Capturing the transmission dynamics of the 2009 Japanese pandemic influenza H1N1 in the presence of heterogeneous immunity.

PURPOSE To explore the heterogeneous transmission dynamics for influenza and identify the optimal serum antibody titer cutoff values for estimating its cumulative incidence. METHODS We constructed a mathematical model describing serologically dependent disease transmission. The diagnostic performances of two serum antibody titer tests (single serum test and paired sera test) were evaluated, and cumulative disease incidence estimators were formulated. The model simulated the 2009 Japanese influenza A/H1N1 epidemic and investigated the optimal cutoff values and cumulative incidence estimates for this epidemic. RESULTS Our assumed model and parameters suggested that the optimal cutoffs for A/H1N1 influenza were 1:20 for the single serum test and a 2-fold increase for the paired sera test. Using these optimal cutoff values, the paired sera test was the most reliable. The cumulative incidence estimate for the pre- and post-epidemic serological data showed that the paired serological data were also more accurately predictive. CONCLUSIONS From a statistical perspective, the currently used cutoff values may be too strict for diagnosing influenza and estimating its incidence. The paired sera test, which was more accurate for diagnosis and cumulative incidence estimation, is the test recommended for seroepidemiological surveillance during an epidemic.

[1]  Gerardo Chowell,et al.  Did Modeling Overestimate the Transmission Potential of Pandemic (H1N1-2009)? Sample Size Estimation for Post-Epidemic Seroepidemiological Studies , 2011, PloS one.

[2]  S. Sridhar.  Heterosubtypic T-Cell Immunity to Influenza in Humans: Challenges for Universal T-Cell Influenza Vaccines , 2016, Front. Immunol..

[3]  Libo Dong,et al.  Cross-reactive antibody responses to the 2009 pandemic H1N1 influenza virus. , 2009, The New England journal of medicine.

[4]  S. Layne.  Human Influenza Surveillance: the Demand to Expand , 2006, Emerging infectious diseases.

[5]  N. Arinaminpathy,et al.  The impact of stratified immunity on the transmission dynamics of influenza , 2017, Epidemics.

[6]  Benjamin J Cowling,et al.  The infection attack rate and severity of 2009 pandemic H1N1 influenza in Hong Kong. , 2010, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[7]  P. Tambyah,et al.  Cost-effectiveness Analysis of Hospital Infection Control Response to an Epidemic Respiratory Virus Threat , 2009, Emerging infectious diseases.

[8]  J. Makkoch,et al.  Determination of antibody response to the human pandemic influenza H1N1 2009 among patients with influenza-like illness and high risk groups. , 2010, Asian Pacific journal of allergy and immunology.

[9]  M. Baguelin,et al.  Assessment of baseline age-specific antibody prevalence and incidence of infection to novel influenza A/H1N1 2009. , 2010, Health technology assessment.

[10]  Ivar Sønbø Kristiansen,et al.  Modeling the cost of influenza: the impact of missing costs of unreported complications and sick leave , 2010, BMC public health.

[11]  M. Lipsitch,et al.  The Severity of Pandemic H1N1 Influenza in the United States, from April to July 2009: A Bayesian Analysis , 2009, PLoS medicine.

[12]  R. Mikolajczyk,et al.  Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases , 2008, PLoS medicine.

[13]  Keiji Fukuda,et al.  Detection of Antibody to Avian Influenza A (H5N1) Virus in Human Serum by Using a Combination of Serologic Assays , 1999, Journal of Clinical Microbiology.

[14]  A. Conlan,et al.  Measuring social networks in British primary schools through scientific engagement , 2010, Proceedings of the Royal Society B: Biological Sciences.

[15]  A. Cook,et al.  2009 influenza A(H1N1) seroconversion rates and risk factors among distinct adult cohorts in Singapore. , 2010, JAMA.

[16]  W. O. Kermack,et al.  A contribution to the mathematical theory of epidemics , 1927 .

[17]  C. Viboud,et al.  First flu is forever , 2016, Science.

[18]  P. Horby,et al.  Influenza Infection Rates, Measurement Errors and the Interpretation of Paired Serology , 2012, PLoS pathogens.

[19]  T. Chieochansin,et al.  Serological Analysis of Human Pandemic Influenza (H1N1) in Thailand , 2010, Journal of health, population, and nutrition.

[20]  A. S. Beare,et al.  The role of serum haemagglutination-inhibiting antibody in protection against challenge infection with influenza A2 and B viruses , 1972, Epidemiology and Infection.

[21]  W. Youden,et al.  Index for rating diagnostic tests , 1950, Cancer.

[22]  Benjamin J. Cowling,et al.  Epidemiological Characteristics of 2009 (H1N1) Pandemic Influenza Based on Paired Sera from a Longitudinal Community Cohort Study , 2011, PLoS medicine.

[23]  N. Perkins,et al.  Optimal Cut-point and Its Corresponding Youden Index to Discriminate Individuals Using Pooled Blood Samples , 2005, Epidemiology.

[24]  N. Andrews,et al.  Incidence of 2009 pandemic influenza A H1N1 infection in England: a cross-sectional serological study , 2010, The Lancet.

[25]  Pia Hardelid,et al.  Age-Specific Incidence of A/H1N1 2009 Influenza Infection in England from Sequential Antibody Prevalence Data Using Likelihood-Based Estimation , 2011, PloS one.