title: INFERNO: a system for early outbreak detection and signature forecasting.
abstract: OBJECTIVE
  Public health surveillance systems that monitor daily disease incidence provide valuable information about threats to public health and enable public health authorities to detect enteric outbreaks rapidly. This report describes the INtegrated Forecasts and EaRly eNteric Outbreak (INFERNO) detection system of algorithms for outbreak detection and forecasting.
  
  
  METHODS
  INFERNO incorporates existing knowledge of infectious disease epidemiology into adaptive forecasts and uses the concept of an outbreak signature as a composite of disease epidemic curves.
  
  
  RESULTS
  Four main components comprise the system: 1) training, 2) warning and flagging, 3) signature forecasting, and 4) evaluation. The unifying goal of the system is to gain insight into the nature of temporal variations in the incidence of infection. Daily collected records are smoothed initially by using a loess-type smoother. Upon receipt of new data, the smoothing is updated; estimates are made of the first two derivatives of the smoothed curve, which are used for near-term forecasting. Recent data and near-term forecasts are used to compute a five level, color-coded warning index to quantify the level of concern. Warning algorithms are designed to balance false detection of an epidemic (Type I errors) with failure to correctly detect an epidemic (Type II errors). If the warning index signals a sufficiently high probability of an epidemic, the fitting of a gamma-based signature curve to the actual data produces a forecast of the possible size of the outbreak.
  
  
  CONCLUSIONS
  Although the system is under development, its potential has been demonstrated through successful use of emergency department records associated with a substantial waterborne outbreak of cryptosporidiosis that occurred in Milwaukee, Wisconsin, in 1993. Prospects for further development, including adjustment for seasonality and reporting delays, are also outlined.
authors.affiliation: Department of Public Health and Family Medicine, Tufts University School of Medicine, Boston, Massachusetts 02111, USA. elena.naumova@tufts.edu
authors.forename: Elena N
authors.initials: EN
authors.lastname: Naumova
authors.forename: E
authors.initials: E
authors.lastname: O'Neil
authors.forename: I
authors.initials: I
authors.lastname: MacNeill
mesh_terms: D000465:Algorithms; D003140:Communicable Disease Control; D003141:Communicable Diseases / Q000453:epidemiology; D003457:Cryptosporidiosis / Q000453:epidemiology; D004196:Disease Outbreaks / Q000517:prevention & control*; D005544:Forecasting; D005759:Gastroenteritis / Q000453:epidemiology; D006801:Humans; D015233:Models, Statistical; D011159:Population Surveillance; D040442:Public Health Informatics / Q000295:instrumentation*; D012189:Retrospective Studies; D014922:Wisconsin / Q000453:epidemiology