I wrote a paper about the herd immunity (the phenomenon, not the policy) that results from epidemic spread years ago with fantastic co-authors @bansallab @laurenmeyers @BjornstadOttar … none of us ever thought this would be suggested as a public health strategy 1/

Much of the focus in network epidemiology at the time was about new epidemics on networks, invasion criteria, R0, final size, etc. We were intrigued by the fact that endemic pathogens don’t see this naive network, they see a “residual network” left behind by prior spread 2/

So naively measuring networks, using diary studies or analogs to online networks, etc, would give us a biased view. The network that WE can measure is unlikely to be the network that a pathogen sees 3/

This is because prior spread occurs in a structured fashion that should result in a residual network of susceptible nodes (individuals) that is less variable than the original network; high contact nodes get exposed, and thus “removed”, at a higher rate 4/

The composition of this residual network, and the subsequent transmission dynamics, are a result of 1) having fewer high contact nodes (direct effect), and 2) a lower density of connections among the remaining nodes (indirect effect analogous to herd immunity) 5/

Interestingly, regardless of the degree of variability in the original network, the residual network was remarkably similar. What differed was the relative size of the direct and indirect effects that got you there 6/

I’m not going to lie … I still think this is a very cool paper not least because, as a young scientist, I got to work with some brilliant mentors and develop a lifelong friendship with a wonderful collaborator, so I’m biased 7/

I stand by the relevance of the result: for endemic pathogens, the world the pathogen sees and the world that we might naively measure (and thus our basis of risk assessment) may be dramatically different because of past direct and indirect processes 8/

The concepts and mathematics are technically applicable in the emerging pathogen case. But to translate this phenomenon to policy requires a lot more thought than just “let it spread” 9/

As a phenomenon, herd immunity is an abstraction that helps to partition the impact of past events. It has been a powerful abstraction in the motivation of vaccination (another fave of mine). It has been an intellectual obscurity in the discussion of epidemic spread 10/

As a predictive concept in epidemic spread and a motivation for policy, herd immunity, reduces individuals to their contacts and ignores their humanity. Worse, it ignores feedbacks between contacts and other vulnerabilities resulting from generations of social inequities 11/

Herd immunity, the phenomenon not the policy, has now become polarized because it makes for a catchy tag line and some have put mathematical convenience above humanity 12/

No one thing will “solve” this pandemic. Vaccines and drugs won’t stop this absent a coherent and comprehensive system of distribution and governance. Herd immunity is an abstraction that can at best be observed but can’t possibly be governed or planned 13/

Unlike vaccines and drugs, herd immunity is not a thing we can easily manipulate. The phenomenon not the policy may provide a helping hand. It may make future interventions easier as population immunity increases. To ignore that is naive. To rely on that is lunacy 14/

If you like ancient history, here’s the original paper from back in the mid aughts https://royalsocietypublishing.org/doi/abs/10.1098/rspb.2006.3636 and there are great follow-ups by @bansallab end/