Simulation

Emerging and re-emerging infectious diseases are a serious threat to global public health. The World Health Organization (WHO) has identified more than 1100 epidemic events worldwide in the last 5 years alone. Recently, the emergence of the novel 2009 influenza A (H1N1) virus and the SARS coronavirus has demonstrated how rapidly pathogens can spread worldwide. This infectious disease threat, combined with a concern over man-made biological or chemical events, spurred WHO to update their International Health Regulations (IHR) in 2005. The new 2005 IHR, a legally binding instrument for all 194 WHO member countries, significantly expanded the scope of reportable conditions, and are intended to help prevent and respond to global public health threats. SAGES aims to improve local public health surveillance and IHR compliance, with particular emphasis on resource-limited settings.

Objective

This paper describes the development of the Suite for Automated Global bioSurveillance (SAGES), a collection of freely available software tools intended to enhance electronic disease surveillance in resource-limited settings around the world.

The evolution of a communicable disease in a human population is not entirely predictable. However, the spreading process can be assumed to vary smoothly in time. The time-dependent infection process can be linked to observations of the epidemic’s evolution by convolving it with a stochastic delay model. In retrospective analyses of epidemics, when the observations are the dates of exhibition of patients’ symptoms, the delay is the incubation period. In case of biosurveillance data, the delay is caused by incubation and a (hospital) visit delay, modeled as independent random variables. A model for observational error is also required. The time-dependent infection/spread rate may be inferred from observations by a deconvolution process. The smooth temporal variation of the infection rate allows its representation using a low dimensional parametric model, and the inference may be performed with relatively little data. For large outbreaks, the data may be available early in the epidemic, allowing timely modeling of the outbreak. Short-term forecasts using the model could thereafter be used for medical planning.

Objective

We present a statistical method to characterize an epidemic of a communicable disease from a time series of patients exhibiting symptoms. Characterization is defined as estimating an unobserved, time-dependent infection rate and associated parameters that completely define the evolution of an epidemic. The problem is posed as one of Bayesian inference, where parameters are inferred with quantified uncertainty. The method is demonstrated on synthetic and historical epidemic data. 

Influenza-like illness (ILI) data is collected by an Influenza Sentinel Provider Surveillance Network at the state (Iowa, USA) level. Historically, the Iowa Department of Public Health has maintained 19 different influenza sentinel surveillance sites. Because participation is voluntary, locations of the sentinel providers may not reflect optimal geographic placement. This study analyzes two different geographic placement algorithms - a maximal coverage model (MCM) and a K-median model. The MCM operates as follows: given a specified radius of coverage for each of the n candidate surveillance sites, we greedily choose the m sites that result in the highest population coverage. In previous work, we showed that the MCM can be used for site placement. In this paper, we introduce an alternative to the MCM - the K-median model. The K-median model, often called the P-median model in geographic literature, operates by greedily choosing the m sites which minimize the sum of the distances from each person in a population to that person’s nearest site. In other words, it minimizes the average travel distance for a population.

Objective

This paper describes an experiment to evaluate the performance of several alternative surveillance site placement algorithms with respect to the standard ILI surveillance system in Iowa.

The 2009 H1N1 novel flu pandemic demonstrates how a rapidly spreading, contagious illness can affect the world’s population in multiple ways including health, economics, education, transportation, and national security. Pandemic disease and the threat of bio-terrorism are prompting the need for a system that integrates disparate data, makes optimal use of the breadth of available health-related analysis and predictive models, and provides timely guidance to decision makers at multiple levels of responsibility.

Objective

Traditional real time surveillance systems such as RODS and ESSENCE have focused on the task of threat detection; however, experience with the use of these systems in pandemic and disaster response settings suggests that a more common application is threat characterization and response management. This paper describes EpiSentry: a novel second generation real-time surveillance software system under development at Lockheed Martin that uses simulation to aid in threat characterization, response management and to provide decision support for disease outbreaks or bio-terror events.

In disease surveillance, an outbreak is often present in more than one data type. If each data type is analyzed separately rather than combined, the statistical power to detect an outbreak may suffer because no single data source captures all the individuals in the outbreak. Researchers, thus, started to take multivariate approaches to syndromic surveillance. The data sources often analyzed include emergency department data, categorized by chief complaint; over-thecounter pharmaceutical sales data collected by the National Retail Data Monitor (NRDM), and some other syndromic data.

Objective

This study proposes a simulation model to generate the daily counts of over-the-counter medication sales, such as thermometer sales from all ZIP code areas in a study region that include the areas without retail stores based on the daily sales collected from the ZIP codes with retail stores through the NRDM. This simulation allows us to apply NRDM data in addition to other data sources in a multivariate analysis in order to rapidly detect outbreaks.