most citedTracking and tracing in the UK: a dynamic causal modelling study

5 citations · 6 across the 4 of their papers we have counts for

collaborators

8 papers

q-bio.QM2020

Dynamic causal modelling of immune heterogeneity

Thomas Parr, Anjali Bhat, Peter Zeidman +4

An interesting inference drawn by some Covid-19 epidemiological models is that there exists a proportion of the population who are not susceptible to infection -- even at the start…

q-bio.NC2020

Parcels and particles: Markov blankets in the brain

Karl J. Friston, Erik D. Fagerholm, Tahereh S. Zarghami +4

At the inception of human brain mapping, two principles of functional anatomy underwrote most conceptions - and analyses - of distributed brain responses: namely functional segrega…

q-bio.PE2020

Effective immunity and second waves: a dynamic causal modelling study

Karl J. Friston, Thomas Parr, Peter Zeidman +11

This technical report addresses a pressing issue in the trajectory of the coronavirus outbreak; namely, the rate at which effective immunity is lost following the first wave of the…

q-bio.NC20201 cited

Sophisticated Inference

Karl Friston, Lancelot Da Costa, Danijar Hafner +2

Active inference offers a first principle account of sentient behaviour, from which special and important cases can be derived, e.g., reinforcement learning, active learning, Bayes…

q-bio.NC2020

Markov Blankets in the Brain

Ines Hipolito, Maxwell Ramstead, Laura Convertino +3

Recent characterisations of self-organising systems depend upon the presence of a Markov blanket: a statistical boundary that mediates the interactions between what is inside of an…

q-bio.QM20205 cited

Tracking and tracing in the UK: a dynamic causal modelling study

Karl J. Friston, Thomas Parr, Peter Zeidman +9

By equipping a previously reported dynamic causal model of COVID-19 with an isolation state, we modelled the effects of self-isolation consequent on tracking and tracing. Specifica…