Dennis R. Powell

Constructing rigorous and broad biosurveillance networks for detecting emerging zoonotic outbreaks.

Determining optimal surveillance networks for an emerging pathogen is difficult since it is not known beforehand what the characteristics of a pathogen will be or where it will emerge. The resources for surveillance of infectious diseases in animals and wildlife are often limited and mathematical modeling can play a supporting role in examining a wide range of scenarios of pathogen spread. We demonstrate how a hierarchy of mathematical and statistical tools can be used in surveillance planning help guide successful surveillance and mitigation policies for a wide range of zoonotic pathogens. The model forecasts can help clarify the complexities of potential scenarios, and optimize biosurveillance programs for rapidly detecting infectious diseases. Using the highly pathogenic zoonotic H5N1 avian influenza 2006-2007 epidemic in Nigeria as an example, we determined the risk for infection for localized areas in an outbreak and designed biosurveillance stations that are effective for different pathogen strains and a range of possible outbreak locations. We created a general multi-scale, multi-host stochastic SEIR epidemiological network model, with both short and long-range movement, to simulate the spread of an infectious disease through Nigerian human, poultry, backyard duck, and wild bird populations. We chose parameter ranges specific to avian influenza (but not to a particular strain) and used a Latin hypercube sample experimental design to investigate epidemic predictions in a thousand simulations. We ranked the risk of local regions by the number of times they became infected in the ensemble of simulations. These spatial statistics were then complied into a potential risk map of infection. Finally, we validated the results with a known outbreak, using spatial analysis of all the simulation runs to show the progression matched closely with the observed location of the farms infected in the 2006-2007 epidemic.

An Integrated Simulation of Pandemic Influenza Evolution, Mitigation and Infrastructure Response

Decision makers, faced with highly complex alternatives for protecting our nations critical infrastructures must understand the consequences of policy options before they enact solutions to prevent and mitigate disasters. An effective way to examine these tradeoffs is to use a computer simulation that integrates high level representations of each infrastructure, their interdependencies and reactions to a variety of potential disruptions. To address this need, the Critical Infrastructure Protection Decision Support System (CIPDSS) project, funded by the Department of Homeland Security Science and Technology Directorate (DHS S&T), has developed a decision support tool that provides insights to help decision makers make risk-informed decisions. With the addition of a disease progression simulation, the CIPDSS tool has a unique ability to provide a high-level, integrated analysis of a pandemic influenza outbreak while representing the impact on critical infrastructures. This simulation models the time-dependent evolution of the disease and can be calibrated to prior data or to other higher fidelity models as appropriate. Mitigation options such as the use of antivirals and vaccines as prophylaxis, treatment or some combination as well as quarantine options can be assessed. Special attention is given to impacts to the population through sickness, targeted quarantine, or fear-based self-isolation and the resulting impacts on critical infrastructure operations.

An epidemiological model of spatial coupling for trips longer than the infectious period.

One of the standard methods of accounting for inter-population disease spread in equation-based epidemiology models is through a transportation operator. Implicit in the use of the transportation operator, however, is an assumption that daily travel volumes are small compared to overall population sizes, an assumption that can break down for modern rates of international travel or local commuter traffic. Alternative types of coupling have been proposed in the limit that trip durations are much shorter than the infectious period. We present an extension of these phenomenological models that relaxes both assumptions. We show that the approach produces more accurate results when assessing the impact of mitigative actions using modern travel volumes.

Measuring the uncertainties of pandemic influenza

It has become critical to assess the potential range of consequences of a pandemic influenza outbreak given the uncertainty about its disease characteristics while investigating risks and mitigation strategies of vaccines, antivirals, and social distancing measures. Here, we use a simulation model and rigorous experimental design with sensitivity analysis that incorporates uncertainty in the pathogen behaviour and epidemic response to show the extreme variation in the consequences of a potential pandemic outbreak in the USA. Using sensitivity analysis we found the most important disease characteristics are the fraction of the transmission that occur prior to symptoms, the reproductive number, and the length of each disease stage. Using data from the historical pandemics and for potential viral evolution, we show that response planning may underestimate the pandemic consequences by a factor of two or more.

Design of an intelligent tutoring system for aircraft recognition

A microcomputer-based intelligent tutoring system for teaching aircraft recognition in support of air defense training is described. An overview of the conceptual design is presented, and the main components are described. The system presents realistic images to the student, dynamically assesses student knowledge, provides individualized feedback, and allows limited student control of the educational path. The design methodology is referenced to standard practice in computer-assisted instruction.