Analysis of COVID-19 evolution in Senegal: impact of health care capacity
This work addresses public health planning for COVID-19 in Senegal, focusing on resource management to prevent system overload, but it is incremental as it applies existing methods to a specific regional context.
The study tackled modeling COVID-19 evolution in Senegal by incorporating a time-dependent health care capacity with logistic growth to assess the impact of anticipation and timing on avoiding overwhelming the system, and used machine learning to project cumulative cases from March to December 2020.
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.