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    Time to FREE America... banned you tube videos and more

    This thread is dedicated to the freedom of america. Freedom from censorship, communism, and tyrannical government overreach... the links, pages and videos on various topics are available at this website https://timetofreeamerica.com if you find one a topic that interests you, copy and paste the link here on this page, lets keep America free!


    Literally Thousands of Doctors and Scientists Have Come Out Against Fauci's Lockdowns Including a Nobel Prize-Winning Biophysicist. The Media Just Doesn't Want You to Know



    By Michael Thau | Jul 13, 2020 6:30 PM ET


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    AP Photo/Alex Brandon


    As RedState’s very own Sister Toldjah reported earlier today, the doubts President Trump recently expressed about the wisdom of Dr. Fauci’s advice on COVID-19 have elicited a chorus of smug accusation from the usual suspects that he’s “ignoring the experts.”


    Sister Toldjah pointed out that it’s hard to know what their complaint even means given how often Fauci and other media-anointed authorities have done total 180s.
    But, even putting aside how their advice seems to change with the political winds, the idea that there’s some scientific consensus in favor of the extreme measures inflicted on us in response to COVID-19 couldn’t be further from the truth.
    Though you don’t hear their perspectives on CNN, countless scientists and doctors have tried to warn us not only that COVID-19 isn’t nearly as deadly as we’ve been led to believe; they’re also confident that the real threat to public health we’re facing is from the lockdowns.
    For example, though the establishment media has somehow failed to make it widely known, in May, over 600 physicians from “all specialties and from all states” signed a public letter to President Trump describing, not COVID-19, but the lockdowns as a “mass casualty incident.” Since the letter first appeared, the number of doctors signing on has grown into the thousands. Their letter warns:
    It is impossible to overstate the short, medium, and long-term harm to people’s health with a continued shutdown. Losing a job is one of life’s most stressful events, and the effect on a persons health is not lessened because it also has happened to 30 million other people. Keeping schools and universities closed is incalculably detrimental for children, teenagers, and young adults for decades to come. The millions of casualties of a continued shutdown will be hiding in plain sight, but they will be called alcoholism, homelessness, suicide, heart attack, stroke, or kidney failure. In youths it will be called financial instability, unemployment, despair, drug addiction, unplanned pregnancies, poverty, and abuse.



    Similarly, way back in April, two California emergency room physicians gave a press conference in which they rejected basically every single premise used to justify the lockdowns. Moreover, unlike Fauci, they actually gave detailed explanations of the reasons behind what they were saying rather than demanding blind obedience.
    Dr. Dan Erickson and Dr. Artin Massihi presented data from all across the world, indicating that Fauci’s response to COVID-19 was entirely out of proportion with the threat it posed. They also explained that isolating healthy people is an unheard-of response that violates the basic tenets of both microbiology and immunology. And, like the thousands of doctors who signed that letter to the president, they described in painful detail the disastrous public health consequences working emergency room physicians on the front lines are seeing every day as a result of these lockdowns.
    “Child molestation is increasing at a severe rate. We can go over multiple cases of children who’ve been molested due to angry family members who are intoxicated, who are home, who have no paycheck. These things last a lifetime. They aren’t like a seasonal flu, they are going to follow these children and affect them in a negative fashion for their entire lives… Spousal abuse. We see people coming in here with black eyes and cuts on their face… Alcoholism…anxiety… depression… suicide is spiking… These are things I’m hearing from E.R.s, talking to my doctors, and talking to people across the country to find out what they’re seeing. [They will] effect people for a lifetime, not for a season.
    In a sign of how much our elites really care about following the science, Youtube banned the video of Dr. Erickson and Dr. Massihi’s press conference even though it not only featured medical experts fully explaining the science behind their conclusions; a local News channel also posted it. Fortunately, the video is still available on other platforms that don’t censor views contradicting the mainstream media’s official narratives.

    Some local news outlets have also acted like journalists instead of gatekeepers and given the public a chance to hear Dr. Erickson’s take.



    But Youtube also censored Laura Ingraham when she interviewed Doctors Erickson and Massihi on Fox News:
    https://twitter.com/qlactaka/status/1255134973918498816 [Twitter has now suspended this account…funny, that.]
    Nor are Erickson and Massihi by any means the only physicians who’ve tried to warn us that the lockdowns Fauci’s pushed have nothing to do with “following the science.” Some have even thus far managed to avoid YouTube censorship.
    As Powerline’s John Hinderaker wrote back in April in a post linking to Drs. Erickson and Massihi’s now censored Youtube video:
    It is perhaps unfortunate that bureaucrat doctors and academic doctors have dominated public discussion of the Wuhan virus. What some would call real doctors–those who actually treat patients–have been little heard.
    Dissent from Fauscism, however, is by no means restricted to working physicians. Eminent scientists with more theoretical backgrounds have also tried to get the word out that the prescriptions Fauci’s pushed on us, far from “following the science,” are, in reality, the worst sort of pseudoscientific quack medicine.
    John P. A. Ioannidis is a professor of medicine at Stanford University and both a professor of epidemiology and population health. When these lockdowns first started kicking in, Dr. Ioannidis published an op-ed titled:
    Again in sharp contrast to Fauci, Dr. Ionnidis broke down his reasons for thinking the threat of COVID-19 was dangerously exaggerated.


