Alex Broadbent’s research while affiliated with University of Johannesburg and other places

This paper argues that lockdown was racist, where “lockdown” refers to a historically situated kind of regulatory response to the Covid-19 pandemic imposing significant restrictions on leaving the home and on activities outside it. We articulate a notion of negligent racism which is objective and does not require intent, and show that lockdown satisfies its definition. The effects of lockdown on Africa significantly disadvantaged its inhabitants relative to the inhabitants of at least some other regions. We show how this suffices to establish the general proposition that lockdown was negligently racist (not merely “sometimes” or “someplace”), given our definitions. We defend our conclusion against two objections: that lockdown was a (moral) necessity (one version of which is the idea that it was a necessary precaution); and that race is explanatorily irrelevant, meaning that to whatever extent our argument is successful, it succeeds merely in showing that lockdown was anti-poor and not that it was racist. Nothing remains to gainsay the conclusion that lockdown was racist.

During the Covid-19 pandemic, the best-performing modelling groups were not always the best-resourced. This paper seeks to understand and learn from notable predictions in two reports by the UK's Scientific Advisory Group for Emergencies (SAGE). In July 2021, SAGE reported that, after the upcoming lifting of restrictions (“Freedom Day”) cases would “almost certainly remain extremely high for the rest of the summer” and that hospitalisations per day would peak between 100 and 10,000. Cases were not “extremely high” and began to decline, while hospitalisations initially lay outside (above) SAGE's confidence bounds, and only came within the expected range when the upper and lower bound moved so far apart as no longer to be useful for policy or planning purposes. The second episode occurred in December 2021, when SAGE projected 600–6000 deaths per day at peak in the scenario where restrictions remained as they were (referred to as “Plan B"). In the event, restrictions did not change, and deaths peaked at 202, well below the lower bound, even though this spanned one order of magnitude. We argue that the fundamental problem was over-reliance on mechanistic approaches to disease modelling, and that a methodologically pluralist approach would have helped. We consider various ways this could have been done, including evaluating past performance and considering data from elsewhere. We show how the South African Covid-19 Modelling Consortium performed better by learning from experience and using multiple methods. We conclude in favour of methodological pluralism in infectious disease modelling, echoing calls for methodological pluralism in recent literature on causal inference.

Suppose there is a public health measure which conflicts with local cultural ways of being, but which the relevant public health authority has the power to impose on the local population. When, if ever, does it have the right to do so? We argue that the right can only accrue when there has been a good negotiation. We stop short of offering a sufficient condition or full justification for a decision on the part of a public health authority to impose a measure on an unwilling population, but provide what we take to be an important necessary condition or prerequisite. We propose that this is a useful amendment of and extension to Medical Cosmopolitanism, an existing approach to intercultural medical disagreement and to “decolonizing medicine” (Broadbent, 2019).

This paper critically evaluates the Suppression Threshold Strategy (STS) for controlling Covid-19 (C-19). STS asserts a "fundamental distinction" between suppression and mitigation strategies, reflected in very different outcomes in eventual mortality depending on whether reproductive number R is caused to fall below 1. We show that there is no real distinction based on any value of R which falls in any case from early on in an epidemic wave. We show that actual mortality outcomes lay on a continuum, correlating with suppression levels, but not exhibiting any step changes or threshold effects. We argue that an excessive focus on achieving suppression at all costs, driven by the erroneous notion that suppression is a threshold, led to a lack of information on how to trade off the effects of different specific interventions. This led many countries to continue with inappropriate intervention-packages even after it became clear that their initial goal was not going to be attained. Future pandemic planning must support the design of "Plan B", which may be quite different from "Plan A".

Public health and the underlying disease processes are complex, often involving the interaction of biologic, social, psychological, economic, and other processes that may be non-linear and adaptive and have other features of complex systems. There is therefore a need to push the boundaries of public health beyond single-factor data analysis and expand the capacity of research methodology to tackle real-world complexities. This paper sets out a way to operationalize complex systems thinking in public health, with particular focus on how epidemiologic methods and data can contribute towards this end. Our proposed framework comprises three core dimensions--patterns, mechanisms, and dynamics--along which complex systems may be conceptualized. These dimensions cover seven key features of complex systems--emergence, interactions, non-linearity, interference, feedback loops, adaptation, and evolution. We relate this framework to examples of methods and data traditionally used in epidemiology. We conclude that systematic production of knowledge on complex health issues may benefit from: formulation of research questions and programs in terms of the core dimensions we identify, as a comprehensive way to capture crucial features of complex systems; integration of traditional epidemiologic methods with systems methodology such as computational simulation modeling; interdisciplinary work; and continued investment in a wide range of data types. We believe that the proposed framework can support systematic production of knowledge on complex health problems, with the use of epidemiology and other disciplines. This will help us understand emergent health phenomena, identify vulnerable population groups, and detect leverage points for promoting public health.

The Covid-19 pandemic was a world event on our intellectual doorstep. What were our duties to respond, and how well did we respond? We published papers, but we did not engage extensively or influentially in public debate. Perhaps we felt we were not experts. Yet in a health crisis, philosophers of medicine can offer not only “conceptual clarification,” but also domain-specific knowledge concerning structural properties of relevant sciences and their social-political uses. I set out three conditions for the kind of contribution I felt was lacking: public, critical, and timely. And I call for us to do more of it.

The initial response to the Covid-19 pandemic was characterised by swift “lockdowns,” a cluster of measures defined by a shared goal of suppressing Covid-19 and a shared character of restricting departure from the home except for specific purposes. By mid-April 2020, most countries were implementing stringent measures of this kind. This essay contends that (1) some epidemiologists played a central role in formulating and promulgating lockdown as a policy and (2) lockdowns were foreseeably harmful to the Global Poor, and foreseeably offered them little benefit, relative to less stringent measures. In view of the widespread commitment to reducing global health inequalities within the profession, this should prompt reflection within the epidemiological community and further work on pandemic response measures more appropriate for the Global Poor.