Some reaction at last.

Over 7,000 Scientists, Doctors Call For COVID Herd Immunity, End To Lockdowns

Authored by Steve Watson via Summit News,

Over six thousand scientists and doctors have signed a petition against coronavirus lockdown measures, urging that those not in the at risk category should be able to get on with their lives as normal, and that lockdown rules in both the US and UK are causing ‘irreparable damage’.

Those who have signed include professors from the world’s leading universities.

Oxford University professor Dr Sunetra Gupta was one of the authors of the open letter that was sent with the petition, along with Harvard University’s Dr Martin Kulldorff and Stanford’s Dr Jay Bhattacharya.

It declares that social distancing and mask mandates are causing ‘damaging physical and mental health impacts.’

The petition, dubbed the Great Barrington Declaration after the town in Massachusetts where it was written, has been signed by close to 73,000 members of the public at time of writing, as well as over 4,700 medical and public health scientists and around 3,200 medical practitioners.

“Those who are not vulnerable should immediately be allowed to resume life as normal,” it notes, adding “Keeping these [lockdown] measures in place until a vaccine is available will cause irreparable damage, with the underprivileged disproportionately harmed.”

“Current lockdown policies are producing devastating effects on short and long-term public health,” the declaration also declares.

It continues, “The results (to name a few) include lower childhood vaccination rates, worsening cardiovascular [heart] disease outcomes, fewer cancer screenings and deteriorating mental health – leading to greater excess mortality in years to come, with the working class and younger members of society carrying the heaviest burden.”

“Keeping students out of school is a grave injustice,” the declaration adds.

“Those who are not vulnerable should immediately be allowed to resume life as normal, it concludes, explaining that “Simple hygiene measures, such as hand washing and staying home when sick should be practiced by everyone to reduce the herd immunity threshold.”

“Schools and universities should be open for in-person teaching. Extracurricular activities, such as sports, should be resumed. Young low-risk adults should work normally, rather than from home,” it emphasises.

Finally, the declaration demands that normal life should resume, stating that “Restaurants and other businesses should open. Arts, music, sport and other cultural activities should resume. People who are more at risk may participate if they wish, while society as a whole enjoys the protection conferred upon the vulnerable by those who have built up herd immunity.”

The declaration echoes President Trump’s words earlier this week when he returned to the White House and asked Americans not to live in fear or let let the virus dominate their everyday lives:

The declaration dovetails with other research that has concluded lockdowns will conservatively “destroy at least seven times more years of human life” than they save.

Germany’s Minister of Economic Cooperation and Development, Gerd Muller, has warned that lockdown measures throughout the globe will end up killing more people than the Coronavirus itself.

In an interview with German newspaper Handelsblatt, Muller warned that the response to the global pandemic has resulted in “one of the biggest” hunger and poverty crises in history.

Muller’s comments come five months after a leaked study from inside the German Ministry of the Interior revealed that the impact of the country’s lockdown could end up killing more people than the coronavirus due to victims of other serious illnesses not receiving treatment.

As we have previously highlighted, in the UK there have already been up to 10,000 excess deaths as a result of seriously ill people avoiding hospitals due to COVID-19 or not having their hospital treatments cancelled.

Professor Richard Sullivan also warned that there will be more excess cancer deaths in the UK than total coronavirus deaths due to people’s access to screenings and treatment being restricted as a result of the lockdown.

His comments were echoed by Peter Nilsson, a Swedish professor of internal medicine and epidemiology at Lund University, who said, “It’s so important to understand that the deaths of COVID-19 will be far less than the deaths caused by societal lockdown when the economy is ruined.”

According to Professor Karol Sikora, an NHS consultant oncologist, there could be 50,000 excess deaths from cancer as a result of routine screenings being suspended during the lockdown in the UK.

In addition, a study published in The Lancet that notes “physical distancing, school closures, trade restrictions, and country lockdowns” are worsening global child malnutrition.

Experts have also warned that there will be 1.4 million deaths globally from untreated TB infections due to the lockdown.

As we further previously highlighted, a data analyst consortium in South Africa found that the economic consequences of the country’s lockdown will lead to 29 times more people dying than the coronavirus itself.

Hundreds of doctors are also on record as opposing lockdown measures, warning that they will cause more death than the coronavirus itself.

Despite citizens across the world being told to observe the lockdown to “save lives,” numerous experts who are now warning that the lockdown could end up costing more lives are being ignored or smeared by the media.

*  *  *

The Great Barrington Declaration

As infectious disease epidemiologists and public health scientists we have grave concerns about the damaging physical and mental health impacts of the prevailing COVID-19 policies, and recommend an approach we call Focused Protection. 

Coming from both the left and right, and around the world, we have devoted our careers to protecting people. Current lockdown policies are producing devastating effects on short and long-term public health. The results (to name a few) include lower childhood vaccination rates, worsening cardiovascular disease outcomes, fewer cancer screenings and deteriorating mental health – leading to greater excess mortality in years to come, with the working class and younger members of society carrying the heaviest burden. Keeping students out of school is a grave injustice. 

Keeping these measures in place until a vaccine is available will cause irreparable damage, with the underprivileged disproportionately harmed.

Fortunately, our understanding of the virus is growing. We know that vulnerability to death from COVID-19 is more than a thousand-fold higher in the old and infirm than the young. Indeed, for children, COVID-19 is less dangerous than many other harms, including influenza. 

As immunity builds in the population, the risk of infection to all – including the vulnerable – falls. We know that all populations will eventually reach herd immunity – i.e.  the point at which the rate of new infections is stable – and that this can be assisted by (but is not dependent upon) a vaccine. Our goal should therefore be to minimize mortality and social harm until we reach herd immunity. 

The most compassionate approach that balances the risks and benefits of reaching herd immunity, is to allow those who are at minimal risk of death to live their lives normally to build up immunity to the virus through natural infection, while better protecting those who are at highest risk. We call this Focused Protection. 

Adopting measures to protect the vulnerable should be the central aim of public health responses to COVID-19. By way of example, nursing homes should use staff with acquired immunity and perform frequent PCR testing of other staff and all visitors. Staff rotation should be minimized. Retired people living at home should have groceries and other essentials delivered to their home. When possible, they should meet family members outside rather than inside. A comprehensive and detailed list of measures, including approaches to multi-generational households, can be implemented, and is well within the scope and capability of public health professionals. 

Those who are not vulnerable should immediately be allowed to resume life as normal. Simple hygiene measures, such as hand washing and staying home when sick should be practiced by everyone to reduce the herd immunity threshold. Schools and universities should be open for in-person teaching. Extracurricular activities, such as sports, should be resumed. Young low-risk adults should work normally, rather than from home. Restaurants and other businesses should open. Arts, music, sport and other cultural activities should resume. People who are more at risk may participate if they wish, while society as a whole enjoys the protection conferred upon the vulnerable by those who have built up herd immunity.

 

 

You can only succeed in burying the truth for so long. Eventually it will emerge. 

 But why? Why did the scientific community spent so much efforts to muddy up what was (almost) obvious from the beginning? Financial interests? 

