Rapid detection of pandemic influenza in the presence of seasonal influenza

BackgroundKey to the control of pandemic influenza are surveillance systems that raise alarms rapidly and sensitively. In addition, they must minimise false alarms during a normal influenza season. We develop a method that uses historical syndromic influenza data from the existing surveillance system 'SERVIS' (Scottish Enhanced Respiratory Virus Infection Surveillance) for influenza-like illness (ILI) in Scotland.MethodsWe develop an algorithm based on the weekly case ratio (WCR) of reported ILI cases to generate an alarm for pandemic influenza. From the seasonal influenza data from 13 Scottish health boards, we estimate the joint probability distribution of the country-level WCR and the number of health boards showing synchronous increases in reported influenza cases over the previous week. Pandemic cases are sampled with various case reporting rates from simulated pandemic influenza infections and overlaid with seasonal SERVIS data from 2001 to 2007. Using this combined time series we test our method for speed of detection, sensitivity and specificity. Also, the 2008-09 SERVIS ILI cases are used for testing detection performances of the three methods with a real pandemic data.ResultsWe compare our method, based on our simulation study, to the moving-average Cumulative Sums (Mov-Avg Cusum) and ILI rate threshold methods and find it to be more sensitive and rapid. For 1% case reporting and detection specificity of 95%, our method is 100% sensitive and has median detection time (MDT) of 4 weeks while the Mov-Avg Cusum and ILI rate threshold methods are, respectively, 97% and 100% sensitive with MDT of 5 weeks. At 99% specificity, our method remains 100% sensitive with MDT of 5 weeks. Although the threshold method maintains its sensitivity of 100% with MDT of 5 weeks, sensitivity of Mov-Avg Cusum declines to 92% with increased MDT of 6 weeks. For a two-fold decrease in the case reporting rate (0.5%) and 99% specificity, the WCR and threshold methods, respectively, have MDT of 5 and 6 weeks with both having sensitivity close to 100% while the Mov-Avg Cusum method can only manage sensitivity of 77% with MDT of 6 weeks. However, the WCR and Mov-Avg Cusum methods outperform the ILI threshold method by 1 week in retrospective detection of the 2009 pandemic in Scotland.ConclusionsWhile computationally and statistically simple to implement, the WCR algorithm is capable of raising alarms, rapidly and sensitively, for influenza pandemics against a background of seasonal influenza. Although the algorithm was developed using the SERVIS data, it has the capacity to be used at other geographic scales and for different disease systems where buying some early extra time is critical.

[1]  A. Monto,et al.  Epidemiology of pandemic influenza: use of surveillance and modeling for pandemic preparedness. , 2006, The Journal of infectious diseases.

[2]  Andrew B. Lawson,et al.  Spatial and syndromic surveillance for public health , 2005 .

[3]  Michael M. Wagner,et al.  Telephone Triage: A Timely Data Source for Surveillance of Influenza-like Diseases , 2003, AMIA.

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

[5]  Benjamin J Cowling,et al.  Methods for monitoring influenza surveillance data. , 2006, International journal of epidemiology.

[6]  M. Jerrett,et al.  Syndromic Surveillance of Norovirus using Over-the-counter Sales of Medications Related to Gastrointestinal Illness. , 2006, The Canadian journal of infectious diseases & medical microbiology = Journal canadien des maladies infectieuses et de la microbiologie medicale.

[7]  Fred Spiring,et al.  Introduction to Statistical Quality Control , 2007, Technometrics.

[8]  E. Lyons,et al.  Pandemic Potential of a Strain of Influenza A (H1N1): Early Findings , 2009, Science.

[9]  A. Wallensten,et al.  Compliance and side effects of prophylactic oseltamivir treatment in a school in South West England. , 2009, Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin.

[10]  D. Cummings,et al.  Strategies for containing an emerging influenza pandemic in Southeast Asia , 2005, Nature.

[11]  N. Verlander,et al.  Evaluation of syndromic surveillance based on National Health Service Direct derived data--England and Wales. , 2005, MMWR supplements.

[12]  Jeremy Ginsberg,et al.  Detecting influenza epidemics using search engine query data , 2009, Nature.

[13]  Beth Ann Griffin,et al.  Early detection of influenza outbreaks using the DC Department of Health's syndromic surveillance system , 2009, BMC public health.

