The CDC has a page hidden relatively deeply on their website that plots “excess” deaths (deaths over the expected average) for each week. All those red plus marks are weeks with detected “excess” deaths.

Most of the excess deaths are attributed to Covid-19, but there have also been some excess deaths attributed to other issues besides Covid-19. For example, more people than average are dying attributed to Alzheimer’s and Dementia:

Heart diseases:

And Diabetes:

This may be because people are avoiding hospitals and dying at home from problems that could have been survivable if they had gone to a hospital, or it could be due to issues with undetected infections.

Similar trends are occurring worldwide. If you are interested in knowing the “excess deaths” for your particular US state, the Washington Post has a nice interactive graph here.
Florida numbers look like this:

Nobody knows for sure what the Case Fatality Rate for Covid-19 is in the general population. (It changes based upon the demographics of the population and the quality of healthcare in the region, and can not be accurately calculated until a large number of people have fully recovered.) Currently the US estimate is hovering around 6% of those infected die, but this is likely inflated by the fact that many cases are asymptomatic and/or are never confirmed by a test.

Estimates from various countries have ranged from 0.07% in Singapore to 6% in the United States. Because many cases may be un-diagnosed this 6% estimate is likely to be high. (In Italy it was 14%.)

But due to the involuntary experiment currently spreading through the prisons of Ohio, along with good reporting by the state, we can generate an estimate of the CFR within the Ohio prison system.

On May 22nd 2020 when I downloaded DRCCOVID-19Information from the https://coronavirus.ohio.gov/ website, the corrections system had 4090 prisoners recovered from COVID-19, with 61 deaths (and 277 inmates still infected).   If we discount the inmates who are still infected (not yet recovered) and only look at those who have recovered (or died) we get a case fatality rate of 61 / (61 + 4090) = 61 / 4151 = .01469525 or 1.469 percent (you can round that up to 1.5%)

Of course, if 200 of those 277 “not yet recovered” are currently on ventilators, this CFR number will go up in the future….but this are the numbers we have to work with today.

So 1.5% of Ohio prisoners who were infected with Covid-19 have died.   (Ohio has 35,464 prisoners in isolation, so approximately 11.7% of them have been infected).

This CFR is 15 times higher than that of the seasonal flu (0.1%).   Of course, because prisoners are not generally representative of the US population (the demographics of prisoners skew young, while oldest adults are more frequently killed by covid-19) this CFR will not be the same as for the general population. But it does give us a ballpark number to think about for a lower limit.

If we take 1.5% CFA times 11.7% infected times the 330 million population of the US, we would get 579,150 deaths. If we let it spread to 60% of the population, that would be 2.97 million deaths.

Of course, other research has estimated the CFR in the US to be hovering around 6%.
But that is likely due to missing many un-diagnosed cases because of lack of testing.
So I would use the 1.5% as a lower bound on the CFR in the general population.

Luckily, most Americans are not housed in prisons and can social distance to more effectively prevent the spread of covid-19, so we have not gotten anywhere near 11.7% of the population infected. Yet.

It is also uncertain if prisoners get better healthcare in prison than the general population, but my guess would be no (at least for the 89% of people who could afford health insurance before the pandemic hit).

Related evidence:

• “Among the 3,711 passengers on the Carnival Corp-owned Diamond Princess cruise ship that was quarantined in Yokahama, Japan in February, 712 tested positive for the coronavirus, according to the CDC. Of the passengers who tested positive, 47% were asymptomatic at the time of testing; nine ultimately died. ”
  9 deaths / 712 confirmed positive = 1.26% case fatality rate. [Source]
• A related scientific paper with similar numbers (1.3%)   can be found here.
• According to the Marshal Project, on May 24th 2020, at least 415 inmates have died in the US, with at least 29,000 infected (which is a CFR of 1.43%).

Covid-19 is a novel coronavirus, and is not a flu (influenza). It is more contagious than the flu, and more deadly than the 2009-2010 N1H1 swine flu.

For example, the first case of the H1N1 flu in America was detected on April 15th 2009.   86 days later, on June 10th 2009, there were 211 recorded deaths (37,246 cases).

The first case of Covid-19 in America was detected on January 20th 2020. 86 days later (April 15th, 2020) there were 32,900 deaths (654,425 cases).

In fact, the CDC’s best estimates place the total death toll of the H1N1 flu for the entire year (April 12, 2009 to April 10, 2010) in the range of 12,469 deaths (8,868-18,306).

In summary, the (86 day) fatalities of covid-19 is already three times higher than the full year (365 day) fatalities from H1N1, even with the physical distancing and safer at home policies attempting to stop transmission.   This is not #JustAnotherFlu.

National officials have stated that we may see 100,000-200,000 deaths from Covid-19. I hope the death toll will be lower, but worry it may be much higher.