I believe there are more important justifications to this question than just financial interests...

Are Global Scientific Elites Trying To Bury The Truth About COVID-19's Origin?

Authored by Col. Lawrence Sellin (Ret.) via Citizens Commission on National Security,

There may be some culpability involved, but the huge resistance being mounted by the international scientific elite, the media and vested financial interests against conducting an objective analysis of the origin of the COVID-19 virus is primarily about money.

If it would be determined that the COVID-19 pandemic resulted from a laboratory leak of a genetically engineered virus, it would not only disrupt the flow of huge sums of research funding, but adversely affect the investments of those vehemently opposed to President Donald Trump’s efforts to make the U.S. economy less dependent on China and, therefore, make the U.S. less vulnerable to Chinese geopolitical blackmail.

There is growing scientific evidence that the COVID-19 pandemic may have resulted from a vaccine development project gone wrong.

Live-attenuated vaccines are a type of vaccine used for smallpox and childhood diseases like measles, mumps, rubella and chickenpox, in which a weakened or “attenuated” form of the virus that causes the disease is manufactured.

Because such vaccines are so similar to the natural infection that they help prevent, they create a strong and long-lasting, even lifetime immune response.

Live-attenuated virus vaccines must possess certain characteristics to be safe and effective.

They must have lower virulence and replication capability than the natural pathogenic form of the virus, but be able to induce a pronounced immune response.

Of additional importance is that the live-attenuated virus vaccines should clear quickly from the body and not revert or mutate back to the natural pathogenic form.

To fulfill those characteristics, certain modifications providing protection strategies, or “circuit breakers,” must be engineered into the viral genome, which are also potential markers of artificial manipulation.

An ad hoc group of scientific investigators known as DRASTIC have compiled a 36-point list to buttress their claim that the COVID-19 virus could have originated in a vaccine development program.

For example, a central mechanism for controlling immune responses is mediated by interferons. The COVID-19 virus seems to have some signatures in its genome which indicate interferon hypersensitivity compared to the coronavirus responsible for the 2002-2003 pandemic.

Another indication that the COVID-19 virus may have been the product of an attempt to produce a live-attenuated virus vaccine is the accumulation of “synonymous mutations” in the spike protein compared to RaTG13, which the global scientific elite claim is the nearest bat coronavirus relative.

The artificial accumulation of synonymous mutations has been described as one method of producing live-attenuated virus vaccines by “deoptimizing” the genetic code and inhibiting replication.

The most striking indication of genetic manipulation of the COVID-19 virus is the presence of the furin polybasic cleavage site, which does not exist in any closely-related bat coronavirus yet identified.

Given its role in the virus-cell or cell-cell membrane fusion process, the DRASTIC team suggests that the insertion of the furin polybasic cleavage site may have been related to a high-risk attempt to produce an intranasal “self-spreading” vaccine spray.

“Self-spreading vaccines are essentially genetically engineered viruses designed to move through populations in the same way as infectious diseases, but rather than causing disease, they confer protection.”

Obviously, much could go wrong using such an approach.

To avoid the scientific equivalent of the Russia collusion hoax, the Trump Administration should not rely on the international scientific elite, the media and vested financial interests to shape the debate, but should appoint an independent and objective task force to determine the true origin of the COVID-19 virus.

Given the power of genetic engineering and the enormous danger when it is recklessly applied, the stakes are just too high not to address this issue honestly and directly.

*  *  *

Lawrence Sellin, Ph.D. is retired from an international career in business and medical research with 29 years of service in the US Army Reserve and a veteran of Afghanistan and Iraq. He is a member of the Citizens Commission on National Security.

Framework for Data Preparation Techniques in Machine Learning (Tutorial)

This is a technical but exceptional article focusing on data preparation. 

It cannot be emphasized enough that data preparation is by far THE most important step in data analysis. Without proper preparation, and a proper framework which we will explore later, data analysis may be worth little.   

The full article can be found at:

https://machinelearningmastery.com/framework-for-data-preparation-for-machine-learning/

Framework for Data Preparation Techniques in Machine Learning

There are a vast number of different types of data preparation techniques that could be used on a predictive modeling project.

In some cases, the distribution of the data or the requirements of a machine learning model may suggest the data preparation needed, although this is rarely the case given the complexity and high-dimensionality of the data, the ever-increasing parade of new machine learning algorithms and limited, although human, limitations of the practitioner.

Instead, data preparation can be treated as another hyperparameter to tune as part of the modeling pipeline. This raises the question of how to know what data preparation methods to consider in the search, which can feel overwhelming to experts and beginners alike.

The solution is to think about the vast field of data preparation in a structured way and systematically evaluate data preparation techniques based on their effect on the raw data.

In this tutorial, you will discover a framework that provides a structured approach to both thinking about and grouping data preparation techniques for predictive modeling with structured data.

After completing this tutorial, you will know:

• The challenge and overwhelm of framing data preparation as yet an additional hyperparameter to tune in the machine learning modeling pipeline.
• A framework that defines five groups of data preparation techniques to consider.
• Examples of data preparation techniques that belong to each group that can be evaluated on your predictive modeling project.

Kick-start your project with my new book Data Preparation for Machine Learning, including step-by-step tutorials and the Python source code files for all examples.

Let’s get started.

Challenge of Data Preparation

Data preparation refers to transforming raw data into a form that is better suited to predictive modeling.

This may be required because the data itself contains mistakes or errors. It may also be because the chosen algorithms have expectations regarding the type and distribution of the data.

To make the task of data preparation even more challenging, it is also common that the data preparation required to get the best performance from a predictive model may not be obvious and may bend or violate the expectations of the model that is being used.

As such, it is common to treat the choice and configuration of data preparation applied to the raw data as yet another hyperparameter of the modeling pipeline to be tuned.

This framing of data preparation is very effective in practice, as it allows you to use automatic search techniques like grid search and random search to discover unintuitive data preparation steps that result in skillful predictive models.

This framing of data preparation can also feel overwhelming to beginners given the large number and variety of data preparation techniques.

The solution to this overwhelm is to think about data preparation techniques in a systematic way.

Framework for Data Preparation

Effective data preparation requires that the data preparation techniques available are organized and considered in a structured and systematic way.

This allows you to ensure that approach techniques are explored for your dataset and that potentially effective techniques are not skipped or ignored.

This can be achieved using a framework to organize data preparation techniques that consider their effect on the raw dataset.

For example, structured machine learning data, such as data we might store in a CSV file for classification and regression, consists of rows, columns, and values. We might consider data preparation techniques that operate at each of these levels.

• Data Preparation for Rows
• Data Preparation for Columns
• Data Preparation for Values

Data preparation for rows may be techniques that add or remove rows of data from the dataset. Similarly, data preparation for columns may be techniques that add or remove columns (features or variables) from the dataset. Whereas data preparation for values may be techniques that change the values in the dataset, often for a given column.

There is one more type of data preparation that does not neatly fit into this structure, and that is dimensionality reduction techniques. These techniques change the columns and the values at the same time, e.g. projecting the data into a lower-dimensional space.