[14]  A. Cook,et al.  Real-Time Epidemic Monitoring and Forecasting of H1N1-2009 Using Influenza-Like Illness from General Practice and Family Doctor Clinics in Singapore , 2010, PloS one.

[15]  P. Sebastiani,et al.  A Bayesian dynamic model for influenza surveillance , 2006, Statistics in medicine.

[16]  M. Keeling,et al.  Epidemiology: Foot-and-mouth disease under control in the UK , 2001, Nature.

[17]  L. Brammer,et al.  Preparing for pandemic influenza: the need for enhanced surveillance. , 1999, Emerging infectious diseases.

[18]  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.

[19]  Francisco Inácio Bastos,et al.  Disease Surveillance: A Public Health Informatics Approach , 2007 .

[20]  Avian influenza: assessing the pandemic threat Avian influenza: assessing the pandemic threat , 2005 .

[21]  A. Nizam,et al.  Containing Pandemic Influenza at the Source , 2005, Science.

[22]  Russ P Lopez.  Disease Surveillance: A Public Health Informatics Approach , 2007 .

[23]  Hsinchun Chen,et al.  Syndromic surveillance systems , 2008, Annu. Rev. Inf. Sci. Technol..

[24]  N. Hunter,et al.  The basic reproduction number for scrapie , 1999, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[25]  S. Leach,et al.  Real-time epidemic forecasting for pandemic influenza , 2006, Epidemiology and Infection.

[26]  N. Verlander,et al.  A national syndromic surveillance system for England and Wales using calls to a telephone helpline. , 2006, Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin.

[27]  S Hashimoto,et al.  Detection of epidemics in their early stage through infectious disease surveillance. , 2000, International journal of epidemiology.

[28]  Pandemic ( H 1 N 1 ) 2009 in England : an overview of initial epidemiological findings and implications for the second wave Executive summary The Health Protection Agency , 2009 .

[29]  L. Hutwagner,et al.  Using laboratory-based surveillance data for prevention: an algorithm for detecting Salmonella outbreaks. , 1997, Emerging infectious diseases.

[30]  D. Cummings,et al.  Strategies for mitigating an influenza pandemic , 2006, Nature.

[31]  J. Taubenberger,et al.  Influenza : the Mother of All Pandemics , 2022 .

[32]  Lori Hutwagner,et al.  Comparing Aberration Detection Methods with Simulated Data , 2005, Emerging infectious diseases.

[33]  R K Griffiths,et al.  Can calls to NHS Direct be used for syndromic surveillance? , 2001, Communicable disease and public health.

[34]  J. Taubenberger,et al.  1918 Influenza: the Mother of All Pandemics , 2006, Emerging infectious diseases.

[35]  Ray Higginson,et al.  The threat of an avian influenza pandemic. , 2005, British journal of nursing.

[36]  Louise Matthews,et al.  New approaches to quantifying the spread of infection , 2005, Nature Reviews Microbiology.

[37]  Nick Andrews,et al.  A Statistical Algorithm for the Early Detection of Outbreaks of Infectious Disease , 1996 .

[38]  J. Kynčl,et al.  Appropriateness of thresholds currently used to describe influenza activity in England. , 2003, Communicable disease and public health.

[39]  Peter A Rogerson,et al.  Approaches to syndromic surveillance when data consist of small regional counts. , 2004, MMWR supplements.

[40]  Andrew B. Lawson,et al.  Spatial and Syndromic Surveillance for Public Health: Lawson/Spatial and Syndromic Surveillance for Public Health , 2005 .

[41]  R. Heathcock,et al.  Oseltamivir adherence and side effects among children in three London schools affected by influenza A(H1N1)v, May 2009 - an internet-based cross-sectional survey. , 2009, Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin.

[42]  Aj Elliot.  Syndromic surveillance: the next phase of public health monitoring during the H1N1 influenza pandemic? , 2009, Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin.

[43]  Simon Cauchemez,et al.  Assessing the severity of the novel influenza A/H1N1 pandemic , 2009, BMJ : British Medical Journal.

[44]  Galit Shmueli,et al.  Early statistical detection of anthrax outbreaks by tracking over-the-counter medication sales , 2002, Proceedings of the National Academy of Sciences of the United States of America.