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Outbreak Detection

As major disease outbreaks are rare, empirical evaluation of statistical methods for outbreak detection requires the use of modified or completely simulated health event data in addition to real data. Comparisons of different techniques will be more reliable when they are evaluated on the same sets of artificial and real data. To this end, we are developing a toolkit for implementing and evaluating outbreak detection methods and exposing this framework via a web services interface.

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Submitted by elamb on

In order to be best prepared to identify health events using electronic disease surveillance systems, it is vital for users to participate in regular exercises that realistically simulate how events may present in their system following disease manifestation in the community. Furthermore, it is necessary that users exercise methods of communicating unusual occurrences to other intra and extra-jurisdictional investigators quickly and efficiently to determine first, if an event actually exits and if one does its characteristics. A simulation exercise held in the National Capital Region (NCR) in the spring of this year exercised a novel format for engaging users while testing the utility of an embedded event communication tool.

 

Objective

This is a description of an innovative design and format used to exercise public health preparedness in a tri-jurisdictional disease surveillance system in the spring of 2006.

Submitted by elamb on

Non-temporal Bayesian network outbreak detection methods only look at data from the most recent day. For example, PANDA-CDCA (PC) only looks at data from the last 24 hours to determine how likely an outbreak is occurring. PC is a Bayesian network disease outbreak detection system that models 12 diseases. A system that looks only at each day's data might signal an outbreak one day and not signal it the next. Cooper et al. obtained such results when evaluating the ability of PC to detect a laboratory validated outbreak of influenza. We hypothesized that temporal modeling would attenuate this problem.

 

Objective

A temporal method for outbreak detection using a Bayesian network is presented and evaluated.

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Submitted by elamb on

A time periodic geographic disease surveillance system based on a cylindrical space-time scan statistic proposed by Kulldorff [1] has been used extensively for disease surveillance along with the SaTScan software. This statistic is based on a circular spatial scan statistic. On the other hand, many different tests have been proposed to detect purely spatial disease clusters. In particular, some spatial scan statistics such as those developed by Duczmal and Assuncao(2004), Patil and Taillie (2004), and Tango and Takahashi(2005) are aimed at detecting irregularly shaped clusters which may not be detected by the circular spatial scan statistic. However, due to the unlimited geometric freedom of cluster shapes, these statistics have a risk to detect quite large and unlikely peculiarly shaped clusters. A flexible spatial scan statistic proposed by Tango and Takahashi[2], which has been used along with the FleXScan software[3], has a parameter K as the pre-set maximum length of neighbors to be scanned, to be avoid detecting a cluster of unlikely peculiar shape. The flexible spatial scan statistic can be easily extended to space-time alerting methods in syndromic surveillance. Objective: This paper proposes a flexible space-time scan statistic for early detection of disease outbreaks.

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Submitted by elamb on

Syndromic surveillance for early warning in military context needs a robust, scalable, flexible, ubiquitous, and interoperable surveillance system. A pilot project fulfilling these aims has been conceived as a collaboration of specialized web-services.

Referenced File
Submitted by elamb on

This paper describes a Bayesian algorithm for diagnosing the CDC Category A diseases, namely, anthrax, smallpox, tularemia, botulism and hemorrhagic fever, using emergency department chief complaints. The algorithm was evaluated on real data and on semi-synthetic data, and this paper summarizes the results of that evaluation.

Referenced File
Submitted by elamb on

Epidemiologists, public health agencies and scientists increasingly augment traditional surveillance systems with alternative data sources such as, digital surveillance systems utilizing news reports and social media, over-the-counter medication sales, and school absenteeism. Similar to school absenteeism, an increase in reservation cancellations could serve as an early indicator of social disruption including a major public health event. In this study, we evaluated whether a rise in restaurant table availabilities could be associated with an increase in disease incidence.

 

Objective

The objective of this study is to evaluate whether trends in online restaurant table reservations can be used as an early indicator for a disease outbreak.

Submitted by hparton on

Reportable disease case data are entered into Merlin by all 67 county health departments in Florida and assigned confirmed, probable, or suspect case status. De-identified reportable disease data from Merlin are sent to ESSENCE-FL once an hour for further analysis and visualization using tools in the surveillance system. These data are available for ad hoc queries, allowing users to monitor disease trends, observe unusual changes in disease activity, and to provide timely situational awareness of emerging events. Based on system algorithms, reportable disease case weekly tallies are assigned an awareness status of increasing intensity from normal to an alert category. These statuses are constantly scrutinized by county and state level epidemiologists to guide disease control efforts in a timely manner, but may not signify definitive actionable information.

 

Objective

In light of recent outbreaks of pertussis, the ability of Florida Department of Health’s (FDOH) Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE-FL) to detect emergent disease outbreaks was examined. Through a partnership with the Johns Hopkins University Applied Physics Laboratory (JHU/APL), FDOH developed a syndromic surveillance system, ESSENCE-FL, with the capacity to monitor reportable disease case data from Merlin, the FDOH Bureau of Epidemiology’s secure webbased reporting and epidemiologic analysis system for reportable diseases. The purpose of this evaluation is to determine the utility and application of ESSENCE-FL system generated disease warnings and alerts originally designed for use with emergency department chief complaint data to reportable disease data to assist in timely detection of outbreaks in promotion of appropriate response and control measures.

Submitted by hparton on

Norovirus infection results in considerable morbidity in the United States where an estimated 21 million illnesses, 70,000 hospitalizations, and 800 deaths are caused by NV annually. Additionally, NV is responsible for approximately 50% of foodborne outbreaks. Between January 2008 and June 2012, 875 NV outbreaks were reported to the Virginia Department of Health (VDH). To assist in detecting possible disease outbreaks such as NV, VDH utilizes the web-based Electronic Surveillance System for Early Notification of Community-based Epidemics (ESSENCE) to monitor and detect public health events across Virginia. ESSENCE performs automated parsing of chief complaint text into 10 syndrome categories, including a non-specific GI syndrome that serves as a proxy for GI illnesses like NV.

 

Objective

To assess the relationship between emergency department and urgent care center chief complaint data for gastrointestinal illness and reported norovirus (NV) outbreaks to develop an early warning tool for NV outbreak activity. The tool will provide an indicator of increasing NV outbreak activity in the community allowing for earlier public health action to mitigate NV outbreaks.

Submitted by hparton on
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