• Data Preparation for Columns + Values

This raises the question of techniques that might apply to rows and values at the same time. This might include data preparation that consolidates rows of data in some way.

• Data Preparation for Rows + Values

Now that we have a framework for thinking about data preparation based on their effect on the data, let’s look at examples of techniques that fit into each group.

Data Preparation Techniques

This section explores the five high-level groups of data preparation techniques defined in the previous section and suggests specific techniques that may fall within each group.

Did I miss one of your preferred or favorite data preparation techniques?
Let me know in the comments below.

Data Preparation for Rows

This group is for data preparation techniques that add or remove rows of data.

In machine learning, rows are often referred to as samples, examples, or instances.

These techniques are often used to augment a limited training dataset or to remove errors or ambiguity from the dataset.

The main class of techniques that come to mind are data preparation techniques that are often used for imbalanced classification.

This includes techniques such as SMOTE that create synthetic rows of training data for under-represented classes and random undersampling that remove examples for over-represented classes.

For more on SMOTE data sampling, see the tutorial:

• SMOTE for Imbalanced Classification with Python

It also includes more advanced combined over- and undersampling techniques that attempt to identify and remove ambiguous examples along the decision boundary of a classification problem and remove them or change their class label.

For more on these types of data preparation, see the tutorial:

• How to Combine Oversampling and Undersampling for Imbalanced Classification

This class of data preparation techniques also includes algorithms for identifying and removing outliers from the data. These are rows of data that may be far from the center of probability mass in the dataset and, in turn, may be unrepresentative of the data from the domain.

For more on outlier detection and removal methods, see the tutorial:

• How to Remove Outliers for Machine Learning

Data Preparation for Columns

This group is for data preparation techniques that add or remove columns of data.

In machine learning, columns are often referred to as variables or features.

These techniques are often required to either reduce the complexity (dimensionality) of a prediction problem or to unpack compound input variables or complex interactions between features.

The main class of techniques that come to mind are feature selection techniques.

This includes techniques that use statistics to score the relevance of input variables to the target variable based on the data type of each.

For more on these types of data preparation techniques, see the tutorial:

• How to Choose a Feature Selection Method for Machine Learning

This also includes feature selection techniques that systematically test the impact of different combinations of input variables on the predictive skill of a machine learning model.

For more on these types of methods, see the tutorial:

• Recursive Feature Elimination (RFE) for Feature Selection in Python

Related are techniques that use a model to score the importance of input features based on their use by a predictive model, referred to as feature importance methods. These methods are often used for data interpretation, although they can also be used for feature selection.

For more on these types of methods, see the tutorial:

• How to Calculate Feature Importance With Python

This group of methods also brings to mind techniques for creating or deriving new columns of data, new features. These are often referred to as feature engineering, although sometimes the whole field of data preparation is referred to as feature engineering.

For example, new features that represent values raised to exponents or multiplicative combinations of features can be created and added to the dataset as new columns.

For more on these types of data preparation techniques, see the tutorial:

• How to Use Polynomial Feature Transforms for Machine Learning

This might also include data transforms that change a variable type, such as creating dummy variables for a categorical variable, often referred to as a one-hot encoding.

For more on these types of data preparation techniques, see the tutorial:

• Ordinal and One-Hot Encodings for Categorical Data

Data Preparation for Values

This group is for data preparation techniques that change the raw values in the data.

These techniques are often required to meet the expectations or requirements of specific machine learning algorithms.

The main class of techniques that come to mind is data transforms that change the scale or distribution of input variables.

For example, data transforms such as standardization and normalization change the scale of numeric input variables. Data transforms like ordinal encoding change the type of categorical input variables.

There are also many data transforms for changing the distribution of input variables.

For example, discretization or binning change the distribution of numerical input variables into categorical variables with an ordinal ranking.

For more on this type of data transform, see the tutorial:

• How to Use Discretization Transforms for Machine Learning

The power transform can be used to change the distribution of data to remove a skew and make the distribution more normal (Gaussian).

For more on this method, see the tutorial:

• How to Use Power Transforms for Machine Learning

The quantile transform is a flexible type of data preparation technique that can map a numerical input variable or to different types of distributions such as normal or Gaussian.

You can learn more about this data preparation technique here:

• How to Use Quantile Transforms for Machine Learning

Another type of data preparation technique that belongs to this group are methods that systematically change values in the dataset.

This includes techniques that identify and replace missing values, often referred to as missing value imputation. This can be achieved using statistical methods or more advanced model-based methods.

For more on these methods, see the tutorial:

• Statistical Imputation for Missing Values in Machine Learning

All of the methods discussed could also be considered feature engineering methods (e.g. fitting into the previously discussed group of data preparation methods) if the results of the transforms are appended to the raw data as new columns.

Data Preparation for Columns + Values

This group is for data preparation techniques that change both the number of columns and the values in the data.

The main class of techniques that this brings to mind are dimensionality reduction techniques that specifically reduce the number of columns and the scale and distribution of numerical input variables.

This includes matrix factorization methods used in linear algebra as well as manifold learning algorithms used in high-dimensional statistics.

For more information on these techniques, see the tutorial:

• Introduction to Dimensionality Reduction for Machine Learning

Although these techniques are designed to create projections of rows in a lower-dimensional space, perhaps this also leaves the door open to techniques that do the inverse. That is, use all or a subset of the input variables to create a projection into a higher-dimensional space, perhaps decompiling complex non-linear relationships.

Perhaps polynomial transforms where the results replace the raw dataset would fit into this class of data preparation methods.

Do you know of other methods that fit into this group?
Let me know in the comments below.

Data Preparation for Rows + Values

This group is for data preparation techniques that change both the number of rows and the values in the data.

I have not explicitly considered data transforms of this type before, but it falls out of the framework as defined.

A group of methods that come to mind are clustering algorithms where all or subsets of rows of data in the dataset are replaced with data samples at the cluster centers, referred to as cluster centroids.

Related might be replacing rows with exemplars (aggregates of rows) taken from specific machine learning algorithms, such as support vectors from a support vector machine, or the codebook vectors taken from a learning vector quantization.

Naturally, these aggregate rows are simply added to the dataset rather than replacing rows, then they would naturally fit into the “Data Preparation for Rows” group described above.

Do you know of other methods that fit into this group?
Let me know in the comments below.

Further Reading

This section provides more resources on the topic if you are looking to go deeper.

Books

• Feature Engineering and Selection, 2019.
• Feature Engineering for Machine Learning, 2018.

 

 

The combination of monopolistic media control and more recent censorship on social medias is having a terrible and toxic effect on debate, not only social but scientific as well. 

One by one, we are sawing the branches on which our society is built. The market was first to go in the aftermath of the 2008 crisis. Followed by politics in 2016 with the outrageous treatment of the president of the United States as soon as it became clear that he would challenge the deep state. And now science whenever it does not fold to political diktat. 

Most people do not realize it yet since on the surface everything looks quiet still but underwater, fundamental current shifts are taking place.

The Pandemic That Killed Debate  

Authored by Stacey Rudin via The American Institute for Economic Research,

Carl Sagan famously said, “the cure for a fallacious argument is a better argument, not the suppression of ideas.” This wisdom has been sadly forsaken during the COVID19 pandemic, when one powerful narrative has taken not only the public, but the scientific community, by storm.

The story is that societies cannot survive the pandemic without society-wide lockdowns until we have a vaccine, despite the fact that we have never had a vaccine for a coronavirus, vaccines usually take many years to develop, and many of them are not all that effective once made.

Penetrating this narrative has been incredibly difficult even for impeccably credentialed scientists.

One might even say that this pandemic killed scientific debate.

Even as evidence proving that lockdowns do not stop the virus rolls in by the truckload, the scientists who argue for a different approach are marginalized, censored, affixed with disparaging labels, and ostracized. Sweden’s chief epidemiologist Anders Tegnell was accused of “leading Sweden to catastrophe” and of “experimenting” on the Swedish people. Nobel Laureate Michael Levitt’s careful studies and models were labeled “lethal nonsense” as he weathered attacks left, right and center. John Ioannidis, one of the world’s most productive scientists, found his studies smeared and ignored. Sunetra Gupta, one of the world’s foremost epidemiologists at The University of Oxford, found that expressing her wide-ranging infectious disease knowledge suddenly made her “unethical and dangerous.”

The latest smear target is neuroradiologist and health policy expert Dr. Scott Atlas, formerly of Stanford.

A longtime lockdown dissenter, his principal and latest offense seems to be agreeing to serve on The White House’s coronavirus task force, although Anthony Fauci - a researcher who funds grants, and who is not a public health expert - is permitted to do so without adverse media coverage. Where Dr. Atlas and Dr. Fauci differ is in their fundamental approach to the virus:

Fauci believes we can never return to normal, while Atlas believes all low-risk groups should do just that, with protective measures targeted towards vulnerable populations.

Atlas believes epidemics end with herd immunity, while Fauci apparently believes they end if you lock down well enough for long enough, and then fundamentally change your way of life because you now have the insight that more pandemics will occur.

Many of Atlas’s former Stanford colleagues publicly took issue with his age-focused pandemic management strategy on September 9, when 98 of them signed a letter leveling the serious accusation of “[fostering] falsehoods and misrepresentations of science.” Omitted from the letter are the alleged misrepresentations and lies, “making scientific discourse difficult.” This injustice was noted by infectious disease expert Martin Kulldorff of Harvard Medical School, who responded with his own letter published - not without some gentle prodding — in the Stanford Daily on September 16. Kulldorff explained his longstanding agreement with Atlas’s position that an age-targeted strategy is needed to minimize casualties as well as collateral damage during the pandemic - “the most compassionate approach . . . is to allow those who are at minimal risk of death to live their lives normally to build up immunity to the virus through natural infection”— and invited the letter’s signatories to publicly debate this strategy.

Among experts on infectious disease outbreaks, many of us have long advocated for an age-targeted strategy, and I would be delighted to debate this with any of the 98 signatories. Supporters include professor Sunetra Gupta at Oxford University, the world’s preeminent infectious disease epidemiologist. Assuming no bias against women scientists of color, I urge Stanford faculty and students to read her thoughts.

Professor Kulldorff received no reply to this offer, so The Soho Forum — a highly respected debate platform — took up the case, personally inviting the scientists to participate in an online, one-on-one debate via Zoom, taking the negative on this resolution:

To minimize mortality and optimize public health, the U.S. should implement a targeted coronavirus strategy that better protects the old and other high-risk groups, while letting children and young adults live close to normal lives.

This offer was emailed to Dr. Philip Pizzo, the chief signatory of Stanford’s letter in opposition to Atlas, who replied simply:

“Thank you for the invitation. We have conveyed what we have to say in our letter and do not have additional comments to offer.”

From both a public policy and scientific standpoint, this blanket refusal to engage in discourse is concerning.

When someone can level an accusation of dishonesty at a public figure, refuse to debate the substance with the accused, and suffer no consequences for this behavior, this stifles the free expression of opinions and ideas. This is not good for anyone except entities trying to control a self-serving narrative, which never turns out well for anyone else — especially those with the least power.

The best system for a humane and compassionate society is one that encourages the free expression of ideas. This practice must be encouraged and rewarded, not stifled and penalized. Ideas should be openly expressed, disagreement voiced, and the undecided parties credited with the intelligence they possess: they listen to both sides, and come to their own conclusions. The alternative — some narrative-maker decides the information that will be provided, withholds contradictory relevant information, and forbids the defense from speaking at all— is fascism. It is tyranny. It is certainly not American. Americans have always known that it is dangerous to restrict debate while placing “authority” in one person or entity: that is why our government is built on checks and balances, on divided bodies of congress, on term limits and the electoral college and and separation of powers.

Experts differ and disagree, on every subject. Intelligent people, coming from various backgrounds and with all manner of life and professional experience, will choose their own side, and once this goes on for long enough, the correct result will arrive. Neither public policy or science is ever completely settled, so the restriction of debate hurts everyone. The voicing of innovative ideas and solutions is what helps us.

We should celebrate people like Scott Atlas who are willing to take the unpopular, minority view — maybe we can learn from them. We should pay careful attention once we know their opponents will not only sling mud, but will not even appear for a debate.

Planned in Advance by Central Banks – A 2020 System Reset

 

Some conspiracy theories are fanciful. Area 51 and ancient advanced civilizations clearly belong to the ghosts and vampires corner of the library. Other theories are less clear and almost always contain some truth when concerning human affairs.  Or to paraphrase Adam Smith: "When powerful men meet, they conspire".

Nowhere is it more true than in finance where decisions are not counted in millions or even billions but in percents of the economy, i.e. trillions. 

It also happens that the fiat money system born in August 1971 when Nixon broke the link between gold and the US dollar is reaching the end of the road with actual value close to zero. Could there be a better reason than the necessity of a global monetary reset for the pandemic circus we have seen in 2020 with the accompanying propaganda and silencing of dissent? 

This article makes a direct link between the gold market over the last few years as a harbinger of things to come and the need to rebuild the US dominated monetary system. 

We are now less than 3 months away from the end of 2020. Will the reset be on schedule? I do not think it matters. The people who control the system are intelligent and flexible. They know how to seize an opportunity when it presents itself and when to engineer one when it does not. They also know how to prepare themselves since they have per definition advance knowledge of what comes next. Which is why timing does not matter.

So what can we do about it? Nothing! Our only possible satisfaction is to understand, as they do, how the system works and witness history in the making. In this respect, the next 12 months will most certainly be spectacular.

 

Planned in Advance by Central Banks – A 2020 System Reset

Submitted by Ronan Manly, BullionStar.com

As early as 2015, I put forward the idea that the year 2020 looked to be a leading contender for a monetary system Reset. 

