# Epidemic Spreading in Urban Areas Using Agent-Based Transportation Models
## 2 Background
### Agent-Based Epidemic Spread Models  
In contrast, Agent-based models (ABMs) allow interactions among individuals (agents) and can overcome the limitations of compartmental and metapopulation models. Modeling epidemic spreading using an agent-based approach pursues the progression of a disease through each individual, and tracks the contacts of each individual with others in the relevant social networks and geographical areas (e.g., co-workers, schoolmates) [18]. The overall system behavior results from the interactions among the multiple single agents of different kinds. The behaviors and interactions of the agents are mostly specified through a simple set of rules or a more sophisticated entity with many interacting rules. It is possible to consider individual variations in the behavioral rules (\"heterogeneity\") and random influences or variations (\"stochasticity\") [26]. This allows an abstraction of human actions by defining autonomous decision-making agents for the complex system. Although these models effectively track the progression of the disease through each individual and track the contacts of each individual with others in the relevant system (social or natural), researchers started to add physical infrastructures such as road networks, and real geographic environments to account for more complex interactions among susceptible and infected individuals. [PERSON] et al. [27] developed a simulation tool called EpiSims, which is a highly resolved agent-based simulation tool, combining realistic estimates of population mobility, based on census and land-use data, with parameterized models for simulating the progress of a disease within a host and of transmission between hosts. The synthetic social connection model is derived from agent activities produced by the Transportation Analysis Simulation System (TRANSIM). [PERSON] and [PERSON] [18] developed an agent-based modeling approach, which integrates geographic information systems (GIS) to simulate a measles outbreak in an urban environment (Burnaby, BC, Canada), as a result of individuals' interactions in a geospatial context. Individuals in a closed population are explicitly represented by agents associated with places where they interact with other agents. They are endowed with mobility, through a transportation network allowing them to move between places within the urban environment. [PERSON] et al. [10] reconstructed the 2003/2004 H3N2  
Figure 2: Schematic representation of a metapopulation model. The system is composed of a heterogeneous network of subpopulations or patches, connected by migration processes. Each patch contains a population of individuals who are characterized with respect to their stage of the disease (e.g., susceptible, infected, removed), and identified with a different color in the picture. Individuals can move from a subpopulation to another on the network of connections among subpopulations. (Taken from [PERSON] and [PERSON] [25]). influenza epidemic in Switzerland with a spatially explicit, individual-based model. The model makes use of disaggregated human travel data of whole Switzerland generated by the open source transport simulation software MATSim. [PERSON] et al. [28] modeled an artificial town populated with 1500 agents to study the propagation rules and control measures of the H1N1 influenza virus. Beside modeling behavior changes due to the epidemic, the other focus is the consideration of public transport which is a high-rate infection place due to high population density. Because agent-based transport models are relatively computationally intensive, it was only in the last decade that they found their way into the modeling and estimation of epidemic spreads. In the same period, considerable progress was also made in modeling traffic flows that have not been incorporated into epidemic spread modeling until now.