Differential Equations and Applications to COVID-19

TL;DR

This paper applies the Verhulst logistic equation to model and predict COVID-19 cases in Senegal, demonstrating the model's effectiveness through data analysis and comparison with actual data from April 2023.

Contribution

The study introduces a retrospective application of the Verhulst logistic model to COVID-19 data in Senegal and evaluates its predictive accuracy using Python.

Findings

The logistic model accurately predicted COVID-19 cases for April 2023.

Python facilitated efficient data analysis and model implementation.

The model's predictions closely matched actual reported cases.

Abstract

This paper focuses on the application of the Verhulst logistic equation to model in retrospective the total COVID-19 cases in Senegal during the period from April 2022 to April 2023. Our predictions for April 2023 are compared with the real COVID-19 data for April 2023 to assess the accuracy of the model. The data analysis is conducted using Python programming language, which allows for efficient data processing and prediction generation.