From April 12, 2009 to April 10, 2010, CDC estimated there were 60.8 million cases (range: 43.3-89.3 million), 274,304 hospitalizations (range: 195,086-402,719), and 12,469 deaths (range: 8868-18,306) in the United States due to the (H1N1)pdm09 virus. – https://www.cdc.gov/flu/pandemic-resources/2009-h1n1-pandemic.html

These numbers indicate that the H1N1 (Swine flu) had a case fatality ratio (CFR) in the US of between .04 and .009 percent. Note that 1 in 3 adults over 60 had antibodies to H1N1, perhaps from an exposure in their childhood.

The World Health Organization reported that 1 in 5 people (20-27%) were infected by H1N1 in the fist year, and “Based on an estimate of around 200,000 deaths, they said the case fatality ratio was probably less than 0.02 percent.” – https://www.reuters.com/article/us-flu-h1n1-pandemic-idUSBRE90O0T720130125

In England the CFR was calculated to be 0.026 percent.

The contagiousness of H1N1 (R₀) is estimated to be between 1.4 and 1.6, which means that every infected person infects 1.4 to 1.6 other people. (As long as this number is above 1.0, the growth rate is exponential.)

The Bad News

Reports from China suggest that with good medical support (i.e. hospitals not overloaded) the case fatality rate for COVID-19 is as low as 0.7 percent. [The CFR in Italy where hospitals are overloaded is currently in the 3.7 – 11 percent range.]

The contagiousness of COVID-19 is estimated to be as high as 2.7 (every infected person infects 2.7 others) with no stay-at-home orders. Seattle is one of the first cities in the US to implement physical distancing procedures and the growth rate there has slowed. One model predicts that the R₀ has been reduced to 1.4 by the physical distancing implemented in Washington State, which is much better than 2.7, but still about as infectious as the H1N1 flu.

If COVID-19 infects 60.8 million people in the US, and has a CFR of 0.7 percent, that is 425,600 deaths.   [Of course, if it infects 60 million people the case fatality rate may jump as all of our hospitals will be overwhelmed, because about 10-15% of infections normally result in hospitalization.] So an R₀ of 1.4 is not good enough.

As a point of comparison, the CDC estimates that smoking results in more than 480,000 deaths in the USA every year. (1 in 5 deaths annually.)

The only way to prevent this is to reduce the contagiousness with extreme physical distancing. In China they were able to bring the R₀ number below 1, allowing the growth rate to fizzle out with severe quarantine methods. South Korea was able to achieve a similar result with ample testing and contact tracing / containment.

Related news stories & Updates

• Dr. Birx predicts up to 200,000 coronavirus deaths ‘if we do things almost perfectly’ – March 31st 2020
• Modeling Covid-19 is hard, here is why.   You may notice that my numbers above are from the simplest possible model.
• This Newsweek story suggests that the CFR is actually closer to 0.2 percent (vs H1N1’s 0.04 percent), which would give only 121,600 deaths in the above calculations.
• This Washington Post story indicates that the US has had 50,000 fatalities due to Covid-19 [April 24th, 2020].

Many people already trust Google with their location data (unless you proactively turned off location history on your android phone and Google maps, Google knows where you have been and how long you spent there.)   Obviously, all of these people are trusting Google to follow it’s privacy policy and not release this information. [Google is not alone here, the major mobile phone carriers also have all this data, and you can’t turn that location tracking off….]

What happens when you start to cough, get a fever, and (can hopefully) get tested for the Coronavirus? If you have a positive result, the local health department should be asking you who you interacted with in the last 5-10 days, and then contact THOSE people to tell them they should self-isolate and get tested if they come down with symptoms.   Presumably you’ll tell them about everybody you remember interacting with, but you might forget that 3 days ago you paid for gas at the counter of the local 7-11, or not know the name of that guy that traded you his Aldi cart for your virus covered quarter so he wouldn’t have to return it to the cart stand to get his own quarter deposit back.

Here is where Google can help everybody. Once you have received a positive diagnosis, your   local health department sends Google your gmail address (google account name).   Google emails you asking for your permission to share your location history for the last 5-10 days with the Health Department.   If you give permission, Google will share that data with the health department, who now knows that you spent 8:10-8:20am last Friday at the 7-11 and 4:30-5pm at Aldi grocery shopping on Tuesday. Having this location history can potentially help the health department, but what they really need is to be able to contact the people you interacted with.

Google can help with this by cross-checking your location history with the location history of everyone else who uses an Android phone (50-70% of the population). People who may have crossed your path could be sent a proactive email letting them know that “From 8:10-8:20am on Friday a person who has subsequently tested positive for coronavirus visited the same 7-11 that you visited from 8:15-8:25am”.   No need to share your name, just the fact that exposure was possible. Those people could be given the option to share their contact information with the local health department by clicking a link. This would allow the local health department to sort by risk, and they might choose to make a followup phone call to those people who spent a considerable amount of time in the same locations as you. (They probably wouldn’t have the manpower to call everyone that was at the same gas station, but if you forgot about the monthly bookclub meeting where 10 people’s phones spent 2 hours near your phone, this could be incredibly useful information for them to have….)

