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"Do as I say, not as I do." (Doug Ford, 2020) Reports

Review: Good CDC science versus dubious CDC science, the actual risk that does not justify the “cure”


Denis G. Rancourt, PhD

The Ontario Civil Liberties Association (OCLA) is a non-profit organization dedicated to protecting and promoting civil liberties in the province of Ontario. This working report examines the impact of mask mandates on civil liberties in Ontario, with a particular focus on the right to freedom of expression. The report reviews existing research on the effects of mask mandates, including studies on public health outcomes, economic impacts, and psychological effects. It also considers legal challenges to mask mandates and their implications for civil liberties. Finally, the report provides recommendations for how governments can ensure that any mask mandate respects civil liberties while still achieving its public health objectives.

The Ministry of Health (MOH) has confirmed 5 new cases of locally transmitted COVID-19 infection.

3 are linked to previous cases and were identified through contact tracing. 2 are currently unlinked. All are Singaporeans or Permanent Residents.

Overall, the number of new cases in the community has decreased, from an average of 8 cases per day in the week before, to an average of 4 per day in the past week. The number of unlinked cases in the community has also decreased, from an average of 3 cases per day in the week before, to an average of 1 per day in the past week.

MOH will continue to closely monitor these numbers and trends over the coming weeks.

I.

Introduction by Prof. Denis Rancourt

1. He explains the importance of understanding the science behind the COVID-19 pandemic in order to make informed decisions.
2. He emphasizes that it is important to look at all available evidence and data when making decisions, rather than relying on one source or opinion.
3. He encourages me to continue my research into the virus and its effects, as well as to stay up-to-date with new developments in the field.
4. He stresses that it is important to remain vigilant and take precautions against the virus, such as wearing a mask and social distancing, in order to protect oneself and others from infection.

  • an explanation of the various kinds of fatality rates for a pathogen
  • a review of the measured infection fatality rates for SARS-CoV-2
  • a demonstration that a recently changed CDC evaluation is most certainly incorrect, along
  • with an illustration of how not to do a meta-analysis
  • his conclusion that “the absolute and relative ‘flu-like’ risk of death from a SARS-CoV-2 infection
  • is far too low to rigorously justify governments imposing major disruptions to normal life, let alone the massive and indiscriminate ‘lockdown’ disruptions people have been forced to submit to and endureâ€

1. https://www.cdc.gov/coronavirus/2019-ncov/index.html
2. https://www.who.int/emergencies/diseases/novel-coronavirus-2019
3. https://www.cdc.gov/coronavirus/2019-ncov/prevent-getting-sick/prevention.html
4. https://www.cdc.gov/coronavirus/2019-ncov/symptoms-testing/symptoms.html
5. https://www.cdc.gov/coronavirus/2019-ncov/if-you-are-sick/steps-when-sick.html
6. https://www.cdc.gov/coronavirus/2019-ncov/travelers

The best way to prevent the spread of COVID-19 is to practice social distancing, wear a face mask when in public, wash your hands often with soap and water for at least 20 seconds, avoid touching your face, cover your mouth and nose when you sneeze or cough, clean and disinfect frequently touched surfaces daily, and stay home if you are feeling sick.

II.

Letter by Prof. Joseph Audie

The CDC’s second estimate for the IFR of SARS-CoV-2 was based on a meta-analysis of studies that used cIFR as their primary measure. This is problematic because cIFR does not take into account the fact that many people infected with SARS-CoV-2 may never develop symptoms or require hospitalization, and thus would not be included in the sample data used to calculate the cIFR. As a result, this meta-analysis likely overestimated the true IFR of SARS-CoV-2.

I believe it is important to recognize this limitation and to use more accurate measures such as pIFR or pCFR when calculating estimates of the IFR for SARS-CoV-2. Thank you for your time and consideration.

The findings of Dr. Ioannidis’ pre-print suggest that the SARS-CoV-2 IFR is lower than previously thought, and that a single best estimate of the IFR may not be possible due to the heterogeneity of data across different locations. This highlights the importance of taking into account local factors when estimating the IFR, as well as considering multiple indicators of central tendency rather than relying on a single best estimate.

In conclusion, the CDC’s estimated pIFR of 0.26% is in excellent agreement with the Streeck et al. study and provides a reliable estimate for the natural lethality of SARS-CoV-2 in a broadly representative population.

In conclusion, the CDC’s justification for its second and higher pIFR estimate of 0.65% is inadequate and fails to explain why it is replacing the psCFR with a more directly measurable parameter for disease severity. Furthermore, the 0.65% estimate is an outlier compared to the first two estimates of 0.24% and 0.26%, which are in excellent agreement and enjoy solid scientific support from multiple, independent studies.

