Logistic growth model and modeling of factors for community case transmission

TL;DR

This paper analyzes COVID-19 community transmission in Senegal using logistic growth models to identify factors influencing spread and to forecast future cases for better control strategies.

Contribution

It introduces a statistical analysis of COVID-19 spread in Senegal with parameter estimation and forecasting to inform intervention measures.

Findings

Estimated growth factors and transmission parameters

Forecasted weekly increase and daily differences

Identified key factors associated with community transmission

Abstract

In this article, we analyze the spread of cases resulting from community transmission of COVID-19 in Senegal in order to identify statistical associations. The identification and knowledge of the factors associated with this community transmission can be a decision support tool to limit the spread of the disease. We estimate parameters and evaluate the growth factor, community rate, weekly increase and daily difference, and make forecasting to help on how to find concrete actions to control the situation.