In this page, we help people decipher the reality of the theory of Herd Immunity. But first a quote from the world’s leading expert on risk.
Q: What is your view on “Herd Immunity”?
Nassim Nicholas Taleb: Criminally stupid. *
What is the theory of Herd Immunity?
First of all it does not actually refer to full immunity to a particular disease within a population. As an ideal state, it refers to there being a theoretical low risk of getting a disease because of a combination of 1) a lot of the population being immune to the disease, hence not contracting it, and so that there’s less of the disease circulating and multiplying in the environment, and 2) a lot of people not able to get the disease directly because a) either they got successfully vaccinated against the disease or b) they previously had the disease and that particular disease does not infect humans twice.
What are the key assumptions behind the herd immunity theory?
Here are some sample assumptions made by herd immunity theory proponents, any, or more than one, of which may be fatally bad assumptions:
- Reinfection Not Possible. For a particular disease, it is often assumed that an individual cannot be reinfected by that disease. However this is not the case of all diseases, and needs to be established in order to be used as an assumption. In the case of a new disease, this cannot be assumed, and the Precautionary Principle requires that we not assume that a recovered individual cannot be reinfected.
- Death Predictability. Total deaths can be predicted is often assumed.
- Relying on Herd Immunity Doesn’t Create New, Worse Strain. A strategy of relying on developing herd immunity vs other possible strategies that might result in less infected people, means more infected people in total. With each infection and each reproduction of the virus, there’s a chance that a new strain of the virus might develop that’s worse than current strains and could dramatically increase the number of deaths.
While these are not key assumptions, these are also often assumed:
- Recovery is 100%. Proponents of herd immunity often assume that an infected individual returns to 100% of their previous capacity, and does not result in a long-term or lifelong disability.
- Non-Hellish Infection Experience. Infection experience is not horrible and does not have a high cost associated with it.
What are some recommended reading articles in relation to Herd Immunity and Covid-19?
Los Alamos Nat’l Lab report shows dominant COVID-19 strain can reinfect survivors https://www.krqe.com/health/coronavirus-new-mexico/los-alamos-natl-labs-report-shows-dominant-covid-19-strain-can-reinfect-survivors/
“No evidence” that recovered COVID-19 patients cannot be reinfected: https://www.reuters.com/article/us-health-coronavirus-who-idUSKCN2270FB
Here’s Why Herd Immunity Won’t Save Us From The COVID-19 Pandemic: https://www.sciencealert.com/why-herd-immunity-will-not-save-us-from-the-covid-19-pandemic
Recovered, almost: China’s early patients unable to shed coronavirus: https://www.reuters.com/article/us-health-coronavirus-china-patients-ins-idUSKCN2240HI
What are considerations for the possibility of herd immunity in the case of a new disease?
Here are some sample considerations worth keeping in mind for anyone exploring the theory, as it relates to a new disease:
- The Precautionary Principle
- Scale-dependent survival rate; if there are huge numbers of infected individuals, the death rate will likely be much higher due to the lack of medical staff, hospital beds, and Intensive Care Units. Medical staff may not be available as they may be fighting infection themselves, or may abandon their jobs to avoid infection or due to burn-out.
- There’s no guarantee of a low ‘apex’, nor that the ‘curve’ will ‘flatten’ in a low bound
- Time to vaccine creation is an uncertain parameter.
- Amount of deaths is uncertain. There may be models that predict ranges of numbers, but those models will have uncertain parameters and also may have flawed assumptions.
- Use of medical capacity by infected humans who later recover can limit availability of medical capacity to more serious cases.
- Possibility of a high upper bound on number of deaths, rather than a low upper bound. The disease could kill a huge number of people.
- “Low-dosage infections” can be contagious. A lot of intellectual folks like to brainstorm ideas to infect small numbers of people to test ideas out, but they often have unstated assumptions that are flawed, such as assuming that they can control the disease from escaping their test group.
- It is also worth noting that contagion is a network phenomenon, with interactions described best by a power-law or similar distribution rather than a simple, uniformly random model, which is what some folks use to create estimates or models.
While we’re sharing these ideas, a couple more notes… Words matter:
Instead of “Social distancing,” say “Physical distancing”
Instead of “Flatten the curve,” say “Crush the curve”