Pandemic response strategies and threshold phenomena.

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 is caused to fall below 1. We show that there is no real distinction based on any value of which falls in any case from early on in an epidemic wave.

Complexity in Epidemiology and Public Health. Addressing Complex Health Problems Through a Mix of Epidemiologic Methods and Data.

Public health and the underlying disease processes are complex, often involving the interaction of biologic, social, psychologic, economic, and other processes that may be nonlinear 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 article sets out a way to operationalize complex systems thinking in public health, with a particular focus on how epidemiologic methods and data can contribute towards this end.

Can you lock down in a slum? And who would benefit if you tried? Difficult questions about epidemiology's commitment to global health inequalities during Covid-19.

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.

Can Robots Do Epidemiology? Machine Learning, Causal Inference, and Predicting the Outcomes of Public Health Interventions.

This paper argues that machine learning (ML) and epidemiology are on collision course over causation. The discipline of epidemiology lays great emphasis on causation, while ML research does not. Some epidemiologists have proposed imposing what amounts to a on ML in epidemiology, requiring it either to engage in causal inference or restrict itself to mere projection.

Corrigendum to "COVID-19 in South Africa" [Global Epidemiology 2 (2020) 100034].

[This corrects the article DOI: 10.1016/j.gloepi.

Clinical characteristics and progression of COVID-19 confirmed cases admitted to a single British clinical centre-A brief case series report.

Objectives: In December 2019, a pneumonia-like illness was first reported in Wuhan-China caused by a new coronavirus named corona virus disease-2019 (COVID-19) which then spread to cause a global pandemic. Most of the available data in the literature is derived from Chinese cohorts and we aim to contribute the clinical experience of a single British clinical centre with the characteristics of a British cohort.

Design: A prospective case series.

Health as a Secondary Property.

In the literature on health, naturalism and normativism are typically characterized as espousing and rejecting, respectively, the view that health is objective and value-free. This article points out that there are two distinct dimensions of disagreement, regarding objectivity and value-ladenness, and thus arranges naturalism and normativism as diagonal opposites on a two-by-two matrix of possible positions. One of the remaining quadrants is occupied by value-dependent realism, holding that health facts are value-laden and objective.

Prediction, Understanding, and Medicine.

What is medicine? One obvious answer in the context of the contemporary clinical tradition is that medicine is the process of curing sick people. However, this "curative thesis" is not satisfactory, even when "cure" is defined generously and even when exceptions such as cosmetic surgery are set aside. Historian of medicine Roy Porter argues that the position of medicine in society has had, and still has, little to do with its ability to make people better.

Intellectualizing Medicine: A Reply to Commentaries on "Prediction, Understanding, and Medicine".

This article is a reply to two critics of my "Prediction, Understanding, and Medicine," published elsewhere in this journal issue. In that essay, I argued that medicine is best understood not as essentially a curative enterprise, but rather as one essentially oriented towards prediction and understanding. Here, I defend this position from several criticisms made of it.

Causality and causal inference in epidemiology: the need for a pluralistic approach.

Causal inference based on a restricted version of the potential outcomes approach reasoning is assuming an increasingly prominent place in the teaching and practice of epidemiology. The proposed concepts and methods are useful for particular problems, but it would be of concern if the theory and practice of the complete field of epidemiology were to become restricted to this single approach to causal inference. Our concerns are that this theory restricts the questions that epidemiologists may ask and the study designs that they may consider.

Causation and prediction in epidemiology: a guide to the "methodological revolution".

There is an ongoing "methodological revolution" in epidemiology, according to some commentators. The revolution is prompted by the development of a conceptual framework for thinking about causation here referred to as the Potential Outcomes Approach (POA), and the mathematical apparatus of directed acyclic graphs that accompanies it. But over and above the mathematics, a number of striking theses about causation are evident, for example: that a cause is something that makes a difference; that a cause is something that humans can intervene on; and that causal knowledge enables one to predict under hypothetical suppositions.

Epidemiological evidence in law: a comment on Supreme Court Decision 2011Da22092, South Korea.

This paper offers a commentary on three aspects of the Supreme Court's recent decision (2011Da22092). First, contrary to the Court's finding, this paper argues that epidemiological evidence can be used to estimate the probability that a given risk factor caused a disease in an individual plaintiff. Second, the distinction between specific and non-specific diseases, upon which the Court relies, is shown to be without scientific basis.

Disease as a theoretical concept: the case of "HPV-itis".

If there is any value in the idea that disease is something other than the mere absence of health then that value must lie in the way that diseases are classified. This paper offers further development of a view advanced previously, the Contrastive Model of Disease: it develops the account to handle asymptomatic disease (previously excluded); and in doing so it relates the model to a broadly biostatistical view of health (where before the model was neutral in the naturalism debate). The developments are prompted by considering cancers featuring viruses as prominent causes, since these appear to amount to cases where the prescriptions of the Contrastive Model could be followed, but aren't.

Risk relativism and physical law.

In two 1959 papers, one coauthored, Jerome Cornfield asserts that 'relative' measures are more useful for causal inference while 'absolute' measures are more useful for public health purposes. In one of these papers (the single-authored one), he asks how epidemiology should respond to the fact that its domain is not a highly 'articulated' one-it is not susceptible to being subsumed under general laws. What is the connection between these issues? There has recently been some backlash against 'risk relativism', and Charles Poole has recently dismantled the mathematical argument for the first claim.