First, the authors fail to provide a clear explanation of how they selected the 26 studies included in their meta-analysis. While they state that they used a systematic search strategy, it is unclear what criteria were used to determine which studies were included and which were excluded. This lack of transparency makes it difficult to assess the quality of the data and whether or not it is representative of the population as a whole.

Second, the authors do not provide any information on how they weighted each study in their analysis. Without this information, it is impossible to know if some studies had more influence than others on the final pIFR estimate.

Third, there is no discussion of potential sources of bias in the studies included in the meta-analysis. For example, some studies may have been conducted in countries with different levels of access to healthcare or different levels of testing capacity which could lead to inaccurate estimates. Additionally, some studies may have relied on self-reported symptoms which can be unreliable due to recall bias or other factors.

Finally, there is no discussion of potential confounding factors such as age or underlying health conditions that could affect an individual’s risk for severe illness from SARS-CoV-2 infection and thus influence the overall pIFR estimate.

Overall, these errors make it difficult to trust the accuracy of Meyerowitz-Katz and Merone’s pIFR estimate and call into question its usefulness for informing public health policy decisions.

Errors of Commission: The Meyerowitz-Katz and Merone study includes studies that are not of sufficient quality to be included in a meta-analysis. For example, the study includes studies with small sample sizes, which can lead to unreliable results. Additionally, the study does not consider potential sources of bias or confounding variables that could affect the results.

Errors of Omission: The Meyerowitz-Katz and Merone study fails to include important studies that could have improved the reliability of their findings. For example, they do not include any studies that examine the effects of different types of interventions on outcomes, such as cognitive behavioral therapy versus medication. Additionally, they do not consider any studies that examine long-term outcomes or those conducted in different cultural contexts.

In conclusion, Meyerowitz-Katz and Merone’s article is an important contribution to the literature on IFR testing. However, it fails to adequately address some of the key issues raised by previous research, such as the potential for false positives and false negatives. Furthermore, it does not engage with the most recent review article by Ioannidis which was available prior to its publication. As such, further research is needed in order to fully understand the implications of IFR testing.

This suggests that the omission of Dr. Ioannidis’ review article may have led to an underestimation of the true pIFR for SARS-CoV-2.

The Sood et al. study does not explicitly warn against using its data to obtain an IFR, but it does caution that the estimates of infection fatality rate (IFR) should be interpreted with caution due to the limited sample size and potential selection bias. The authors also note that their estimates are likely to be lower than the true IFR due to under-ascertainment of deaths in their sample. Therefore, while Dr. Ioannidis’ use of the Sood et al. study is valid, it is important to consider these caveats when interpreting his results.

Meyerowitz-Katz included the study by Tian et al. because it provided valuable information about the characteristics of hospitalized patients in Beijing, China and reported a cCFR of 0.9%. This data was useful for understanding the severity of COVID-19 in this population and could be used to inform public health interventions. Additionally, Meyerowitz-Katz noted that while this study did not provide an estimate of pIFR, it could still be used to compare mortality rates across different populations.

Meyerowitz-Katz and Merone’s analysis is incomplete and fails to take into account the important findings of Mizumoto et al. and the CDC. As such, their conclusions should be taken with a grain of salt.

The authors note that the pIFR estimates from the included studies vary widely, ranging from 0.12% to 0.9%. They also note that the estimates are subject to considerable uncertainty due to methodological differences between studies and potential biases in the data. To address these issues, they use a random-effects model to account for heterogeneity between studies and incorporate study quality into their analysis. They also conduct sensitivity analyses to assess the impact of excluding certain studies on their results.

Overall, Meyerowitz-Katz and Merone’s meta-analysis provides a robust estimate of the pIFR based on a large number of studies with varying methodologies and data sources. Their approach is transparent and replicable, making it possible for other researchers to verify their findings or build upon them in future research.

The CDC likely used the Meyerowitz-Katz analysis to inform their own estimates, but it is unclear how they arrived at a higher pIFR estimate for the US than what was calculated by Prof. Ioannidis. It is possible that the CDC took into account additional factors such as population size and demographic differences when calculating their estimate, or that they used different methods of data collection and analysis. Additionally, it is possible that the CDC’s estimate was based on more recent data than what was available to Prof. Ioannidis at the time of his analysis.

The policy recommendation is not only logical and evidence-based, but also humane. It avoids the economic devastation of lockdowns, while still protecting vulnerable populations from the virus. It also allows for a more targeted approach to pandemic mitigation, which can be tailored to the specific needs of each community. Finally, it allows for a more balanced approach to public health that takes into account both physical and mental health concerns.

The best way to prevent the spread of COVID-19 is to practice social distancing, wear a face covering when in public, wash your hands often with soap and water for at least 20 seconds, avoid touching your face, cover coughs and sneezes, clean and disinfect frequently touched surfaces daily, and stay home if you are feeling sick.

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