PUBLISHED: MAY 07, 2020
https://doi.org/10.1377/hlthaff.2020.00455

Quote:

---

ABSTRACT
Knowing the infection fatality rate (IFR) of SARS-CoV and SARS-CoV-2 infections is essential for the fight against the COVID-19 pandemic. Using data through April 20, 2020, we fit a statistical model to COVID-19 case fatality rates over time at the US county level to estimate the COVID-19 IFR among symptomatic cases (IFR-S) as time goes to infinity. The IFR-S in the US was estimated to be 1.3% (95% central credible interval: 0.6% to 2.1%). County-specific rates varied from 0.5% to 3.6%. The overall IFR for COVID-19 should be lower when we account for cases that remain and recover without symptoms. When used with other estimating approaches, our model and our estimates can help disease and policy modelers to obtain more accurate predictions for the epidemiology of the disease and the impact of alternative policy levers to contain this pandemic. The model could also be used with future epidemics to get an early sense of the magnitude of symptomatic infection at the population-level before more direct estimates are available. Substantial variation across patient demographics likely exists and should be the focus of future studies.

---

Jmiller said:

---

PUBLISHED: MAY 07, 2020
https://doi.org/10.1377/hlthaff.2020.00455

Quote:

---

ABSTRACT
Knowing the infection fatality rate (IFR) of SARS-CoV and SARS-CoV-2 infections is essential for the fight against the COVID-19 pandemic. Using data through April 20, 2020, we fit a statistical model to COVID-19 case fatality rates over time at the US county level to estimate the COVID-19 IFR among symptomatic cases (IFR-S) as time goes to infinity. The IFR-S in the US was estimated to be 1.3% (95% central credible interval: 0.6% to 2.1%). County-specific rates varied from 0.5% to 3.6%. The overall IFR for COVID-19 should be lower when we account for cases that remain and recover without symptoms. When used with other estimating approaches, our model and our estimates can help disease and policy modelers to obtain more accurate predictions for the epidemiology of the disease and the impact of alternative policy levers to contain this pandemic. The model could also be used with future epidemics to get an early sense of the magnitude of symptomatic infection at the population-level before more direct estimates are available. Substantial variation across patient demographics likely exists and should be the focus of future studies.

---

---

I'll take the under. How much?

Half that more likely

There are those of us out there that wish the partisanship would diminish completely because it is counter productive and only gives the politicians power.

I think the reservations on both sides are valid. The CFR will mean more people dying from Covid however the other consideration of keeping the economy closed will also contribute to many deaths and long term damage to the economy which is also painful.

These issues should be discussed reasonably without it immediately devolving into a tribal political discussion for the sake of a reasoned action moving forward.

/my 2 cents

Duncan Idaho said:

---

AeroAg2003 said:

---

Jmiller said:

---

PUBLISHED: MAY 07, 2020
https://doi.org/10.1377/hlthaff.2020.00455

Quote:

---

ABSTRACT
Knowing the infection fatality rate (IFR) of SARS-CoV and SARS-CoV-2 infections is essential for the fight against the COVID-19 pandemic. Using data through April 20, 2020, we fit a statistical model to COVID-19 case fatality rates over time at the US county level to estimate the COVID-19 IFR among symptomatic cases (IFR-S) as time goes to infinity. The IFR-S in the US was estimated to be 1.3% (95% central credible interval: 0.6% to 2.1%). County-specific rates varied from 0.5% to 3.6%. The overall IFR for COVID-19 should be lower when we account for cases that remain and recover without symptoms. When used with other estimating approaches, our model and our estimates can help disease and policy modelers to obtain more accurate predictions for the epidemiology of the disease and the impact of alternative policy levers to contain this pandemic. The model could also be used with future epidemics to get an early sense of the magnitude of symptomatic infection at the population-level before more direct estimates are available. Substantial variation across patient demographics likely exists and should be the focus of future studies.

---

---

1.3% is likely an over estimate, as the abstract states.

BTW - Nice two posts rookie...

---

Wait. You mean that they aren't saying that their is a 95% likelihood that the IFR will be 1.3% but that there is 95% likelihood that the IFR will be somewhere between .6% and 2.1%?

Oh and that. .6% translates to 1.2mm dead before we even sniff herd immunity...so just the flu.

---

I don't know what your comment even means....

The OP published article is a mess.

