Fractional-order deterministic epidemic model for the spread and control of HIV/AIDS with special reference to Mexico and India

This paper introduces a deterministic fractional-order epidemic model (FOEM) for studying the transmission dynamics of the human immunodeficiency virus (HIV) and acquired immunodeficiency syndrome (AIDS). The model highlights the substantial role of unaware and undetected HIV-infected individuals in spreading the disease. Control strategies, such as wielding condoms, level of preventive measures to avoid infection, and self-strictness of susceptibles in sexual contact, have been incorporated into the study. The basic reproduction number R0 alpha has been derived, which suggests the conditions for ensuring the persistence and elimination of the disease. Further, to validate the model, actual HIV data taken from Mexico and India separately have been used. The disease dynamics and its control in both countries are analyzed broadly. The values of biological parameters are estimated at which numerical solutions better match the actual data of HIV patients in the case of fractional-order (FO) instead of integer-order (IO). Moreover, in the light of R0 alpha, our findings forecast that the disease will abide in the population in Mexico, and at the same time, it will die out from India after a long time.(c) 2023 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.

Automated summary

This paper presents a deterministic fractional-order epidemic model (FOEM) to study the transmission dynamics of HIV and AIDS. The model highlights the role of undetected and unaware HIV-infected individuals in spreading the disease. Control strategies and their impact on disease persistence or elimination are analyzed using actual HIV data from Mexico and India. The results show that the disease will persist in Mexico but eventually die out in India after a long time, based on the derived basic reproduction number R0 alpha and its implications on disease dynamics.