Simulation

The aerosol release of a pathogen during a bioterrorist incident may not always be caught on environmental sensors - it may be too small, may consist of a preparation that is coarse and heavy (and consequently precipitates quickly) or may simply have occurred in an uninstrumented location. In such a case, the first intimation of an event is the first definitive diagnosis of a patient. Being able to infer the size of the attack, its time, and the dose received has important ramifications for planning a response. Estimates drawn from such a short observation period will have limited accuracy, and hence establishing confidence levels (i.e., error bounds) on these estimates is an major concern. These estimates of outbreak characteristics can be also be used as initial conditions for epidemic models to predict the evolution of disease (along with error bounds in the predictions), in particular, communicable diseases in which the contagious period starts soon after infection (e.g., plague).

In this paper, we will consider anthrax and smallpox as our model pathogens. Since the contagious period of smallpox usually starts after the long incubation period (7–17 days), and the early epoch will consist only of index cases, we will model it as a non-contagious disease. Inputs will be obtained from simulated outbreaks as well as from the Sverdlovsk anthrax outbreak of 1979.

Objective

This paper presents a method that infers the number of infected people, the time of infection and the dose received from an aerosol release of a pathogen during a bioterrorism incident. Inputs into the inference process are the number of new diagnosed patients showing symptoms each day as observed over a short duration (3–4 days) during the early epoch of the outbreak.

The United States Environmental Protection Agency (U.S. EPA) has developed a prototype contamination warning system (CWS) for drinking water in response to Homeland Security Presidential Directive 9 (HSPD9). The goal of HSPD9 and the CWS is to expedite contamination containment and emergency response, thereby minimizing public health and economic impacts.

U.S. EPA’s conceptual CWS system, named WaterSentinel, is currently being pilot tested by U.S. EPA and its research partners. WaterSentinel is a multi-faceted approach involving water quality monitoring at optimal locations throughout the drinking water distribution system, enhanced security monitoring at key water utility infrastructure assets, consumer complaint surveillance, and innovative uses of public health surveillance data streams.

Objective

This paper summarizes the use and evaluation of various types of public health surveillance data for the early detection of chemical and biological contamination of drinking water.

On 27 April 2005, a simulated bioterrorist event—the aerosolized release of Francisella tularensis in the men’s room of luxury box seats at a sports stadium—was used to exercise the disease surveillance capability of the National Capital Region (NCR). The objective of this exercise was to permit all of the health departments in the NCR to exercise inter-jurisdictional epidemiological investigations using an advanced disease surveillance system. Actual system data could not be used for the exercise as it both is proprietary and contains protected, though de-identified, health information about real people; nor is there much historical data describing how such an outbreak would manifest itself in normal syndromic data. Thus, it was essential to develop methods to generate virtual health care records that met specific requirements and represented both ‘normal’ endemic visits (the background) as well as outbreak-specific records (the injects).

Objective

This paper describes a flexible modeling and simulation process that can create realistic, virtual syndromic data for exercising electronic biosurveillance systems.

In a classical surveillance system one looks for disturbances in the number of cases, but in a spatio-temporal system, not only the number of cases observed but also where they are located is reported. What location is reported, and to which degree of accuracy it is reported are important. At one extreme les near-perfect information about each case, as with contact tracing; at the other extreme we have no information about location; viz. just that the patient exisits, or a temporal system. From maximum spatial precision to no spatial precision, one gains in speed of reporting and privacy; but one loses power to detect outbreaks. For example, in Ozonoff et al. we see that more than one address is better than just a single one. This general point is intuitively appealing, and can be demonstrated. 

Objective

This paper quantifies the effect of not providing full information about the location of patients when dealing with spatio-temporal systems in syndromic surveillance. The study investigates the loss of power to detect clusters when aggregation takes place. 

Varied approaches have been used by syndromic surveillance systems for aberration detection. However, the performance of these methods has been evaluated only across a small range of epidemic characteristics.

Objective

We conducted a large simulation study to evaluate the detection properties of 6 different algorithms across a range of outbreak characteristics.

