In some influenza seasons, morbidity and mortality closely follow the expected seasonal variation. In these years, approaches such as SerflingÃs model and seasonal-based syndromic outbreak detectors, in use in EARS, work well. In other years, though, short but intense variations occur in addition to the longer term seasonal variation. These intense outbreaks, which are often multimodal, have important implications for both syndromic surveillance and influenza epidemiology. Unfortunately, they are both difficult to characterize and poorly understood. In this paper, we apply techniques from time-frequency distribution theory to identify the temporal location, duration, and amplitude of intense outbreaks occurring in the presence of longer time scale variations.
ISDS Conference
The objective of this work was to elucidate clinical features for distinguishing inhalational anthrax (IA) from ED patients.
This research aims to determine the catchment area of Miami Children's Hospital Emergency Department (ED). The purpose is to identify pediatric populations and territories within Miami-Dade County that are insufficiently covered by this hospital's ED.
The National Poison Data System (NPDS) is maintained and operated by the American Association of Poison Control Centers (AAPCC) for the analysis, visualization, and reporting of call data from all 61 regional poison centers (PCs) in cooperation with CDC's National Center for Environmental Health (NCEH). NCEH collaborates with AAPCC toxicologists using NPDS to facilitate early recognition and monitoring of illness due to intentional or unintentional chemical or toxin exposures. NPDS algorithms identify statistically significant increases in callers' reported signs and symptoms - 131 clinical effects (CEs) such as rash and diarrhea - for detection of national poison exposure anomalies. Each day AAPCC toxicologists make decisions about NPDS anomalies' public health importance. Regional PCs are contacted as required for additional information about potentially important anomalies. NPDS also allows for individual case tracking through user-defined 'case-based definitions.' This additional method is especially useful during an outbreak when the agent and/or symptoms of affected persons are known.
To determine if ticks on dogs can provide early warning for tick-borne diseases in people.
BioSense is a national program designed to improve the nation’s capabilities for conducting disease detection, monitoring, and real-time situational awareness. Currently, BioSense receives near real-time data from non-federal hospitals, as well as national daily batched data from the Departments of Defense and Veteran’s Affairs facilities. These data are analyzed, visualized, and made simultaneously available to public health at local, state, and federal levels through the BioSense application.
Objective:
In this paper we present summary information on the non-federal hospitals currently sending data to the BioSense system and describe this distribution by hospital type, method of data delivery as well as patient class and patient health indicator.
Benchmarking of temporal surveillance techniques is a critical step in the development of an effective syndromic surveillance system. Unfortunately, holding “bakeoffs” to blindly compare approaches is a difficult and often fruitless enterprise, in part due to the parameters left to the final user for tuning. In this paper, we demonstrate how common analytical development and analysis may be coupled with realistic data sets to provide insight and robustness when selecting a surveillance technique.
OBJECTIVE
This paper compares the robustness and performance of three temporal surveillance techniques using a twofold approach: 1) a unifying statistical analysis to establish their common features and differences, and 2) a benchmarking on respiratory, influenza-like ill-nesses, upper GI, and lower GI complaint time series from the Harvard Pilgrim Health Care (HPHC).
Historical data are essential for development of detection algorithms. Spatio-temporal data, however, are difficult to come by due to variety of issues concerning patient confidentiality. Several approaches have been used to generate benchmark data using statistical methods. Here, we demonstrate how to generate benchmark data using a discrete event model simulating inter- and intra-contact network transmission dynamics of infectious diseases in space and time using publicly available population data.
OBJECTIVE
The objective of this study is to generate benchmark data from a discrete event model simulating the transmission dynamics of an infectious disease within and between contact networks in urban settings using real population data. Such data can be used to test the performance of various temporal and spatio-temporal detection algorithms when real data are scarce or cannot be shared.
A Bayesian Network (BN) is a probabilistic graphical model representing dependencies and relationships. The structure of the network and conditional probabilities capture an expert’s view of a system. BN have been applied to the public health domain for research purposes, but have not been used directly by the end users of public health systems. As BN technology becomes more and more accepted in the public health domain, the data fusion visualization becomes a critical component of the overall system design. The tools developed utilize computer assisted analysis on BN in the public health domain, provide a concise view of the data for better decision support, and shorten the decision making phase allowing rapid dissemination of information to public health.
OBJECTIVE
This paper describes the use and visualization of BNs to better assists public health users. The Data Fusion Visualization (DFV) provides an intuitive graphical interface that supports users in three ways. The first is by providing a seamless drill down interpretation of a dataset. The second is by providing an intuitive interpretation of BN. Finally, by abstracting the visualization from the underlying model, the DFV is capable of masking inter-operating BNs into a single visualization. The DFV provides a graphical representation of BN Network Data Fusion.
Syndromic surveillance needs to be (1) transparent, (2) actionable, and (3) flexible. Traditional frequentist approaches to syndromic surveillance, such as cusum charts and scan statistics, tend to fail on all three criteria. First, the validity of the assumptions is generally difficult to check and the methods are hard to modify; second, the false positive rate makes it impossible to be both sensitive to true signal and resistant to spurious signal; and third, the implementation usually requires significant hand-tinkering to adjust background rates for known seasonal affects and other identifiable influences.
OBJECTIVE
This paper describes a Bayesian approach to syndromic surveillance. The method provides more interpretable inference than traditional frequentist approaches. Bayesian methods avoid many of the problems associated with alpha levels and multiple comparisons, and make better use of prior information. The technique is illustrated on simulated data.