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      A Space–Time Permutation Scan Statistic for Disease Outbreak Detection

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          The ability to detect disease outbreaks early is important in order to minimize morbidity and mortality through timely implementation of disease prevention and control measures. Many national, state, and local health departments are launching disease surveillance systems with daily analyses of hospital emergency department visits, ambulance dispatch calls, or pharmacy sales for which population-at-risk information is unavailable or irrelevant.

          Methods and Findings

          We propose a prospective space–time permutation scan statistic for the early detection of disease outbreaks that uses only case numbers, with no need for population-at-risk data. It makes minimal assumptions about the time, geographical location, or size of the outbreak, and it adjusts for natural purely spatial and purely temporal variation. The new method was evaluated using daily analyses of hospital emergency department visits in New York City. Four of the five strongest signals were likely local precursors to citywide outbreaks due to rotavirus, norovirus, and influenza. The number of false signals was at most modest.


          If such results hold up over longer study times and in other locations, the space–time permutation scan statistic will be an important tool for local and national health departments that are setting up early disease detection surveillance systems.


          A new, flexible method for disease outbreak surveillance and its application to emergency department data from New York City

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          Most cited references 48

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          Prospective time periodic geographical disease surveillance using a scan statistic

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            Modified Randomization Tests for Nonparametric Hypotheses

             Meyer Dwass (1957)
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              Syndromic surveillance in public health practice, New York City.

              The New York City Department of Health and Mental Hygiene has established a syndromic surveillance system that monitors emergency department visits to detect disease outbreaks early. Routinely collected chief complaint information is transmitted electronically to the health department daily and analyzed for temporal and spatial aberrations. Respiratory, fever, diarrhea, and vomiting are the key syndromes analyzed. Statistically significant aberrations or "signals" are investigated to determine their public health importance. In the first year of operation (November 15, 2001, to November 14, 2002), 2.5 million visits were reported from 39 participating emergency departments, covering an estimated 75% of annual visits. Most signals for the respiratory and fever syndromes (64% and 95%, respectively) occurred during periods of peak influenza A and B activity. Eighty-three percent of the signals for diarrhea and 88% of the signals for vomiting occurred during periods of suspected norovirus and rotavirus transmission.

                Author and article information

                Role: Academic Editor
                PLoS Med
                PLoS Medicine
                Public Library of Science (San Francisco, USA )
                March 2005
                15 February 2005
                : 2
                : 3
                1Department of Ambulatory Care and Prevention, Harvard Medical School and Harvard Pilgrim Health Care Boston, MassachusettsUnited States of America
                2New York City Department of Health and Mental Hygiene, New York New YorkUnited States of America
                3New York Academy of Medicine, New York New YorkUnited States of America
                4Departamento de Estatistica, Universidade Federal de Minas Gerais Belo Horizonte, Minas GeraisBrazil
                University of California at Los Angeles United States of America
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author Contributions: MK and FM designed the study. MK, RA, and FM developed the statistical methodology. MK and RH analyzed the data. MK, RH, JH, RA, and FM contributed to writing the paper.

                *To whom correspondence should be addressed. E-mail: martin_kulldorff@ 123456hms.harvard.edu
                Copyright: © 2005 Kulldorff et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
                Research Article
                Infectious Diseases
                Epidemiology/Public Health
                Medical Informatics
                Medical Informatics
                Public Health



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