Herd Immunity Assisted Pandemic Control

By Foss Tighe

Since the start of the pandemic, there have been numerous articles about how Herd Immunity is a pipe dream and that it plays no rational role in the management of the COVID-19 pandemic. However, I believe that in countries like the United States, Brazil and Mexico, where attempts to control the virus via traditional science driven epidemiology have failed it is quite likely that immunity resulting from recovery from COVID-19 is already playing a role in the continuing outbreak in the Unites States and other countries.

All of the arguments against counting on immunity from COVID-19 recovery are based on the assumption that we would count on herd immunity alone to control the virus. But we know from experience that, even when there is no or little government action, as soon as people start dying people alter then behaviors to reduce their risks.

So for example, a recent NPR podcast argues, for Herd Immunity to work it would require somewhere between 50 and 80% of the population to become infected and recover before Herd Immunity would come into play. This estimate is based on the theoretical infection rate of COVID-19. Epidemiologists refer to the theoretical infection rate as R-naught or R(0). It represents the average number of people who will become infected for each case of the disease in a population where everyone is susceptible to the disease. So if R(0) is greater than 1, each infection, on average, will infect more than one other person, which results in the total number of new infections growing over time. If R(0) is less than one, than on average each infection generates less than 1 new infection, and the number new of infections declines over time. Estimates of the theoretical R(0) for COVID-19 are generally in the range from 2.0 to 5.0. An R(0) of 5, means that the effective reproduction will not get down to 1 until 80% of the population has been infected and is immune. The math is actually pretty straight forward, to get from 5 to 1 you need an 80% reduction. If R(0) is 3, then you need a 66% reduction to get from 3 to 1. With a R(0) of 3.0, once 66% of the people are immune, then two out of the three people an existing case would have infected, would have already had the disease, so now each case is only generating on average only 1 new case. Once we get below 1, cases naturally decline.

Those who feel herd immunity plays no role in the control of the virus add a little more math to reveal how horrific the thought of herd immunity might be. If 66% of the population needs to be infected, that is over 200 million people in the US. Even if we count on a very low case fatality rate of say 0.25% (Less than 1 percent), we are still talking about 5 million deaths. That is how they rest their case. For good measure some articles will point out that the strength and duration of the immunity achieved by recovery from COVID-19 is still unknown.

Important Assumptions in Anti-Herd Immunity Argument

But the theoretical R(0) has a hidden assumption generally not stated. It assumes that the people involved are like cells in a Petri dish. But people are not cells in a Petri dish and many real world things impact the actual effective reproduction rate of the virus. Things like social customs, hand shaking, population density, mask wearing, social distancing and existing immunity all act of the theoretical rate to produce a real world rate. For a more detailed discussion of what can effect the rate see my blog post. The effective reproduction rate, sometimes referred to at R(t) is how epidemiologists refer the actual real-world rate at which infections occur. There are a number of web sites that estimate R(t), the site re.live provides a daily updated estimate of R(t) for each state. These estimates are far from perfect. We don’t really know how many new cases are occurring each day in the United States, so estimates of R(t) look at things like the number of new cases, the number of tests and the positivity rate in an attempt to estimate the number of new infections. Its not an exact science. But these kind of calculations are used to rate the risk for outbreak by the Federal Coronavirus task force and webs sites such CovidActNow.org. When the pandemic finally peaked in the early epi-center of New York, I remember Governor Coumo announcing their public health experts were then estimating the effective R(t) as being at 0.7, this was a huge victory because there was clear evidence that the new infection rate was in decline in New York State.

Immunity in the Context of the Real World

According to rt.live the effective R(t) in the United States all fall in a fairly narrow range from 0.82 to 1.36. Quite far from the theoretical R(0) of 2.0 to 5.0. If 11% more of the US population were to become infected and recover, and that recovery generated effective immunity at least for some period of time measured in months or years, the effective R(t) for all States would fall below 1.0, and new infections in all 50 states would go into decline. This assumes of course that all the other activities that people are doing across the country to mitigate infection would continue. Eleven percent of the US is still 36 million people. Currently we are seeing around 50,000 new detected cases each day, recognizing that many other cases go un-detected, we might estimate 100,000 new infections each day. At that rate it would take about a year to infect an additional 11% of the US population.

Even with luck with vaccine development it is likely we will have a COVID-19 problem in the United States for at least the next year. During that time it is likely, immunity from recovered individuals will play an increasing role in the suppression of new infections. If we had a national strategy for controlling the virus, it would be wise to include an understanding of the role that this type of immunity can and will play. If we are not going to really control the virus in the United States, we might want to think about about our ability to influence which segments of our communities get infected. If we are going to allow 100,000 people per day to be infected with COVID-19 we might be wise to think about trying to influence who they are, and how we can protect those at high risk, while the march of infections continues without an end in sight. Right now, poorly formed national strategy can be basically defined as hide under a rock until a vaccine arrives. This strategy places the poor, women and people of color at increased risk because they will have to continue doing the dangerous stuff, while the rest of us work from home.

A small amount of immunity can go a long way to helping control the current pandemic in the United States. It would be prudent to recognize this and stop the somewhat partisan “Herd immunity” bashing.

3 thoughts on “Herd Immunity Assisted Pandemic Control

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