Infectious Disease

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.

Bordetella Pertussis outbreaks cause morbidity in all age groups, but the infection is most dangerous for young infants. Pertussis is difficult to diagnose, especially in its early stages, and definitive test results are not available for several days. Because of temporal and geographic variability of pertussis outbreaks, delay in diagnostic test results and ramifications of incorrect management decisions at the point of care, pertussis represents a prototypical disease where realtime public health surveillance data might inform, guide and improve medical decision making. Previously, we showed that diagnostic accuracy for meningitis can be improved when information about recent, local disease incidence is accounted for. Here, we quantify the contribution of epidemiologic context to a clinical prediction model for pertussis using a state public health data stream.

Objective

To explore the integration of epidemiological context – current population-level disease incidence data – into a clinical prediction model for pertussis.

The Internet has created an information revolution that spans across all knowledge domains and removes temporal and geographic barriers. Various disparate tools allow individuals to communicate with each other across these barriers. We also have an abundance of electronic resources containing health information locked inside free text components. The lack of integration of these tools and electronic resources has prevented harnessing of information for use in integrated and novel ways. We developed an application for 'Semantic Processing and Integration of Distributed Electronic Resources for Epidemiology' or EpiSPIDER (http://www.epispider.org), an integrative web-based information processing system that uses these tools and electronic resources to create an information environment for enhancing the surveillance of emerging infectious disease threats to global health.

This poster describes the practical integration of Early Event Detection (EED) into the daily operation of a medium sized public health department to improve surveillance for, and response to, outbreaks of communicable disease.

The objective of this communication is to demonstrate an approach for modeling time-distributed effects of exposures to cases of infection which can be utilized in syndromic surveillance systems for characterizing, detecting, and forecasting a potential outbreak.

The objective of this research is to describe infectious disease surveillance on military population on board ships.

The increased threat of bioterrorism and naturally occurring diseases, such as pandemic influenza, continually forces public health authorities to review methods for evaluating data and reports. The objective of bio-surveillance is to automatically process large amounts of information in order to rapidly provide the user with a situational awareness. Most systems currently deployed in health departments use only statistical algorithms to filter data for decision-making. These algorithms are capable of high sensitivity, but this sensitivity comes at the cost of excessive false positives [2], especially when multiple syndrome groups and data types are processed.

Objective

An intelligent information fusion approach is proposed to identify and provide early alerting of naturally-occurring disease outbreaks, as well as bioterrorist attacks, while reducing false positives. The proposed system statistically preprocesses information from multiple sources and fuses it in a manner comparable with the domain expert's decision-making process. Currently, system users lower the false alarm rate by "explaining away" the statistical data anomalies with alternative hypotheses derived from external, non-syndromic knowledge. We seek to incorporate this heuristic decision-making into a probabilistic network that accepts the outputs of statistical algorithms in a hybrid model of domain knowledge and data inference.