    Since then, Dr. Ioannidis has researched the prevalence of the COVID-19 virus, which indicates that its fatality rate is likely comparable to the flu’s
    Media airheads like CNN’s S.E. Cupp excoriated President Trump for ignoring the science when he criticized Fauci. Meanwhile, in the real world, Nobel Prize-winning biophysicist Michael Levitt has been hammering away at how idiotic this lockdown is in terms that make the president’s remarks seem mild:
    Apparently, S. E. Cupp doesn’t think Nobel Prize-winning biophysicists know anything about science.
    Likewise, for Oxford University’s Centre for Evidence-Based Medicine. They’ve been presenting cold hard data since the day these lockdowns began showing that Fauci‘s testimony to congress that COVID-19 is “at least ten times more lethal than the flu” had no basis in scientific reality.
    Former chief of neuroradiology at Stanford University Medical Center, Dr. Scott W. Atlas, warned us way back in April that, Fauci’s lockdown, rather than being based on science, contradicted the “empirical evidence” as well as the “fundamental principles of biology established for decades.” According to Dr. Atlas’s expert opinion:
    The overwhelming evidence all over the world consistently shows that a clearly defined group — older people and others with underlying conditions — is more likely to have a serious illness requiring hospitalization and more likely to die from COVID-19. Knowing that, it is a commonsense, achievable goal to target isolation policy to that group… The appropriate policy, based on fundamental biology and the evidence already in hand, is to institute a more focused strategy like some outlined in the first place: Strictly protect the known vulnerable, self-isolate the mildly sick and open most workplaces and small businesses with some prudent large-group precautions… Let’s stop underemphasizing empirical evidence while instead doubling down on hypothetical models. Facts matter.



    I could go on listing working physicians and distinguished scientists who are opposed to Fauci’s lockdown all day. And, like those already listed above but unlike Fauci, the reasons for their opinions would be clearly explained, and their assessments wouldn’t change with the shifting political winds.
    But there’s a more general point that’s crucial here.
    S. E Cupp, Pete Buttigieg, and all the others who defend Fauci by chanting “follow the science” don’t have a clue what they’re talking about. Nothing could be less scientific than blindly following the word of some alleged authority and trying to bully everyone else into doing so by mindlessly regurgitating some idiotic catchphrase. Science is about believing things for a reason, not because some media-anointed expert insists on them.
    Science is also about research. And if Cupp, Buttigieg, and the rest had the slightest interest in discovering whether our response to COVID-19 was justified, they would have spent a half-hour doing some and found any number of prominent scientists and working physicians who believe these lockdowns have violated the basic tenets of science and altogether reject Fauci’s scaremongering about COVID-19.
    In fact, one scientist whose opinion both Cupp and Buttigieg respect published an article in the New England Journal of Medicine just 11 days before Fauci’s March 11 testimony to congress. And, just like all the many dissenters cited above, this highly respected researcher totally rejected Fauci’s frightening claim that COVID-19 is “at least ten times more lethal than the flu.”
    According to this researcher, whose opinions are also often featured in the mainstream media:
    The overall clinical consequences of Covid-19 may ultimately be more akin to those of a severe seasonal influenza (which has a case fatality rate of approximately 0.1%).


    The author of that New England Journal of Medicine piece was none other than Dr. Faucihimself. Apparently, the “expert opinion” to which S. E. Cupp and Pete Buttigieg are trying to bully the rest of us into joining them in blind allegiance is for the rubes and not something Fauci wanted to present to other experts unlikely to fall for bogus stats.
    So the next time someone tries to convince you that COVID-19 is substantially more deadly than the seasonal flu, just tell them they need to shut up and listen to Dr. Fauci.

    ***Did you know that the research on COVID-19 has repeatedly shown that around half of us have preexisting “crossover immunity” from prior contact with very common but harmless variant strains?How could you when top public health officials like Anthony Fauci and CDC Director Robert Redfield have not only withheld the data but gone so far as to commit perjury by denying it before Congress?Find out more on how the real COVID-19 science is being suppressed:Trump’s New C19 Advisor Cites Research Showing Widespread Immunity! Calls out CDC Head Redfield’s False Testimony to Stunned Press

    ***Were you aware that the Chinese Communist Party started hyping COVID-19 in their official English language publication way back on January 1st? In the ensuing months, they conducted a far-reaching propaganda campaign to convince Western nations to commit suicide by imposing these unprecedented anti-science lockdowns and make billions selling us shoddy medical supplies along the way. The CCP even used a mass of fake social media accounts to attack dissenting Governor Kristi Noem for having enough brains and courage to buck the tied and not impose their lockdown-weapon on South Dakota.
    For the details on how China attacked America with what their top military strategist has called a “new concept of weapon” see:The ‘Whistleblower’ Who Told Tucker Carlson COVID-19 Is a Chinese Bioweapon May Be Playing a Very Devious Game



    The disastrous policies and shocking misinformation peddled by those charged with safeguarding America’s public health these past six months may be more than just incompetence or corruption. In some cases, it might be treason.The ‘Whistleblower’ Who Told Tucker Carlson COVID-19 Is a Chinese Bioweapon May Be Playing a Very Devious Game

    ***And check out the rest of my work for everything else the media isn’t telling you about COVID-19.But steel your resolve. It’s much worse than you even think.

