Influenza A and B epidemic criteria based on time-series analysis of health services surveillance data

Many countries now have epidemiological surveillance systems using health services-based indicators that allow detection of influenza epidemics. However, there is no accepted criterion for defining an influenza epidemic. An epidemic criterion has been developed, based on a time-series analysis of health services-based indicators collected on a weekly basis by a surveillance network implemented in the Paris region since 1984: the Groupe Régional d'Observation de la Grippe (GROG). For each new season, an epidemic threshold is independently defined for each health services-based indicator as the upper limit of the one-sided confidence interval of the expected value calculated from the weekly differences between the observed number of events and those predicted by a SARIMA model fitted on the non-epidemic data of previous seasons. Epidemic criteria for influenza A and B are then defined from the combination of both viral indicators and epidemic thresholds of individual health services-based indicators. Among health indicators, sick-leave data collected from GP's or the Health Insurance system, emergency home medical visits, and influenza-like-illness reported by GP's are the most sensitive indicators for the early recognition of epidemics. The exceeding of the above mentioned thresholds combined with virological data allows the specific detection of influenza A or B epidemics. This time-series method of analysing surveillance data provides early and reliable recognition of these epidemics.

[1]  P. Krause,et al.  Sales of Nonprescription Cold Remedies: A Unique Method of Influenza Surveillance , 1979, Pediatric Research.

[2]  A. Langmuir,et al.  Excess mortality from epidemic influenza, 1957-1966. , 1974, American journal of epidemiology.

[3]  P. Quénel,et al.  Sensitivity, specificity and predictive values of health service based indicators for the surveillance of influenza A epidemics. , 1994, International journal of epidemiology.

[4]  J. Lentino,et al.  Influenza A among hospital personnel and patients. Implications for recognition, prevention, and control. , 1989, Archives of internal medicine.

[5]  M. T. Paixão,et al.  Influenza and the 'spotter' general practitioner. , 1988, The Journal of the Royal College of General Practitioners.

[6]  R. Douglas,et al.  Respiratory syncytial virus and influenza. Practical community surveillance. , 1976, American journal of diseases of children.

[7]  Harvey V. Fineberg,et al.  The Swine Flu Affair: Decision-Making on a Slippery Disease , 1979 .

[8]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .

[9]  A. Kendal,et al.  Do family physicians make good sentinels for influenza? , 1993, Archives of family medicine.

[10]  W. Dab,et al.  A new influenza surveillance system in France: The Ile-De-France “GROG”. I. Principles and methodology , 1989, European Journal of Epidemiology.

[11]  Rod Ellis,et al.  Principles and methodology , 1985 .

[12]  A. Flahault,et al.  A routine tool for detection and assessment of epidemics of influenza-like syndromes in France. , 1991, American journal of public health.

[13]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .

[14]  S B Thacker,et al.  An evaluation of influenza mortality surveillance, 1962-1979. II. Percentage of pneumonia and influenza deaths as an indicator of influenza activity. , 1981, American journal of epidemiology.

[15]  Hirotugu Akaike,et al.  On the Likelihood of a Time Series Model , 1978 .

[16]  George E. P. Box,et al.  Time Series Analysis: Forecasting and Control , 1977 .

[17]  R. B. Hogan,et al.  Laboratory diagnosis of Asian influenza. , 1958, Public health reports.

[18]  C. Stuart-harris,et al.  Epidemiology of influenza in man. , 1979, British medical bulletin.

[19]  S B Thacker,et al.  The persistence of influenza A in human populations. , 1986, Epidemiologic reviews.

[20]  S. Thacker,et al.  Persistence of influenza A by continuous close-contact transmission: the effect of non-random mixing. , 1990, International journal of epidemiology.

[21]  R. Douglas,et al.  Prophylaxis and treatment of influenza. , 1990, The New England journal of medicine.

[22]  P. Quénel,et al.  A new influenza surveillance system in France: The Ile-De-France “GROG”. 2. Validity of indicators (1984–1989) , 1991, European Journal of Epidemiology.

[23]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

[24]  H. Fineberg,et al.  The Swine Flu Affair , 2005 .

[25]  S B Thacker,et al.  An evaluation of influenza mortality surveillance, 1962-1979. I. Time series forecasts of expected pneumonia and influenza deaths. , 1981, American journal of epidemiology.

[26]  G. Box,et al.  On a measure of lack of fit in time series models , 1978 .

[27]  C. Chatfield,et al.  Box‐Jenkins Seasonal Forecasting: Problems in a Case‐Study , 1973 .

[28]  D M Perrotta,et al.  Acute respiratory disease hospitalizations as a measure of impact of epidemic influenza. , 1985, American journal of epidemiology.

[29]  R. Serfling Methods for current statistical analysis of excess pneumonia-influenza deaths. , 1963, Public health reports.

[30]  Hope-Simpson Re,et al.  Epidemic mechanisms of type A influenza. , 1979 .

[31]  S. Thacker,et al.  From the centers for disease control. Improved accuracy and specificity of forecasting deaths attributed to pneumonia and influenza. , 1981, The Journal of infectious diseases.