mike529

Introduction

Throughout the pandemic many people have been frustrated with public health guidance as either too strict, too loose, or simply incoherent. In this article I will argue that the public health guidance makes sense when viewed from the perspective or their actual goals, which are based on making people feel safe, even if it seems nonsensical when viewed from the perspective of risk mitigation.

Risk Mindset:

Many people, especially mathematically inclined ones, rely on a risk based framework for evaluating the proper COVID mitigation decisions. In this framework each mitigation has a cost associated with it (loss of quality of life, economic damages, etc.) as well as a corresponding benefit in terms of reduction in COVID deaths.

Mitigation	Cost	Benefit

mike529

Introduction:

Recently a paper was published which got attention by estimating that 100% mask wearing in the population would cause a 25% reduction in the effective transmission number (shortened to transmissiblity throughout).

This study was observational and so inferring causality is always difficult. Thanks to the excellent data availability I was able to replicate and attempt to validate the model.

Based on my analysis, this study does not provide good evidence for such a causal link, and there is evidence to believe that the observed correlation is spurious.

Model Details:

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Introduction:

The standard model for explaining COVID transmission has a serious problem with the data. In the United States, despite seemingly large differences in policy and behavior, the difference in infection rates has been relatively small.

In this article I will explain why this convergence is so surprising for the standard model and how a relatively small modification can give much more sensible results. I will also discuss some of the other suggested approaches and why they are not able to adequately solve this problem.

The Problem:

If we look at the infection data as of November 2021, before California and New York had their winter surge, we see widely varying COVID infection rates. Vermont, the least infected state, had less than 3% of its population infected. The most infected state (excluding ones with major outbreaks before lockdown) was North Dakota with almost 28% of its population infected.

mike529

Introduction:

In the first few months of the pandemic, the infection rates of COVID varied widely between different states. By November 1st, the least infected state, Vermont, had less than 3% of its population infected. The most infected state (excluding the ones with major outbreaks before lockdown) was North Dakota with nearly 28% of its population infected.

At the same time the approach and strictness of the lockdown in different states seemed to vary widely, and more strict states seemed to have better results than those which were more loose. Florida and California came to represent these two approaches. Neither had the best, or the worst results but they did show differences. From May to November, people in Florida spent about 20% more time outside the home, than those in California did. In turn Florida had about 20% of its population infected, while California only had 12% infected.

At first glance this seems perfectly natural, this large increase in mobility corresponds to Florida's looser restr

mike529

The Book of Esther opens with a seemingly bizarre story.

Ahasuerus (Xerxes I) is feasting with his ministers and asks his queen to come and show off her beauty. The queen refuses and in a rage Ahasuerus has her banished and decrees that all men should speak their own language, to better rule in their homes.

There seems to be little connection between the goal, that men should be rulers in their homes, and this decree. However groundbreaking archeological research by Dr. Icke helps us explain this connection, as well as many other smaller mysteries throughout the rest of the book.

Historians now believe that following his defeat by the Greeks, Xerxes returned to Persia in a paranoiac state, convinced that many of his closest confidantes were actually Anunnaki in disguise seeking to overthrow his regime. However nothing in the historical record indicates why he came to believe this.

Only by combin

mike529

Introduction:

In my previous article I discussed some of the basic theories about how COVID spreads and how it can be effectively fought. This article does not require having read that article but will refer to the homogenous SEIR model and heterogenous models.

This article will argue that our current strategy of complete suppression is infeasible and not justifiable. Instead we need to adapt our strategy and find a sustainable level of infections and lockdown restrictions until we have an effective vaccine.

Why We Need a New Strategy:

The success of Asian countries in quickly suppressing COVID has led to the hope that a similar strategy could work elsewhere. After a short and sharp lockdown, we could reopen with minimal restrictions and rely

mike529

Introduction:

Over the past six months of the COVID epidemic a sharp divide has emerged.

In one camp, most public health experts believe that the lockdowns in place are keeping us from unmitigated disaster and millions of deaths. In the other camp, a disparate group of scientists have argued that the lockdowns are unnecessary and causing far more damage than what would be caused by COVID if life was allowed to return mostly to normal.

Most people are aware of these two camps, and may have read some of the articles arguing in one direction or the other. However most articles are either at a very high level, and don't explain the specifics of the strategy and beliefs; Or they are at a very technical level, and are too bogged down with mathematics and terminology to be comprehensible.

In this article I want to blaze a middle path, providing enough detail to understand the beliefs and arguments of both the camps, while not being so technical. To aid in this goal I have developed interactive tools to visuali

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mike529

Questions arising from COVID Data:

In the course of the COVID-19 outbreak, there have been many features of the data which do not seem to have made sense.

In this article I will explain how the standard SEIR model used as the basis for most of the analysis fails to account for these features of the data. I will then propose a new model for understanding the disease's spread which more adequately explains these strange features.

Highly Different Spread:

Spain and Portugal started out with very different outbreaks, with Spain significantly more affected than Portugal. However after the lockdown they have converged to very similar growth rates. If there is some factor which makes Portugal less susceptible to the diseases, why has it not lead to the spread being lower while under lockdown?

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