The study published in Harvard Data Science Review, focuses on the spread of epidemics and specifically the current COVID-19 coronavirus pandemic, but the models are applicable to many other disciplines as well.

Models that seek to estimate epidemic trajectories fall into two classes: structural models that seek to capture the causal mechanisms underlying disease transmission, and time series models that are oblivious to the precise mechanism and extract changing trends from historical data. When there is uncertainty about many clinical and transmission aspects of disease, as in the current pandemic, “structural models are forced to rely on many assumptions and unknowns,” the study says.

The study’s new class of time series models therefore provides a “simple and transparent” route to predict an epidemic’s trajectory – and can be adapted to include additional factors including seasonal or day-of-the-week effects.

Specifically, the paper formulates a class of models in which the growth rate of the cumulative series – for example the total number of COVID-19 infections to date – changes over time. “Allowing the trend to be time-varying introduces further flexibility and enables the effects of changes in policy to be tracked and evaluated,” the study says.

The paper – entitled “Time series models based on growth curves with applications to forecasting coronavirus” – is co-authored by Andrew Harvey of the University of Cambridge, and by Dr Paul Kattuman, Reader in Economics at Cambridge Judge Business School. https://hdsr.mitpress.mit.edu/pub/ozgjx0yn/release/2