Noun: heterogeneity. Definition: “the quality or state of being diverse in character or content.”
Viruses, bacteria, fungi, plants, animals, and eco-systems all display heterogeneity in one way or another, and of course, so too do human beings; different appearances, behaviours, preferences, and socio-economic circumstances for instance.
To completely ignore heterogeneity is to ignore reality.
In urban planning, this can mean ignoring choice, for example, decision-making based on the long-standing assumption that everyone aspires to live in a suburban or quasi-suburban setting with a car-dominated way of life.
In economics, this can mean developing decentralisation policies that ignore the inherent productivity (and upward socio-economic mobility) benefits of large, densely populated urban areas. In law, this can mean drafting legislation that ignores chronic socio-economic inequality, and imposes huge financial penalties, some of which can consume a full week’s worth of income for minimum wage workers, for minor infractions.
What about public health?
Dr Gabriela Gomes, a mathematician, professor and researcher at the University of Strathclyde, has been modeling the covid19 pandemic in many countries around the world. Unlike simpler techniques – like those used to predict 2.2 million deaths in the US alone (current figure is roughly 220,000) – her models have attempted to take into account heterogeneity. In this instance, referring to the difference in patterns of interaction between different types of people. For example, younger adults are typically more socially active and have more contacts than do the elderly.
Much of her work has been used to determine herd immunity thresholds, that is, the point at which people infected with a virus infect less than one other person on average. In this regard, taking into account the effects of heterogeneity and natural infection, she finds typical herd immunity thresholds much closer to a population-wide immunity level of 10-20 per cent, than to the higher values of 60 per cent plus quoted by other modelers that ignore heterogeneity.
In fact, her research suggests that due to heterogeneity, immunity gained via natural infection is more efficient at slowing down the spread of the virus than random vaccination. It is precisely those people who socialise and mix the most who are more likely to be infected, likely to attain a level of immunity the quickest, and therefore in theory, facilitate a swifter end to the epidemic phase of the outbreak.
Heterogeneity is also intimately tied to risk of mortality upon infection. Dr John Ioannidis, the renowned Stanford epidemiologist who was “virtually” attacked with pitchforks for suggesting that the Infection Fatality Rate (IFR) of SARS-CoV-2 was in the range of 0.2 to 0.3 per cent in Santa Clara County, California, just had a commentary/analysis peer-reviewed and published by the Bulletin of the WHO. It estimates a global median IFR of 0.23 per cent. A number approximately in line with recent pronouncements by the WHO that roughly 1 in 10, or 750 million, people have already been infected, and a recorded global death toll of roughly 1.1 million, equating to an implied IFR of 0.16 per cent. For those under the age of 70, the median IFR estimate is 0.05 per cent.
In fact, Sir David Spiegelhalter, a statistician at the University of Cambridge, wrote in a Medium article that, “compared to a 20-year-old, an 80-year-old had approximately 1000 times the risk of dying.” As Ioannidis said in an interview with Medscape, “age predicts mortality even better than comorbidities.”
Given the heterogeneity in patterns of contact and risk of mortality, it is quite possible (likely?) that attempts to suppress the virus through homogenous population restrictions, may be more harmful than age-targeted measures. Why? Eventually everyone, regardless of age becomes nearly equally susceptible to risk of infection, albeit in a delayed fashion.
Dr Wesley Pegden of Carnegie Mellon University and Dr Maria Chikina of the University of Pittsburgh School of Medicine developed a model, and published a study, showing that an approach focusing on lowering viral transmission among older people, could greatly reduce the overall number of deaths and ICU utilisation.
This idea was supported by a study published in the British Medical Journal by Ken Rice, Ben Wynne, Victoria Martin, and Graeme J Ackland. They concluded that, “school closures and isolation of younger people would increase the total number of deaths, albeit postponed to second and subsequent waves.” Perhaps their most important conclusion was that, “nevertheless, in all mitigation scenarios...the final death toll depends primarily on the age distribution of those infected and not the total number.”
While lockdowns and or intense social distancing to attempt to protect everyone equally sound virtuous in theory, in reality, the young tend to be more vulnerable to the deleterious side effects of such measures. The cure, for them, may be worse than the disease.
Protecting the most vulnerable may be more challenging in a society with more multigenerational households. One suggestion that has been put forward is to provide temporary housing in vacant hotel and guesthouse rooms for extremely high-risk people living in multi-generational settings. Expensive? Yes. More expensive than shutting down vast sectors of the economy; preventing swathes of relatively low-risk younger people from working; and then borrowing and pumping billions into ensuring they do not, at minimum, starve to death due to instant economic vulnerability?