Immunity Agent-Based Model (IABM) for epidemiological systems

COVID-19 was one of the pandemic episodes that has afflicted humanity in the last centuries and may not be the last. In science, the intense spread of the SARS-CoV-2 virus boosted the development and improvement of models, techniques, studies, and analyses that mobilized a large part of the scientific community. Many biomedical studies explore the immune response efficiency to avoid the infection caused by viruses in an attempt to provide subsidies to clarify how the pathogen affects the organism, however, few socio-environmental studies deal with this subject. In this sense, this work aims to present the Immunity Agent-Based Model (IABM), a computational model to replicate pathogen spread scenarios whose course is determined by the physiological characteristics of the individuals that form the community exposed to the pathogen. A set of rules are defined to represent the complex actions taken by the immune system during an infection process. The dynamic within the host considers innate response (non-specialized cells) and humoral response development by the work orchestrated by B and T cells. On a broader scale, the SEIR compartmental model drives the epidemiological state transitions of the agents. Simulations using the Monte Carlo method show that IABM can efficiently replicate virus spreading dynamics, presenting epidemiological curves and state transitions governed by the dynamic between immune response and viral load. The results display a significant variability of the innate and humoral responses of the agents, as well as different levels of viral load. Different recovery periods were observed, showing individuals whose infection uptime is much longer than others, suggesting the emergence of individuals with a possible long-term infection condition. IABM can easily be adapted to be proper for analysis of the spread of pathogens that cause different respiratory diseases.