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    https://redstate.com/michael_thau/2020/07/13/many-medical-experts-were-against-lockdowns-the-media-just-didnt-want-us-know-n247652
    Last edited by HFO3; 11-23-2020 at 08:22 PM.

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    The CDC numbers on Covid 19

    COVID-19 Pandemic Planning Scenarios

    Updated Sept. 10, 2020


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    Summary of Recent Changes
    Updated September 10, 2020:

    • The Infection Fatality Ratio parameter has been updated to include age-specific estimates
    • The parameter for Number of Days from Symptom Onset to Seeking Outpatient Care—which was based on influenza care seeking data—has been replaced with the Median Number of Days from Symptom Onset to SARS-CoV-2 Test among SARS-CoV-2 Positive Patients
    • A new parameter for the likelihood of an infection being reported has been added: The Ratio of Estimated Infections to Reported Case Counts



    CDC and the Office of the Assistant Secretary for Preparedness and Responseexternal icon (ASPR) have developed five COVID-19 Pandemic Planning Scenarios that are designed to help inform decisions by public health officials who use mathematical modeling, and by mathematical modelers throughout the federal government. Models developed using the data provided in the planning scenario tables can help evaluate the potential effects of different community mitigation strategies (e.g., social distancing). The planning scenarios may also be useful to hospital administrators in assessing resource needs and can be used in conjunction with the COVID-19Surge Tool.
    Each scenario is based on a set of numerical values for biological and epidemiological characteristics of COVID-19 illness, which is caused by the SARS-CoV-2 virus. These values—called parameter values—can be used in models to estimate the possible effects of COVID-19 in U.S. states and localities. This document was first posted on May 20, 2020, with the understanding that the parameter values in each scenario would be updated and augmented over time, as we learn more about the epidemiology of COVID-19. The September 10 update is based on data received by CDC through August 8, 2020.
    In this update, age-specific estimates of Infection Fatality Ratios have been updated, one parameter measuring healthcare usage has been replaced with the median number of days from symptom onset to positive SARS-CoV-2 test, and a new parameter has been included: Ratio of Estimated Infections to Reported Case Counts, which is based on recent serological data from a commercial laboratory survey in the U.S.1
    New data on COVID-19 are available daily, yet information about the biological aspects of SARS-CoV-2 and epidemiological characteristics of COVID-19 remain limited, and uncertainty remains around nearly all parameter values. For example, current estimates of infection-fatality ratios do not account for time-varying changes in hospital capacity (e.g., in bed capacity, ventilator capacity, or workforce capacity) or for differences in case ascertainment in congregate and community settings or in rates of underlying health conditions that may contribute to a higher frequency of severe illness in those settings. A nursing home, for example, may have a high incidence of infection (due to close contacts among many individuals) and severe disease (due to a high rate of underlying conditions) that does not reflect the frequency or severity of disease in the broader population of older adults. In addition, the practices for testing nursing home residents for SARS-CoV-2 upon identification of a positive resident may be different than testing practices for contacts of confirmed cases in the community. Observed parameter values may also change over time (e.g., the percentage of transmission occurring prior to symptom onset will be influenced by how quickly and effectively both symptomatic people and the contacts of known cases are quarantined).
    The parameters in the scenarios:

    • Are estimates intended to support public health preparedness and planning.
    • Are not predictions of the expected effects of COVID-19.
    • Do not reflect the impact of any behavioral changes, social distancing, or other interventions.



    The Five Scenarios

    The five COVID-19 Pandemic Planning Scenarios (Box 1) represent a range of possible parameters for COVID-19 in the United States. All parameter values are based on current COVID-19 surveillance data and scientific knowledge.

    • Scenarios 1 through 4 are based on parameter values that represent the lower and upper bounds of disease severity and viral transmissibility (moderate to very high severity and transmissibility). The parameter values used in these scenarios are likely to change as we obtain additional data about the upper and lower bounds of disease severity and the transmissibility of SARS-CoV-2, the virus that causes COVID-19.
    • Scenario 5 represents a current best estimate about viral transmission and disease severity in the United States, with the same caveat: the parameter values will change as more data become available.

    Parameter values that vary among the Pandemic Planning Scenarios are listed in Table 1, while parameter values common to all five scenarios are listed in Table 2. Definitions of the parameters are provided below, and the source of each parameter value is indicated in the Tables.


