Forecasting Epidemics

[I am always grateful to Andrew Harvey for keeping me updated on his latest work. The post below is adapted from private correspondence with Andrew, but any errors of commission or omission are mine alone.]

I blogged previously on early-vintage Harvey-Kattuman work. Their latest is "A Farewell to R: Time-Series Models for Tracking and Forecasting Epidemics". (Also see here.) You can think of it as a farewell to R, as their title indicates, or as a vastly-improved approach to tracking R in real time, producing both nowcast point estimates and confidence intervals.

Looks extremely promising, in contrast to traditional/current "best-practice" methods, as documented for example in Spectator magazine on Sage vs reality, as of Saturday (22 January):

        Sage ‘scenarios’ vs actual: an update | The Spectator.

(Sage is a body of distinguished epidemiologists who have been advising the UK government. They have a track record of pessimistic scenarios based on traditional/current "best practice" methods, and they have been spectacularly wrong. At the beginning of last September they were forecasting between 1500 and 7000 UK hospital admissions per day by the end of September. The Harvey-Kattuman model was predicting 1000. In the end it was around 800.)