Discrete Gompertz and Generalized Logistic Models for early monitoring of the COVID-19 pandemic in Cuba

TL;DR

This paper introduces discrete versions of Gompertz and Generalized Logistic models for early COVID-19 monitoring in Cuba, demonstrating accurate short-term predictions using real data and detailed parameter estimation methods.

Contribution

It develops and applies discrete growth models for epidemic forecasting, providing a novel approach to early monitoring with detailed methodology and implementation code.

Findings

Accurate short-term COVID-19 predictions in Cuba

Discrete models outperform continuous ones in early stages

Methodology includes detailed parameter estimation and code

Abstract

For the last few years there has been a resurgence in the use of phenomenological growth models for predicting the early dynamics of infectious diseases. These models assume that time is a continuous variable whereas in the present contribution, the discrete versions of Gompertz and Generalized Logistic models are used for early monitoring and short-term forecasting of the spread of an epidemic in a region. The time-continuous models are represented mathematically by first-order differential equations while their discrete versions are represented by first-order difference equations that involve parameters that should be estimated prior to forecasting. The methodology for estimating such parameters is described in detail. Real data of COVID-19 infection in Cuba is used to illustrate this methodology. The proposed methodology was implemented for the first thirty-five days, being able to predict with very good precision the data reported for the following twenty days. The codes implemented to study the Gompertz model in differences are included in an appendix with each step of the methodology identified.