    The Parameter Values: Definitions

    Parameter values that vary across the five COVID-19 Pandemic Planning Scenarios (Table 1) include measures of viral transmissibility, disease severity, and pre-symptomatic and asymptomatic disease transmission. Age-stratified estimates are provided, where sufficient data are available.
    Viral Transmissibility

    • Basic reproduction number (R0): The average number of people that one person with SARS-CoV-2 is likely to infect in a population without any immunity (from previous infection) or any interventions. R0 is an estimate of how transmissible a pathogen is in a population. R0 estimates vary across populations and are a function of the duration of contagiousness, the likelihood of infection per contact between a susceptible person and an infectious person, and the contact rate.2

    Disease Severity

    • Infection Fatality Ratio (IFR): The number of individuals who die of the disease among all infected individuals (symptomatic and asymptomatic). This parameter is not necessarily equivalent to the number of reported deaths per reported case because many cases and deaths are never confirmed to be COVID-19, and there is a lag in time between when people are infected and when they die. This parameter also reflects the existing standard of care, which may vary by location and may be affected by the introduction of new therapeutics.

    Pre-symptomatic and Asymptomatic Contribution to Disease Transmission

    A pre-symptomatic case of COVID-19 is an individual infected with SARS-CoV-2, who has not exhibited symptoms at the time of testing, but who later exhibits symptoms during the course of the infection. An asymptomatic case is an individual infected with SARS-CoV-2, who does not exhibit symptoms during the course of infection. Parameter values that measure the pre-symptomatic and asymptomatic contribution to disease transmission include:

    • Percentage of infections that are asymptomatic: The percentage of persons who are infected with SARS-CoV-2 but never show symptoms of disease. Asymptomatic cases are challenging to identify because individuals do not know they are infected unless they are tested over the course of their infection, which is typically only done systematically as a part of a scientific study.
    • Infectiousness of asymptomatic individuals relative to symptomatic individuals: The contribution to transmission of SARS-CoV-2 from asymptomatic individuals compared to the contribution to transmission of SARS-CoV-2 from symptomatic individuals. For example, a parameter value of 50% means that an asymptomatic individual is half as infectious as a symptomatic individual, whereas a parameter value of 100% means that an asymptomatic individual is just as likely to transmit infection as a symptomatic individual.
    • Percentage of transmission occurring prior to symptom onset: Among symptomatic cases, the percentage of new cases of COVID-19 due to transmission from a person with COVID-19 who infects others before exhibiting symptoms (pre-symptomatic).

    Parameter values that do not vary across the five Pandemic Planning Scenarios (Table 2) are:

    • Level of pre-existing immunity to COVID-19 in the community: The percentage of the U.S. population that had existing immunity to COVID-19 prior to the start of the pandemic beginning in 2019.
    • Ratio of estimated infections to reported case counts: The estimated number of infections divided by the number of reported cases. The level of case detection likely varies by the age distribution of cases, location, and over time.
    • Time from exposure to symptom onset: The number of days from the time a person has contact with an infected person that results in COVID-19 infection and the first appearance of symptoms.
    • Time from symptom onset in an individual and symptom onset of a second person infected by that individual: The number of days from the time a person becomes symptomatic and when the person who they infect becomes symptomatic.

    Additional parameter values common to the five COVID-19 Pandemic Planning Scenarios are these ten measures of healthcare usage:

    • Median number of days from symptom onset to SARS-CoV-2 test among SARS-CoV-2 positive patients
    • Median number of days from symptom onset to hospitalization
    • Median number of days of hospitalization among those not admitted to the ICU
    • Median number of days of hospitalization among those admitted to the ICU
    • Percentage of patients admitted to the ICU among those hospitalized
    • Percentage of patients on mechanical ventilation among those hospitalized (includes both non-ICU and ICU admissions)
    • Percentage of patients who die among those hospitalized (includes both non-ICU and ICU admissions)
    • Median number of days on mechanical ventilation
    • Median number of days from symptom onset to death
    • Median number of days from death to reporting of that death

    These healthcare-related parameters (Table 2) are included to assist in assessment of resource needs as the pandemic progresses.
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    [COLOR=#FFFFFF !important]Box 1 Description of the Five COVID-19 Pandemic Planning Scenarios

    For each Pandemic Planning Scenario:

    • Parameter value for viral transmissibility is the Basic Reproduction Number (R0)
    • Parameter value for disease severity is the Infection Fatality Ratio (IFR)
    • Parameter values for the pre-symptomatic and asymptomatic contribution to disease transmission are:
      • Percentage of transmission occurring prior to symptom onset (from pre-symptomatic individuals)
      • Percentage of infections that are asymptomatic
      • Infectiousness of asymptomatic individuals relative to symptomatic individuals

    For Pandemic Scenarios 1-4:

    • These scenarios are based on parameter values that represent the lower and upper bounds of disease severity and viral transmissibility (moderate to very high severity and transmissibility). The parameter values used in these scenarios are likely to change as we obtain additional data about the upper and lower bounds of disease severity and viral transmissibility of COVID-19.

    For Pandemic Scenario 5:

    • This scenario represents a current best estimate about viral transmission and disease severity in the United States, with the same caveat: that the parameter values will change as more data become available.


