id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-130240-bfnav9sn	Friston, Karl J.	Dynamic causal modelling of COVID-19	2020-04-09		.txt	text/plain	13594	688	51	The model is based upon ensemble or population dynamics that generate outcomes, like new cases and deaths over time. 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. Specifically, the posterior densities (i.e., Bayesian beliefs) over states and parameters-and the precision of random fluctuations-are optimised by maximising a variational bound on the model's marginal likelihood, also known as model evidence. This figure reports the differences among countries in terms of selected parameters of the generative model, ranging from the effective population size, through to the probability of testing its denizens. In this example (based upon posterior expectations for the United Kingdom and Bayesian parameter averages over countries), death rates (per day) decrease progressively with social distancing.	./cache/cord-130240-bfnav9sn.txt	./txt/cord-130240-bfnav9sn.txt