Super-Spreaders and the Need for New Prediction Models

Super-Spreaders and the Need for New Prediction Models, by Jonathon Kay.

Absent isolation or other precautionary measures, the average socially active COVID-19 infectee will transmit the disease to an average of about 2.4 people. i.e., the R0 value is 2.4. But super-spreaders can spread a disease to dozens or hundreds.

Super-spreading isn’t confined to COVID-19. Mary Mallon [aka “Typhoid Mary”] (an asymptomatic typhoid carrier) famously infected more than 50 Americans. …

In a 2016 paper, South Korean doctor Byung Chul Chun noted that the MERS outbreak could be summarized as:

an explosive epidemic by infrequent super-spreaders. The number of secondary cases in the transmission tree was extremely skewed. Among 186 confirmed cases, 166 cases (89.2%) did not lead to any secondary cases, but 5 (2.7%) super-spreaders lead to 154 secondary cases. …

At the most basic level of analysis, computer modelers apply an R0 figure to some baseline pool of infected individuals, and then iterate the spread of the disease exponentially over time…. R0, being a mean or median value, “does not capture the heterogeneity of transmission among infected persons.” …

One of the long-term effects of the COVID-19 crisis might be to accelerate a shift toward models that are less rooted in traditional R0 frameworks. … In Wuhan, according to unpublished CDC data, the observed R0 for COVID-19 went from 3.86 to 0.32 in just a few weeks. On the Diamond Princess cruise ship, the rate went from about 15 to less than two once isolation protocols commenced. …

What differentiates SSE [super-spreader events] generators from other infected individuals? The CDC authors go through a lengthy laundry list of possible factors, including possible variations between multiple disease sub-types. But the unfortunate bottom line is that they don’t know.

From Seattle to South Korea, many of the biggest outbreaks were fuelled by a small handful of very sick, highly symptomatic people who drifted along for days before their condition was correctly treated and isolated. (In South Korea, some have noted, the problem was exacerbated by patients who went “doctor shopping,” spreading their germs in many different clinics.) Test everyone who is symptomatic, and test them early, and you will prevent SSEs.

We also need to be increasingly wary of computer models that apply a traditional R0-based approach to a novel coronavirus amidst a real-time public-health mobilization campaign whose speed and scale are likely unprecedented in human history. …

Of course, the law of large numbers applies to all systems. And if we were resigned to a mass spread of COVID-19 throughout our societies, it probably would be fine to fall back on traditional model, since we’d be talking about daily infection rates on the scale of many tens or hundreds of thousands, or even millions, and so individual variations would be less meaningful. But as my Quillette boss Claire Lehmann has vigorously asserted, we are very much not resigned to that; and so instead find ourselves with many countries battling to keep their symptomatic case loads in three or four figures. This is on a scale that permits SSEs to assume a large — and perhaps even dominant — role in transmission mechanics.

Not enough data, and reliance on models that are fundamentally flawed. It’s beginning to sound like climate change. It’s a scenario that allows plenty of wiggle room in which the politically mischievous can bend things to suit themselves, such as by funding only “scientists” who produce results that suit their ends.