    Scenario 1:

    • Lower-bound values for virus transmissibility and disease severity
    • Lower percentage of transmission prior to onset of symptoms
    • Lower percentage of infections that never have symptoms and lower contribution of those cases to transmission

    Scenario 2:

    • Lower-bound values for virus transmissibility and disease severity
    • Higher percentage of transmission prior to onset of symptoms
    • Higher percentage of infections that never have symptoms and higher contribution of those cases to transmission

    Scenario 3:

    • Upper-bound values for virus transmissibility and disease severity
    • Lower percentage of transmission prior to onset of symptoms
    • Lower percentage of infections that never have symptoms and lower contribution of those cases to transmission

    Scenario 4:

    • Upper-bound values for virus transmissibility and disease severity
    • Higher percentage of transmission prior to onset of symptoms
    • Higher percentage of infections that never have symptoms and higher contribution of those cases to transmission

    Scenario 5:

    • Parameter values for disease severity, viral transmissibility, and pre-symptomatic and asymptomatic disease transmission that represent the best estimate, based on the latest surveillance data and scientific knowledge. Parameter values are based on data received by CDC through August 8, 2020.





    Table 1. Parameter Values that vary among the five COVID-19 Pandemic Planning Scenarios. The scenarios are intended to advance public health preparedness and planning. They are not predictions or estimates of the expected impact of COVID-19. The parameter values in each scenario will be updated and augmented over time, as we learn more about the epidemiology of COVID-19. Additional parameter values might be added in the future (e.g., population density, household transmission, and/or race and ethnicity).
    Parameter Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5: Current Best Estimate
    R0* 2.0 4.0 2.5
    Infection Fatality Ratio† 0-19 years: 0.00002
    20-49 years: 0.00007
    50-69 years: 0.0025
    70+ years: 0.028
    0-19 years: 0.0001
    20-49 years: 0.0003
    50-69 years: 0.010
    70+ years: 0.093
    0-19 years: 0.00003
    20-49 years: 0.0002
    50-69 years: 0.005
    70+ years: 0.054
    Percent of infections that are asymptomatic§ 10% 70% 10% 70% 40%
    Infectiousness of asymptomatic individuals relative to symptomatic¶ 25% 100% 25% 100% 75%
    Percentage of transmission occurring prior to symptom onset** 30% 70% 30% 70% 50%

