/day.



Last month

Last week

Growth rate (%/day)

Fairfax County









S. Alexandria








Annandale/Fall Church









 N. Arlington




 S. Arlington








Recently, the trends show two groups of rates for Fairfax County: McLean is doing the best.  All the other locations are grouped at a higher number -- every region is seeing small upticks in the last few weeks. 



In Vienna, the average daily case count has doubled; I do not why (I have not been out and about). While, the growth rate may have stabilized, the absolute numbers remain higher than they have been since May.  

Local Safety/Risk

I am attempting a different means of discussing risk. Risk is very personal-- people will react to COVID differently; however, we know how many people have died (and how many cases) for each age group in VA 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 an infection fatality rate (IFR).  We also know how co-morbidities play in. 


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


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 are 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 per day is about one in three million. That is a baseline but none of us likely knows anyone who will not get into 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   4.09e-08   3.68e-07   1.55e-06   3.80e-06   1.06e-05   2.41e-05   3.89e-05   7.28e-05 
         5   0.00e+00   2.04e-07   1.84e-06   7.75e-06   1.90e-05   5.28e-05   1.20e-04   1.94e-04   3.63e-04 
        15   0.00e+00   6.07e-07   5.46e-06   2.31e-05   5.64e-05   1.57e-04   3.58e-04   5.77e-04   1.08e-03 
        50   0.00e+00   1.97e-06   1.77e-05   7.48e-05   1.83e-04   5.10e-04   1.16e-03   1.87e-03   3.50e-03 
       100   0.00e+00   3.78e-06   3.41e-05   1.44e-04   3.52e-04   9.80e-04   2.23e-03   3.60e-03   6.73e-03 
       500   0.00e+00   1.41e-05   1.27e-04   5.35e-04   1.31e-03   3.65e-03   8.31e-03   1.34e-02   2.51e-02 
      1000   0.00e+00   2.04e-05   1.84e-04   7.76e-04   1.90e-03   5.29e-03   1.20e-02   1.94e-02   3.63e-02 
      2000   0.00e+00   2.45e-05   2.21e-04   9.32e-04   2.28e-03   6.35e-03   1.45e-02   2.33e-02   4.37e-02 


#exposure        0-9      10-19      20-29      30-39      40-49      50-59      60-69      70-79        80+ 
         1   0.00e+00   4.09e-07   3.68e-06   1.55e-05   3.04e-05   5.30e-05   7.24e-05   7.78e-05   1.46e-04 
         5   0.00e+00   2.04e-06   1.84e-05   7.75e-05   1.52e-04   2.64e-04   3.61e-04   3.88e-04   7.26e-04 
        15   0.00e+00   6.07e-06   5.46e-05   2.31e-04   4.51e-04   7.86e-04   1.07e-03   1.15e-03   2.16e-03 
        50   0.00e+00   1.97e-05   1.77e-04   7.48e-04   1.46e-03   2.55e-03   3.48e-03   3.74e-03   7.00e-03 
       100   0.00e+00   3.78e-05   3.41e-04   1.44e-03   2.81e-03   4.90e-03   6.70e-03   7.20e-03   1.35e-02 
       500   0.00e+00   1.41e-04   1.27e-03   5.35e-03   1.05e-02   1.82e-02   2.49e-02   2.68e-02   5.02e-02 
      1000   0.00e+00   2.04e-04   1.84e-03   7.76e-03   1.52e-02   2.64e-02   3.61e-02   3.88e-02   7.27e-02 

      2000   0.00e+00   2.45e-04   2.21e-03   9.32e-03   1.82e-02   3.18e-02   4.34e-02   4.67e-02   8.73e-02 

Age Distribution: 

I am not updating this section for the time being except for the charts.  I will leave it here as is for a while longer--at times it can be very interesting.  This is particularly so when specific age groups do not follow other groups. For example, teens and 20-somethings surged in early September while the other age groups did not due to the outbreaks at colleges.


The colleges are now shown in cases per day per 100K students.  Not that the outbreaks at Radford and JMU early stand out, but all colleges are doing ok, particularly when adjusted to the risk of the college age population.  

College Communities:

When I started talking about communities the focus was on the safety for incoming students. Unfortunately, that concept has 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 VA Tech (New River, and Central Shenandoah health districts), we see that, starting about four weeks ago, the number of cases for non-college age citizens has been 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 senior citizens have been infected; there have been 12 cases to 24 cases per day in those groups. Furthermore, in the five months from the beginning of the epidemic to early September, there was an average of 10 deaths per month; in the last seven weeks there have been 35 deaths. This is concerning as the return of students has appeared to result in 21 extra deaths in the Commonwealth today in just the New River and Central Shenandoah health districtsNone of the additional deaths were of student age. This trend has been observed elsewhere in the country where the college students infect the more vulnerable populations.



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