Impact of demographic disparities in social distancing and vaccination on influenza epidemics in urban and rural regions of the United States

Abstract

Background Self-protective behaviors of social distancing and vaccination uptake vary by demographics and affect the transmission dynamics of influenza in the United States. By incorporating the socio-behavioral differences in social distancing and vaccination uptake into mathematical models of influenza transmission dynamics, we can improve our estimates of epidemic outcomes. In this study we analyze the impact of demographic disparities in social distancing and vaccination on influenza epidemics in urban and rural regions of the United States. Methods We conducted a survey of a nationally representative sample of US adults to collect data on their self-protective behaviors, including social distancing and vaccination to protect themselves from influenza infection. We incorporated this data in an agent-based model to simulate the transmission dynamics of influenza in the urban region of Miami Dade county in Florida and the rural region of Montgomery county in Virginia. Results We compare epidemic scenarios wherein the social distancing and vaccination behaviors are uniform versus non-uniform across different demographic subpopulations. We infer that a uniform compliance of social distancing and vaccination uptake among different demographic subpopulations underestimates the severity of the epidemic in comparison to differentiated compliance among different demographic subpopulations. This result holds for both urban and rural regions. Conclusions By taking into account the behavioral differences in social distancing and vaccination uptake among different demographic subpopulations in analysis of influenza epidemics, we provide improved estimates of epidemic outcomes that can assist in improved public health interventions for prevention and control of influenza. Electronic supplementary material The online version of this article (10.1186/s12879-019-3703-2) contains supplementary material, which is available to authorized users.

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“…To the best of our knowledge, most of the previous studies had shown that demographic characteristics are often associated with preventive behaviors during epidemics of infectious diseases such as influenza [ 18 ] or COVID-19 [ 19 , 20 ]. Taking this into account, various psychosocial methods, such as the Health Belief Model (HBM), the Stages of Change Model, and the Social Cognition Model, are proposed to predict the practice of preventive behavior at the individual level.…”

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“…We build on our modeling and simulation framework for epidemic spread 3 – 9 using an individual level synthetic social contact network 5 , 10 —which represents each individual in the population along with their demographic attributes (e.g., age, gender, income), and their social interactions. The main steps in the first-principles based construction of synthetic populations and social contact networks are: (1) construct a synthetic population by using US Census and other commercial databases; (2) assign daily activities to individuals within each household using activity and time-use surveys (American Time Use Survey data and National Household Travel Survey Data); (3) assign a geo-location to each activity of each person based on data from Dun and BradStreet, land-use, Open Street Maps etc.…”

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“…We build on our modeling and simulation framework for epidemic spread [3][4][5][6][7][8][9] using an individual level synthetic social contact network 5,10 -which represents each individual in the population along with their demographic attributes (e.g., age, gender, income), and their social interactions. The main steps in the first-principles based construction of synthetic populations and social contact networks are: (i) construct a synthetic population by using US Census and other commercial databases; (ii) assign daily activities to individuals within each household using activity and time-use surveys (American Time Use Survey data and National Household Travel Survey Data); (iii) assign a geo-location to each activity of each person based on data from Dun and BradStreet, land-use, Open Street Maps etc.…”

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