    *The best estimate representative of the point estimates of R0 from the following sources:
    Chinazzi M, Davis JT, Ajelli M, et al. The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak. Science. 2020;368(6489):395-400; Imai N., Cori, A., Dorigatti, I., Baguelin, M., Donnelly, C. A., Riley, S., Ferguson, N.M. (2020). Report 3: Transmissibility of 2019-nCoV. Online report
    Li Q, Guan X, Wu P, et al. Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus-Infected Pneumonia. N Engl J Med. 2020;382(13):1199-1207
    Munayco CV, Tariq A, Rothenberg R, et al. Early transmission dynamics of COVID-19 in a southern hemisphere setting: Lima-Peru: February 29th-March 30th, 2020 [published online ahead of print, 2020 May 12]. Infect Dis Model. 2020; 5:338-345
    Salje H, Tran Kiem C, Lefrancq N, et al. Estimating the burden of SARS-CoV-2 in France [published online ahead of print, 2020 May 13] [published correction appears in Science. 2020 Jun 26;368(6498):]. Science. 2020;eabc3517.
    The range of estimates for Scenarios 1-4 represent the upper and lower bound of the widest confidence interval estimates reported in: Li Q, Guan X, Wu P, et al. Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus-Infected Pneumonia. N Engl J Med. 2020;382(13):1199-1207.
    Substantial uncertainty remains around the R0 estimate. Notably, Sanche S, Lin YT, Xu C, Romero-Severson E, Hengartner N, Ke R. High Contagiousness and Rapid Spread of Severe Acute Respiratory Syndrome Coronavirus 2. Emerg Infect Dis. 2020;26(7):1470-1477 (https://dx.doi.org/10.3201/eid2607.200282external icon) estimated a median R0 value of 5.7 in Wuhan, China. In an analysis of 8 Europe countries and the US, the same group estimated R0 of between 4.0 and 7.1 in the pre-print manuscript: Ke R., Sanche S., Romero-Severson, & E., Hengartner, N. (2020). Fast spread of COVID-19 in Europe and the US suggests the necessity of early, strong and comprehensive interventions. medRxiv.
    † These estimates are based on age-specific estimates of infection fatality ratios from Hauser, A., Counotte, M.J., Margossian, C.C., Konstantinoudis, G., Low, N., Althaus, C.L. and Riou, J., 2020. Estimation of SARS-CoV-2 mortality during the early stages of an epidemic: a modeling study in Hubei, China, and six regions in Europe. PLoS medicine, 17(7), p.e1003189. Hauser et al. produced estimates of IFR for 10-year age bands from 0 to 80+ year old for 6 regions in Europe. Estimates exclude infection fatality ratios from Hubei, China, because we assumed infection and case ascertainment from the 6 European regions are more likely to reflect ascertainment in the U.S. To obtain the best estimate values, the point estimates of IFR by age were averaged to broader age groups for each of the 6 European regions using weights based on the age distribution of reported cases from COVID-19 Case Surveillance Public Use Data (https://data.cdc.gov/Case-Surveillan...Data/vbim-akqf). The estimates for persons ≥70 years old presented here do not include persons ≥80 years old as IFR estimates from Hauser et al., assumed that 100% of infections among persons ≥80 years old were reported. The consolidated age estimates were then averaged across the 6 European regions. The lower bound estimate is the lowest, non-zero point estimate across the six regions, while the upper bound is the highest point estimate across the six regions.
    § The percent of cases that are asymptomatic, i.e. never experience symptoms, remains uncertain. Longitudinal testing of individuals is required to accurately detect the absence of symptoms for the full period of infectiousness. Current peer-reviewed and preprint studies vary widely in follow-up times for re-testing, or do not include re-testing of cases. Additionally, studies vary in the definition of a symptomatic case, which makes it difficult to make direct comparisons between estimates. Furthermore, the percent of cases that are asymptomatic may vary by age, and the age groups reported in studies vary. Given these limitations, the range of estimates for Scenarios 1-4 is wide. The lower bound estimate approximates the lower 95% confidence interval bound estimated from: Byambasuren, O., Cardona, M., Bell, K., Clark, J., McLaws, M. L., & Glasziou, P. (2020). Estimating the extent of true asymptomatic COVID-19 and its potential for community transmission: systematic review and meta-analysis. Available at SSRN 3586675. The upper bound estimate approximates the upper 95% confidence interval bound estimated from: Poletti, P., Tirani, M., Cereda, D., Trentini, F., Guzzetta, G., Sabatino, G., Marziano, V., Castrofino, A., Grosso, F., Del Castillo, G. and Piccarreta, R. (2020). Probability of symptoms and critical disease after SARS-CoV-2 infection. arXiv preprint arXiv:2006.08471. The best estimate is the midpoint of this range and aligns with estimates from: Oran DP, Topol EJ. Prevalence of Asymptomatic SARS-CoV-2 Infection: A Narrative Review [published online ahead of print, 2020 Jun 3]. Ann Intern Med. 2020; M20-3012.
    ¶ The current best estimate is based on multiple assumptions. The relative infectiousness of asymptomatic cases to symptomatic cases remains highly uncertain, as asymptomatic cases are difficult to identify, and transmission is difficult to observe and quantify. The estimates for relative infectiousness are assumptions based on studies of viral shedding dynamics. The upper bound of this estimate reflects studies that have shown similar durations and amounts of viral shedding between symptomatic and asymptomatic cases: Lee, S., Kim, T., Lee, E., Lee, C., Kim, H., Rhee, H., Park, S.Y., Son, H.J., Yu, S., Park, J.W. and Choo, E.J., Clinical Course and Molecular Viral Shedding Among Asymptomatic and Symptomatic Patients With SARS-CoV-2 Infection in a Community Treatment Center in the Republic of Korea. JAMA Internal Medicine; Zou L, Ruan F, Huang M, et al. SARS-CoV-2 Viral Load in Upper Respiratory Specimens of Infected Patients. N Engl J Med. 2020;382(12):1177-1179; and Zhou R, Li F, Chen F, et al. Viral dynamics in asymptomatic patients with COVID-19. Int J Infect Dis. 2020; 96:288-290. The lower bound of this estimate reflects data indicating that viral loads are higher in severe cases relative to mild cases (Liu Y, Yan LM, Wan L, et al. Viral dynamics in mild and severe cases of COVID-19. Lancet Infect Dis. 2020;20(6):656-657) and data showing that viral loads and shedding durations are higher among symptomatic cases relative to asymptomatic cases (Noh JY, Yoon JG, Seong H, et al. Asymptomatic infection and atypical manifestations of COVID-19: Comparison of viral shedding duration [published online ahead of print, 2020 May 21]. J Infect. 2020; S0163-4453(20)30310-8).
    ** The lower bound of this parameter is approximated from the lower 95% confidence interval bound from: He, X., Lau, E.H., Wu, P., Deng, X., Wang, J., Hao, X., Lau, Y.C., Wong, J.Y., Guan, Y., Tan, X. and Mo, X. (2020). Temporal dynamics in viral shedding and transmissibility of COVID-19. Nature medicine, 26(5), pp.672-675. The upper bound of this parameter is approximated from the higher estimates of individual studies included in: Casey, M., Griffin, J., McAloon, C.G., Byrne, A.W., Madden, J.M., McEvoy, D., Collins, A.B., Hunt, K., Barber, A., Butler, F. and Lane, E.A. (2020). Estimating pre-symptomatic transmission of COVID-19: a secondary analysis using published data. medRxiv.The best estimate is the geometric mean of the point estimates from these two studies.

