HealthMap, a team of researchers, epidemiologists and software developers at Boston Children's Hospital founded in 2006, is an established global leader in utilizing online informal sources for disease outbreak monitoring and real-time surveillance of emerging public health threats. The freely available Web site 'healthmap.org' and mobile app 'Outbreaks Near Me' deliver real-time intelligence on a broad range of emerging infectious diseases for a diverse audience including libraries, local health departments, governments, and international travelers.
Outbreak Detection
For its January 2011 Literature Review, the ISDS Research Committee invited Daniel B. Neill, PhD, Assistant Professor of Information Systems at Carnegie Mellon University, to present his paper, "An Empirical Comparison of Spatial Scan Statistics for Outbreak Detection," published in the International Journal of Health Geographics.
Presenter
Daniel B. Neill, PhD, Assistant Professor of Information Systems, Carnegie Mellon University
Date and Time
Thursday, January 27, 2011
Host
Krista Kniss and Scott Epperson, from the CDC Influenza Division, will be joining the ISDS Public Health Practice Committee this coming Monday, October 22, for a two-part discussion of influenza surveillance in the United States and abroad. The first presentation by Krista Kniss will discuss the differences between the U.S. influenza surveillance system and how influenza surveillance is conducted in other countries, specifically those with few resources. The second presentation by Scott Epperson will discuss the evolution and current status of influenza A viruses both in humans and swine.
Description
HealthMap collects and aggregates information from online sources to generate outbreak alerts based on disease and geographic location. This project will assess the timeliness and sensitivity of HealthMap based on outbreak posts from EpiCom, the Florida Department of Health’s disease outbreak and health incident alert network.
Objective
To assess the outbreak detection utility of HealthMap, a publically available event-based biosurveillance system utilizing various internet-based media resources to identify outbreaks, at the state and local level. Results may help determine whether HealthMap should be monitored more closely as a supplementary surveillance tool.
Description
In the event of a large-scale public health crisis, successfully detecting and assessing health threats and monitoring population health status over a sustained period of time is likely to require integration of information from multiple sources. In addition, this information must be shared at varying levels of detail both among different agencies or organizations within an affected locality and among response participants at local, state, and federal levels of government. In early 2007, the International Society for Disease Surveillance (ISDS) proposed a project to support member initiated consultations on priority unresolved questions in the field of syndromic surveillance (SS) research, development, or practice. The Duval County Health Department sought and obtained ISDS support to address the use of SS data in combination with other human health and veterinary surveillance data, environmental sampling data, and plume modeling results in the event of an airborne bioterrorist (BT) attack. To date, the development of SS in Florida has mainly focused on systems that monitor information from emergency department (ED) visits. In addition, because SS development was decentralized and managed primarily by county health departments, various systems were used in Florida, including ESSENCE, STARS, EARS and BioDefend.
Objective
The objective of this consultation was to develop expert, consensus-based recommendations for use of SS in combination with other human health, animal health, and environmental data sources to improve situational awareness in the event of a large-scale public health emergency. The consultation, convened by the Duval County, Florida, Health Department, involved other local and state public health offi cials from Florida who addressed this question in the context of a hypothetical BT attack scenario in Duval County. Insights arising from the consultation will be used to strengthen public health surveillance capacities as part of both local and state emergency preparedness efforts in Florida. The approach used by the consultation may be useful to other health departments seeking to enhance their emergency situational awareness capacity.
Referenced File
Description
Effective infectious disease public health surveillance systems are often lacking in resource poor settings. In response, the World Health Organization (WHO) put forword recommnded standards for public health surveillence.[1] Following the recommendations, the WHO Regional Office for Africa (AFRO) in 1998 proposed the Integrated Dieases Surveillance and Response (IDSR) strategy for the prompt detection and response to key communicable diseases in the African region.[2,3] In 2003, Cameroon adopted the IDSR-strategy to fortify surveillance in the country. We describe cholera surveillance within IDSR-strategy, and assess whether its goal of data analysis and rapid response at the district level have been met.
Objective
To describe cholera public health surveillance systems in Cameroon within its hierarchical health system
Description
The New York City (NYC) syndromic surveillance system has monitored syndromes from NYC emergency department (ED) visits since 2001, using the temporal and spatial scan statistic in SaTScan for aberration detection. Since our syndromic system was initiated, alternative methods have been proposed for outbreak identification. Our goal was to evaluate methods for outbreak detection and apply the best performing method(s) to our daily analysis of syndromic data.
Objective
To evaluate temporal and spatial aberration detection methods for implementation in a local syndromic surveillance system.
Description
Booz Allen Hamilton is developing a novel bio-surveillance prototype tool, the Digital Disease Detection Dashboard (D4) to address the questions fundamental to daily biosurveillance analysis and decision making: is something unusual happening (e.g., is an outbreak or novel disease emerging)?, What is the probability that what I’m seeing is by chance?, How confident am I that this data is really detecting a signal?, Why is this happening and can I explain it?; and How many cases should I expect? (e.g., magnitude of event over time). These questions focus on detection, confidence, variance, and forecasting and D4 integrates a number of diverse analytical tools and methods that are crucial to a complete biosurveillance program.
Objective
To develop a web-enabled Digital Disease Detection Dashboard (D4) that allows users to statistically model and forecast multiple data streams for public health biosurveillance. D4 is a user-friendly, cloudenabled, and R Shiny-powered application that provides intuitive visualization enabling immediate situational awareness through interactive data displays and multi-factor analysis of traditional and non-traditional data feeds. The objective of D4 is to support public health decision making with high confidence across all four aspects of the biosurveillance continuum—detection, investigation, response, and prevention.
Kyasannur Forest Disease (KFD) is a tick borne viral disease first reported in Shimoga district of Karnataka, India. On January 6th 2015, the disease has spread to neighbouring state, Kerala and a forest guard from Sulthan Bathery, Wayanad who had disposed the monkey carcass was succumbed to the disease following confirmation of the disease from Manipal institute of virology. Spot surveillance of the area by Health department revealed 15 more fever cases among women working as fire line workers. Out of these twelve cases were confirmed to be KFD.
Description
We implemented the CDC EARS algorithms in our DADAR (Data Analysis, Detection, and Response) situational awareness platform. We encountered some skepticism among some of our partners about the efficacy of these algorithms for more than the simplest tracking of seasonal flu.
We analyzed several flu outbreaks observed in our data, including the H1N1 outbreaks in 2009, and noted that, using the C1 algorithm, even with our adjustable alerting thresholds, there was an uncomfortable number of false alarms in the noisy steady-state data, when the number of reported cases of flu-like symptoms was less than five per day.
We developed an algorithm, RecentMax, that could offer better performance in analyzing our flu data.
Objective
To develop an algorithm for detecting outbreaks of typical transmissible diseases in time series data that offers better sensitivity and specificity than the CDC EARS C1/C2/C3 algorithms while offering much better noise handling performance.