Does the Effectiveness of Control Measures Depend on the Influenza Pandemic Profile?

Background Although strategies to contain influenza pandemics are well studied, the characterization and the implications of different geographical and temporal diffusion patterns of the pandemic have been given less attention. Methodology/Main Findings Using a well-documented metapopulation model incorporating air travel between 52 major world cities, we identified potential influenza pandemic diffusion profiles and examined how the impact of interventions might be affected by this heterogeneity. Clustering methods applied to a set of pandemic simulations, characterized by seven parameters related to the conditions of emergence that were varied following Latin hypercube sampling, were used to identify six pandemic profiles exhibiting different characteristics notably in terms of global burden (from 415 to >160 million of cases) and duration (from 26 to 360 days). A multivariate sensitivity analysis showed that the transmission rate and proportion of susceptibles have a strong impact on the pandemic diffusion. The correlation between interventions and pandemic outcomes were analyzed for two specific profiles: a fast, massive pandemic and a slow building, long-lasting one. In both cases, the date of introduction for five control measures (masks, isolation, prophylactic or therapeutic use of antivirals, vaccination) correlated strongly with pandemic outcomes. Conversely, the coverage and efficacy of these interventions only moderately correlated with pandemic outcomes in the case of a massive pandemic. Pre-pandemic vaccination influenced pandemic outcomes in both profiles, while travel restriction was the only measure without any measurable effect in either. Conclusions Our study highlights: (i) the great heterogeneity in possible profiles of a future influenza pandemic; (ii) the value of being well prepared in every country since a pandemic may have heavy consequences wherever and whenever it starts; (iii) the need to quickly implement control measures and even to anticipate pandemic emergence through pre-pandemic vaccination; and (iv) the value of combining all available control measures except perhaps travel restrictions.

[1]  Maurice G. Kendall,et al.  PARTIAL RANK CORRELATION , 1942 .

[2]  J. H. Ward Hierarchical Grouping to Optimize an Objective Function , 1963 .

[3]  L. A. Rvachev,et al.  A mathematical model for the global spread of influenza , 1985 .

[4]  A. Flahault,et al.  Modelling the 1985 influenza epidemic in France. , 1988, Statistics in medicine.

[5]  P. Haggett,et al.  Statistical modelling of measles and influenza outbreaks , 1993, Statistical methods in medical research.

[6]  Hadi Dowlatabadi,et al.  Sensitivity and Uncertainty Analysis of Complex Models of Disease Transmission: an HIV Model, as an Example , 1994 .

[7]  A S Perelson,et al.  Emergence of drug resistance during an influenza epidemic: insights from a mathematical model. , 1998, The Journal of infectious diseases.

[8]  M. D. McKay,et al.  A comparison of three methods for selecting values of input variables in the analysis of output from a computer code , 2000 .

[9]  M E Halloran,et al.  Estimation of the efficacy of live, attenuated influenza vaccine from a two-year, multi-center vaccine trial: implications for influenza epidemic control. , 2000, Vaccine.

[10]  Richard J. Beckman,et al.  A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output From a Computer Code , 2000, Technometrics.

[11]  M. Meltzer,et al.  Effectiveness and cost-benefit of influenza vaccination of healthy working adults: A randomized controlled trial. , 2000, JAMA.

[12]  F. Hayden,et al.  Perspectives on antiviral use during pandemic influenza. , 2001, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[13]  J. Oxford,et al.  Effectiveness of oseltamivir in preventing influenza in household contacts: a randomized controlled trial. , 2001, JAMA.

[14]  G. Glass,et al.  Assessing the impact of airline travel on the geographic spread of pandemic influenza , 2003 .

[15]  Susan Mallett,et al.  A population-dynamic model for evaluating the potential spread of drug-resistant influenza virus infections during community-based use of antivirals. , 2003, The Journal of antimicrobial chemotherapy.

[16]  J. H. Ellis,et al.  Modeling the Spread of Annual Influenza Epidemics in the U.S.: The Potential Role of Air Travel , 2004, Health care management science.

[17]  R. Belshe,et al.  Safety and efficacy of trivalent inactivated influenza vaccine in young children: a summary for the new era of routine vaccination , 2004, The Pediatric infectious disease journal.

