Infectious Disease

Disease screening facilitates the reduction of disease prevalence in two ways: (1) by preventing transmission and (2) allowing for treatment of infected individuals. Hospitals choosing an optimal screening level must weigh the benefits of decreased prevalence against the costs of screening and subsequent treatment. If screening decisions are made by multiple decision units (DU, e.g., hospital wards), they must consider the disease prevalence among admissions to their unit. Thus, the screening decisions made by one DU directly affect the disease prevalence of the other units when patients are shared. Because of this interdependent relationship, one DU may have an incentive to "free-ride" off the screening decisions of others as the disease prevalence declines. On the other hand, DUs may find it futile to invest in screening if they admit a large number of infected patients from neighbors who fail to screen properly. This problem is important in determining the optimal level of unit autonomy, since increasing a unit's level of autonomy in screening effectively increases the total number of DUs.

Objective

To analyze optimal disease screening in strategic multi-unit settings, and determine how the level of unit autonomy may effect screening decisions.

Argus is an event-based, multi-lingual surveillance system which captures and analyzes information from publicly available Internet media. Argus produces reports that summarize and contextualize indications and warning (I&W) of emerging threats, and makes these reports available to the system's users. The significance of the Escherichia coli (EHEC) outbreak analyzed here lies primarily in the fact that it raised epidemiological questions and public health infrastructure concerns that have yet to be resolved, and required the development of new resources for detecting and responding to newly-emerging epidemics.

Objective

To demonstrate how event-based biosurveillance, using direct and indirect I&W of disease, provides early warning and situational awareness of the emergence of infectious diseases that have the potential to cause social disruption and negatively impact public health infrastructure, trade, and the economy. Specifically, tracking of I&W during the 2011 enterohaemorrhagic EHEC O104:H4 outbreak in Germany and Europe was selected to illustrate this methodology.

There are currently no federal laws mandating the reporting of infectious diseases to public health authorities. Reporting requirements reside at the state level and such laws do not apply to federal agencies including the VA. Heretofore, VA's reporting of infectious diseases to public health authorities has been strictly voluntary, and has been accomplished via traditional methods (phone, mail, and fax) that are highly prone to human error, create a significant administrative burden, and do not adequately safeguard the privacy of Veterans' data. Previously, without a reporting mandate applicable to VA facilities, public health authorities have had an incomplete picture of the VA contribution to the overall infectious disease burden existing in the larger population. Moreover, at a national level, the VA has not had the ability to monitor the prevalence of the various infectious diseases within its own 151 hospitals and 827 community-based outpatient clinics. Nor has the VA been able to meet the spirit of the Health Information Technology for Clinical and Economic Health Act's Meaningful Use requirements, mandating electronic exchange of information.

Objective

In June 2013, in anticipation of the passage of proposed federal legislation (S 875 and HR 1792), the Department of Veterans Affairs (VA) issued a Directive requiring mandatory reporting of infectious diseases to various public health authorities (VHA Directive 2013-008). In terms of implementation strategies, the ideal is to build on an existing technology, optimize the quality and completeness of reporting, and minimize additional work burdens on VA staff.

This abstract describes an Electronic Surveillance System for Infectious Disease Outbreaks used by all federal levels in Germany and comments on timelyness and comprehensiveness of informations about outbreak settings and infection sources.

As part of the epidemiological investigation of an outbreak of Q fever in a factory in Scotland, we aimed to utilise a spatial scan statistic to aid in identification of areas associated with increased relative risk of infection.

Multiple data sources are used in a variety of biosurveillance systems. With the advent of new technologies, globalization, high performance computing, and "big data" opportunities, there are seemingly unlimited potential data streams that could be useful in biosurveillance. Data streams have not been universally defined in either the literature or by specific biosurveillance systems. The definitions and framework that we have developed enable a characterization methodology that facilitates understanding of data streams and can be universally applicable for use in evaluating and understanding a wide range of biosurveillance activities- filling a gap recognized in both the public health and biosurveillance communities.

Objective

To develop a data stream-centric framework that can be used to systematically categorize data streams useful for biosurveillance systems, supporting comparative analysis

Epidemic dynamics of dengue fever are driven by complex interactions between hosts, vectors and viruses that are influenced by environmental and climatic factors [1]. The development of new methods to identify such specific characteristics becomes crucial to better understand and control spatiotemporal transmission. We concentrated our efforts on applying sequential pattern mining [2] to an epidemiological and meteorological dataset to identify potential drivers of dengue fever outbreaks.

Objective

We used a data mining method based on sequential patterns extraction to identify local meteorological drivers of dengue fever epidemics in French Guiana.

ICD-9-CM codes have been proposed to be used as adjuncts to existing public health reporting systems and are commonly used for public health surveillance and research purposes. However these codes have been found to have variable accuracy for both healthcare billing as well as for disease classification due to both coding and physician errors, and these codes have never been comprehensively validated for their use for surveillance. Quantification of the positive predictive value for ICD-9 CM diagnosis codes is crucial for assessing their utility for public health disease surveillance and research.

Objective

To quantify the positive predictive values of ICD-9 CM diagnosis codes for public health surveillance of communicable diseases.