The role of subway travel in an influenza epidemic: a New York City simulation.

The interactions of people using public transportation in large metropolitan areas may help spread an influenza epidemic. An agent-based model computer simulation of New York City's (NYC's) five boroughs was developed that incorporated subway ridership into a Susceptible-Exposed-Infected-Recovered disease model framework. The model contains a total of 7,847,465 virtual people. Each person resides in one of the five boroughs of NYC and has a set of socio-demographic characteristics and daily behaviors that include age, sex, employment status, income, occupation, and household location and membership. The model simulates the interactions of subway riders with their workplaces, schools, households, and community activities. It was calibrated using historical data from the 1957-1958 influenza pandemics and from NYC travel surveys. The surveys were necessary to enable inclusion of subway riders into the model. The model results estimate that if influenza did occur in NYC with the characteristics of the 1957-1958 pandemic, 4% of transmissions would occur on the subway. This suggests that interventions targeted at subway riders would be relatively ineffective in containing the epidemic. A number of hypothetical examples demonstrate this feature. This information could prove useful to public health officials planning responses to epidemics.

Including the Group Quarters Population in the US Synthesized Population Database.

In 2005, RTI International researchers developed methods to generate synthesized population data on US households for the US Synthesized Population Database. These data are used in agent-based modeling, which simulates large-scale social networks to test how changes in the behaviors of individuals affect the overall network. Group quarters are residences where individuals live in close proximity and interact frequently. Although the Synthesized Population Database represents the population living in households, data for the nation's group quarters residents are not easily quantified because of US Census Bureau reporting methods designed to protect individuals' privacy.Including group quarters population data can be an important factor in agent-based modeling because the number of residents and the frequency of their interactions are variables that directly affect modeling results. Particularly with infectious disease modeling, the increased frequency of agent interaction may increase the probability of infectious disease transmission between individuals and the probability of disease outbreaks.This report reviews our methods to synthesize data on group quarters residents to match US Census Bureau data. Our goal in developing the Group Quarters Population Database was to enable its use with RTI's US Synthesized Population Database in the Modeling of Infectious Diseases Agent Study.

Protecting health care workers: a pandemic simulation based on Allegheny County.

BACKGROUND AND OBJECTIVES: The Advisory Committee on Immunization Practices has identified health care workers (HCWs) as a priority group to receive influenza vaccine. Although the importance of HCW to the health care system is well understood, the potential role of HCW in transmission during an epidemic has not been clearly established. METHODS: Using a standard SIR (Susceptible-Infected-Recovered) framework similar to previously developed pandemic models, we developed an agent-based model (ABM) of Allegheny County, PA, that incorporates the key health care system features to simulate the spread of an influenza epidemic and its effect on hospital-based HCWs. FINDINGS: Our simulation runs found the secondary attack rate among unprotected HCWs to be approximately 60% higher (54.3%) as that of all adults (34.1%), which would result in substantial absenteeism and additional risk to HCW families. Understanding how a pandemic may affect HCWs, who must be available to treat infected patients as well as patients with other medical conditions, is crucial to policy makers' and hospital administrators' preparedness planning.

Synthesized Population Databases: A US Geospatial Database for Agent-Based Models.

Agent-based models simulate large-scale social systems. They assign behaviors and activities to "agents" (individuals) within the population being modeled and then allow the agents to interact with the environment and each other in complex simulations. Agent-based models are frequently used to simulate infectious disease outbreaks, among other uses.RTI used and extended an iterative proportional fitting method to generate a synthesized, geospatially explicit, human agent database that represents the US population in the 50 states and the District of Columbia in the year 2000. Each agent is assigned to a household; other agents make up the household occupants.For this database, RTI developed the methods for generating synthesized households and personsassigning agents to schools and workplaces so that complex interactions among agents as they go about their daily activities can be taken into accountgenerating synthesized human agents who occupy group quarters (military bases, college dormitories, prisons, nursing homes).In this report, we describe both the methods used to generate the synthesized population database and the final data structure and data content of the database. This information will provide researchers with the information they need to use the database in developing agent-based models.Portions of the synthesized agent database are available to any user upon request. RTI will extract a portion (a county, region, or state) of the database for users who wish to use this database in their own agent-based models.