    Table 2. Parameter Values Common to the Five COVID-19 Pandemic Planning Scenarios. The parameter values are likely to change as we obtain additional data about disease severity and viral transmissibility of COVID-19.
    Parameter values are based on data received by CDC through August 8, 2020, including COVID-19 Case Surveillance Public Use Data (https://data.cdc.gov/Case-Surveillan...Data/vbim-akqf); data from the Hospitalization Surveillance Network (COVID-NET) (through August 1); and data from Data Collation and Integration for Public Health Event Response (DCIPHER).
    Pre-existing immunity
    Assumption, ASPR and CDC
    No pre-existing immunity before the pandemic began in 2019. It is assumed that all members of the U.S. population were susceptible to infection prior to the pandemic.
    Time from exposure to symptom onset* ~6 days (mean)
    Time from symptom onset in an individual and symptom onset of a second person infected by that individual† ~6 days (mean)
    Mean ratio of estimated infections to reported case counts, Overall (range)§ 11 (6, 24)
    Parameter Values Related to Healthcare Usage
    Median number of days from symptom onset to SARS-CoV-2 test among SARS-CoV-2 positive patients (interquartile range)¶
    Overall: 3 (1, 6) days
    Median number of days from symptom onset to hospitalization (interquartile range)**
    18-49 years: 6 (3, 10) days50-64 years: 6 (2, 10) days≥65 years: 4 (1, 9) days
    Median number of days of hospitalization among those not admitted to ICU (interquartile range) ††
    18-49 years: 3 (2, 5) days50-64 years: 4 (2, 7) days≥65 years: 6 (3, 10) days
    Median number of days of hospitalization among those admitted to ICU (interquartile range)††,§§
    18-49 years: 11 (6, 20) days50-64 years: 14 (8, 25) days≥65 years: 12 (6, 20) days
    Percent admitted to ICU among those hospitalized††
    18-49 years: 23.8%50-64 years: 36.1%≥65 years: 35.3%
    Percent on mechanical ventilation among those hospitalized. Includes both non-ICU and ICU admissions††
    18-49 years: 12.0%50-64 years: 22.1%≥65 years: 21.1%
    Percent that die among those hospitalized. Includes both non-ICU and ICU admissions†† 18-49 years: 2.4%
    50-64 years: 10.0%
    ≥65 years: 26.6%
    Median number of days of mechanical ventilation (interquartile range)**
    Overall: 6 (2, 12) days
    Median number of days from symptom onset to death (interquartile range)**
    18-49 years: 15 (9, 25) days50-64 years: 17 (10, 26) days≥65 years: 13 (8, 21) days
    Median number of days from death to reporting (interquartile range)¶¶
    18-49 years: 19 (5, 45) days50-64 years: 21 (6, 46) days≥65 years: 19 (5, 44) days
    * McAloon, C.G., Collins, A., Hunt, K., Barber, A., Byrne, A., Butler, F., Casey, M., Griffin, J.M., Lane, E., McEvoy, D. and Wall, P. (2020). The incubation period of COVID-19: A rapid systematic review and meta-analysis of observational research. medRxiv.
    † He, X., Lau, E.H., Wu, P., Deng, X., Wang, J., Hao, X., Lau, Y.C., Wong, J.Y., Guan, Y., Tan, X. and Mo, X. (2020). Temporal dynamics in viral shedding and transmissibility of COVID-19. Nature medicine, 26(5), pp.672-675.
    § The point estimate is the geometric mean of the location specific point estimates of the ratio of estimated infections to reported cases, from Havers, F.P., Reed, C., Lim, T., Montgomery, J.M., Klena, J.D., Hall, A.J., Fry, A.M., Cannon, D.L., Chiang, C.F., Gibbons, A. and Krapiunaya, I., 2020. Seroprevalence of antibodies to SARS-CoV-2 in 10 sites in the United States, March 23-May 12, 2020. JAMA Internal Medicine. The lower and upper bounds for this parameter estimate are the lowest and highest point estimates of the ratio of estimated infections to reported cases, respectively, from Havers et al., 2020.
    ¶ Estimates only include symptom onset dates between March 1, 2020 – July 15, 2020. Estimates represent time to obtain SARS-CoV-2 tests among cases who tested positive for SARS-CoV-2. Estimates based on and data from Data Collation and Integration for Public Health Event Response (DCIPHER).
    ** Estimates only include symptom onset dates between March 1, 2020 – July 15, 2020 to ensure cases have had sufficient time to observe the outcome (hospital discharge or death). Data for 17 year olds and under are suppressed due to small sample sizes.
    †† Based on data reported to COVID-NET by Aug 1, 2020. Data for 17 year olds and under are suppressed due to small sample sizes. https://gis.cdc.gov/grasp/COVIDNet/COVID19_5.html.
    §§ Cumulative length of stay for persons admitted to the ICU, inclusive of both ICU and non-ICU days.
    ¶¶ Estimates only include death dates between March 1, 2020 – July 15, 2020 to ensure sufficient time for reporting. Data for 17 year olds and under are suppressed due to small sample sizes.


    References

    • Havers, F.P., Reed, C., Lim, T., Montgomery, J.M., Klena, J.D., Hall, A.J., Fry, A.M., Cannon, D.L., Chiang, C.F., Gibbons, A. and Krapiunaya, I., 2020. Seroprevalence of antibodies to SARS-CoV-2 in 10 sites in the United States, March 23-May 12, 2020. JAMA Internal Medicine.
    • Dietz K. The estimation of the basic reproduction number for infectious diseases. Stat Methods Med Res. 1993;2:23–41.



