“Inferring change points in the spread of COVID-19 reveals the effectiveness of interventions”

J. Dehning et al., Science 369, eabb9789 (2020). DOI: 10.1126/science.abb9789

Source code and data.

Note: This is not a classical approach to assessing strength of interventions using either counterfactuals or other kinds of causal inference. Accordingly, the argument for the effectiveness of interventions is weaker than it might be.

About ecoquant

See https://wordpress.com/view/667-per-cm.net/ Retired data scientist and statistician. Now working projects in quantitative ecology and, specifically, phenology of Bryophyta and technical methods for their study, notably Macrophotography. Some photos of mine: https://www.flickr.com/photos/198372469@N03/
This entry was posted in American Association for the Advancement of Science, American Statistical Association, Bayesian, Bayesian computational methods, causal inference, causation, changepoint detection, coronavirus, counterfactuals, COVID-19, epidemiology, SARS-CoV-2, state-space models, statistical series, time series. Bookmark the permalink.

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