title: Identifying the relative priorities of subpopulations for containing infectious disease spread.
abstract: 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.
authors.affiliation: Department of Computer Science, Hong Kong Baptist University, Hong Kong SAR.
authors.forename: Shang
authors.initials: S
authors.lastname: Xia
authors.forename: Jiming
authors.initials: J
authors.lastname: Liu
authors.forename: William
authors.initials: W
authors.lastname: Cheung
mesh_terms: D003140:Communicable Disease Control / Q000379:methods*; D018562:Disease Transmission, Infectious / Q000517:prevention & control*; D006292:Health Priorities; D006801:Humans; D053118:Influenza A Virus, H1N1 Subtype; D007251:Influenza, Human / Q000453:epidemiology; D032541:Mass Vaccination / Q000379:methods* / Q000592:standards; D008954:Models, Biological; D013037:Spatial Behavior