"Back to the future" projections for COVID-19 surges

by   J. Sunil Rao, et al.

We argue that information from countries who had earlier COVID-19 surges can be used to inform another country's current model, then generating what we call back-to-the-future (BTF) projections. We show that these projections can be used to accurately predict future COVID-19 surges prior to an inflection point of the daily infection curve. We show, across 12 different countries from all populated continents around the world, that our method can often predict future surges in scenarios where the traditional approaches would always predict no future surges. However, as expected, BTF projections cannot accurately predict a surge due to the emergence of a new variant. To generate BTF projections, we make use of a matching scheme for asynchronous time series combined with a response coaching SIR model.


Modeling Effect of Lockdowns and Other Effects on India Covid-19 Infections Using SEIR Model and Machine Learning

The SEIR model is a widely used epidemiological model used to predict th...

When and How to Lift the Lockdown? Global COVID-19 Scenario Analysis and Policy Assessment using Compartmental Gaussian Processes

The coronavirus disease 2019 (COVID-19) global pandemic has led many cou...

Generalized logistic growth modeling of the COVID-19 outbreak in 29 provinces in China and in the rest of the world

The COVID-19 has been successfully contained in China but is spreading a...

COVID-19 cases prediction using regression and novel SSM model for non-converged countries

Anticipating the quantity of new associated or affirmed cases with novel...

An Analysis of International Use of Robots for COVID-19

This article analyses data collected on 338 instances of robots used exp...

Please sign up or login with your details

Forgot password? Click here to reset