Spreadsheet of San Francisco Bay Area Covid-19 Data and Charts(docs.google.com) |
Spreadsheet of San Francisco Bay Area Covid-19 Data and Charts(docs.google.com) |
Who gets tested is a moving target. Stanford a short time ago did a free-for-all testing binge in order to collect data, but finished that and is now restricting tests to people requiring specific risk factors to give a test.
The first time I tried to get a test from another provider I just wasn't able, they didn't know of anywhere that would test me outside of hospitalization-type symptoms.
So testing is uneven and not very available, any stats need to include some metric for the criteria to get tests in the first place.
In other words, there is likely an enormous population with no symptoms or mild symptoms who couldn't get tested if they tried.
After two video appointments with separate providers I was able to get tested yesterday and the result came back negative about 22 hours later. It took me about 8 hours of effort and time to get that done, a luxury many people do not have.
Is there any value in people self-selecting into personal choice testing? You could get infected tomorrow, for instance...
If we wanted a full picture of community spread we'd need a top-down random sample, not self-selection, no?
After you have this info, you can compare the rates to what kind of testing policies the areas have, and make some initial conclusions.
https://paroj.github.io/arewedeadyet/#rate
The good news is that the US has gone from a 30+% daily growth rate 10 days ago down to a 15% growth rate and falling. We need to keep falling into the negative rates to solve this problem.
for example the bay area has recently been seeing some days with single digit growth rates. shelter in place IS WORKING, but it's going to take time / we may need some additional measures. I was just reading it may also be spread in the air from breathing.
* Overcrowded hospitals is what leads to large jumps in fatality rates.
* It only lags the date of infection by about a week.
* It also isn't subject to external factors like availability of tests. (Though availability of hospital beds is a factor later on)
There are some limited stats to the very far right side of the "SF Bay Area Actuals" sheet.
Anecdotally, Bay Area is seeing <10% positivity rates
In other places with higher testing, such as Australia, the CFR is 0.6% or less. This implies that the true number of cases is 4-5 times higher... probably a lot more.
That the published infection and mortality rates are so low strains credulity in the extreme, especially when much smaller-population countries at similar proximity to the equator but greater distance from China have higher case rates (i.e Brazil, Ecuador, the UAE).
I am updating it regularly.
I wish you had the new cases per day graphed for all the bay area counties because that is what I monitor.
I'll add a new cases graph for each county.
We made one here from the NYT dataset on MintData [1]:
(note: I think we need to update the cumulative counter, we'll be fixing that shortly)
@andfrob happy to get you free/unlimited access to MintData if you're interested in making similar visualizations, please DM me if this would be helpful.
For example, San Diego has zipcode breakdown here: https://www.sandiegocounty.gov/content/sdc/hhsa/programs/phs...
SF County - https://www.sfdph.org/dph/alerts/coronavirus.asp
San Mateo County - https://www.smchealth.org/coronavirus
Alameda County excluding Berkeley - http://www.acphd.org/2019-ncov.aspx
Berkeley - https://www.cityofberkeley.info/coronavirus/
Santa Clara County - https://www.sccgov.org/sites/phd/DiseaseInformation/novel-co...
Marin County has an (ominously named) dashboard - https://coronavirus.marinhhs.org/surveillance
Have you been able to find data on # of tests carried out?
California does report them on aggregate, but the purpose of this sheet was to focus on the Bay Area.
But really we want more than just accurate statistics, we want to minimize damage. Any increase in testing is good testing, and triaging testing to highest risk individuals makes sense when your capacity is limited.
The consequences though are that reported statistics are often just wrong. Skewed towards higher negative outcomes and comparisons between dates are flawed without much additional information.
[1] https://newsroom.questdiagnostics.com/COVIDTestingUpdates
That's what a pandemic needs. If it is very deadly very quickly it kills its transmission vectors before they can transmit. If it is entirely symptom free, it is very evolutionarily successful, but no one cares because there aren't any negative effects.
There is an "optimum" of disease characteristics for maximum damage and we seem to be experiencing one.
The bottom line is that it seems to be very difficult to prevent a majority of the world population from getting this disease and the result is going to be a global fatality rate of somewhere in the neighborhood of 1%.
It will be hard to trace and isolate if this is the case.
[1] https://sfist.com/2020/04/01/cdc-director-coronavirus-25-per... [2] https://nationalpost.com/news/world/in-iceland-free-coronavi...
The CFR should end up being about 1.5% (or possibly somewhat higher).
https://www.who.int/emergencies/diseases/novel-coronavirus-2...
Doesn't account for lack of testing, of course.
Deaths / (Deaths + Recoveries) would be more like it, and that's a scary number.