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BioSurveillance

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.

Submitted by teresa.hamby@d… on

The National Biosurveillance Integration Center (NBIC) serves to enable early warning and shared situational awareness of acute biological events and support better decisions through rapid identification, characterization, localization, and tracking. As part of the U.S. Department of Homeland Security, NBIC integrates biosurveillance information across the domains of human, animal, and plant health, as well as food and the environment using a variety of human and technological sources.

The June 2011 meeting of the Public Health Practice Committee featured a discussion on the June 2011 Report of the National Biosurveillance Advisory Subcommittee (NBAS). Leading the discussion was Pamela S. Diaz, MD, the Director of Biosurveillance Coordination at the Office of Surveillance, Epidemiology and Laboratory Services (OSELS) at the Centers for Disease Control and Prevention (CDC).

Presenter

Pakistan being a subtropical region is highly susceptible to water-borne, air-borne and vector-borne infectious diseases (IDs). Each year, millions of its people are exposed to, and infected with, deadly pathogens including hepatitis, tuberculosis, malaria, and now-a-days dengue fever (DF). Monitoring and response management to natural or man-made IDs is non-existent in the country due to lack of robust infrastructure for health surveillance. DF outbreaks in 2005-2011 alone resulted in more than 50,000 infections and about 1500 people lost their lives.

Submitted by uysz on

The past decade has seen the rise of many new diseases, and the re-emergence of others which were thought to have been brought under control. This is the combined result of the expansion of global trade and travel, the increases in populations of both humans and animals, and environmental changes. As a result, there should be an effective collaboration among different institutions in each country, and close international cooperation with different stakeholders. The MBDS (Mekong Basin Disease Surveillance) cooperation is a self-organized sub-regional network commenced in 2001.

Submitted by uysz on
Description

Currently, there is an abundance of data coming from most of the surveillance environments and applications. Identification and filtering of responsive messages from this big data ocean and then processing these informative datasets to gain knowledge are the two real challenges in today’s applications.

Use of Analytics has revolutionized many areas. At LongRiver Infotech, we have used various Machine Learning techniques (Regression, Classification, Text Analytics, Decision Trees, Clustering etc.) in different types of applications. These methodologies are abstracted in a generic platform, which can be put to use in many public health and surveillance applications, which are enumerated here.

Objective

To summarize ways in which Analytics, Machine Learning (ML) and Natural Language Processing (NLP) can improve accuracy and efficiency in bio surveillance and public health practices. We also discuss the use of this framework in typical surveillance applications (Integration with Devices/Sensors, Web/Mobile, Clinical Records, Internet queries, Social/News media).

Submitted by teresa.hamby@d… on

Zoonotic diseases constitute about 70% of the emerging or reemerging diseases in the world; they affect many animals, cause many economic loses, and have a negative effect on public health. As a tropical country, Cuba is not exempt from the occurrence of this type of illness. There are many risk factors present such as climate change, natural disasters, bird migrations, vector species, the entry of Cuban travelers into endemic areas, the increase of commercial and touristic exchange, and the increase of agricultural activities including animals raised in urban areas.

Submitted by uysz on
Description

The APCC hotline fields daily calls regarding potential animal intoxications from the US, its territories, and Canada. We explored the value of these data for identifying increased occurrences of intoxications related to livestock and poultry species, toxicant product categories, clinical syndromes, and illness severity. These data proved valuable for identifying risks of toxicant exposures by species, product category, and season. In addition to identifying intoxication risks to animal health, these data could be used to monitor for infectious outbreaks that may initially be confused for intoxications.

Objective

To describe the value of the American Society for the Prevention of Cruelty to Animals (ASPCA) Poison Control Center (APCC) livestock animal calls as a passive data stream for biosurveillance of number of calls, species affected, toxicant exposures, and clinical syndromes.

 

Submitted by Magou on
Description

Previous research identifies social media as an informal source of near-real time health data that may add value to disease surveillance systems by providing broader access to health data across hard-toreach populations. This indirect health monitoring may improve public health professionals’ ability to detect disease outbreaks faster than traditional methods and to enhance outbreak response. The Philippines consists of over 7,000 islands and is prone to meteorological (storms), hydrological (floods), and geophysical disasters (earthquakes and volcanoes). In these situations, evacuation centers are used for safety and medical attention and often house up to 50K people each for 2 or more months, sometimes with unclean water sources and improper sanitation. Consequently, these conditions are a perfect venue for communicable disease transmission and have been proposed to cause disease outbreaks weeks after the original disaster occurred. Coined the social media capital of the world1, the Philippines provides a perfect opportunity to evaluate the potential of social media use in disease surveillance.

Objective

To determine the potential of Twitter data as an early warning of a likely communicable disease outbreak following a natural disaster, and if successful, develop an open-source algorithm for use by interested parties.

Submitted by Magou on