Case Detection

Argus is an event-based surveillance system which captures information from publicly available Internet media in multiple languages. The information is contextualized and indications and warning (I&W) of disease are identified. Reports are generated by regional experts and are made available to the system's users. In this study a small-scale disease event, plague emergence, was tracked in a rural setting, despite media suppression and a low availability of epidemiological information.

Objective

To demonstrate how event-based biosurveillance can be utilized to closely monitor disease emergence in an isolated rural area, where medical information and epidemiological data are limited, toward identifying areas for public health intervention improvements.

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.

Commonly used syndromic surveillance methods based on the spatial scan statistic first classify disease cases into broad, pre-existing symptom categories ("prodromes") such as respiratory or fever, then detect spatial clusters where the recent case count of some prodrome is unexpectedly high. Novel emerging infections may have very specific and anomalous symptoms which should be easy to detect even if the number of cases is small. However, typical spatial scan approaches may fail to detect a novel outbreak if the resulting cases are not classified to any known prodrome. Alternatively, detection may be delayed because cases are lumped into an overly broad prodrome, diluting the outbreak signal.

Objective

We propose a new text-based spatial event detection method, the semantic scan statistic, which uses free-text data from Emergency Department chief complaints to detect, localize, and characterize newly emerging outbreaks of disease.

The status of each Intensive Care Unit (ICU) patient is routinely monitored and a number of vital signs are recorded at sub-second frequencies which results in large amounts of data. We propose an approach to transform this stream of raw vital measurements into a sparse sequence of discrete events. Each such event represents significant departure of an observed vital sequence from the null distribution learned from reference data. Any substantial departure may be indicative of an upcoming adverse health episode. Our method searches the space of such events for correlations with near-future changes in health status. Automatically extracted events with significant correlations can be used to predict impending undesirable changes in the patient's health. The ultimate goal is to equip ICU physicians with a surveillance tool that will issue probabilistic alerts of upcoming patient status escalations in sufficient advance to take preventative actions before undesirable conditions actually set in.

Objective

To present a statistical data mining approach designed to: 1. Identify change points in vital signs which may be indicative of impending critical health events in ICU patients and 2. Identify utility of these change points in predicting the critical events.

Disease surveillance data often has an underlying network structure (e.g. for outbreaks which spread by person-to-person contact). If the underlying graph structure is known, detection methods such as GraphScan (1) can be used to identify an anomalous subgraph which might be indicative of an emerging event. Typically, however, the network structure is unknown, and must be learned from unlabeled data, given only the time series of observed counts (e.g. daily hospital visits for each zip code).

Objective

Our goal is to learn the underlying network structure along which a disease outbreak might spread, and use the learned network to improve the timeliness and accuracy of outbreak detection.

Lymphatic filariasis is one of the most prevalent of the tropical diseases, but is also the most neglected.Though significant advances have been made in the understanding both the disease and its control, there is general lack of information about its socioeconomic effects, prevalence and distribution in most endemic societies. Presently, there is global effort towards the elimination of the disease by 2020. The success of this programme depends largely on the use of simple, non-invasive procedures to identify endemic communities. Limb elephantiasis is one of the chronic symptoms of lymphatic filariasis that could be easily diagnosed by persons with minimum training. Therefore, the prevalence of elephantiasis could serve as a useful tool to determine the occurrence and spread of lymphatic filariasis in endemic communities.

Objective

This paper describes how limb elephantiasis was used to determine the occurrence and spread of lymphatic filariasis in Kano state, Nigeria as well as the use of the results for further epidemiological studies.

States and localities are using biosurveillance for a variety purposes including event detection, situational awareness, and response. However, little is known about the impact of biosurveillance on the operational components and functioning of the public health system and the added value of biosurveillance to traditional surveillance methods. A deeper understanding of how state and local public health systems use biosurveillance data and the factors that facilitate and impede its utility are needed to inform efforts to improve public health surveillance.

Objectives

A goal of the case studies was to assess the impact of biosurveillance on public health system preparedness, detection and response for a range of public health threats.

In addition to monitoring Emergency Department chief complaint data and pharmacy sales as indicators of outbreaks, the New York State Department of Health (NYSDOH) Syndromic Surveillance System also monitors information from the CDC’s Early Event Detection and Situational Awareness System, BioSense. BioSense includes Department of Defense (DOD) and Veterans Affairs (VA) outpatient clinical data (ICD-9-CM diagnoses and CPT procedure codes), and LabCorp test order data. Within NYS excluding New York City, there are a total of 7 DOD and 60 VA hospitals and/or clinics reporting to the BioSense system, located across 41 of 57 counties.

BioSense includes a Sentinel Alert system, which monitors for diagnoses of CDC-classified Category A, B, and C diseases that have been reported from DOD and VA facilities. Sentinel Alerts are issued for single disease records, and can be followed up at local discretion to assess for public health significance and to determine whether the source of the disease might be intentional.

Objective

To describe the NYSDOH's experience with the monitoring of Sentinel Alerts generated for NYS within the CDC’s BioSense application, following up each alert with local health department staff to determine case resolution, and providing user-level feedback to the CDC to effect system improvements.

Objective

There were two objectives of this analysis. First, apply text-processing methods to free-text clinician notes extracted from the VA electronic medical record for automated detection of Influenza-Like-Illness. Secondly, determine if use of data from free-text clinical documents can be used to enhance the predictive ability of case detection models based on coded data.

Syndromic surveillance may be suited for detection of emerging respiratory disease elevations that could pass undiagnosed. The syndromes under surveillance should then retrospectively reflect known respiratory pathogen activity. To validate this for respiratory syndromes we analyzed dutch medical registration data from 1999-2003 (national hospital discharge diagnoses and causes of death). We assume that syndromes with a good reflection of pathogen activity have the potential ability to reflect unexpected respiratory pathogen activity in prospective surveillance.

Objective

As a validation for syndromic surveillance we studied whether respiratory syndromes indeed reflect the activity of respiratory pathogens. Therefore we retrospectively estimated the temporal trend of two respiratory syndromes by the seasonal dynamics of common respiratory pathogens.