Jennifer Mathieu

A model-based tool to predict the propagation of infectious disease via airports.

Summary Epidemics of novel or re-emerging infectious diseases have quickly spread globally via air travel, as highlighted by pandemic H1N1 influenza in 2009 (pH1N1). Federal, state, and local public health responders must be able to plan for and respond to these events at aviation points of entry. The emergence of a novel influenza virus and its spread to the United States were simulated for February 2009 from 55 international metropolitan areas using three basic reproduction numbers (R 0): 1.53, 1.70, and 1.90. Empirical data from the pH1N1 virus were used to validate our SEIR model. Time to entry to the U.S. during the early stages of a prototypical novel communicable disease was predicted based on the aviation network patterns and the epidemiology of the disease. For example, approximately 96% of origins (R 0 of 1.53) propagated a disease into the U.S. in under 75 days, 90% of these origins propagated a disease in under 50 days. An R 0 of 1.53 reproduced the pH1NI observations. The ability to anticipate the rate and location of disease introduction into the U.S. provides greater opportunity to plan responses based on the scenario as it is unfolding. This simulation tool can aid public health officials to assess risk and leverage resources efficiently.

Mission Effectiveness and European Airspace: U.S. Air Force CNS/ATM Planning for Future Years

The U.S. Air Force (USAF) uses the term Communication Navigation Surveillance/Air Traffic Management (CNS/ATM) for capabilities that allow its aircraft to use civil airspace and air traffic control services. The resulting ability to interoperate with air traffic control systems around the world supports the USAFs global, multi-faceted mission, but entails great expense in on-board equipage and training. It is important to understand the trade-offs that the USAF must make in assessing the value of specific CNS/ATM capabilities. In this paper, we describe a model-driven analysis to assess mission effectiveness. The analysis is accomplished via a five-step process involving military route generation, simulation of the civilian airspace activity, simulation of delay events for military flights, and evaluation of several figures of merit. The software modules used for these activities were a combination of existing packages and some newly-developed programs. The analysis accounts for planned USAF CNS/ATM capabilities by aircraft type, as well as any airspace and operational restrictions that might be encountered in specific geographic regions when the aircraft does not have the required capabilities. Two layers of interactions are investigated: first, within the USAF enterprise, specifically, between the fighter and tankers assets and, second, between the USAF and civilian ATM.

Multi-Scale Modeling of Bacterial Bioremediation

Bacterial bioremediation of sites contaminated with heavy metals such as chromium, copper, zinc, cadmium, and lead is facilitated by adding amendments (e.g. apatite—a phosphate mineral, chitin, acetate, etc.) to stimulate indigenous bacterial growth and metal reduction. The metals can be sorbed, degraded, transformed, or immobilized by various iron- and sulfate- reducing bacteria. The interaction of bacterial species populations in bioremediation systems is hypothesized to be more efficient at metal remediation than one species of bacteria. For example, phosphate solublizing bacteria might provide increased access to phosphate allowing faster growth rates for aerobic bacterial communities. The bioremediation system is modeled here using two simulation methodologies: Agent-Based at the individual bacterium scale and System Dynamics at the population scale, with the goal of observing patterns at these different scales. A simplified estuarine environment was defined with an oxygen gradient, amendment concentrations, and metal concentrations. Three bacterial populations were selected and behavioral rules were developed based on their growth requirements. An Agent-Based model was developed based on simple rules and environment structure, and the general pattern of their behavior was validated using experimental parameters from the literature. Similarly, a population scale model was developed for the three populations. Traditionally, these modeling paradigms have been used separately to describe biological systems such as bioremediation. The strength of the Agent-Based model is the visualization of the zones of bacterial growth. The strength of the System Dynamics model is the visualization of the model structure. One possibility for model integration is in parameter estimation. Exploring the simulation space with System Dynamics and running detailed simulations using the Agent-Based model may also be beneficial in augmenting experimental data (i.e., both micro and macro) to gain a greater understanding of bioremediation systems.

Evaluation of Crop Evapotranspiration Rates for Use in Fault Detection in Hydroponic Systems

Early fault detection of problems in hydroponic production systems necessities looking at rate variables such as water uptake. Crop transpiration rate is directly related to the quality of the crop. By placing a small production system on a scale, it was shown that a measurable change occurred within 20 minutes. It was found that air velocity must be manipulated or measured when trying to model evapotranspiration in greenhouses. Using the simple Penman-Monteith evapotranspiration equation gave a reasonable prediction for the 4-hour evapotranspiration data (R2 = 0.70) using constant resistance terms. Dynamically determined resistance parameters would improve this correlation and are necessary for the 1-hour and 20-minute data. Preliminary results support that using the evapotranspiration rate of a crop, it is possible to detect a sudden copper toxicity in the root zone.