The Limits to Learning an SIR Process: Granular Forecasting for Covid-19

by   Jackie Baek, et al.

A multitude of forecasting efforts have arisen to support management of the ongoing COVID-19 epidemic. These efforts typically rely on a variant of the SIR process and have illustrated that building effective forecasts for an epidemic in its early stages is challenging. This is perhaps surprising since these models rely on a small number of parameters and typically provide an excellent retrospective fit to the evolution of a disease. So motivated, we provide an analysis of the limits to estimating an SIR process. We show that no unbiased estimator can hope to learn this process until observing enough of the epidemic so that one is approximately two-thirds of the way to reaching the peak for new infections. Our analysis provides insight into a regularization strategy that permits effective learning across simultaneously and asynchronously evolving epidemics. This strategy has been used to produce accurate, granular predictions for the COVID-19 epidemic that has found large-scale practical application in a large US state.



There are no comments yet.


page 1

page 2

page 3

page 4


A Mathematical Dashboard for the Analysis of Italian COVID-19 Epidemic Data

A data analysis of the COVID-19 epidemic is proposed on the basis of the...

Estimating the case fatality rate of a disease during the course of an epidemic with an application to COVID-19 in Argentina

We present an accurate estimator of the case fatality rate that can be c...

Mid-Epidemic Forecasts of COVID-19 Cases and Deaths: A Bivariate Model applied to the UK

The evolution of the COVID-19 epidemic has been accompanied by accumulat...

Limits of epidemic prediction using SIR models

The Susceptible-Infectious-Recovered (SIR) equations and their extension...

An Optimal Control Approach to Learning in SIDARTHE Epidemic model

The COVID-19 outbreak has stimulated the interest in the proposal of nov...

Simulation of Covid-19 epidemic evolution: are compartmental models really predictive?

Computational models for the simulation of the severe acute respiratory ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.