Outbreak Detection

Syndromic surveillance is an investigational approach used to monitor trends of illness in communities. It relies on pre-diagnostic health data rather than laboratory-confirmed clinical diagnoses. Its primary purpose is to detect disease outbreaks, incidents and unusual public health events earlier than possible with traditional public health surveillance methods.

Objective

To describe how epidemiological principles are utilized to distinguish a real alert from statistically significant alerts in order to monitor and create daily reports in the Miami-Dade County Health Department using Electronic Surveillance System for the Early Notification of Community Based Epidemics. 

Graph theory concepts are well established in epidemiology, with particular success as a description of agent-based modeling. An agent-based viewpoint leads to conclusions about the spatial distribution of links: infection is more likely among individuals in close proximity. In this analysis, we seek evidence of these temporal-spatial links though the properties of random geometric graphs.

Our investigation begins with the interpoint distance distribution (IDD) approaches referenced, which provide a promising approach to detect outbreaks that are localized in both space and time. Using a Mahalanobis-based metric, this distribution is compared to an expected distribution derived from historical records.

Unfortunately, when applied to a complex data set such as from Children’s Hospital Boston, the IDD provides inadequate power. Emergency Department chief complaints from 1/1/2000-12/31/2004 were used to identify patients with infectious respiratory illness based on a triage process.

As in most realistic catchments, the historic density of patients varies greatly over the catchment area.

Objective

This paper uses geometric random graph concepts to develop early detection algorithms for the real-time detection and localization of outbreaks.

The impetus for the development of many first syndromic surveillance systems was the hope of detecting infectious disease outbreaks earlier than with traditional surveillance. Various data sources have been suggested as potential disease indicators. Researchers have analyzed many of these, including those resulting from behaviors that change due to illness, such as purchasing medications, missing school or work, and using health care call centers or the internet to obtain health information. To define the prodromal behavior of patients presenting for care of acute illnesses, we initiated a pilot survey in the emergency room and acute care clinics at Walter Reed Army Medical Center.

Objective

This study describes the results of a survey given to patients to determine if any changes occurred in their behavior secondary to the illness that could potentially be tracked and used to detect a disease outbreak.

Early and reliable detection of anomalies is a critical challenge in disease surveillance. Most surveillance systems collect data from multiple data streams but the majority of monitoring is performed at univariate time series level. Purely statistical methods used in disease surveillance look at each time series separately and tend to generate a large number of false alarms. Support Vector Machines can be used to develop rich multivariate models that allow detecting abnormal relationships between different time series leading to greatly reduced number of false alarms.

Objective

This paper depicts a novel method for reliable detection of disease outbreaks. The methodology and initial results obtained on ESSENCE data are presented.

Identifying potential biases and confounders that may affect data quality is an important consideration when evaluating surveillance systems. Having the benefit of predictable temporal trends is a key requirement to improve upon the specificity of detecting outbreaks. Identification of factors that impact on the reliability of the temporal trends observed in the data may provide for the ability to improve the capability to identify aberrations in those trends. During a retrospective study of a dataset of microbiology orders from the veterinary teaching hospital at The Ohio State University for 2003 we noticed regular intervals when increases in the number of culture orders were not accompanied by proportional increases in the number of isolates. These instances appeared to occur at intervals that coincided with the clinical rotation of senior veterinary students within the hospital.

Objective

This paper reports on a potential confounder discovered during an investigation of microbiology orders in a veterinary teaching hospital as a possible data source for outbreak detection.

Tuberculosis (TB) has reemerged as a global public health epidemic in recent years. TB remains a serious public health problem among certain patient populations, and is prevalent in many urban areas. The World Health Organization estimates that approximately nine million individuals will develop active TB disease and more than two million will die from TB. The global burden of TB remains enormous, and will likely rank high among public health problems in the coming decades. Although evaluating local disease clusters leads to effective prevention and control of TB, there are few, if any, spatiotemporal comparisons for epidemic diseases. In this study, we used the space-time scan statistic to identify where and when the prevalence of TB is high in Fukuoka Prefecture. The ability to detect disease outbreaks is important for local and national health departments to minimize morbidity and mortality through timely implementation of disease prevention and control measures. Because the statistic meets these needs completely, results that are effective and practical for public health officials are expected from this study.

