Identifying the Relative Priorities of Subpopulations for Containing Infectious Disease Spread

In response to the outbreak of an emerging infectious disease, e.g., H1N1 influenza, public health authorities will take timely and effective intervention measures to contain disease spread. However, due to the scarcity of required resources and the consequent social-economic impacts, interventions may be suggested to cover only certain subpopulations, e.g., immunizing vulnerable children and the elderly as well as closing schools or workplaces for social distancing. Here we are interested in addressing the question of how to identify the relative priorities of subpopulations for two measures of disease intervention, namely vaccination and contact reduction, especially when these measures are implemented together at the same time. We consider the measure of vaccination that immunizes susceptible individuals in different age subpopulations and the measure of contact reduction that cuts down individuals’ effective contacts in different social settings, e.g., schools, households, workplaces, and general communities. In addition, we construct individuals’ cross-age contact frequency matrix by inferring basic contact patterns respectively for different social settings from the socio-demographical census data. By doing so, we present a prioritization approach to identifying the target subpopulations that will lead to the greatest reduction in the number of disease transmissions. We calculate the relative priorities of subpopulations by considering the marginal effects of reducing the reproduction number for the cases of vaccine allocation by age and contact reduction by social setting. We examine the proposed approach by revisiting the real-world scenario of the 2009 Hong Kong H1N1 influenza epidemic and determine the relative priorities of subpopulations for age-specific vaccination and setting-specific contact reduction. We simulate the influenza-like disease spread under different settings of intervention. The results have shown that the proposed approach can improve the effectiveness of disease control by containing disease transmissions in a host population.

[1]  M. van Boven,et al.  Optimizing infectious disease interventions during an emerging epidemic , 2009, Proceedings of the National Academy of Sciences.

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

[3]  Aravind Srinivasan,et al.  Modelling disease outbreaks in realistic urban social networks , 2004, Nature.

[4]  Stefano Merler,et al.  Determinants of the Spatiotemporal Dynamics of the 2009 H1N1 Pandemic in Europe: Implications for Real-Time Modelling , 2011, PLoS Comput. Biol..

[5]  J.A.P. Heesterbeek.  A Brief History of R0 and a Recipe for its Calculation , 2002, Acta biotheoretica.

[6]  Eric H. Y. Lau,et al.  The Effective Reproduction Number of Pandemic Influenza: Prospective Estimation , 2010, Epidemiology.

[7]  Matt J. Keeling,et al.  Targeting vaccination against novel infections: risk, age and spatial structure for pandemic influenza in Great Britain , 2010, Journal of The Royal Society Interface.

[8]  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.

[9]  Maria A. Kazandjieva,et al.  A high-resolution human contact network for infectious disease transmission , 2010, Proceedings of the National Academy of Sciences.

[10]  Gail E. Potter,et al.  The Transmissibility and Control of Pandemic Influenza A (H1N1) Virus , 2009, Science.

[11]  B. Cowling,et al.  Estimation of the Serial Interval of Influenza , 2009, Epidemiology.

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

[13]  C. Macken,et al.  Modeling targeted layered containment of an influenza pandemic in the United States , 2008, Proceedings of the National Academy of Sciences.

[14]  Jan Medlock,et al.  Optimizing allocation for a delayed influenza vaccination campaign , 2009, PLoS currents.

[15]  J. Medlock,et al.  Optimizing Influenza Vaccine Distribution , 2009, Science.

[16]  Benjamin J. Cowling,et al.  School Closure and Mitigation of Pandemic (H1N1) 2009, Hong Kong , 2010, Emerging infectious diseases.

[17]  Ira M. Longini,et al.  Optimizing Vaccine Allocation at Different Points in Time during an Epidemic , 2010, PloS one.

[18]  J. Watmough,et al.  Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission. , 2002, Mathematical biosciences.

[19]  O. Diekmann,et al.  On the definition and the computation of the basic reproduction ratio R0 in models for infectious diseases in heterogeneous populations , 1990, Journal of mathematical biology.

[20]  M. Postma,et al.  Mitigation of pandemic influenza: review of cost–effectiveness studies , 2009, Expert review of pharmacoeconomics & outcomes research.

[21]  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.

[22]  W. Edmunds,et al.  Estimating the costs of school closure for mitigating an influenza pandemic , 2008, BMC public health.

[23]  T. Kamigaki,et al.  Major Issues and Challenges of Influenza Pandemic Preparedness in Developing Countries , 2008, Emerging infectious diseases.

[24]  Ken Eames,et al.  "Herd immunity": a rough guide. , 2011, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[25]  Lai Ming Ho,et al.  Estimating Infection Attack Rates and Severity in Real Time during an Influenza Pandemic: Analysis of Serial Cross-Sectional Serologic Surveillance Data , 2011, PLoS medicine.

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

[27]  P E Fine,et al.  Herd immunity: history, theory, practice. , 1993, Epidemiologic reviews.

[28]  O Diekmann,et al.  The construction of next-generation matrices for compartmental epidemic models , 2010, Journal of The Royal Society Interface.

[29]  Steven Riley,et al.  Modelling the Proportion of Influenza Infections within Households during Pandemic and Non-Pandemic Years , 2011, PloS one.

[30]  Alessandro Vespignani,et al.  Inferring the Structure of Social Contacts from Demographic Data in the Analysis of Infectious Diseases Spread , 2012, PLoS Comput. Biol..

[31]  Walter A Orenstein,et al.  Vaccine shortages: history, impact, and prospects for the future. , 2006, Annual review of public health.

[32]  Cecile Viboud,et al.  Vaccinating to Protect a Vulnerable Subpopulation , 2007, PLoS medicine.