Application of Event-Based Biosurveillance to Disease Emergence in Isolated Regions

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

May 02, 2019

Mining Intensive Care Vitals for Leading Indicators of Adverse Health Events

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.

May 02, 2019

Use of CDC's Epidemic Information Exchange system as a Disease Surveillance Tool

Epi-X is an internet-based secure website for the exchange of information regarding developing public health events. Reports are exchanged with state epidemiologists, state health officers, and other key public health officials. Provisional and secure information is regularly posted on Epi-X. The Epi-X user base is restricted to public health officials at the local, state, federal, and international levels. Private health-care practitioners who do not otherwise hold a government position are not given access to Epi-X.

May 02, 2019

Detecting Previously Unseen Outbreaks with Novel Symptom Patterns

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.

May 02, 2019

Game-Theoretic Surveillance Approaches for Hospital-Associated Infections

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.

May 02, 2019

Fast Graph Structure Learning from Unlabeled Data for Outbreak Detection

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

May 02, 2019

Use of emergency department data for case finding following a community anthrax exposure

On 24 December 2009, a female New Hampshire resident was confirmed to have gastrointestinal anthrax on the basis of clinical findings and laboratory testing. Her source of anthrax was not immediately known, so the New Hampshire Department of Health and Human Services, in conjunction with several other state and federal agencies, conducted a comprehensive epidemiologic investigation, which included active surveillance to identify any additional anthrax cases from a similar exposure.

June 20, 2019

Identification and tracking of heat-related illnesses using syndromic surveillance

The summer of 2010 in Maryland was characterized by unusually high temperatures. This type of increased and prolonged heat can potentially make residents sick, and extreme exposure can even kill people at highest risk. Numerous deaths throughout the state were attributed to this heat wave.

June 26, 2019

Identifying water contamination from syndromic surveillance signals

The EPA Water Security initiative contamination warning system detection strategy involves the use of multiple monitoring and surveillance components for timely detection of drinking water contamination in the distribution system. The public health surveillance (PHS) component of the contamination warning system involves the analysis of health-related data to identify disease events that may stem from drinking water contamination.

June 26, 2019

Improved diagnosis of group A streptococcal pharyngitis using real-time biosurveillance

Group A Streptococcal (GAS) pharyngitis, the most common bacterial cause of acute pharyngitis, causes more than half a billion cases annually worldwide. Treatment with antibiotics provides symptomatic benefit and reduces complications, missed work days and transmission. Physical examination alone is an unreliable way to distinguish GAS from other causes of pharyngitis, so the 4-point Centor score, based on history and physical, is used to classify GAS risk.

June 26, 2019

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