One of the challenges facing developers and users of automated disease surveillance systems is being able to accurately evaluate the performance of their systems for the wide variety of public health threats that are possible. A variety of methods have been used in the past to create data sets for use in testing algorithm performance. Synthetic data has been created using agent-based simulations where data is created based on the hypothesized activity of individuals with contagious diseases. This data is only as accurate as the social models and variety of assumptions which must be made permit. Real data containing elevated levels of respiratory and gastrointestinal activity have been used to evaluate the ability of algorithms to detect the elevated levels. Routine unvalidated outbreaks are typically not public health emergencies and may not represent signals of interest. Another approach is to use real background data and inject a variety of different types of synthetic cases representing various types of outbreaks on top of that background.

With the introduction of the American Health Information Community (AHIC) Minimum Data Set (MDS), the public health surveillance community should have the potential to obtain greater specificity for alerts generated in automated systems. The introduction of these additional data elements increases the complexity of algorithms using linked data elements. Creating synthetic data sets that accurately estimate relationships among chief complaint, pharmacy, laboratory and radiology is an added complexity in creating synthetic outbreaks for performance evaluation.

Objective

The objectives of this presentation are to describe the need for synthetic data containing the elements of the AHIC MDS. Approaches for creating synthetic data with MDS data elements will be presented and methods for insuring maintenance of confidentiality will be discussed.

The Early Aberration Reporting System was developed at the Centers for Disease Control and Prevention to help assist local and state health officials to focus limited resources on appropriate activities of public health surveillance. Outbreaks of

infectious diseases are indicated in multiple spatial and temporal data sources, such as emergency department visits, drug store sales, and ambulatory clinic visits. Based on this premise, we provided correlated data sets and investigated disease clusters.

Objective

We present a pilot study of simulation of correlated outbreak signals for early aberration reporting and evaluating detection methods.

With the widespread deployment of near real time population health monitoring, there is increasing focus on spatial cluster detection for identifying disease outbreaks. These spatial epidemiologic methods rely on knowledge of patient location to detect unusual clusters. In hospital administrative data, patient location is collected as home address but use of this precise location raises privacy concerns. Regional locations, such as center points of zip codes, have been deployed in many existing systems. However, this practice could distort the geographic properties of the raw data and affect subsequent spatial analyses. The impact of location error due to centroid assignment on the statistical analyses underlying these systems requires study.

Objective

To investigate the impact of address precision (exact latitude and longitude versus the center points of zip codes) on spatial cluster detection.

Irregularly shaped cluster finders frequently end up with a solution consisting of a large zone z spreading through the map, which is merely a collection of the highest valued regions, but not a geographically sound cluster. One way to amenize this problem is to introduce penalty functions to avoid the excessive freedom of shape of z. The compactness penalty K(z) is a function used to reduce the scan value of irregularly shaped clusters, based on its geometric shape. Another penalty is the cohesion function C(z), a measure of the absence of weak links, or underpopulated regions within the cluster which disconnect it when removed. It was mentioned in that such weak links could be responsible for a diminished power of detection in cluster finder algorithms. Methods using those penalty functions present better performance. The geometric  compactness is not entirely satisfactory, although, because it has a tendency to avoid potentially interesting irregularly shaped clusters, acting as a low-pass filter. The cohesion function penalty method, although, has slightly less specificity.

Objective

We introduce a novel spatial scan algorithm for finding irregularly shaped disease clusters maximizing simultaneously two objectives: the regularity of shape and the internal cohesion of the cluster.

Existing statistical methods can perform well in detecting simulated bioterrorism events. However, these methods have not been well-evaluated for detection of the type of respiratory and gastrointestinal events of greatest interest for routine public health practice. To assess whether a syndromic surveillance system can detect these outbreaks, we constructed simulated outbreaks based on public health interest and experience. We then inserted these outbreaks into real data. We assessed whether the simulated outbreaks could be detected using a battery of detection methods, including model-adjusted scan statistics and space-time permutation scan statistics.

Objective

We used simulation methods to assess the performance of two distinct anomaly-detection approaches, each under a variety of parameter settings, with respect to their ability to detect outbreaks of commonly occurring events of public health importance.