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.


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).


May 02, 2019

A bootstrapping method to improve cohort identification

The research reported in this paper is part of a larger effort to achieve better signal-to-noise ratio, hence accuracy, in pharmacovigilance applications. The relatively low frequency of occurrence of adverse drug reactions leads to weak causal relations between the reaction and any measured signal. We hypothesize that by grouping related signals, we can enhance detection rate and suppress false alarm rate.



June 07, 2019

A Voronoi based scan for space-time cluster detection in point event data

Scan statistics are highly successful for the evaluation of space-time clusters. Recently, concepts from the graph theory were applied to evaluate the set of potential clusters. Wieland et al. introduced a graph theoretical method for detecting arbitrarily shaped clusters on the basis of the Euclidean minimum spanning tree of cartogram transformed case locations, which is quite effective, but the cartogram construction step of this algorithm is computationally expensive and complicated.



June 07, 2019

Document classification toward efficient event-based biosurveillance

Event-based biosurveillance is a practice of monitoring diverse information sources for the detection of events pertaining to human health. Online documents, such as news articles on the Internet, have commonly been the primary information sources in event-based biosurveillance. With the large number of online publications as well as with the language diversity, thorough monitoring of online documents is challenging. Automated document classification is an important step toward efficient event-based biosurveillance.

June 14, 2019

Building an automated Bayesian case detection system

Current practices of automated case detection fall into the extremes of diagnostic accuracy and timeliness. In regards to diagnostic accuracy, electronic laboratory reporting (ELR) is at one extreme and syndromic surveillance is at the other.

June 18, 2019


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