an independent organization acting as a data clearing-house between the reporting laboratories and public health departments. The system ran in tandem with conventional reporting methods. Laboratory test orders and results were aggregated and mapped to a common nomenclature. Reports were delivered through a secure Internet connection to the Kansas City Health Department (KCHD); during the first...

Syndromic surveillance is the regular collection, analysis, and interpretation of real-time and near-real-time indicators of diseases and other outbreaks by public health organizations. Motivated by the threat of bioterrorism, syndromic surveillance systems are being developed and implemented around the world. In a 2004 systematic review of publicly available information, 115 surveillance syste...

The emergence of the 2009 pandemic influenza A(H1N1) virus in North America and its subsequent global spread highlights the public health need for early warning of infectious disease outbreaks. Event-based biosurveillance, based on local- and regional-level Internet media reports, is one approach to early warning as well as to situational awareness. This study analyses media reports in Mexico c...

South and South-East Asian countries are currently in the midst of a new epidemic of Dengue Fever. This paper presents disease surveillance systems in Sri Lanka and India, monitoring a handful of communicable diseases termed as notifiable. These systems typically require 15-30 days to communicate field data to the central Epidemiology Units, to be then manually processed (Prashant & Waidyanatha...

Emerging infectious diseases (EIDs), the majority of which are zoonotic, represent a tremendous challenge for public health and biosurveillance infrastructure across the globe. Due to the complexity of zoonotic pathogens, it is essential that research and response to EIDs be a transdisciplinary effort. And while crisis and circumstance may be the initial catalyst for responding to an outbreak, ...

This paper investigates Bayesian modeling of unknown causes of events in the context of disease-outbreak detection. We introduce a Bayesian approach that models and detects both (1) known diseases (e.g., influenza and anthrax) by using informative prior probabilities and (2) unknown diseases (e.g., a new, highly contagious respiratory virus that has never been seen before) by using relatively n...

Background The 21st century has seen advances in many aspects of global disease surveillance.1 These advances have been driven by heightened concerns over perceived threats to public health both from natural pathogens and from bioterrorism. These concerns have led to mandated improvements at the international level, through revision of International Health Regulations of the World Health Organi...