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Global Infectious Disease Surveillance and Detection: Assessing the Challenges—Finding Solutions: Workshop Summary
and the European Commission’s Medical Intelligence System (MedISys), which is available only to European Union member states. GPHIN, a primary source of electronic surveillance for WHO, also serves a host of government institutions, nongovernmental agencies and organizations, academic institutions, and private companies, who pay between 30,000 and 200,000 Canadian dollars per year in subscription fees, depending on the specific services provided.
HealthMap is a freely accessible, automated network that collects information from multiple web-based data sources on infectious outbreaks (currently news wires, Really Simple Syndication (RSS) feeds, ProMED mailing lists, and EuroSurveillance and WHO alerts). The network then organizes and displays this information in real time as graphic “maps” featuring geography, time, and infectious disease agent. In their contribution to this chapter, workshop presenter John Brownstein of Harvard Medical School and his colleagues at Children’s Hospital Boston discuss their efforts to evaluate the HealthMap system with reference to four characteristics that have been used to evaluate syndromic surveillance systems: data acquisition; information characterization; signal interpretation; and dissemination. The authors’ preliminary evaluation of HealthMap according to these criteria appears to demonstrate that the aggregation of multiple sources of data—each potentially biased or otherwise flawed—increases the sensitivity and timeliness of alerts while reducing false alarms.
The concluding paper of the chapter describes a different sort of electronic surveillance network: one powered by cell phones, enabling observers in some of the world’s most remote and impoverished communities to report disease outbreaks. The authors are workshop speakers Pamela Johnson of Voxiva, a company that provides information technology to establish surveillance networks in low-resource settings, and David Blazes, of the U.S. Naval Medical Research Center Detachment in Lima, Peru, which used an Internet- and cell phone-based electronic system developed by Voxiva to support disease surveillance by the Peruvian navy along that country’s coast and remote rivers. This experience is presented as a case study in surveillance and evaluated according to the Centers for Disease Control and Prevention (CDC) guidelines for public health surveillance systems. The authors also share lessons gleaned from six years of building surveillance systems, based on cell phones and other cost-effective information technologies, for use in low-resource environments.
Workshop participants raised a series of issues in response to the presentations upon which the papers in this chapter are based. A detailed account of this discussion appears in the Summary and Assessment section, “Considerations for Surveillance Networks.” Discussants were especially concerned about the potentially devastating economic consequences to a country—particularly a developing country—of being labeled (accurately or inaccurately) as harboring a feared infectious disease. In his contribution to Chapter 4, speaker Will Hueston assesses the tradeoff between health and development inherent in the release of surveillance information such as HealthMap’s geographic depictions of outbreak reports.