The average person that dies of COVID loses 10 years of life expectancy on average.
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Well, let's look at what the economists actually say then:

http://www.igmchicago.org/surveys/po...vid-19-crisis/

Question A: A comprehensive policy response to the coronavirus will involve tolerating a very large contraction in economic activity until the spread of infections has dropped significantly.

88% agree, 5% uncertain

Question B: Abandoning severe lockdowns at a time when the likelihood of a resurgence in infections remains high will lead to greater total economic damage than sustaining the lockdowns to eliminate the resurgence risk.

80% agree, 14% uncertain
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Thank goodness we had him to tell us to wash our hands and brainwash the public with a constant barrage of this social distancing crap. :-)
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Wait, you mean the average person that contracts COVID, correct?
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of course not, no one is saying he is. His advice is about public health. You want to make your argument above, no reasonable person would disagree. Any pandemic will always be about balance.

The scientist and public health professionals tell us the truth based on facts. Then what their educated opinions of what might happen in the future. Science doesn’t get to make the final decision. But, science and facts matter.

We don’t have to be economists to see the economy is tanking due to the voluntary shut downs.

Where’s the line balancing human deaths and economic pain? Who gets to decide?
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I'm not a health expert and I don't claim to be-however my background is in statistics.

My only question to your statement is what epidemiologist modeling is done to support that claim?

For economics and statistics, for example, modeling 99% of the time is not in real time and is based off historic data and used to implement recommendations in the future and not in real time.
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No.
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Findings:

Results: Using the standard WHO life tables, YLL per COVID-19 death was 14 for men and 12 for women. After adjustment for number and type of LTCs, the mean YLL was slightly lower, but remained high (13 and 11 years for men and women, respectively). The number and type of LTCs led to wide variability in the estimated YLL at a given age (e.g. at ≥80 years, YLL was >10 years for people with 0 LTCs, and <3 years for people with ≥6).
Conclusions: Deaths from COVID-19 represent a substantial burden in terms of per-person YLL, more than a decade, even after adjusting for the typical number and type of LTCs found in people dying of COVID-19. The extent of multimorbidity heavily influences the estimated YLL at a given age. More comprehensive and standardised collection of data on LTCs is needed to better understand and quantify the global burden of COVID-19 and to guide policy-making and interventions.

https://wellcomeopenresearch.org/articles/5-75/v1

Methodology:

The standard approach for calculating years of life lost is to apply the distribution of ages among those who died from a specific cause to a standard life-table. For the purposes of international comparison, we opted to use the WHO 2010 Global Burden of Diseases table as the reference13, which presents YLL by age, but not by sex or extent of multimorbidity. This method involves summing the expected years of life remaining from the table according to the number (or for the mean YLL the proportion) of people dying within each age-band. We applied the age distribution of COVID-19 deaths in Italy from published data to estimate the YLL.

...

Long-term condition prevalence and correlation models. This first stage of our modelling aimed to estimate the prevalence and correlation between specific LTCs among people dying with COVID-19.

We utilised aggregate data on COVID-19 deaths from the Istituto Superiore di Sanità in Italy. Since we were unable to obtain IPD for the Italian case-series of deaths from COVID-19, we had to infer the joint prevalence of LTCs from the summarised information available, i.e. the marginal distribution of multimorbidity counts (the row sums, or total number of diseases for each patient, wherein counts of ≥3 LTCs were collapsed into the single category of 3+) and the marginal distributions of LTC frequency (the columns sums, or the total number of patients with each LTC). To that end, we developed a Bayesian latent process model of disease prevalence and correlation and fitted it using Markov chain Monte Carlo (MCMC) to both elements in the published data. This analysis was applied jointly to the small number of deaths that had occurred in Scotland, primarily to aid convergence in Bayesian model fitting by providing some information about the correlation between LTCs15. The Scottish subset of the data contained a partial record of known LTCs for individual patients, but the multimorbidity count per patient, as well as the marginal frequency of each LTC, were missing (hence, modelled as latent). Bayesian priors for the correlations between diseases were specified with a tendency to zero (shrinkage). Numerical investigations indicated little sensitivity of convergence to the strength of shrinkage, so we opted for weak shrinkage as a precautionary approach. This model gave us the full matrix of correlations between every combination of LTCs at the level of individuals, therefore providing us with a complete dependence structure of LTCs presented within the sample of COVID-19 mortalities. In order to propagate uncertainty through the analysis, from this fitted model (effective sample size of MCMC 410) we simulated 10,000 notionally “typical” patients, with plausible combinations of LTCs (under the combined Italian and Scottish data).

To test the sensitivity of our findings to the estimated correlations, we also estimated the YLL under two opposite extremes (i) that LTCs were independent and (ii) that LTCs were highly correlated. Unlike the Bayesian LTC mode, these sensitivity analyses did not use the information on the multimorbidity counts from the ISS report, but only the proportion of patients with each of the eleven comorbidities. For the “independent” scenario we created 11 vectors comprising 1s and 0s (respectively with and without the long term condition) corresponding in length to the number of patients. We then sampled from these vectors with replacement to obtain 10,000 simulated patients. For the “highly correlated” scenario we first sorted each vector, then combined them to form a 710x11 matrix, then sampled each row with replacement to obtain 10,000 simulated patients.
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What? Make it more amenable to a vast preponderance of the population sitting around the house for months or years?

It's not politics, it's human nature, people do things that reap rewards. Free market capitalism isn't perfect but it recognizes that fundamental biological drive and rewards productivity.
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That’s a bullshit statement and your smart enough to know that. There is no way to know that with any amount of accuracy.
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Yeah, it's not like there's a study and a link I just posted that determines how the information was found or anything.
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:-):-):-)
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Wrong. This is the age of data analytics.
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Looks to me like Fauci is a football fan.
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That study Hamas posted was full of assumptions and large margin for error. He knows that.
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