Timely outbreak detection, and monitoring of morbidity and mortality among Katrina evacuees, and needs assessment for better planning and response were urgent information intensive priorities during Katrina relief efforts at Houston, and called for immediate deployment of a real-time surveillance and needs assessment system ad hoc, in order to collect and analyze relevant data at the scene. Initial requirement analysis revealed the following capabilities as essential to sustain effective response within the shelters:

• The ability to securely collect and integrate data from evacuees seeking any form of health services from all care providers (academic, volunteers, federal, NGOs and international aid organizations, etc), including demographic information, vital signs, chief complaints, disabilities, chronic conditions, current and past medications, traumas and injuries, exposure to toxic materials, clinical laboratory results, past medical history, discharge notes and diagnoses, and ability to collect free text entries for any other information (similar to a full-blown electronic medical records system).

• Proactive survey of demographic profile, physical and mental health status, as well as special needs assessment (e.g., dialysis, medications, etc) from all evacuees.

• The ability to collect uniform information, using any network-enabled device available: PCs, tablets, and handheld devices. 

• The ability to classify observations by processing sign and symptom, chief complaint, medication, and other diagnostic data (including free text entries) through ad-hoc definition of concepts such as (Gastrointestinal, Respiratory, Fever and Rash, etc). 

Objective

This paper presents lessons learned from leveraging Internet-based technologies and Services Oriented Architecture in providing timely, novel, and customizable solutions, just in time and for preparedness against unprecedented events such as natural disasters (e.g., Katrina) or terrorism.

Sixty-one percent of known disease-causing agents that infect humans can also infect animals [1]. While humans are the primary reservoir for only 3% of zoonoses, detection of zoonotic disease outbreaks remains mostly dependant on the identification of human cases [2]. Very few of the diseases that are a threat to humans are reportable in pets. Over onethird of American households include at least one pet [3]. Pets can present with clinical signs of disease earlier than people after becoming infected at the same time [4]. Pets can also become infected first and act as a source of infection for humans [5]. Detection of an outbreak in pets may then provide for warning of an outbreak that could affect humans.

Objective

This paper describes occurrences of possible co-morbidity in pets and humans discovered in a retrospective study of veterinary microbiology records and through the application of syndromic surveillance methods in a prospective outbreak detection system using veterinary laboratory orders.

Evaluation is a major topic in order to enhance syndromic surveillance. In May 2004, a CDC working group developed a framework for evaluating public health surveillance systems for early detection of outbreaks. This framework has been used to evaluate some civilian and also some military syndromic surveillance systems, as the French system 2SE FAG (Surveillance spatiale des épidémies au sein des forces armées en Guyane) and the UK system RMS (Real time Medical Surveillance). Those systems have been set up since the 2002 Prague summit. But because the objectives and the functioning of those systems have some military specificities, the current CDC framework was not totally adapted for their evaluation. This study presented a proposal of a new framework for evaluating military syndromic surveillance systems.

Objective

The objective of this study was to propose a new framework for evaluating military surveillance systems for early detection of outbreaks. This one was based on the French and UK military real time surveillance systems.

Los Angeles County Department of Health Services is currently testing SaTScan’s space-time permutation model to assist in identifying aberrant illness activity in the community and determine it’s ability to detect outbreaks through analyzing real-time syndromic data. SaTScan could be useful especially due to its ability to provide geographic locations of outbreaks in the community.

Objective

To determine the usefulness of SaTScan as an outbreak and illness cluster detection tool in syndromic surveillance and to compare to a purely temporal CUSUM algorithm.