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Outbreak Detection

The use of syndromic surveillance systems to detect illness and outbreaks in the mid 1990s in New York City resulted in recommendations for increased use of these systems for detection of bioterrorist agents, and tracking influenza throughout the region. Discussions on approaches to best respond to surveillance system signals led to initial efforts to organize a coordinating group of various public health agencies throughout the New York City region. These efforts were strengthened after the events of September 11, 2001, and resulted in the development of a regional workgroup consisting of epidemiologists and other staff from all state, county, and municipal health departments who operate, respond to, or oversee public health preparedness surveillance systems throughout the greater New York City metropolitan area.

 

Objective

The rapid and effective coordination of the multi-jurisdictional communications and response to a surveillance system signal are an important goal of public health preparedness planning. This goal is particularly challenging if the signal indicates a possible risk that could adversely affect populations in multiple states and municipalities. This paper examines the value of a regional workgroup in the activation, integration, and coordination of multiple surveillance systems along with efforts to coordinate risk communication messaging. Recommendations for the development of similar groups in other regions are discussed.

Referenced File
Submitted by hparton on

Mandatory notification to public health of priority communicable diseases (CDs) is a cornerstone of disease prevention and control programs. Increasingly, the addresses of CD cases are used for spatial monitoring and cluster detection and public health may direct interventions based on the results of routine spatial surveillance. There has been little assessment of the quality of addresses in surveillance data and the impact of address errors on public health practice.

We launched a pilot study at the Montreal Public Health Department, wherein our objective was to determine the prevalence of address errors in the CD surveillance data. We identified address errors in 25% of all reported cases of communicable diseases from 1995 to 2008. We also demonstrated that address errors could bias routine public health analyses by inappropriately flagging regions as having a high or low disease incidence, with the potential of triggering misguided outbreak investigations or interventions. The final step in our analysis was to determine the impact of address errors on the spatial associations of campylobacter cases in a simulated point source outbreak.

 

Objective

To examine, via a simulation study, the potential impact of residential address errors on the identification of a point source outbreak of campylobacter.

Referenced File
Submitted by hparton on

An interdisciplinary team convened by ISDS to translate public health use-case needs into well-defined technical problems recently identified the need for new pre-syndromic surveillance methods that do not rely on existing syndromes or pre-defined illness categories1. Our group has recently developed Multidimensional Semantic Scan (MUSES), a pre-syndromic surveillance approach that (1) uses topic modeling to identify newly emerging syndromes that correspond to rare or novel diseases; and (2) uses multidimensional scan statistics to identify emerging outbreaks that correspond to these syndromes and are localized to a particular geography and/or subpopulation2,3. Through a blinded evaluation on retrospective free-text ED chief complaint data from NYC DOHMH, we demonstrate that MUSES has great potential to serve as a safety net for public health surveillance, facilitating a rapid, targeted, and effective response to emerging novel disease outbreaks and other events of relevance to public health that do not fit existing syndromes and might otherwise go undetected.

Objective: We present a new approach for pre-syndromic disease surveillance from free-text emergency department (ED) chief complaints, and evaluate the method using historical ED data from New York City's Department of Health and Mental Hygiene (NYC DOHMH).

Submitted by elamb on

Infectious disease outbreaks, such as the Ebola outbreak in West Africa, highlight the need for surveillance systems to quickly detect outbreaks and provide data to prevent future pandemics. The World Health Organization (WHO) developed the Joint External Evaluation (JEE) tool to conduct country-level assessments of surveillance capacity. However, considering that outbreaks begin and are first detected at the local level, national-level evaluations may fail to identify capacity improvements for outbreak detection. The gaps in local surveillance system processes illuminate a need for investment in on-the-ground surveillance improvements that may be lower cost than traditional surveillance improvement initiatives, such as enhanced training or strengthening data transfer mechanisms before building new laboratory facilities. To explore this premise, we developed a methodology for assessing surveillance systems with special attention to the local level and applied this methodology to the malaria outbreak surveillance system in Mashonaland East, Zimbabwe.

Objective: To conduct a field-based assessment of the malaria outbreak surveillance system in Mashonaland East, Zimbabwe.

Submitted by elamb on

The multivariate Bayesian scan statistic (MBSS) enables timely detection and characterization of emerging events by integrating multiple data streams. MBSS can model and differentiate between multiple event types: it uses Bayes’ Theorem to compute the posterior probability that each event type Ek has affected each space-time region S. Results are visualized using a ‘posterior probability map’ showing the total probability that each location has been affected. Although the original MBSS method assumes a uniform prior over circular regions, and thus loses power to detect elongated and irregular clusters, our Fast Subset Sums (FSS) method assumes a hierarchical prior, which assigns non-zero prior probabilities to every subset of locations, substantially improving detection power and accuracy for irregular regions.

