by James Lyons-Weiler, PhD, Popular Rationalism, ©2024
(Apr. 15, 2024) — Biology has few laws. One of them, Farr’s Law, is violated when we try to control the spread of infectious diseases. Given the unpredictability of complex, non-linear dynamics, chaos could result…
When we think about the effects of non-sterilizing vaccination in herds of animals, we have to realize that chickens given respiratory virus vaccines are not given early, effective antiviral and other treatments for respiratory ailments.
By fighting against early, effective treatments, Fauci and company treated the human population like a herd of animals.
Then, in their mantra “Flatten the Curve,” Fauci et al. made certain to defeat Farr’s Law. In Fauci’s own words, the real reasons for Flattening the Curve were to prevent too many people from acquiring immunity through natural infection and, to a lesser degree, to protect hospitals that had received billions in training funds to be ready for pandemics after Ebola.
Farr’s Law
Named after William Farr, a British epidemiologist in the 19th century, Farr’s Law describes the pattern of many epidemics, including their rise and fall in a roughly symmetrical pattern, which a bell-shaped curve can often approximate. Farr noticed this pattern during the smallpox epidemic. This law suggests that epidemics tend to rise and fall in a roughly symmetrical pattern that can often be approximated by a normal distribution, mathematically expressing how the number of new cases increases rapidly and then declines at a similar rate.
In the context of infectious diseases, the pattern of an exponential rise in infections followed by a subsequent drop is commonly referred to as an “epidemic curve.” This term is used to describe the graphical representation of the number of new cases over time during an outbreak of an infectious disease. The shape of the curve can provide insights into the dynamics of the disease’s spread.
The typical epidemic curve for an infectious disease that spreads rapidly and then decreases can often be described as having the following phases:
1. Initial Phase: There are only a few cases in this phase, which could potentially increase slowly as the disease begins to find susceptible hosts.
2. Exponential Growth Phase: During this phase, the number of new cases increases rapidly—often exponentially—as each infected individual transmits the infection to multiple others.
3. Peak Phase: This is the point at which the rate of new cases reaches its maximum. It does not necessarily mean that the number of cases has stopped increasing, but the rate of increase has begun to slow down.
4. Decline Phase: After the peak, the number of new cases begins to decrease. This decline can be gradual or rapid, depending on various factors, including public health interventions (like vaccination, social distancing, and quarantine), the depletion of susceptible individuals, or changes in the pathogen itself.
5. Tail Phase: This is the final phase, during which new cases trickle in at a low level. The epidemic is ending, but lingering isolated cases or small clusters may still appear.
The overall pattern is determined by the reproduction number R0. Very early on, I (and others) used this and a simple logistic grown model to study the likelihood of success in their calls to “flatten the curve.”
Here’s the CEBM’s figure from April, 2020:
Three Countries With No COVID-19 Vaccines Fared Far Better
Deaths per million (OWID) from the US, Burundi, Eritrea and Madagascar are shown in this figure.
Only one of these countries deployed COVID-19 vaccines.
Read the rest here.