“We knew this would happen.”
The phrase has become the lament of experts. A grammatical sleight of hand propels past knowledge into the present as it punctuates time by separating before from after. The complaint is the opposite of pre-emption, a declaration of living in the wake, a dirge in the face of warnings unheeded. Let us name this knowledge in the present moment: virology, epidemiology, public health, biosecurity.
In the first part of this essay, I explored disruptions to supply chains and logistical networks occasioned by the COVID-19 pandemic. A focus was capital’s attempts to externalise the risks and cut the losses associated with this upheaval by pursuing claims of force majeure, a legal doctrine that allows release from contractual obligations in the face of unforeseeable events and obstacles. Here, I query this invocation of the pandemic’s unforeseeability in relation to claims that the emergence and transmission of coronavirus in human worlds was anticipatable.
Anticipation is not prediction
“We knew this would happen” is not a claim for the predictability of the COVID-19 outbreak. It may be a complaint for a lack of preparedness, a reprimand for underspending in public health and research, or a plea to put expert knowledge in the seat of politics, but it is not an assertion of powers of divination or prophecy. Global health experts have presented the question of the occurrence of a viral pandemic as a matter not of if but of when and where.
Like the proprietary models used by logisticians to map the economic impact of the virus, the epidemiological models used to enact pandemic simulations proceed by assembling a portfolio of future courses that are neither necessary nor impossible. They do not deliver knowledge with the quality of prediction. As Sven Opitz writes, these models “embody a different temporal logic vis-à-vis the future. Instead of giving an account of a dated future incident, they chart a series of futures.”[i]
The exact time and place at which a virus jumps the barrier between nonhuman animals and humans remains irreducibly unknown. Tracing the diffusion of a virus after this emergence is a more viable task, given that paths of infection tend to follow transport and trading routes. Human designs for the mobility of things open routes of human travel and vice versa. In the name of reducing capital’s turnover time, logistical thinking has amassed databases that allow sophisticated analysis and tracking of the movement of people and goods across space. As Kim Moody writes, “the virus has moved through the circuits of capital and the humans that labor in them, and not solely by random ‘community’ transmission.” This coincidence of viral diffusion and human mobility is the reason why so many epidemiological models use logistics data.
Soviet cybernetician Leonid A. Rvachev and colleagues pioneered the integration of logistics data into epidemiological models. Beginning in 1967 by modelling the geographical spread of influenza among Soviet cities[ii], Rvachev extended the technique in the 1980s to evaluate the effect of air travel on the global spread of influenza.[iii] Adapted to leverage information on the topology of transport networks, these spatially explicit models have evolved to estimate epidemic arrival times as effective distances.
Topological models differ from territorially discrete clinical pathway models, which remain little changed since Daniel Bernoulli’s modelling of smallpox diffusion in the 18th century, and today remain important for predicting healthcare demand. An example of topological modelling of the current pandemic is work carried out at Berlin’s Robert Koch Institute, which produced models and diagrams of the early global diffusion of COVID-19. The title of the 2020 book by Chinese scholars Ming Liu, Jie Cao, Jing Liang and MingJun Chen, Epidemic-logistics Modeling: A New Perspective on Operations Research, illustrates just how close the knowledge practices of epidemiology and logistics have moved.
The knowledge and practice of logisticians are not external to ways of knowing the emergence and circulation of viruses, as force majeure claims imply, but are indeed crucial to the most sophisticated current methods of modelling and anticipating viral outbreaks. Beyond the mobilisation of contagion as a basic social category, logistical ways of knowing and exercising power impact directly on the epistemic practices governing public reactions and policy responses to COVID-19.
In recent times, knowledge, law and state power have intersected to address the outbreak with declarations of emergency. The resulting conditions of encapsulation, sociality at a distance, inequality of impact, denial and secluded death provide testbeds for new techniques and technologies of governance. Familiar players such as tech companies and online retailers are stepping up but novel combinations of state, capital, self-regulation and superintendence are likely to emerge.
Demonstrating the intimacy of logistics and epidemiology provides some clues to the paths this experimentation may take. A provisional name for these incipient forms of administration and rule is virologistics.
Brett Neilson is Professor at the Institute for Culture and Society at Western Sydney University.
[i] Opitz, Sven. 2017. Simulating the World: The Digital Enactment of Pandemics as a Mode of Global Self-Observation. European Journal of Social Theory 20(3): 392-416.
[ii] Baroyan, O.V. and Leonid A. Rvachev. 1967. Deterministic Models of Epidemics for a Territory with a Transport Network. Cybernetics and Systems Analysis 3: 55-61.
[iii] Rvachev, Leonid A. and Ira M. Longini. 1985. A Mathematical Model for the Global Spread of Influenza. Mathematical Biosciences 75(1): 3–22.