Objective

We propose a new, computationally efficient Bayesian method for detection and visualization of irregularly shaped clusters. This Generalized Fast Subset Sums (GFSS) method extends our recently proposed MBSS and FSS approaches, and substantially improves timeliness and accuracy of event detection.

Referenced File
Submitted by teresa.hamby@d… on

Syndromic surveillance systems significantly enhance the ability of Public Health Units to identify, quantify, and respond to disease outbreaks. Existing systems provide excellent classification, identification, and alerting functions, but are limited in the range of statistical and mapping analyses that can be done. Currently available commercial off-the-shelf (COTS) statistical and GIS packages provide a much broader range of analytical and visualization tools, as well as the capacity for automation through user-friendly scripting languages. This study retrospectively evaluates the use of these packages for surveillance using syndromic data collected in Ottawa during the 2009 pH1NI outbreak.

 

Objective

The objective of this study was to create and evaluate a system that uses customized scripts developed for COTS statistical and GIS software to (1) analyze syndromic data and produce regular reports to public health epidemiologists, containing the information they would need to detect and manage an ILI outbreak, and (2) facilitate the generation more detailed analyses relevant to specific situations using these data.

Referenced File
Submitted by hparton on

One objective of public health surveillance is detecting disease outbreaks by looking for changes in the disease occurrence, so that control measures can be implemented and the spread of disease minimized. For this purpose, the Florida Department of Health (FDOH) employs the Electronic Surveillance System for Early Notification of Community-based Epidemics (ESSENCE). The current problem was spawned by a laborintensive process at the FDOH: authentic outbreaks were detected by epidemiologists inspecting ESSENCE time series and derived event lists. The corresponding records indicated that patients arrived at an ED within a short interval, often less than 30minutes. The time-of-arrival (TOA) task was to develop and automate a capability to detect events with clustered patient arrival times at the hospital level for a list of subsyndrome categories of concern to the monitoring counties.

 

Objective

This presentation discusses the approach and results of collaboration to enable a solution of a hospital TOA monitoring problemin syndromic surveillance applied to public health data at the hospital level for county monitoring.

Referenced File
Submitted by hparton on

Legionellosis is a respiratory disease that can lead to serious illness such as pneumonia, and can even result in death. Since 2010, increased reports of legionellosis have been received in Toronto during the summer months and led to a five-fold increase by 2012. This underscored the need to rule out common sources through a rapid assessment of exposure data (i.e., locations visited) for any spatio-temporal links. Legionella bacteria from a single source can affect individuals at distances as great as 10 km (1) but dispersion of Legionella bacteria is generally within 1 km of the source (2). This information was used to describe an area of potential risk around each exposure location. Adding temporal information from dates of potential exposures could provide a useful tool for outbreak detection. An automated tool was developed to link spatial and temporal data to assess need for further follow up.

Objective:

To develop an outbreak detection tool which uses spatial information related to temporally clustered legionellosis cases reported in Toronto, Canada.

Submitted by elamb on

Despite the number of infections, hospitalizations, and deaths from influenza each year, developing the ability to predict the timing of these outbreaks has remained elusive. Public health practitioners have lacked a reliable, easy-to-implement method for predicting the onset of a period of elevated influenza incidence in a community. We (a team of statisticians, epidemiologists, and clinicians) have developed a model to help public health practitioners develop simple, adaptable, data-driven rules to define a period of increased disease incidence in a given location. We call this method the Above Local Elevated Respiratory illness Threshold (ALERT) algorithm. The ALERT algorithm is a simple method that defines a period of elevated disease incidence in a community or hospital that systematically collects surveillance data on a particular disease.

Objective

Our objective was to develop a simple, easy-to-use algorithm to predict the onset of a period of elevated influenza incidence in a community using surveillance data.

Submitted by elamb on

The Electronic Integrated Disease Surveillance System (EIDSS) is a computer-based disease reporting application funded under the Cooperative Biological Engagement Program of the U.S. Defense Threat Reduction Agency. EIDSS deployment includes the Republics of Georgia (GG) and Azerbaijan (AJ) where personnel in the Ministry of Health and the Ministry of Agriculture in each country enter case-based disease reports. The potential benefits obtained through surveillance of infectious diseases across species have been widely discussed. A limitation of such practice has been the paucity of single applications that collect information about disease in both human and other animal populations (Scotch 2009). A unique feature of EIDSS is the use of a single platform to enter reports of disease in humans and other animals. Records are stored in a common database enabling ready access to information on multiple diseases and provide a quantitative linkage between human and animal data. An integrated analysis and reporting (AVR) module further supports timely investigation of disease events across the epizootic barrier.

Objective

We describe an electronic disease reporting system that integrates case-based disease information from humans and other animals in a single database and examine the utility for supporting disease surveillance functions through access to longitudinal case reports of multiple diseases across multiple species provided by the system.

Submitted by elamb on
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