Dynamic causal modelling of COVID-19.

@article{Friston2020DynamicCM,
  title={Dynamic causal modelling of COVID-19.},
  author={Karl J. Friston and T. Parr and P. Zeidman and A. Razi and G. Flandin and J. Daunizeau and O. Hulme and A. J. Billig and V. Litvak and R. Moran and C. Price and C. Lambert},
  journal={Wellcome open research},
  year={2020},
  volume={5},
  pages={
          89
        }
}
  • Karl J. Friston, T. Parr, +9 authors C. Lambert
  • Published 2020
  • Medicine, Biology, Computer Science
  • Wellcome open research
  • 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… CONTINUE READING
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