Emerging Infectious Disease: A Computational Multi-agent Model

Abstract

In today's global society there exists a need to understand and predict the behavior of vector-borne diseases. With globalization, human groups tend to interact with other groups that can have one or multiple types of viruses. Currently, there are many mathematical models for studying patterns of emerging infectious diseases. These mathematical models are based on differential equations and can become unmanageable due to many parameters involved. With this in mind, we design and implement a simple spatial computational multi-agent model that can be used as a tool to analyze and predict the behavior of emerging infectious diseases. Our novel computational agent-based model integrated with evolution and phylogeny to simulate and understand emerging infectious diseases, which enables us to prevent or control outbreaks of infectious diseases in an effective and timely manner. Our multi-agent spatial-temporal model contributes to epidemiology, public health and computational simulation in several folds: First, our simulation offers an effective way to train public policy decision-makers who will respond to emergent outbreaks of infectious diseases in an appropriately and timely manner. Second, our model has the potential to aid real-time disease control and decision making. Third, our model uniquely takes evolution of viruses into account. Evolution of viruses means their genomic DNA/RNA sequence can mutate and compete for subpopulations of hosts (human, birds/pets). Our implementation provides graphical representation of the results by conducting a set of experiments under various settings.