Monitoring and Forecasting COVID-19: Heuristic Regression, Susceptible-Infected-Removed Model and, Spatial Stochastic.

ABSTRACT: The COVID-19 pandemic has had worldwide devastating effects on human lives, highlighting the need for tools to predict its development. The dynamics of such public-health threats can often be efficiently analyzed through simple models that help to make quantitative timely policy decisions. We benchmark a minimal version of a Susceptible-Infected-Removed model for infectious diseases (SIR) coupled with a simple least-squares Statistical Heuristic Regression (SHR) based on a lognormal distribution. We derive the three free parameters for both models in several cases and test them against the amount of data needed to bring accuracy in predictions. The SHR model is ≈ ±2% accurate about 20 days past the second inflexion point in the daily curve of cases, while the SIR model reaches a similar ac

SUBMITTER: de Andres PL 

PROVIDER: S-EPMC7611421 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature