Daily Status, Sept. 30: Off the NY/NJ Quarantine list
Daily Status, Sept. 30
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 around 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 are largely ignored. The trends are clearly visible in the figure below.
With this, we have completed the transition from COVID-19 being an urban disease to being a rural disease. 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). Over the last few weeks, that seems to be improving. We are not out of the woods, but I am far more optimistic than I was even a week ago.
NOVA: .985-- GMU
Central VA: .968-- VCU
Hampton Roads/Eastern VA: 0.957 -- W&M, CNU & ODU
SW VA: 0.962- VT & Radford
NW VA: 0.956-- JMU & UVA
The state as a whole is is decreasing with Rt=0.980
The following table shows the number per 100k for each region. Again, NOVA and Eastern VA are doing the best, and NW & SW are doing the worst, but are showing improvements as the college cases are getting somewhat under control -- or at least better than they were.
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. Note that the nose-dive in the chart today is 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.
Growth rate (%/day)
Last 10 days
% that had COVID***
* estimated from the number of cases in the zip codes associated with the university removing the pre-student arrival case rate
Large Scale Community Spread:
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Other Sites: John's Hopkins
Kids can pass covid to parents: Pediatric SARS-CoV-2: Clinical Presentation, Infectivity, and Immune Responses