I reached that conclusion based on a trend I had spotted in the gold repatriation timelines announced by a number of European central banks beginning in 2013. And who better to know the inside plans for a future and much needed reset of the financial system than the world’s most powerful central banks, the unaccountable and secretive institutions where gold is at the heart of their balance sheets, and would be the natural and reserve asset anchor in any new international monetary system.

Besides, the global financial crisis that began in 2007/2008 never really ended. It was merely patched up, prolonged, and put on life support by central bank interventions in the form of unceasing quantitative easing (QE), asset buying, and artificially low interest rates. 

From Frankfurt to Vienna 

The first piece of evidence was the by now famous gold repatriation exercise by Germany’s Bundesbank (Buba) to move nearly 700 tonnes of gold from the vaults of the New York Fed and Banque de France to Frankfurt, which the Bundesbank announced in a press release on 16 January 2013 as follows:

“By 2020, the Bundesbank intends to store half of Germany’s gold reserves in its own vaults in Germany. The other half will remain in storage at its partner central banks in New York and London" 

“To this end, the Bundesbank is planning a phased relocation of 300 tonnes of gold from New York to Frankfurt as well as an additional 374 tonnes from Paris to Frankfurt by 2020.“

This was followed on 28 May 2015 by an announcement from Austria’s central bank, the Oesterreichische Nationalbank (OeNB), that it too would repatriate gold stored in London back to its vaults in Vienna in what it referred to as the adoption of a ‘2020 gold storage policy’. To wit: 

“The OeNB adopts 2020 gold storage policy:

Recently, the Governing Board of the OeNB adopted the 2020 gold storage policy  

By the year 2020, 50% of Austria’s gold reserves are to be held in Austria (OeNB and Münze Österreich AG), 30% in London and 20% in Switzerland.“

To me, the OeNB’s 2020 plan following a similar statement by its German neighbour BuBa suggested a coordinated strategy by these central banks to regain control of as much gold reserve assets as they could in advance of a pre-planned system reset deadline of 2020. 

Which is why on the same day, on 28 May 2015, more than 5 years ago, I pointed out this in a Tweet:

2020 System Reset – Tweet from 28 May 2015   

In February 2016, while attending the World Money Fair in Berlin, we recorded a BullionStar Perspectives video interview about central banking trends, in which I again referred to this 2020 System Reset. See the 1 minute video segment below for the relevant discussion:

BullionStar Perspectives interview 2016 – Referring to Central Banks and a System Reset in 2020. Source 

In August 2017, the German Bundesbank in a press release again tellingly referred to the hard and fast 2020 deadline announcing that it had completed its gold repatriation from New York and Paris ahead of schedule:

“Bundesbank completes gold transfer ahead of schedule  

50.6 per cent of Germany’s gold reserves are now stored in Germany. This goal was set out by the Bundesbank in 2013 and was scheduled to be achieved by 2020 at the latest. “This closes out the entire gold storage plan – around three years ahead of the time we were aiming for,” said Bundesbank Executive Board member Carl-Ludwig Thiele to representatives of the press."

“At the latest" and “ahead of the time we were aiming for" – Unusually precise and urgent wording, but illuminating and logical when interpreted in the context of a pre-planned 2020 System Reset deadline. It was likewise with Germany’s neighbour to the south, where in March 2019, the Austrian central bank announced that: 

“Regarding the milestones reached in 2018, Governor Nowotny pointed out that “the repatriation of gold reserves to Austria was completed in 2018, ahead of schedule.'"

There’s that exact phrase again “ahead of schedule". Here we see that both banks had a schedule of 2020, and were relieved to have achieved their repatriations ahead of this 2020 schedule. 

Dutch jump the Gun – In Secret

But it wasn’t just the central banks of Germany and Austria who were busy planning gold repatriating in the 2013-2015 period ahead of the 2020 deadline. In Germany’s neighbour to the north-west, the Dutch central bank, De Nederlandsche Bank (DNB), was, over October and November 2014, secretively repatriating 122.5 tonnes of gold from the vaults of the New York Fed to the DNB vaults in Amsterdam, but only announcing the gold transfers on 21 November 2014, after they had been completed. 

Positioned between the 2013 Bundesbank and 2015 OeNB repatriation announcements, both of which referenced the 2020 deadline, the Dutch gold transfers of 2014 make most sense when seen in terms of the same motivations, but given that the gold transfers were executed in secret and in a very short timeframe, there was no advance announcement.

However, in one of the most explicit statements of central banks in explaining why it holds substantial physical gold reserves, DNB tellingly commented on its website in April 2019 that gold plays the role in rebuilding a collapsed finanical system: 

“Gold is …the anchor of trust for the financial system. If the system collapses, the gold stock can serve as a basis to build it up again. Gold bolsters confidence in the stability of the central bank’s balance sheet and creates a sense of security."

Troops guarding central bank of Hungary’s gold repatriated from London 

Poland and Hungary Follow Suit

Beyond the Dutch, let’s not forget Germany’s neighbour to the east, Poland, which while late to the central European gold repatriation club, made up for lost time in the first half of 2019 when the Polish central bank, the National Bank of Poland (NBP), announced a massive purchase of 100 tonnes of gold at the Bank of England, after which it promptly flew all of this 100 tonnes of gold back to Warsaw in a series of air transfers, the last of which was in November 2019. 

The rationale of the Polish central bank in doing this was, in its own words, because:

“Gold is the ‘most reserve’ reserve asset: it … is a kind of confidence anchor, especially in times of tensions and crises. …Gold secures Poland’s financial strength even in extremely unfavorable conditions." 

The Polish operation followed similar moves in October 2018 by Austria’s neighbour to the east, the Hungarian central bank, Magyar Nemzeti Bank (MNB), where the MNB increased its gold reserves ten-fold from 3.1 tonnes to 31.5 tonnes with the purchase of 28.4 tonnes of gold in London, and in the following weeks, promptly repatriated all of this gold back to Hungary. Tellingly, in its October 2018 comment, the Hungarian central bank also made reference to structural changes in the monetary system:

“When raising domestic gold reserves to 31.5 tonnes, the MNB paid attention to the international and regional role played by gold in central bank reserves. 

While gold has a confidence-building effect in normal times, and can play a role in stabilizing and defending, it is also a benefit in extreme market environments, deeper geopolitical crises, and structural changes in the international financial system.“

As Germany’s and Austria’s contiguous neighbours and close associates, Poland and Hungary would be in an ideal position to observe the behind the scenes operations of the Bundesbank and Austrian central bank in preparing for a system reset and would naturally want to also augment their gold reserve assets in advance of such a reset. 

A Widely Known Plan among Top Central Bankers? 

Beyond the central European gold repatriation club, the board of directors of the Bank for International Settlements (BIS) in Basel, Switzerland and the Ministers of Finance and central bank Governors of the Group of Ten (G10) which is also operated from the BIS offices in Basel, would be privy to a 2020 deadline for a planned financial system reset. Which is why their meeting briefs and meeting minutes are classified top secret and will never in your lifetime see the light of day.

The secretive and unaccountable Bank for International Settlements (BIS), Switzerland, the central bankers’ central bank.