    Archive
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    Tens of thousands of coronavirus tests have been double-counted, officials admit

    Two samples taken from the same patient are being recorded as two separate tests in the Government's official figures
    ByMason Boycott-Owen and Paul Nuki, GLOBAL HEALTH SECURITY EDITOR, LONDON21 May 2020 • 9:00pm


    Tens of thousands of Covid-19 tests have been double-counted in the Government’s official tally, public health officials have admitted.
    Diagnostic tests which involve taking saliva and nasal samples from the same patient are being counted as two tests, not one.
    The Department of Health and Social Care and Public Health England each confirmed the double-counting.
    This inflates the daily reported diagnostic test numbers by over 20 per cent, with that proportion being much higher earlier on in the crisis before home test kits were added to the daily totals.
    Almost 350,000 more tests have been reported in Government data than people tested since the start of the pandemic.
    The discrepancy is in large part explained by the practice of counting salvia and nasal samples for the same individual twice.
    Public Health England oversee the testing of patients who are seriously ill in hospital, as well as the most critical key workers.
    The test involves a swab from the mouth and nose as well as a sample of saliva. Although both of these are taken from the same patient, they are counted twice by the Government in its daily data.



    It is not the first time the Government has been caught massaging the testing data. It was accused last month of including thousands of home tests which had been posted but not completed in a bid to reach its target of 100,000 tests.
    Jon Ashworth MP, Labour’s Shadow Health Secretary, said: “Ministers have already received an embarrassing slap on the wrists for their dodgy spin on testing figures. It seems they haven’t learnt their lesson. We need absolute transparency in the presentation of these figures”.
    The Government announced at the beginning of May that it would be extending its target from 100,000 tests per day to 200,000 tests per day. But so far it has only hit the 100,000 target nine times in the 20 days since its introduction.
    Global health experts said the Government should stop fixating on its arbitrary targets and instead focus on making testing work to drive down Covid-19 infections in the UK.



    Nicola Stonehouse, Professor of Molecular Virology at the University of Leeds, said: “I don’t think it’s helpful to be simply focused on the numbers of tests. We should concentrate on using our testing intelligently and combining testing with contact tracing."
    PHE said that there were other reasons why one person may receive more than one test.
    These included repeating a test after receiving inconclusive results and double checking a negative result.
    Devi Sridhar, Professor of Global Public Health at the University of Edinburgh, said: “Instead of fixating on the exact number of tests, we should be looking at the ratio of confirmed cases to total number of people tested (and bringing this percentage down), the speed of tests and getting results to individuals.
    "All while setting up a massive public health infrastructure with testing sites around the country for regular use by social care workers, health workers, and even towards teachers as capacity grows. The number of tests required is linked to how much transmission is occurring."
    Both Public Health England and the Department for Health and Social Care did not offer further comment.







    https://www.telegraph.co.uk/global-health/science-and-disease/tens-thousands-coronavirus-tests-have-double-counted-officials/?fbclid=IwAR0S7D5eisLiLKLNwB_3dcu60GJy17Vv1qqMQTSM gWs5XFr8aJtInin4W4Q

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    Quote Originally Posted by heckler7 View Post
    Democraps are talking about de-funding the police...and I'm over here like defund the ATF.

    Do they not realize there are alot of cnc machines and 3d printers.......

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    Quote Originally Posted by Joocey View Post
    Democraps are talking about de-funding the police...and I'm over here like defund the ATF.

    Do they not realize there are alot of cnc machines and 3d printers.......
    if you defund the ATF how will the cartels get those fully automatic weapons from us.

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    Thanks for starting this. I been thinking we need a "police state" thread of some kind. So much is going on and so much *more* will be.

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    I think we all need to start using Bing over google.

    I heard it isn’t biased or manipulative as google.


    Sent from my iPhone using Tapatalk

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    Quote Originally Posted by Multislacking View Post
    Thanks for starting this. I been thinking we need a "police state" thread of some kind. So much is going on and so much *more* will be.

    We are in uncharted waters and we need to keep each other sharp with The Word and with situational awareness of the current “climate” . That and I love all of you guys here on asf, your'e all really my 2nd family yeah and if you’re one of those I dont like and youre reading this, I still love ya despite your stupidity/ignorance or whatever it is that keeps you from agreeing with me LOL


    Quote Originally Posted by Augustine5I View Post
    I think we all need to start using Bing over google.

    I heard it isn’t biased or manipulative as google.


    Sent from my iPhone using Tapatalk

    I changed over to duckduckgo.com and I get much better search results, I reverse google and I ndontn miss google at all. I especially do not miss having to prove that I’m not a damn robot lol

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    Quote Originally Posted by heckler7 View Post
    if you defund the ATF how will the cartels get those fully automatic weapons from us.
    Fully automatic weapons? lolol They get them from Nicaragua and other South American countries.

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    Worked for GOOG IT for a year, retarded company for sure...
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    Quote Originally Posted by Augustine5I View Post
    I think we all need to start using Bing over google.

    I heard it isn’t biased or manipulative as google.


    Sent from my iPhone using Tapatalk
    Isn’t Bing a Microsoft search engine?
    You know, the ones who brought us MSM.

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    Quote Originally Posted by tommygunz View Post
    Isn’t Bing a Microsoft search engine?
    You know, the ones who brought us MSM.
    Yeah, bing isn't any better than google. I hear it works better on porn searches though...

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    My favorite socialist at the moment...talking about our wonderful msm....


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    Quote Originally Posted by Multislacking View Post
    Yeah, bing isn't any better than google. I hear it works better on porn searches though...
    So it should be called boing?

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