Have You “Herd” About Covid-19 Immunity?

Have You “Herd” About COVID-19 Immunity:

How many people need to be infected to prevent further infection?

            The fields of epidemiology, immunology, and infectious disease intersect in the theory of herd immunity (stay tuned for why I call it a theory).  The theory presses the statistics of epidemiology into the battle between immunology and infectious disease in an attempt to tease out an almost magical number.  This number then becomes the percentage of the population which must have been exposed or infected by the stated microbe in order to slow the spread among non-infected individuals.

            Let’s take the example of COVID 19 which the article in focus addresses after clarifying a few terms.  Epidemiology is the field which studies how often a particular disease occurs or spread in a population. An epidemiologist may consider the rates of heart disease in different groups, based on different risk factors, and watch how the rates change over time or with some medical intervention.  In regards to an infection, they look at how fast and how often it spreads from one person to another and within groups say in a city or a state or a nation.  Immunology is the study of how our bodies defends themselves against infections and crosses over with infectious disease, the practice of medicine addressing infections. 

            The theory of herd immunity involves all three of these fields and leans heavily on mathematical models of probability and statistics.  I call it a theory since it has never been proven and by its nature, it is not truly provable.  In general terms, it does seem to make sense and hold true that when a high enough percent of a group has been infected and become immune, that a given infection will slow down its spread and maybe even fizzle out in that group.  In the specific situation of one chosen infection, identifying the exact number is less definite.  Many factors including a lot of probability go into mathematical models for something like COVID 19.  Age, gender, smoking status, rates of diabetes, frequency of social interaction, time of year, and much more contribute dynamically.  Change any one factor and the estimate will change.  Some factors change from day to day, week to week, and month to month.  At best epidemiology can offer an estimated range.

            We turn our attention back to COVID19 now.  Why should we care about this calculated number anyway?  Well, from a public health perspective, we could say that once our nation or even just a region reaches that magical number, then we can expect the rate of spread to lower.  That hindsight can influence public health decisions.  On the other hand, the number is used often as the percentage o fa population that is required to be vaccinated in order to slow the spread.  This is a more proactive approach.  The former is more like counting how many were shot so we know that shootings will go down versus the latter picture of how many body armors do we need to hand out to prevent shootings.

            By now, you may realize that my main focus is not on the article’s proclamation that a new model indicates herd immunity is possibly closer to 43% than other predictions of 60%.  I honestly don’t care that much about another mathematical model which is just a theory.  Yes, I believe that as more infections occur, less people can become infected.  That is logical.  I just don’t think we are as smart as we think we are in regards to COVID19.  Herd immunity predictions, in my opinion, lie in the realm of predicting next months weather.  We can say it might rain and will likely be warm, but until the day comes, it is just a guess.  It may be a “scientific” guess based on patterns and 50 measured factors, but ultimately it is an educated guess.

            In functional medicine, we aim to go beyond guesses, and aim for as deep an understanding as possible.  In doing so, we acknowledge the limits of our knowledge.  If only epidemiologists and public health experts promoting these mathematical models would be as transparent.  Until then, take the time to understand the basics of their proclamations so you can sort the silly from the fruitful.  To overcome 2020 and beyond, we can’t sit back and let others follow such mathematical models without scrutiny.

Original Article:

Tom Britton, Frank Ball, Pieter Trapman. A mathematical model reveals the influence of population heterogeneity on herd immunity to SARS-CoV-2. Science, June 23, 2020; DOI: 10.1126/science.abc6810

Thanks to Science Daily:

University of Nottingham. “COVID-19: Herd immunity threshold could be lower, study finds.” ScienceDaily. ScienceDaily, 23 June 2020. <www.sciencedaily.com/releases/2020/06/200623111329.htm>.