Key questions for modelling COVID-19 exit strategies.

Combinations of intense non-pharmaceutical interventions (lockdowns) were introduced worldwide to reduce SARS-CoV-2 transmission. Many governments have begun to implement exit strategies that relax restrictions while attempting to control the risk of a surge in cases. Mathematical modelling has played a central role in guiding interventions, but the challenge of designing optimal exit strategies in the face of ongoing transmission is unprecedented. Here, we report discussions from the Isaac Newton Institute 'Models for an exit strategy' workshop (11-15 May 2020). A diverse community of modellers who are providing evidence to governments worldwide were asked to identify the main questions that, if answered, would allow for more accurate predictions of the effects of different exit strategies. Based on these questions, we propose a roadmap to facilitate the development of reliable models to guide exit strategies. This roadmap requires a global collaborative effort from the scientific community and policymakers, and has three parts: (i) improve estimation of key epidemiological parameters; (ii) understand sources of heterogeneity in populations; and (iii) focus on requirements for data collection, particularly in low-to-middle-income countries. This will provide important information for planning exit strategies that balance socio-economic benefits with public health.

Forensic likelihood ratio: Statistical problems and pitfalls.

This article is a response to the position papers published in the Science & Justice virtual special issue on measuring and reporting the precision of forensic likelihood ratios. I point out a number of serious statistical errors in some of these papers. These issues need to be properly addressed before the philosophical debate can be conducted in earnest.

Deep determinism and the assessment of mechanistic interaction.

Given two variables that causally influence a binary response, we formalize the idea that their effects operate through a common mechanism, in which case we say that the two variables interact mechanistically. We introduce a mechanistic interaction relationship of "interference" that is asymmetric in the two causal factors. Conditions and assumptions under which such mechanistic interaction can be tested under a given regime of data collection, be it interventional or observational, are expressed in terms of conditional independence relationships between the problem variables, which can be manipulated with the aid of causal diagrams. The proposed method is able, under appropriate conditions, to test for interaction between direct effects, and to deal with the situation where one of the two factors is a dichotomized version of a continuous variable. The method is illustrated with the aid of a study on heart disease.

Probabilistic sensitivity analysis in health economics.

Health economic evaluations have recently become an important part of the clinical and medical research process and have built upon more advanced statistical decision-theoretic foundations. In some contexts, it is officially required that uncertainty about both parameters and observable variables be properly taken into account, increasingly often by means of Bayesian methods. Among these, probabilistic sensitivity analysis has assumed a predominant role. The objective of this article is to review the problem of health economic assessment from the standpoint of Bayesian statistical decision theory with particular attention to the philosophy underlying the procedures for sensitivity analysis.

A Formal Treatment of Sequential Ignorability.

Taking a rigorous formal approach, we consider sequential decision problems involving observable variables, unobservable variables, and action variables. We can typically assume the property of extended stability, which allows identification (by means of "[Formula: see text]-computation") of the consequence of a specified treatment strategy if the "unobserved" variables are, in fact, observed-but not generally otherwise. However, under certain additional special conditions we can infer simple stability (or sequential ignorability), which supports [Formula: see text]-computation based on the observed variables alone. One such additional condition is sequential randomization, where the unobserved variables essentially behave as random noise in their effects on the actions. Another is sequential irrelevance, where the unobserved variables do not influence future observed variables. In the latter case, to deduce sequential ignorability in full generality requires additional positivity conditions. We show here that these positivity conditions are not required when all variables are discrete.

Response to: DNA identification by pedigree likelihood ratio accommodating population substructure and mutations.

Mutation models are important in many areas of genetics including forensics. This letter criticizes the model of the paper 'DNA identification by pedigree likelihood ratio accommodating population substructure and mutations' by Ge et al. (2010). Furthermore, we argue that the paper in some cases misrepresents previously published papers.Please see related letter: http://www.investigativegenetics.com/content/2/1/8.