Another hint that preparations for a global reset were being made in advance of 2020 came from the surprise announcement at the end of March by the Bank of Russia, Russia’s central bank, that it would abruptly cease buying gold for Russia’s sovereign gold reserves, buying activity that it had relentlessly pursued for the previous 12 years since 2008.

As to how widespread central bank preparations over the last decade for a reset involving gold have been is not clear, but those preparations may have even followed the approach put forward by Paul Brodsky and Lee Quaintance in May 2012, in which they described central banks and governments working behind the scenes in a coordinated re-distribution of the world’s monetary gold reserves among themselves in a more equitable manner. Behind the scenes gold redistribution is an intriguing possibility, and would explain why the large central bank gold holders continue to have large portions on their gold holdings still stored in foreign vaults in New York and London. 

The Reset Trigger – The Covid Plandemic 

While gold repatriation in the central bank sphere from 2013 to 2019 was a clue of what lay ahead in terms of Reset preparedness, all the while the existing financial system continued to be propped up with accelerating quantitative easing and increasing intervention. As 2020 approached I again proposed the reset theory based on the evidence of gold repatriation activity, and tweeted this, in fact on 30 December 2019.

But what would be the Reset trigger that the central banks were waiting for? As 2020 began, we did not have to wait long to find out, for within a short few weeks, as if on cue, the Deus ex machina trigger conveniently arrived in the form of the now evidently orchestrated Covid plandemic, a WHO coordinated endgame play announced in Geneva, Switzerland on 11 March, which has allowed the world’s most powerful central banks such as the US Fed, ECB, Bank of England and Bank of Japan to engage in ‘all in’ interventions one last time so as to prop up the debt and derivative laden financial system, while providing the cover to tee up the monetary Reset.

Full details of these central bank interventions, which began in March, can be read about here in a BullionStar article from 2 May, but some quotes from that May article are provided for background:

“QE COVID 

Over the last two months, major central banks and governments across the globe have unleashed a series of monetary and fiscal interventions on markets and economies which are unprecedented in their magnitude and which are boarding on the destruction of the current financial system.

While the global spread of coronavirus COVID-19 provided the trigger and the pretext for the current full-spectrum quantitative easing, money printing, asset purchases and economic bailouts, the size and scope of the current assault on free markets makes all previous central bank and government interventions look insignificance in comparison“ 

As to the Reset, the shaping of public opinion has now been put into play by that other elitist Swiss based elitist institution, the World Economic Forum (WEF) (of Davos meeting fame), which conveniently had waiting in the wings, its ‘Great Reset’ strategy, released to the public on 3 June 2020 from its headquarters in Geneva.

Minutely planned and highly detailed, the WEF Great Reset strategy is an elitist blueprint to usher in sweeping forms of global population control using the Covid plandemic as a smokescreen. In much the same way, the world’s central banks have now used the Covid trigger as a smokescreen to prop up financial markets one last time before they usher in their ‘planned in advance’ monetary Reset. 

If top central bankers not only knew of a 2020 Reset and its Covid trigger, but actively planned years in advance with prior knowledge to prepare for such a reset event, the legal implications are grave and serious. Only time will tell.

The last word for now goes to German physician, Dr. Heiko Schöning, who, speaking from London on 27 September, succinctly explains how the creation of the Covid plandemic points to powerful banks and their private controlling interests, while being used by these powerful banks to reset the financial system.

How "democracy" really works nowadays

 

If you still believe that you leave in a "Democratic" system this article is a must read. This is not a "political" leaflet from a few communists trying to undermine the "system". Just a description of what the US political machine has become and how it really works nowadays.

“There are Trillions at Stake.”

Posted on October 3, 2020 by sundance

 https://www.theburningplatform.com/2020/10/04/there-are-trillions-at-stake/

With 30-days left before the election perhaps it’s worthwhile remembering what all of this opposition is about…. Something 99% of American voters do not quite understand.

Congress doesn’t actually write legislation. The last item of legislation written by congress was sometime around the mid 1990’s. Modern legislation is sub-contracted to a segment of DC operations known as K-Street. That’s where the lobbyists reside.

Lobbyists write the laws; congress sells the laws; lobbyists then pay congress lucrative commissions for passing their laws. That’s the modern legislative business in DC.

When we talk about paying-off politicians in third-world countries we call it bribery. However, when we undertake the same process in the U.S. we call it “lobbying”.

CTH often describes the system with the phrase: “There are Trillions at Stake.” The process of creating legislation is behind that phrase. DC politics is not quite based on the ideas that frame most voter’s reference points.

With people taking notice of DC politics for the first time; and with people not as familiar with the purpose of DC politics; perhaps it is valuable to provide clarity.

Most people think when they vote for a federal politician -a House or Senate representative- they are voting for a person who will go to Washington DC and write or enact legislation. This is the old-fashioned “schoolhouse rock” perspective based on decades past. There is not a single person in congress writing legislation or laws.

In modern politics not a single member of the House of Representatives or Senator writes a law, or puts pen to paper to write out a legislative construct. This simply doesn’t happen.

Over the past several decades a system of constructing legislation has taken over Washington DC that more resembles a business operation than a legislative body. Here’s how it works right now.

Outside groups, often called “special interest groups”, are entities that represent their interests in legislative constructs. These groups are often representing foreign governments, Wall Street multinational corporations, banks, financial groups or businesses; or smaller groups of people with a similar connection who come together and form a larger group under an umbrella of interest specific to their affiliation.

Sometimes the groups are social interest groups; activists, climate groups, environmental interests etc. The social interest groups are usually non-profit constructs who depend on the expenditures of government to sustain their cause or need.

The for-profit groups (mostly business) have a purpose in Washington DC to shape policy, legislation and laws favorable to their interests. They have fully staffed offices just like any business would – only their ‘business‘ is getting legislation for their unique interests.

These groups are filled with highly-paid lawyers who represent the interests of the entity and actually write laws and legislation briefs.

In the modern era this is actually the origination of the laws that we eventually see passed by congress. Within the walls of these buildings within Washington DC is where the ‘sausage’ is actually made.

Again, no elected official is usually part of this law origination process.

Almost all legislation created is not ‘high profile’, they are obscure changes to current laws, regulations or policies that no-one pays attention to. The passage of the general bills within legislation is not covered in media. Ninety-nine percent of legislative activity happens without anyone outside the system even paying any attention to it.

Once the corporation or representative organizational entity has written the law they want to see passed – they hand it off to the lobbyists.

The lobbyists are people who have deep contacts within the political bodies of the legislative branch, usually former House/Senate staff or former House/Senate politicians themselves.

The lobbyist takes the written brief, the legislative construct, and it’s their job to go to congress and sell it.

“Selling it” means finding politicians who will accept the brief, sponsor their bill and eventually get it to a vote and passage. The lobbyist does this by visiting the politician in their office, or, most currently familiar, by inviting the politician to an event they are hosting. The event is called a junket when it involves travel.

Often the lobbying “event” might be a weekend trip to a ski resort, or a “conference” that takes place at a resort. The actual sales pitch for the bill is usually not too long and the majority of the time is just like a mini vacation etc.

