Analysis of COVID-19 evolution in Senegal: impact of health care capacity

TL;DR

This paper models COVID-19 progression in Senegal by integrating a time-dependent healthcare capacity with logistic growth and employs machine learning to project case numbers, emphasizing the importance of timely response to prevent healthcare system overload.

Contribution

It introduces a compartmental model with dynamic healthcare capacity and applies machine learning for case projection, providing insights into epidemic management in Senegal.

Findings

Healthcare capacity growth can prevent system overload.

Timely anticipation is crucial for effective epidemic response.

Machine learning projections align with observed case trends.

Abstract

We consider a compartmental model from which we incorporate a time-dependent health care capacity having a logistic growth. This allows us to take into account the Senegalese authorities response in anticipating the growing number of infected cases. We highlight the importance of anticipation and timing to avoid overwhelming that could impact considerably the treatment of patients and the well-being of health care workers. A condition, depending on the health care capacity and the flux of new hospitalized individuals, to avoid possible overwhelming is provided. We also use machine learning approach to project forward the cumulative number of cases from March 02, 2020, until 1st December, 2020.