RANDOM TESTING THE POPULATION COULD BE THE KEY TO GETTING OUT OF A LOCKDOWN EARLIER

TESTING, TESTING AND MORE TESTING

Friston KJ, Parr T, Zeidman P et al. Dynamic causal modelling of COVID-19 [version 1; peer review: awaiting peer review]. Wellcome Open Res 2020, 5:89 (https://doi.org/10.12688/wellcomeopenres.15881.1

(https://doi.org/10.12688/wellcomeopenres.15881.1)

Abstract This technical report describes a dynamic causal model of the spread of coronavirus through a population. The model is based upon ensemble or population dynamics that generate outcomes, like new cases and deaths over time. The purpose of this model is to quantify the uncertainty that attends predictions of relevant outcomes. By assuming suitable conditional dependencies, one can model the effects of interventions (e.g., social distancing) and differences among populations (e.g., herd immunity) to predict what might happen in different circumstances. Technically, this model leverages state-of-the-art variational (Bayesian) model inversion and comparison procedures, originally developed to characterise the responses of neuronal ensembles to perturbations. Here, this modelling is applied to epidemiological populations—to illustrate the kind of inferences that are supported and how the model per se can be optimised given timeseries data. Although the purpose of this paper is to describe a modelling protocol, the results illustrate some interesting perspectives on the current pandemic; for example, the nonlinear effects of herd immunity that speak to a self-organised mitigation process.

Dynamic causal modelling of COVID-19 [version 1; peer review: awaiting peer review]

10.1126/sciadv.aba2282

In both natural and engineered systems, communication often occurs dynamically over networks ranging from highly structured grids to largely disordered graphs. To use, or comprehend the use of, networks as efficient communication media requires understanding of how they propagate and transform information in the face of noise. Here, we develop a framework that enables us to examine how network structure, noise, and interference between consecutive packets jointly determine transmission performance in complex networks governed by linear dynamics. Mathematically, normal networks, which can be decomposed into separate low-dimensional information channels, suffer greatly from readout noise. Most details of their wiring have no impact on transmission quality. Non-normal networks, however, can largely cancel the effect of noise by transiently amplifying select input dimensions while ignoring others, resulting in higher net information throughput. Our theory could inform the design of new communication networks, as well as the optimal use of existing ones.

Efficient communication over complex dynamical networks: The role of matrix non-normality

10.31234/osf.io/mpxra

Self-report dominates research that considers the impact of technology on people and society. However, errors of measurement may obscure any genuine associations between technology use and mental health. We explored how different ways of measuring technology use, through psychometric scales, subjective estimates and objective logs leads to highly distorted associations between screen-time and health. Across two pre-registered designs, including: iPhone (n=199) and Android (n=46), we observed that measuring smartphone use via self-reports inflates any effect size between smartphone use and mental health symptomology (depression, anxiety, and stress). The size of the relationship was fourfold in study one, and nearly threefold in study two when employing a smartphone addiction scale in comparison to objective logs. Consequently, and beyond smartphones, any research which administers self-reports as a measure of problematic behaviors is likely to have findings which exaggerate any associations with mental health.

Subjective reports overstate the relationship between screen time and mental health