The size of the indulgence within the event, the amount of money the lobbyist is spending, is customarily related to the scale of benefit within the bill the sponsoring business entity is pushing. If the sponsoring business or interest group can gain a lot of financial benefit from the legislation they spend a lot on the indulgences.

Recap: Corporations, mostly modern multinationals (special interest group), write the legislation. The corporations then contract the lobbyists.  Lobbyists then take the law and go find politician(s) to support it. Politicians get support from their peers using tenure and status etc. Eventually, if things go according to norm, the legislation gets a vote.

Within every step of the process there are expense account lunches, dinners, trips, venue tickets and a host of other customary financial way-points to generate/leverage a successful outcome. The amount of money spent is proportional to the benefit derived from the outcome.

The important part to remember is that the origination of the entire process is EXTERNAL to congress.

Congress does not write laws or legislation, special interest groups do. Lobbyists are paid, some very well paid, to get politicians to go along with the need of the legislative group.

When you are voting for a Congressional Rep or a U.S. Senator you are not voting for a person who will write laws. Your rep only votes on legislation to approve or disapprove of constructs that are written by outside groups and sold to them through lobbyists who work for those outside groups.

While all of this is happening the same outside groups who write the laws are providing money for the campaigns of the politicians they need to pass them. This construct sets up the quid-pro-quo of influence, although much of it is fraught with plausible deniability.

This is the way legislation is created.

If your frame of reference is not established in this basic understanding you can often fall into the trap of viewing a politician, or political vote, through a false prism.

The modern origin of all legislative constructs is not within congress.

“We have to pass the bill to, well, find out what is in the bill” etc. ~ Nancy Pelosi 2009

“We rely upon the stupidity of the American voter” ~ Johnathan Gruber 2011, 2012.

“If Congress isn’t going to convene until the bill is ready to vote on… who the hell is writing the bill?” ~ Tom Massie, 2020

Once you understand this process you can understand how politicians get rich.

When a House or Senate member becomes educated on the intent of the legislation, they have attended the sales pitch; and when they find out the likelihood of support for that legislation; they can then position their own (or their families) financial interests to benefit from the consequence of passage. It is a process similar to insider trading on Wall Street, except the trading is based on knowing who will benefit from a legislative passage.

The legislative construct passes from K-Street into the halls of congress through congressional committees. The law originates from the committee to the full House or Senate. Committee seats which vote on these bills are therefore more valuable to the lobbyists. Chairs of these committees are exponentially more valuable.

Now, think about this reality against the backdrop of the 2016 Presidential Election. Legislation is passed based on ideology. In the aftermath of the 2016 election the system within DC was not structurally set-up to receive a Donald Trump presidency.

If Hillary Clinton had won the election, her Oval Office desk would be filled with legislation passed by congress which she would have been signing. Heck, she’d have writer’s cramp from all of the special interest legislation, driven by special interest groups that supported her campaign, that would be flowing to her desk.

Why?

Simply because the authors of the legislation, the originating special interest and lobbying groups, were spending millions to fund her campaign. Hillary Clinton would be signing K-Street constructed special interest legislation to repay all of those donors/investors.

Congress would be fast-tracking the passage because the same interest groups also fund the members of congress.

President Donald Trump winning the election threw a monkey wrench into the entire DC system…. In early 2017 the modern legislative machine was frozen in place.

The “America First” policies represented by candidate Donald Trump were not within the legislative constructs coming from the K-Street authors of the legislation. There were no MAGA lobbyists waiting on Trump ideology to advance legislation based on America First objectives.

As a result of an empty feeder system, in early 2017 congress had no bills to advance because all of the myriad of bills and briefs written were not in line with President Trump policy. There was simply no entity within DC writing legislation that was in-line with President Trump’s America-First’ economic and foreign policy agenda.

Exactly the opposite was true. All of the DC legislative briefs and constructs were/are antithetical to Trump policy. There were hundreds of file boxes filled with thousands of legislative constructs that became worthless when Donald Trump won the election.

Those legislative constructs (briefs) representing tens of millions of dollars worth of time and influence were just sitting there piled up in boxes under desks and in closets amid K-Street and the congressional offices. Legislation needed to be in-line with an entire new political perspective, and there was no-one, no special interest or lobbying group, currently occupying DC office space with any interest in synergy with Trump policy.

Think about the larger ramifications within that truism. That is also why there was/is so much opposition.

No legislation provided by outside interests means no work for lobbyists who sell it. No work means no money. No money means no expense accounts. No expenses means politicians paying for their own indulgences etc.

Politicians were not happy without their indulgences, but the issue was actually bigger. No K-Street expenditures also means no personal benefit; and no opportunity to advance financial benefit from the insider trading system. Republicans and democrats hate the presidency of Donald Trump because it is hurting them financially.

President Trump is not figuratively hurting the financial livelihoods of DC politicians; he’s literally doing it. President Trump is not an esoteric problem for them; his impact is very real, very direct, and hits almost every politician in the most painful place imaginable, the bank account.

In the pre-Trump process there were millions upon millions, even billions that could be made by DC politicians and their families. Thousands of very indulgent and exclusive livelihoods attached to the DC business model. At the center of this operation is the lobbying and legislative purchase network. The Big Club.

Without the ability to position personal wealth and benefit from the system, why would a politician stay in office? It is a fact the income of many long-term politicians on both wings of the uniparty bird were completely disrupted by Trump winning the 2016 election. That is one of the key reasons why so many politicians retired in 2018.

When we understand the business of DC, we understand the difference between legislation with a traditional purpose and modern legislation with a financial and political agenda.

When we understand the business of DC we understand why the entire network hates President Donald Trump.

Lastly, this is why -when signing legislation- President Trump often says “they’ve been trying to get this through for a long time” etc. Most of the legislation that is passed by congress, and signed by President Trump in his first term; is older legislative proposals, with little indulgent value that were shelved in years past.

Example: Criminal justice reform did not carry a financial benefit to the legislative bodies, and there was no financial interest funding the politicians to pass the bill. If you look at most of the bills President Trump has signed, with the exception of a few economic bills, they stem from congressional construction many years, even decades, ago.

Think about it carefully and you’ll see it. The “First step act”, “Right to Try”, etc. were all shelved by Boehner, Pelosi, Ryan, McConnell, Reid and others before them. When the value of legislation is measured by the financial underwriting and payoffs behind it, what type of legislative calendar does that require?….

How Three Prior Pandemics Triggered Massive Societal Shifts

Fascinating examples of how 3 previous pandemics deeply transformed the societies of their time. The scope of Covid-19 is not comparable to these pandemics but its timing right at the end of a long societal growth cycle means that the impact may be just as deep.

 

How Three Prior Pandemics Triggered Massive Societal Shifts

Authored by Andrew Latham via TheConversation.com,

Before March of this year, few probably thought disease could be a significant driver of human history.

Not so anymore. People are beginning to understand that the little changes COVID-19 has already ushered in or accelerated – telemedicine, remote work, social distancing, the death of the handshake, online shopping, the virtual disappearance of cash and so on – have begun to change their way of life. They may not be sure whether these changes will outlive the pandemic. And they may be uncertain whether these changes are for good or ill.

