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.

What are the underlying transmission patterns of COVID-19 outbreak? An age-specific social contact characterization

“…The underlying transmission patterns of COVID-19 among different populations are difficult to characterize because they are complex and related to various observations and disease-related factors, including the number of confirmed cases, the potential risks brought by unconfirmed cases, the distribution of different case categories (indigenous/imported) in different regions/cities, the population distribution of different age-groups, the social contact patterns in different settings (e.g., households, schools, workplaces, and public places), the extent of interventions implemented in different regions/cities, etc.. To address this challenging issue in a fundamental way, we examine an essential factor that characterizes the disease transmission patterns: the interactions among people. 20,21 Specifically, we examine the interactions in terms of the social contact patterns among the population of different age-groups. To characterize the age-specific social contact-based transmission, we divide a city's population into seven age-groups: 0-6 years old (children); 7-14 (primary and junior high school students); 15-17 (high school students); 18-22 (university and college students); 23-44 (young/middleaged people); 45-64 years old (middle-aged/elderly people); and 65 or above (elderly people).…”

“…Next, we represent the overall age-specific social contact matrix as a linear combination of the above four matrices: 21 ,…”

“…For the infectivity, we set a ii = 1.0 (for i = 1, …, 7) according to Xia et al's work. 21 For the susceptibility b ii , as it represents the probability of being infected when a susceptible individual is exposed to infectious contacts, we estimate it as follows: For each G i , we first calculate its infected population ratio r i by dividing the number of infected cases in G i by P i , i.e., r i = n i /P i . With r i calculated for all 7 age-groups, we then obtain a multiplier, 1/min{r 1 , …, r 7 }, through normalizing the smallest r i to 1, and inflate all other infected population ratios by 1/min{r 1 , …, r 7 }.…”

Imitation dynamics of vaccine decision-making behaviours based on the game theory

“…d'Onofrio et al [12] assumed that the perceived risk of vaccination is a function with respect to the incidence and studied an susceptible-infected-removed (SIR) transmission model with dynamic vaccine demand based on an imitation mechanism. A common assumption in these existing vaccination models on imitation dynamics is that individuals are homogeneously mixed or heterogeneously mixed on social networks [9,11,37]. This cannot capture the feature of different transmission rates for childhood diseases which we mentioned earlier.…”

Research priorities in modeling the transmission risks of H7N9 bird flu

“…Meanwhile, age-specific and location-specific human immunities to AIVs, which may be highly related to historical exposures of the host population, are also of critical importance for the spread of the disease [46-49]. Once the human-to-human transmission is confirmed, more impact factors should be carefully considered, such as human mobility and contact patterns [50,51], and social influence [52-54]. We agree with Professor Wiwanitkit in this regard and believe that it would be essential for us to study the spread of AIVs from a novel, complex systems perspective.…”