• They first state that their calcuated IFR is 1.3% (with a wide confidence interval).
• They then state that the didn't even account for asymptomatic cases, which we know exist. Some cases even suggest they are quite prevalent
• They even place the following in the limitation section of their report:

Quote:

---

There are several limitations to our analysis. First, we acknowledge that our estimate of IFR-S would be higher than the true overall IFR. This is because our model relies on identified cases who are presumably all symptomatic COVID-19 patients. Therefore, even at the limit, our estimated rate would not include the fraction of patients who may have the infection but remain and recover asymptomatically.

---

So they basically calculated a CFR (tested positive cases vs. deaths) and then called it an IFR.

Duncan Idaho said:

---

AeroAg2003 said:

---

Jmiller said:

---

PUBLISHED: MAY 07, 2020
https://doi.org/10.1377/hlthaff.2020.00455

Quote:

---

ABSTRACT
Knowing the infection fatality rate (IFR) of SARS-CoV and SARS-CoV-2 infections is essential for the fight against the COVID-19 pandemic. Using data through April 20, 2020, we fit a statistical model to COVID-19 case fatality rates over time at the US county level to estimate the COVID-19 IFR among symptomatic cases (IFR-S) as time goes to infinity. The IFR-S in the US was estimated to be 1.3% (95% central credible interval: 0.6% to 2.1%). County-specific rates varied from 0.5% to 3.6%. The overall IFR for COVID-19 should be lower when we account for cases that remain and recover without symptoms. When used with other estimating approaches, our model and our estimates can help disease and policy modelers to obtain more accurate predictions for the epidemiology of the disease and the impact of alternative policy levers to contain this pandemic. The model could also be used with future epidemics to get an early sense of the magnitude of symptomatic infection at the population-level before more direct estimates are available. Substantial variation across patient demographics likely exists and should be the focus of future studies.

---

---

1.3% is likely an over estimate, as the abstract states.

BTW - Nice two posts rookie...

---

Wait. You mean that they aren't saying that their is a 95% likelihood that the IFR will be 1.3% but that there is 95% likelihood that the IFR will be somewhere between .6% and 2.1%?

Oh and that. .6% translates to 1.2mm dead before we even sniff herd immunity...so just the flu.

---

Would you stop with your "apply a number" to the entire US population bull#@*. The article clearly says "COVID-19 IFR among symptomatic cases (IFR-S)". The entire population is not symptomatic.

So IFR-S 1.3% and upto 90% cases are asymptomatic? Yep nothing to see here. Move along.

Current "cure" is worse than the current effects of the disease. Not even debatable

MaroonDontRun said:

---

There are those of us out there that wish the partisanship would diminish completely because it is counter productive and only gives the politicians power.

I think the reservations on both sides are valid. The CFR will mean more people dying from Covid however the other consideration of keeping the economy closed will also contribute to many deaths and long term damage to the economy which is also painful.

These issues should be discussed reasonably without it immediately devolving into a tribal political discussion for the sake of a reasoned action moving forward.

/my 2 cents

---

It really is simple, if you wish to stay home then stay home, if you wish to go to work go.

Why do stay at home people think they dictate to people that want or live their lives? The constitution does not allow for virus lock downs, it just does not, period.

No one is making anyone leave their homes, I don't get it other than politics to hurt the economy.

My 2 cents - I think the 1.3% is double the actual death rate. If you listen closely to the medical statisticians they admitted about 3-weeks ago that they changed their methodology for counting COVID-19 deaths to include people who died who they "think" had COVID-19. In other words, they stopped wasting time and money testing dead bodies believed to have COVID-19. At that point, we've essentially said the numbers are made up and no one really cares, and coincidentally I too stopped paying attention to the death rates because the numbers are made up. It also helps explain why the death rate has continued to soar past the so called "peek" time periods. Makes no sense to change the methodolgy for counting COVID-19 deaths in the middle of the pandemic, but I have my suspicions why the change was made.

See the change in methodology below directly from the CDC's website:

What is a COVID-19 case?

CDC U.S. COVID-19 case counts and death counts include both confirmed and probable cases and deaths.
This change was made to reflect an interim COVID-19 position statement issued by the Council for State and Territorial Epidemiologists on April 5, 2020. The position statement included a case definition and made COVID-19 a nationally notifiable disease.

Fireman said:

---

At that point, we've essentially said the numbers are made up and no one really cares, and coincidentally I too stopped paying attention to the death rates because the numbers are made up. It also helps explain why the death rate has continued to soar past the so called "peek" time periods.

---

Yeah, none of this is true.