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.

Figure 1. Contact patterns inferred from the socio-demographical census data of Hong Kong.

We consider disease transmissions among individuals between 0 and 85+ years old and divide them into 18 age groups. The contact matrices are generated corresponding to the likelihoods of individuals mixing together within respective social settings: A household (), B school (), C workplace (), D general community (). E The overall contact matrix is calculated as the linear combination of the four setting-specific contact matrices. The combination coefficient of each matrix denotes the ratio of effective contacts occurring in that social setting. F The population size in each age group.

Figure 2. The baseline scenario of disease spread.

We calibrate the proposed disease model according to the 2009 Hong Kong H1N1 influenza epidemic. In doing so, we collect the laboratory-confirmed cases of H1N1 infection reported by the Centre for Health Protection (CHP) of Hong Kong Public Health Department for 200 days since the disease onsite in early May 2009 . A The temporal dynamics of disease spread in terms of the proportion of the newly infected cases reported each day to the total number of disease infections. B A comparison of the observed and estimated age-specific attack rates.

Figure 3. The number of reported infections in different age groups during the spread of H1N1 influenza in Hong Kong.

We collected the laboratory-confirmed cases of infection reported by the Centre for Health Protection (CHP) of Hong Kong Public Health Department for 200 days since the disease onsite in early May 2009 .

Figure 4. Prioritization of age groups for vaccine allocation during the course of disease spread.

Figure 5. Prioritization of social settings for individuals contact reduction in different stages of disease spread.

Figure 6. Prioritization of age groups and social settings for implementing age-specific vaccination and setting-specific contact reduction concurrently in different stages of disease spread.

Figure 7. Disease dynamics under the intervention measures of vaccine allocation and contact reduction.

Baseline scenario without any intervention (black solid curve); contact reduction only in schools (blue solid curve), households (red solid curve), workplaces (yellow solid curve), and general communities (green solid curve); vaccination only (black dash curve); vaccination and contact reduction in schools (blue dash curve), households (red dash curve), workplaces (yellow dash curve), and general communities (green dash curve).