Commentary: Coronavirus and the Perils of Disease Modeling

by Peter C. Earle


I’m pleased to announce the third in AIER’s book series on the virus: Coronavirus and Disease Modeling. This follows Coronavirus and Economic Crisis and Coronavirus and Economic Recovery. The third book focuses on the fallacies of epidemiological modeling and the social and economic planning that instituted its inspiration.

Everything from anthills to traffic patterns to slime molds to schools and herds of animals and cities are observed, studied, and analyzed for insights into how aggregates operate. The conditions under which living things move in lockstep, and the advantages they gain by doing so, are a fertile subcategory of study across both the physical and social sciences: within zoology, botany, microbiology, economics, sociology, and so on. The body collective dominates scientific inquiry.


But in every amassed group—even those with what barely passes for consciousness, at least from the human perspective—there are individuals that break away. They move outside, against, or in any event away from the community or prevailing trend: the bird that flees the flock, the bee that abandons the hive.

In the human world, and in particular the arts, outsiders tend to be celebrated. Writers, musicians, poets, painters, sculptors, and actors are so often misfits, and their creations embrace the plight and purpose of accidental or intentional nonconformists. Inventors, scientists, and other creators are at times notably eccentric, as were their forefathers, the shamans (some interesting speculation about who is beyond the scope of this article) and healers.

Across the spectrum of life, deviation from the norm occurs, despite good reasons for flocking behaviors: efficiencies of scale, mutual protection, rudimentary information sharing, network benefits, and so on. But to borrow a programming adage, are mavericks a bug or a feature? After decades and perhaps centuries of discarding or ignoring them, science appears (however slowly) to be coming around to the study of outlier behavior. According to mathematical biologist Corina Tarnita of Princeton University:

We spent a long time trying to understand how things synchronize. No one has really been [as] interested in the single cells that don’t seem to do anything, or the lazy ants, or the wildebeests that for some reason decide not to migrate, or the locusts that peel off. We’ve just never really paid attention.

Published in March 2020, just about when the lockdowns pursuant to the novel coronavirus pandemic began, Tarnita and team’s PLoS paper “Eco-evolutionary significance of loners” ponders (albeit contextually): “Are loners incidental byproducts of large-scale coordination attempts, or are they part of a mosaic of life-history strategies?”

The article continues:

Collective behaviors, in which a large number of individuals exhibit some degree of behavioral coordination, are frequent across the tree of life and across spatiotemporal scales: from microbial aggregates to the great wildebeest migration, from locust swarming to synchronized bamboo flowering, from fish schooling to mechanical adaptation in honeybee clusters. Intriguingly, however, such coordination is sometimes imperfect, and “out-of-sync” individuals (henceforth loners) have been reported in several of these systems. For instance, in locusts, population crowding prompts a transition from a solitary phase, in which individuals repel one another, to a gregarious phase, in which they attract each other. Experiments show, however, that not all individuals undergo this transition, even if exposed to long periods of crowding.

And this goes on elsewhere in the animal kingdom as well.

In wildebeest, hundreds of thousands of individuals coordinate with each other and organize herding migrations, but resident populations that fail to migrate also exist. Similarly, wildebeest calving times are highly coordinated, but some fraction of the calves are born outside the calving period.

Other research points in a similar direction. Are these individuals and klatches a product of pure randomness, biological flukes, or cogs in a deeper, evolutionary machine? Although that question has not been a scientific priority, there have been some empirical trailheads: “Theoretical investigations of such loner behaviors have been sparse, but the handful of existing studies have suggested that, at least in some systems, they could be a means of spatiotemporal niche partitioning that promotes diversity.” There is no evidence that the propensity to idiosyncratic behavior is heritable, and thus “there exists no empirical evidence, in any system, that loners are anything more than chance stragglers[.]”

The authors go on to explain how in the cellular slime mold Dictyostelium discoideum (a strange and fascinating creature with the ability to convert from thousands of unicellular amoeba to a multicellular, “slug like” creature) there are certain cells which do not, as the vast majority do, join in its multicellular life stage. While in that state they remain “invulnerable to the threats of the multicellular stage but capable of reachieving multicellularity via their offspring,” their behavior “may constitute insurance against such threats and therefore be critical” to the colony’s survival. Broader still,

[B]eyond multicellularity and sociality, [the] results have potential implications for the broadly analogous loner behaviors identified across a variety of systems in which some form of coordination or synchronization is observed, from insects to vertebrates to plants. Our findings represent the first demonstration that loner behaviors can indeed . . . exhibit significant ecological consequences . . . While the mechanisms underlying the existence of loners are likely different across systems, the widespread existence of loners and the possibility that they could in fact be shaped by selection suggest an interesting conjecture: that, in general, imperfect coordination among individuals may enable evolution to shape population-partitioning strategies . . .and thus for system-level robustness.

It is of course possible to take conclusions far further than is warranted. And there is a vast difference between noisy gene expression, which can sometimes account for differences between otherwise identical individuals, and human choice. But a growing acceptance that individuals and small dissident clusters are consequential, and thus critical to holistic understanding of systems, is a huge step forward for scientific inquiry in the natural and social sciences.

Of particular importance to the topics covered in this book, there are hints that even computational approaches to the social sciences are at the cusp of embracing this awareness. As an interdisciplinary 2015 George Mason University master’s thesis notes, with respect to agent-based models (ABMs),

One value of ABMs is that they allow for collection of all details of the characteristics and behavior over time of every individual agent and the environment, theoretically enabling the analysis of micro-level interactions between individuals and within small groups. In practice, however, the volume of raw data generated by each run of a model (thousands of which might be done to test a range of parameters) makes it difficult to identify unusual interactions, and analysis of models ends up being done by aggregating data and reporting overall trends.

And thus, as a corrective, the author discusses “using computer-generated narratives describing the behavior of an individual agent as an alternate method for evaluating the micro-level behavior of an ABM,” and goes on to detail ways to identify and study “interesting” individual agents. Doing so may provide deeper insights or highlight flaws in the assumptions or design of economic, epidemiological, or any other such simulation.

In the meantime, it is jarring to think that highly-trained people who themselves tend toward independent and often iconoclastic thought are so late to arrive at the notion that individuals of any sort straying from a community, in any case exhibiting drift, is nontrivial. The rogue elephant here, the person refusing to wear a mask there: perhaps intransigence, but perhaps responding to an arcane dictate beyond the explicitly knowable.

Computer models and simulations have fed fear and enlivened tyranny. Many millions of people in the real world continue to suffer and, indeed, die under pandemic policies wrought by the output of iteratively-run code. The scientific recognition of the social value of the asocial, in which our individual hopes, fears, personalities, and relationships are no longer assumed away or subsumed by mathematical averages, is a subtle but monumental breakthrough. Here’s hoping it continues.

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Peter C. Earle is an economist and writer who joined AIER in 2018 and prior to that spent over 20 years as a trader and analyst in global financial markets on Wall Street. His research focuses on financial markets, monetary issues, and economic history. He has been quoted in the Wall Street Journal, Reuters, NPR, and in numerous other publications. Pete holds an MA in Applied Economics from American University, an MBA (Finance), and a BS in Engineering from the United States Military Academy at West Point.








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One Thought to “Commentary: Coronavirus and the Perils of Disease Modeling”

  1. 83ragtop50

    The real problem with disease modeling is that diseases do not care a whit about a model.

    It is the same reason that the weather forecasts are so often wrong. Nobody told the weather that it had to follow the model. And weather modeling has been applied to the same weather for decades. And they are still wrong.