Three previous plagues could yield some clues about the way COVID-19 might bend the arc of history. As I teach in my course “Plagues, Pandemics and Politics,” pandemics tend to shape human affairs in three ways.

First, they can profoundly alter a society’s fundamental worldview.

Second, they can upend core economic structures.

And, finally, they can sway power struggles among nations.

Sickness spurs the rise of the Christian West

The Antonine plague, and its twin, the Cyprian plague – both now widely thought to have been caused by a smallpox strain – ravaged the Roman Empire from A.D. 165 to 262. It’s been estimated that the combined pandemics’ mortality rate was anywhere from one-quarter to one-third of the empire’s population.

While staggering, the number of deaths tells only part of the story. This also triggered a profound transformation in the religious culture of the Roman Empire.

On the eve of the Antonine plague, the empire was pagan. The vast majority of the population worshipped multiple gods and spirits and believed that rivers, trees, fields and buildings each had their own spirit.

Christianity, a monotheistic religion that had little in common with paganism, had only 40,000 adherents, no more than 0.07% of the empire’s population.

Yet within a generation of the end of the Cyprian plague, Christianity had become the dominant religion in the empire.

How did these twin pandemics effect this profound religious transformation?

Rodney Stark, in his seminal work “The Rise of Christianity,” argues that these two pandemics made Christianity a much more attractive belief system.

While the disease was effectively incurable, rudimentary palliative care – the provision of food and water, for example – could spur recovery of those too weak to care for themselves. Motivated by Christian charity and an ethic of care for the sick – and enabled by the thick social and charitable networks around which the early church was organized – the empire’s Christian communities were willing and able to provide this sort of care.

Pagan Romans, on the other hand, opted instead either to flee outbreaks of the plague or to self-isolate in the hope of being spared infection.

This had two effects.

First, Christians survived the ravages of these plagues at higher rates than their pagan neighbors and developed higher levels of immunity more quickly. Seeing that many more of their Christian compatriots were surviving the plague – and attributing this either to divine favor or the benefits of the care being provided by Christians – many pagans were drawn to the Christian community and the belief system that underpinned it. At the same time, tending to sick pagans afforded Christians unprecedented opportunities to evangelize.

Second, Stark argues that, because these two plagues disproportionately affected young and pregnant women, the lower mortality rate among Christians translated into a higher birth rate.

The net effect of all this was that, in roughly the span of a century, an essentially pagan empire found itself well on its way to becoming a majority Christian one.

The plague of Justinian and the fall of Rome

The plague of Justinian, named after the Roman emperor who reigned from A.S. 527 to 565, arrived in the Roman Empire in A.D. 542 and didn’t disappear until A.D. 755. During its two centuries of recurrence, it killed an estimated 25% to 50% of the population – anywhere from 25 million to 100 million people.

This massive loss of lives crippled the economy, triggering a financial crisis that exhausted the state’s coffers and hobbled the empire’s once mighty military.

In the east, Rome’s principal geopolitical rival, Sassanid Persia, was also devastated by the plague and was therefore in no position to exploit the Roman Empire’s weakness. But the forces of the Islamic Rashidun Caliphate in Arabia – which had long been contained by the Romans and Sasanians – were largely unaffected by the plague. The reasons for this are not well understood, but they probably have to do with the caliphate’s relative isolation from major urban centers.

Caliph Abu Bakr didn’t let the opportunity go to waste. Seizing the moment, his forces swiftly conquered the entire Sasanian Empire while stripping the weakened Roman Empire of its territories in the Levant, the Caucasus, Egypt and North Africa.

Pre-pandemic, the Mediterranean world had been relatively unified by commerce, politics, religion and culture. What emerged was a fractured trio of civilizations jockeying for power and influence: an Islamic one in the eastern and southern Mediterranean basin; a Greek one in the northeastern Mediterranean; and a European one between the western Mediterranean and the North Sea.

This last civilization – what we now call medieval Europe – was defined by a new, distinctive economic system.

Before the plague, the European economy had been based on slavery. After the plague, the significantly diminished supply of slaves forced landowners to begin granting plots to nominally “free” laborers – serfs who worked the lord’s fields and, in return, received military protection and certain legal rights from the lord.

The seeds of feudalism were planted.

The Black Death of the Middle Ages

The Black Death broke out in Europe in 1347 and subsequently killed between one-third and one-half of the total European population of 80 million people. But it killed more than people. By the time the pandemic had burned out by the early 1350s, a distinctly modern world emerged – one defined by free labor, technological innovation and a growing middle class.

Before the Yersinia pestis bacterium arrived in 1347, Western Europe was a feudal society that was overpopulated. Labor was cheap, serfs had little bargaining power, social mobility was stymied and there was little incentive to increase productivity.

But the loss of so much life shook up an ossified society.

Labor shortages gave peasants more bargaining power. In the agrarian economy, they also encouraged the widespread adoption of new and existing technologies – the iron plow, the three-field crop rotation system and fertilization with manure, all of which significantly increased productivity. Beyond the countryside, it resulted in the invention of time and labor-saving devices such as the printing press, water pumps for draining mines and gunpowder wea turn, freedom from feudal obligations and a desire to move up the social ladder encouraged many peasants to move to towns and engage in crafts and trades. The more successful ones became wealthier and constituted a new middle class. They could now afford more of the luxury goods that could be obtained only from beyond Europe’s frontiers, and this stimulated both long-distance trade and the more efficient three-masted ships needed to engage in that trade.

The new middle class’s increasing wealth also stimulated patronage of the arts, science, literature and philosophy. The result was an explosion of cultural and intellectual creativity – what we now call the Renaissance.

Our present future

None of this is to argue that the still-ongoing COVID-19 pandemic will have similarly earth-shattering outcomes. The mortality rate of COVID-19 is nothing like that of the plagues discussed above, and therefore the consequences may not be as seismic.

But there are some indications that they could be.

Will the bumbling efforts of the open societies of the West to come to grips with the virus shattering already-wavering faith in liberal democracy, creating a space for other ideologies to evolve and metastasize?

In a similar fashion, COVID-19 may be accelerating an already ongoing geopolitical shift in the balance of power between the U.S. and China. During the pandemic, China has taken the global lead in providing medical assistance to other countries as part of its “Health Silk Road” initiative. Some argue that the combination of America’s failure to lead and China’s relative success at picking up the slack may well be turbocharging China’s rise to a position of global leadership.

Finally, COVID-19 seems to be accelerating the unraveling of long-established patterns and practices of work, with repercussions that could affet the future of office towers, big cities and mass transit, to name just a few. The implications of this and related economic developments may prove as profoundly transformative as those triggered by the Black Death in 1347.

Ultimately, the longer-term consequences of this pandemic – like all previous pandemics – are simply unknowable to those who must endure them. But just as past plagues made the world we currently inhabit, so too will this plague likely remake the one populated by our grandchildren and great-grandchildren.