Analytic Biosurveillance Methods for Resource-Limited Settings

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 Organization,2 and also in the United States at the national level.3 Particular concern exists for surveillance in resourcelimited settings (i.e., areas with limited access to medical care; inadequate or no laboratory diagnostic capability; insufficient numbers of first responders, care providers, and public health workers; and sometimes deficiencies in fundamental hygienic needs such as clean drinking water).4, 5 As a result of these issues, such regions are vulnerable to outbreaks of diseases, such as cholera and typhoid fever, that are not seen in advantaged settings. ublic health surveillance faces many challenges in geographic regions lacking modern technology and infrastructure. This article addresses the role of analytic methods in such regions and evaluates temporal alerting algorithms using both authentic and simulated data sets. Evaluation analyses give the technical background for the statistical methods provided by the Johns Hopkins University Applied Physics Laboratory (APL) Suite for Automated Global Electronic bioSurveillance (SAGES), a collection of modular, open-source software tools to enable electronic surveillance in resource-limited settings. Included in the evaluation are only those statistical methods that are broadly applicable to multiple evolving-background time-series behaviors with limited data history. Multiple detection performance measures are defined, and a practical means of combining them is applied to recommend preferred alerting methods for common scenarios. Effective usage of these methods is discussed in the context of routine health-monitoring operations. Analytic Biosurveillance Methods for ResourceLimited Settings