[18]  C. Viboud,et al.  A Bayesian MCMC approach to study transmission of influenza: application to household longitudinal data , 2004, Statistics in medicine.

[19]  C. Fraser,et al.  Factors that make an infectious disease outbreak controllable. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[20]  J. Robins,et al.  Transmissibility of 1918 pandemic influenza , 2004, Nature.

[21]  A. Nizam,et al.  Containing pandemic influenza with antiviral agents. , 2004, American journal of epidemiology.

[22]  Erratum: Assessing the impact of airline travel on the geographic spread of pandemic influenza (European Journal of Epidemiology (2003) 18 (1605-1072)) , 2004 .

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

[24]  A. Flahault,et al.  A mathematical model for the European spread of influenza , 1994, European Journal of Epidemiology.

[25]  M Elizabeth Halloran,et al.  Strategy for distribution of influenza vaccine to high-risk groups and children. , 2005, American journal of epidemiology.

[26]  M. Halloran,et al.  Finding optimal vaccination strategies for pandemic influenza using genetic algorithms. , 2005, Journal of theoretical biology.

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

[28]  A. Gumel,et al.  Assessing the role of basic control measures, antivirals and vaccine in curtailing pandemic influenza: scenarios for the US, UK and the Netherlands , 2007, Journal of The Royal Society Interface.

[29]  Mark A. Miller,et al.  Synchrony, Waves, and Spatial Hierarchies in the Spread of Influenza , 2006, Science.

[30]  E. Lofgren,et al.  Influenza Seasonality: Underlying Causes and Modeling Theories , 2006, Journal of Virology.

[31]  W. Edmunds,et al.  Delaying the International Spread of Pandemic Influenza , 2006, PLoS medicine.

[32]  Alessandro Vespignani,et al.  The role of the airline transportation network in the prediction and predictability of global epidemics , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[33]  Keiji Fukuda,et al.  Nonpharmaceutical Interventions for Pandemic Influenza, International Measures , 2006, Emerging infectious diseases.

[34]  N. Ferguson,et al.  Will travel restrictions control the international spread of pandemic influenza? , 2006, Nature Medicine.

[35]  A. Flahault,et al.  Strategies for containing a global influenza pandemic. , 2006, Vaccine.

[36]  E. D. Kilbourne Influenza Pandemics of the 20th Century , 2006, Emerging infectious diseases.

[37]  F. Brauer,et al.  Simple models for containment of a pandemic , 2006, Journal of The Royal Society Interface.

[38]  F. Carrat,et al.  A 'small-world-like' model for comparing interventions aimed at preventing and controlling influenza pandemics , 2006, BMC medicine.

[39]  J. Hyman,et al.  Transmission Dynamics of the Great Influenza Pandemic of 1918 in Geneva, Switzerland: Assessing the Effects of Hypothetical Interventions , 2022 .

[40]  C. Macken,et al.  Mitigation strategies for pandemic influenza in the United States. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

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

[42]  M. Tibayrenc Encyclopedia of infectious diseases : modern methodologies , 2007 .

[43]  Alessandro Vespignani,et al.  Modeling the Worldwide Spread of Pandemic Influenza: Baseline Case and Containment Interventions , 2007, PLoS medicine.

[44]  David J. Philp,et al.  The Waiting Time for Inter-Country Spread of Pandemic Influenza , 2007, PloS one.

[45]  M. G. Roberts,et al.  Model-consistent estimation of the basic reproduction number from the incidence of an emerging infection , 2007, Journal of mathematical biology.

[46]  Joshua M. Epstein,et al.  Controlling Pandemic Flu: The Value of International Air Travel Restrictions , 2007, PloS one.

[47]  B. Levin,et al.  Antiviral Resistance and the Control of Pandemic Influenza , 2007, PLoS medicine.

[48]  Steven Riley,et al.  Optimizing the Dose of Pre-Pandemic Influenza Vaccines to Reduce the Infection Attack Rate , 2007, PLoS medicine.

[49]  N. Ferguson,et al.  Time lines of infection and disease in human influenza: a review of volunteer challenge studies. , 2008, American journal of epidemiology.

[50]  C. Fraser,et al.  Reducing the impact of the next influenza pandemic using household-based public health interventions. , 2006, Hong Kong medical journal = Xianggang yi xue za zhi.