Home Publications · Oden Institute
Assessment of Predictability of a Class of Models of Growth of Coronavirus 19 Cases as an Exercise in Predictive Computational Science
Abstract
A report from the Spring 2020 graduate course *Foundations of Predictive Computational Science* at the Oden Institute, taught by J. Tinsley Oden with Prashant K. Jha. The class was set a single question as its final assignment: take a simple growth model of disease cases, and establish how far it can actually be trusted to predict. Each solution calibrates the model against roughly forty days of reported COVID-19 case data from the United States, Japan and South Korea, validates it against a later window, and predicts a quantity of interest — the total case count at around one hundred days — with its uncertainty. The solutions differ in the metrics and validation tolerances they choose, which is part of the point. The report's own conclusion is the honest one: the spread of predicted quantities suggests the simple model **cannot** capture the real trend. What the exercise demonstrates is not a forecast but the procedure — how to establish whether a prediction is worth anything before relying on it.
Written up from the class solutions, and a compact demonstration of the argument that runs through this whole program: a model’s usefulness is a claim that has to be established, not assumed.