A Logistic Fractional Model with Control Measures for Cumulative Cases of COVID-19

Abstract

The curve of cumulative cases of individuals infected by COVID-19 shows similar growth to the logistic curve in the period referring to each epidemic "wave'', as each peak of active cases is called. Considering that in pandemic scenarios it is common to seek control measures based on previous experiences. In this paper, we model the curve of cumulative cases through a logistic model with infected removal to include the control measures in the dynamics. This model is based on fractional differential equations to also include the memory effect. We study the scenario of the first two "waves'' in the analyzed countries: Brazil, China, Italy, and Switzerland. Scenarios with and without control measures are compared, proving the importance of control measures such as isolation. Moreover, this model makes it possible to determine the portion of the population that did not participate in the dynamics of the spread of the disease, as well as to analyze how the number of infected people reduced in each country.

Keywords

• Logistic model with removal
• fractional differential equations
• social isolation.