2020/11/24 by Karl Friston, Guillaume Flandin, Friston, Karl J. +3
Computer Science · Mathematics · #Advanced Causal Inference Techniques #FOS: Biological sciences #FOS: Physical sciences #Machine Learning in Healthcare #Physics and Society (physics.soc-ph) #Populations and Evolution (q-bio.PE)
paper · pdf · doi:10.48550/arxiv.2011.12400
openalex publication_date 2020/11/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This technical report describes the rationale and technical details for the dynamic causal modelling of mitigated epidemiological outcomes based upon a variety of timeseries data. It details the structure of the underlying convolution or generative model (at the time of writing on 6-Nov-20). This report is intended for use as a reference that accompanies the predictions in following dashboard: https://www.fil.ion.ucl.ac.uk/spm/covid-19/dashboard