A Stochastic Model for Hepatitis B Patients Using Two Sources of Transmission

Abstract

The Hepatitis B (HBV) infection has led to a global pandemic. Addressing this issue requires the collaborative efforts of medical professionals, social workers, mathematicians, and statisticians to analyse various facets of the infection and its transmission. A particularly intriguing aspect of this research is estimating when an infected individual becomes seropositive. This brings us to the concept of the antigenic diversity threshold. This threshold refers to a specific level of antigenic diversity in the invading pathogen, beyond which the human immune system fails, resulting in seropositivity. In this paper, we derive the expected time to seroconversion by considering the antigenic diversity threshold comprising two elements: the natural threshold level of the human immune system and the threshold associated with antiretroviral therapy (ART). Additionally, numerical examples are provided.Keywords: Skin Lesion, Deep Learning, Ceroscopy, Classification, Neural Network, Melanoma.