Nidhi Kiranbhai Parikh

Modeling the effect of transient populations on epidemics in Washington DC

Large numbers of transients visit big cities, where they come into contact with many people at crowded areas. However, epidemiological studies have not paid much attention to the role of this subpopulation in disease spread. We evaluate the effect of transients on epidemics by extending a synthetic population model for the Washington DC metro area to include leisure and business travelers. A synthetic population is obtained by combining multiple data sources to build a detailed minute-by-minute simulation of population interaction resulting in a contact network. We simulate an influenza-like illness over the contact network to evaluate the effects of transients on the number of infected residents. We find that there are significantly more infections when transients are considered. Since much population mixing happens at major tourism locations, we evaluate two targeted interventions: closing museums and promoting healthy behavior (such as the use of hand sanitizers, covering coughs, etc.) at museums. Surprisingly, closing museums has no beneficial effect. However, promoting healthy behavior at the museums can both reduce and delay the epidemic peak. We analytically derive the reproductive number and perform stability analysis using an ODE-based model.

Planning and response in the aftermath of a large crisis: an agent-based informatics framework

We present a synthetic information and modeling environment that can allow policy makers to study various counter-factual experiments in the event of a large human-initiated crisis. The specific scenario we consider is a ground detonation caused by an improvised nuclear device in a large urban region. In contrast to earlier work in this area that focuses largely on the prompt effects on human health and injury, we focus on co-evolution of individual and collective behavior and its interaction with the differentially damaged infrastructure. This allows us to study short term secondary and tertiary effects. The present environment is suitable for studying the dynamical outcomes over a two week period after the initial blast. A novel computing and data processing architecture is described; the architecture allows us to represent multiple co-evolving infrastructures and social networks at a highly resolved temporal, spatial, and individual scale. The representation allows us to study the emergent behavior of individuals as well as specific strategies to reduce casualties and injuries that exploit the spatial and temporal nature of the secondary and tertiary effects. A number of important conclusions are obtained using the modeling environment. For example, the studies decisively show that deploying ad hoc communication networks to reach individuals in the affected area is likely to have a significant impact on the overall casualties and injuries.

Summarizing Simulation Results Using Causally-Relevant States

As increasingly large-scale multiagent simulations are being implemented, new methods are becoming necessary to make sense of the results of these simulations. Even concisely summarizing the results of a given simulation run is a challenge. Here we pose this as the problem of simulation summarization: how to extract the causally-relevant descriptions of the trajectories of the agents in the simulation. We present a simple algorithm to compress agent trajectories through state space by identifying the state transitions which are relevant to determining the distribution of outcomes at the end of the simulation. We present a toy-example to illustrate the working of the algorithm, and then apply it to a complex simulation of a major disaster in an urban area.

Cover Your Cough! Quantifying the Benefits of a Localized Healthy Behavior Intervention on Flu Epidemics in Washington DC

We use a synthetic population model of Washington DC, including residents and transients such as tourists and business travelers, to simulate epidemics of influenza-like illnesses. Assuming that the population is vaccinated at the compliance levels reported by the CDC, we show that additionally implementing a policy that encourages healthy behaviors (such as covering your cough and using hand sanitizers) at four major museum locations around the National Mall can lead to very significant reductions in the epidemic. These locations are chosen because there is a high level of mixing between residents and transients. We show that this localized healthy behavior intervention is approximately equivalent to a 46.14% increase in vaccination compliance levels.

A comparison of multiple behavior models in a simulation of the aftermath of an improvised nuclear detonation

We describe a large-scale simulation of the aftermath of a hypothetical 10kT improvised nuclear detonation at ground level, near the White House in Washington DC. We take a synthetic information approach, where multiple data sets are combined to construct a synthesized representation of the population of the region with accurate demographics, as well as four infrastructures: transportation, healthcare, communication, and power. In this article, we focus on the model of agents and their behavior, which is represented using the options framework. Six different behavioral options are modeled: household reconstitution, evacuation, healthcare-seeking, worry, shelter-seeking, and aiding & assisting others. Agent decision-making takes into account their health status, information about family members, information about the event, and their local environment. We combine these behavioral options into five different behavior models of increasing complexity and do a number of simulations to compare the models.

Integrating Behavior and Microsimulation Models

Microsimulations focus on modeling routine activities of individuals and have been used for modeling and planning urban systems like transportation, energy demand, and epidemiology. On the other hand, planning for emergency situations (e.g., disasters) needs to account for human behavior which is not routine or pre-planned but depends upon the current situation like the amount of physical damage or safety of family. Here, we focus on modeling the aftermath of a hypothetical detonation of an improvised nuclear device in Washington DC. We review various behavior models from the literature and provide motivation for our model which is conceptually based on the formalism of decentralized semi-Markov decision processes with communication, using the framework of options. We describe our approach for integrating behavior and microsimulation models where the behavior model specifies context-dependent behaviors (like looking for family members, sheltering, evacuation, and search and rescue) and the synthetic population provides information about demographics and infrastructures. We present results from a number of simulation runs.