The key privacy preserving idea is that sharing your location data would be optional.
If you chose to share your location history, Google would anonymously notify those people who may have intersected with your travel history, and then THEY would have the choice to share their contact information (or not) with the health department (not you). Most people (who have not turned off location history) already “trust” google with their location history, so proactively cross-checking to find intersections with anonymous notifications should not be seen as a major a privacy violation.

Obviously, it would be scary to receive an email from Google telling you that you might have used the same gas-pump as somebody infected with the coronavirus, and there would certainly be a LOT of false positives (If the cross-checking algorithm was too aggressive, it might pair up everybody who stopped at the same stoplight at the same time in their commute, for example….).   I trust that the UX (User Experience) people (and lawyers) at Google could write the emails in a suitably non-threatening way “Out of an abundance of caution, we wanted to let you know that there is a small possibility that you shared the same location as a person who later tested positive for the coronavirus.”

I argue that a little bit of scaring would be a net social benefit. If you were receiving a daily email with a list of the times and places you were near an infected person, social distancing would go up immediately. If Google is more worried about scaring away their users than the global good, they could make a website that people would have to deliberately visit to find out if any coronavirus positive intersections were found for their location history, but this would remove significant portions of the benefit.

I’m sure that many smart people inside Google have already come up with this idea and there are internal debates about privacy and public relations going on internally between them and the lawyers. We should tell Google that this is a perfect time to “Not be Evil”.

Links:

Singapore has created an application like this, but it requires that everybody install the same application to trade encrypted contact information. So either the government makes it a requirement that you run the application to leave your house, or it is not as effective.

MIT has released an app that can log your location history completely privately, but it does not collect contact information from others, and has no way to cross-correlate with other’s tracking history.

Apple & Google are working together on ways for iPhones and Android devices to work natively with contact tracing applications.
https://www.apple.com/newsroom/2020/04/apple-and-google-partner-on-covid-19-contact-tracing-technology/

After the FCC auctioned off some of the HDTV spectrum for cell phone (5G) use, a few stations had to change their channel allocations. Here is a list of the over the air (OTA) channels I can pick up from the west side of Orlando.

• 2_1 – NBC (WESH-DT) VHF-11
  • 2_2 – Me TV
• 6_1 – CBS (WKMG-DT)
  • 6_2 – Dabl   WKMG-DT
  • 6_3 – Cozi
  • 6_4 – Start TV
  • 6_5 – Decades
• 9_1 – WFTV-HD   (ABC) (UHF-35)
  • 9_2 – LAFF
  • 9_3 – Escape
• 10_1 – Diya TV (Indian? classic cinema) [Schedules Direct does not have a lineup yet]
  • 10_2 – Orange (county government TV)
  • 10_3 – Vision   (orange TV)   (WLOQ?)
  • 10_4 – This TV
  • 10_5 – News Net
  • 10_6 – JTV
  • 10_9 – open
• 15_1 – WDSC-HD (PBS 15 – Daytona Beach)
  • 15_2 – WDSC-ED The Florida Channel
  • 15_3 – WDSC-WV (Worldview)
• 18_1 – WKCF-DT   CW
  • 18_2 Justice
  • 18_3 estrell
• 24_1 – WUCF-HD (PBS 24 UHF -34)
  • 24_2 create
  • 24_3 kids
• 27-2 – 27 – WRDQ
  • 27_2 – Antenna
  • 27_4 – Grit TV
• 31_1 – T31 WTMO CD (TeleMundo Orlando )
  • 31_2 – WTMO-SD
  • 31_3 – TELEXITOS (Xitos)
• 35_1 – WOFL-DT (FOX)
  • 35_2 – LIGHT
• 43_1 WVEN-TV (Univision) UHF-22
  • 43_2 – GET TV
  • 43_3 – Bounce
  • 43_4 – Escape
  • 43_5 – Quest
• 52_1   TBN-HD (Trinity Broadcasting Network) (WHVL-TV Digital 52, UHF 32)
  • 52_2 Hillsng (Hillsing)
  • 52_3 – Smile
  • 52_4 – Enlace
  • 52_5 – JUCE
• 55_1 WACX-D1 (SuperChannel )
  • 55_2 – WAXC-D2
  • 55_3 – WACX-D3
  • 55_4 – WACX-D4
  • 55_5 – WACX-D5
  • 55_6 – WACX-D6
  • 55_7 – WACX-D7
  • 55_8 – WACX-D8
  • 55_9 – WACX-D9
• 56_1 – ION (WOPX)   Physical Channel 48
  • 56_2 – QUBO
  • 56_3 – ION Plus
  • 56_4 – Shop (sponsored Television programming)
  • 56_5 – QVC
  • 56_6 – HSN
• 65_1 – WRBW-DT     UHF-28 (fox 35 plus, my network TV)
  • 65_2 Movies!
  • 65_3 H&I
  • 65_4 Buzzr
• 68_1 – WEFS-HD Educational   (Eastern Florida State College) [minimal signal, can’t receive reliably]
  • 68_2 – WEFS-CL (Classic Arts)
  • 68_3 – WEFS-NS (NASA Educational)
  • 68_4 – WEFS-FL (Florida Channel)