Dynamic causal modelling of mitigated epidemiological outcomes
Karl J. Friston, Guillaume Flandin, Adeel Razi
TL;DR
This paper presents a detailed dynamic causal modelling framework for predicting mitigated epidemiological outcomes using various timeseries data, providing a technical reference for COVID-19 impact analysis.
Contribution
It introduces a novel convolution-based generative model for epidemiological data, enhancing the accuracy of outcome predictions under mitigation strategies.
Findings
Model effectively captures epidemic dynamics
Provides reliable predictions for policy planning
Serves as a technical reference for epidemiological modelling
Abstract
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
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Taxonomy
TopicsMachine Learning in Healthcare · Advanced Causal Inference Techniques