Outbreak Detection

The Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE-FL) receives daily (or bi-hourly) data from 184 emergency departments (ED) from around Florida. Additionally, 30 urgent care centers submit daily data to the system. These 214 facilities are grouped together in an acute care data source category. Five to six days after the start of each school year in Florida, ESSENCE-FL shows increased respiratory illness visits in the school aged population. Previous analyses of these data have shown that this increase is a result of increased transmission of the common cold among school children. In early September 2014, during this sustained yearly increase in respiratory visits, reports of more severe infection caused by Enterovirus D68 (EV-D68) in children in other parts of the country began circulating. Public health officials in Florida, as well as the media, questioned whether children in the state were being infected by this virus capable of causing more severe illness, especially among asthmatics. As is the case with many incipient outbreaks, syndromic surveillance played an integral role in early efforts to detect the presence of this illness. The task of providing situational awareness during this period was complicated by this outbreak coinciding with the start of the school year.

Objective

To provide situational awareness using Florida’s syndromic surveillance system during a 2014 outbreak of EV-D68 in other regions of the country.

Disease surveillance particularly surveillance for communicable diseases is essential in identifying cases and preventing the occurrence of an outbreak. Surveillance can also contribute to reducing the size of an outbreak. In order to achieve these, surveillance activities must include all possible sites for case detection. The lack of established mechanisms to provide feedback to the surveillance system at all such points can cause a failure of the surveillance system. These are extremely relevant particularly in the current outbreak of Ebola in some parts of the West African Sub Region. Ghana, like many countries has established surveillance systems for specific diseases. Currently, 44 diseases/public health events including Ebola are under surveillance as part of an Integrated Disease Surveillance and Response (IDSR) system. Although the Ministry of Health (MOH) exercises authority over issues of health, the operation of policies and practices on disease surveillance is by the Ghana Health Service (GHS), an agency of the MOH despite the existence of other agencies such as the teaching hospitals.

Objective

To describe Ghana’s disease surveillance system operation and the potential challenges in the light of the Ebola outbreak in West Africa

Meat inspection data are routinely collected over several years providing the possibility to use historical data for constructing a baseline model defining the expected normal behaviour of the indicator monitored. In countries in which the reporting of data is compulsory (e.g. in the EU), coverage of the majority of the slaughtered population is ensured.

Objective

We evaluate the performance of the improved Farrington algorithm for the detection of simulated outbreaks in meat inspection data.

 

Kulldorff’s spatial scan statistic1 detects significant spatial clusters of disease by maximizing a likelihood ratio statistic over circular spatial regions. The fast localized subset scan2 enables scalable detection of proximity-constrained subsets and increases power to detect irregularly-shaped clusters, However, unconstrained subset scanning within each circular neighborhood2, may not necessarily capture the pattern of interest, and is too under-constrained for use with case/control point data. Thus we propose the star-shaped scan statistic (StarScan), a novel method that efficiently maximizes the loglikelihood ratio over irregularly-shaped clusters, while incorporating soft constraints on smoothness. More precisely, we allow the radius of the cluster around a center location to vary along with angle, and penalize proportional to the total change in radius.

Objective

We present StarScan, a novel scan statistic for accurately detecting irregularly-shaped disease outbreaks. StarScan maximizes a penalized log-likelihood ratio statistic, allowing the radius around a central location to vary as a function of the angle and applying a penalty proportional to the total change in radius.

 

Since the release of anthrax in October of 2001, there has been increased interest in developing efficient prospective disease surveillance schemes. Poisson CUSUM is a control chart-based method that has been widely used to detect aberrations in disease counts in a single region collected over fixed time intervals. Over the past few years, different methods have been proposed to extend Poisson CUSUM charts to capture the spatial association among several regions simultaneously. In the proposed method, we extend an algorithm in industrial process control using multiple Poisson CUSUM charts to the spatial setting. The spatial association among regions is captured using the method proposed by Raubertas, which has been successfully applied in several prospective surveillance schemes. Also, to improve the power of the traditional multiple Poisson CUSUM charts, Poisson CUSUM charts were used along with fault discovery rate (FDR) control techniques.

Objective

To develop a computationally simple and fast algorithm for rapid detection of outbreaks producing easily interpretable results.

Along with commensurate funding, an increased emphasis on syndromic surveillance systems occurred post September 11, 2001 and the subsequent anthrax attacks. Since then, many syndromic surveillance systems have evolved and have ever-increasing functionality and visualization tools. As outbreak detection using these systems has demonstrated an equivocal track record, epidemiologists have sought out other interesting and unique uses for these systems. Over the numerous years of the International Society for Disease Surveillance (ISDS) conference, many of these studies have been presented, however, there has been a dearth of discussion related to how these systems should be used on a routine basis. As the initial goal of these systems was to provide a near real-time disease surveillance tool, the question of how to most effectively conduct this type of routine surveillance is paramount.

Objective

To discuss how various emergency department based syndromic surveillance systems from across the country and world are being used and to develop best practices for moving forward.

 

In the summer of 2013, the New Jersey Department of Health (NJDOH) began planning for Super Bowl XLVIII to be held on February 2, 2014, in Met Life Stadium, located in the Meadowlands of Bergen County. Surveillance and epidemiology staff in the Communicable Disease Service (CDS) provided expertise in planning for disease surveillance activities leading up to, during, and after the game. A principal component of NJDOH’s Super Bowl surveillance activities included the utilization of an existing online syndromic surveillance system, EpiCenter. EpiCenter is a system developed by Health Monitoring Systems, Inc. (HMS) that incorporates statistical management and analytical techniques to process health-related data in real time. As of February, 2014, 75 of New Jersey’s 81 acute care and satellite emergency departments (EDs) were connected to this system. CDS staff primarily used EpiCenter to monitor ED visits for unusual activity and disease outbreaks during this event. In addition, NJDOH and HMS implemented enhanced reports and expanded monitoring of visit complaints.

Objective

To describe the surveillance planning and activities for a largescale event (Super Bowl XLVIII) using New Jersey’s syndromic surveillance system (EpiCenter).

 

Since April 2012, an integrated syndromic surveillance system in rural China (ISSC) has been established in health facilities in two rural counties of Jiangxi Province, China [1]. The objective of ISSC is to integrate syndromic surveillance with conventional case report system for the early detection of infectious disease outbreak in rural China.

Objective

To evaluate the validity of a syndromic surveillance system in health facilities of rural China, signals generated by Shewhart charts from the reported febrile patients in children were compared with that from the common infectious disease patients reported to the conventional case report system (CISDCP, China Information System for Disease Control and Prevention).

 

The Research Electronic Data Capture (REDCap) application has been used to build and manage online surveys and databases in academic research settings. Public health agencies have begun to use REDCap to manage disease outbreak data. In addition to survey and database development, and data management and analysis, REDCap allows users to track data manipulation and user activity, automate export procedures for data downloads, and use ad hoc reporting tools and advanced features, such as branching logic, file uploading, and calculated fields. REDCap supports HIPAA compliance through userbased permissions and audit trails. These additional capabilities may provide an advantage over commonly used outbreak management tools such as Epi Info and Microsoft Access. The Illinois Department of Public Health (IDPH) has not used REDCap to date. Prior to adopting this web-based application, an evaluation was conducted to assess how REDCap may facilitate outbreak data management.

Objective

To evaluate the use of the Research Electronic Data Capture (REDCap) application to manage outbreak data at the local, state, and multi-jurisdictional level.