TEAM - Transmission of Epidemic Among Membranes

The COVID-19 pandemic has underscored the crucial role of computational simulation in understanding, predicting, and controlling infectious disease dynamics, as well as supporting data-driven decision-making in public health. Within this context, the biologically inspired membrane computing has been shown to be promising for modeling complex epidemiological systems, due to its population-based inherent parallelism and compartmental structure. Two models stand out for their complementary strengths among the existing works that adopt this paradigm. One, known as LOIMOS, focuses on detailed representations of infection and symptom progression, offering a biologically rich modeling of disease stages. The other, referred to as MVT, introduces behavioral dynamics, allowing individuals to adapt their actions based on perceived risk, personal willingness to vaccinate, and inter-provincial mobility preferences. This work combines the core ideas of LOIMOS and MVT into a unified simulation framework, referred to as TEAM (Transmission of Epidemic Among Membranes), which integrates biological accuracy with behavioral flexibility. The goal is to create a general-purpose model adaptable to various infectious diseases beyond COVID-19, usable as a decision support system. Central challenges included resolving formal and structural differences between the two source models and harmonizing their rule-based dynamics.