Gabriela Gomes is Professor of Mathematics and Statistics at University of Strathclyde Glasgow and corresponding author of the recent paper, "Herd immunity thresholds for SARS-CoV-2 estimated from unfolding epidemics," one of the papers arguing that the herd immunity threshold is on the order of 10-20% in the European communities they studied rather than the 60-80% more commonly cited, and she's here to talk about herd immunity and COVID-19.

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Show Notes

00:00:01 Introduction

00:01:28 Gabriela's body of research showing the importance of individual variation in susceptibility to infectious diseases.

00:04:25 Conventional models that don't take into account individual variation overestimate the size of epidemics and overestimate the effect of interventions.

00:05:21 How has her research been received by her colleagues?

00:09:30 The evolution of immunity has to be through the action of natural selection on variation, making it logically necessary that variation exists.

00:11:54 Nutrition scientists underestimated variation in nutritional requirements but have been much more receptive than infectious disease epidemiologists to incorporating it into their models.

00:15:51 The ability to identify a single necessary factor in infectious disease spread has biased the field toward a ground-up mechanistic model to vet the inclusion of variables.

00:22:34 What is herd immunity?

00:26:11 Tuberculosis disappeared because socioeconomic conditions improved.

00:29:06 Until the vaccine, measles cycled in and out of herd immunity as new infants entered the susceptible population.

00:31:13 Measles is much more transmissible than SARS-CoV-2.

00:32:21 SARS was contained before it had a chance to spread.

00:34:08 Herd immunity is not the end of concern about a disease.

00:34:33 The herd immunity threshold for COVID will be crossed many times, but crossing it the first time is the end of the "pandemic phase."

00:39:16 The seasonality of the flu is influenced not only by weather and social patterns that influence transmissibility, but also by cyclically crossing in and out of herd immunity. Viral mutations can be the trigger to temporarily cycle out of herd immunity.

00:46:57 Why conventional herd immunity threshold calculations say the threshold is 60-80% for COVID, but why Gabriela's model says 10-20%.

00:51:45 How do we know what the degree of variation is?

00:54:56 Gabriela's conclusions are similar to those reached by other researchers when looking at NYC and Chicago.

00:56:48 As long as susceptibility is correlated to infectiousness, natural infections will remove the most infectious people early on.

01:01:26 Is it possible that lockdowns and social distancing are solely responsible for everything we attribute to herd immunity?

01:08:30 How likely is it that fading immunity and reinfection would render the herd immunity model useless?

01:11:19 What does the New York City data tell us about herd immunity and reinfection?

01:15:32 What does Spain's second wave tell us?

01:19:21 We discuss Harvard Professor Miguel Hernán's suggestion on Twitter that NYC is doing so much better than Madrid because NYC had superior testing, contact tracing, and phased reopening.

01:23:16 Misunderstanding herd immunity has huge economic consequences.

01:25:13 Should we be looking at cases, hospitalizations, deaths, or seroprevalence as our primary metric?

01:35:28 We discuss University of Minnesota epidemiologist Michael Osterholm's suggestion that scientists modeling a 20% herd immunity threshold are "not connected with real-world thinking" and that the much higher infection rates in prisons and a South Korean call center show that the herd immunity threshold cannot be anywhere near as low as 20%.

01:41:05 Gabriela is hoping to find more collaborators to extend the modeling to more communities.

01:41:55 Why approaching herd immunity rapidly allows you to massively overshoot the threshold

01:46:10 How has her research been received by peer review, the press, and her colleagues?

01:49:00 What developments will she be looking for that would falsify her model?

01:50:39 What are the policy implications of her research?

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Disclaimer

I am not a medical doctor and this is not medical advice. Nor is this financial or legal advice, nor is anything contained herein to be construed as an explicit accusation against any individual or organization mentioned. My goal is to seek the truth and to empower you with information. Please make all health decisions yourself, consulting sources you trust, including a caring health care professional.

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