A space-time permutation scan statistic for disease outbreak detection.
BACKGROUND
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.


RESULTS
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.


CONCLUSIONS
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.
Kulldorff
Martin
M
Department of Ambulatory Care and Prevention, Harvard Medical School and Harvard Pilgrim Health Care, Boston, Massachusetts, USA. martin_kulldorff@hms.harvard.edu
Heffernan
Richard
R
Hartman
Jessica
J
Assunção
Renato
R
Mostashari
Farzad
F
D003627:Data Interpretation, Statistical; D004196:Disease Outbreaks; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data*; D005843:Geography; D006801:Humans; D008962:Models, Theoretical; D009519:New York City / Q000453:epidemiology; D011159:Population Surveillance / Q000379:methods*; D013997:Time Factors
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