AUTHOR=Stolfi Paola , Vergni Davide , Castiglione Filippo 

TITLE=An agent-based multi-level model to study the spread of gonorrhea in different and interacting risk groups

JOURNAL=Frontiers in Applied Mathematics and Statistics

VOLUME=Volume 9 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/applied-mathematics-and-statistics/articles/10.3389/fams.2023.1241538

DOI=10.3389/fams.2023.1241538

ISSN=2297-4687

ABSTRACT=Mathematical modeling has emerged as a crucial component in understanding the epidemiology of infectious diseases. In fact, contemporary surveillance efforts for epidemic or endemic infections heavily rely on mathematical and computational methods.This study presents a novel agent-based multi-level model that depicts the transmission dynamics of gonorrhoea, a sexually transmitted infection (STI) caused by the bacterium Neisseria gonorrhoeae. This infection poses a significant public health challenge as it is endemic in numerous countries, and each year sees millions of new cases, including a concerning number of drug-resistant cases commonly referred to as gonorrhoea superbugs or super gonorrhoea. These drug-resistant strains exhibit a high level of resistance to recommended antibiotic treatments.The proposed model incorporates a multi-layer network of agents' interaction representing the dynamics of sexual partnerships. It also encompasses a transmission model, which quantifies the probability of infection during sexual intercourse, and a within-host model, which captures the immune activation following gonorrhoea infection in an individual.The uniqueness of this research lies in its objective to accurately depict the influence of distinct sexual risk groups and their interaction on the prevalence of gonorrhoea. This is achieved through a combination of agent-based modeling, which effectively captures intricate interactions among various risk groups, and probabilistic modeling, which enables a theoretical exploration of sexual network characteristics and contagion dynamics. This approach facilitates the identification of interpretable parameters from epidemiological data for a more comprehensive understanding of the disease evolution.