Network Systems Science and Advanced Computing | Population Health Informatics | Social, Cognitive, and Behavioral Science

Health Disparities

Contacts
Sponsor

National Institutes of Health (NIH)

Health disparities are preventable differences in the burden of disease, injury, violence, or opportunities to achieve optimal health that are experienced by socially disadvantaged populations. Populations can be defined by factors such as race or ethnicity, gender, education or income, disability, geographic location (e.g., rural or urban), or sexual orientation. Health disparities are directly related to the historical and current unequal distribution of social, political, economic, and environmental resources.

Project Overview

This project incorporates social behavior into mathematical models of infectious disease transmission dynamics to help improve our understanding of the impact of different control and prevention strategies for pandemics and epidemics. Individual behavior, disease dynamics, and interventions coevolve across multiple scales to create statistically and epidemiologically significant differences in the efficacy and social equity of public health policies such as infectious disease control strategies.

This project extends well-studied computational simulations to include people's behaviors relevant to infectious disease epidemics, in order to study the feedback between population-level effects and individual-level behavior. In particular, we determine the sensitivity of outcomes to health related behaviors such as social distancing and vaccination uptake. A survey was conducted to estimate the variability of behaviors across communities and to inform the model parameters.

Findings
  1. 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 and 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. Epidemic scenarios wherein the social distancing and vaccination behaviors are uniform versus non-uniform across different demographic subpopulations were compared. Results show 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. By accounting for the behavioral differences in social distancing and vaccination uptake among different demographic subpopulations, we provide improved estimates of epidemic outcomes that can assist in improved public health interventions for prevention and control of influenza (Singh et al. BMC Infectious Disease 2019).
  2. We study the vaccine allocation problem in the context of seasonal Influenza spread in the United States. We develop a novel national scale flu model that integrate both short and long distance travel, which are known to be important determinants of the spread of Influenza. We also design a greedy algorithm for allocating the vaccine supply at a county level. Our results show significant improvement over the current baseline, which involves allocating vaccines based on the state population (S Venkatramanan et al. 2017, ICHI).
  3. We analyze structural properties of 3 semantic networks, representing a conceptual map of vaccine beliefs and demonstrative of vaccine sentiment on social media, using twitter data. Overall, this study demonstrated the unique potential for semantic networks, as part of a much-needed interdisciplinary effort, to enhance meaningful understanding of highly-complex public health issues such as vaccine hesitancy (G Kang et al. Vaccine 2017).
  4. We built a computational framework for studying health disparities among cohorts based on individual level features, such as age, gender, income, etc. We apply this framework to find health disparities among subpopulations in an influenza epidemic and evaluate vaccination prioritization strategies to achieve specific objectives. We explore the heterogeneities in individuals' demographic and socioeconomic attributes as the potential cause of health disparities (Wang et al. 2018, WWW Journal Special Issue).
  5. We evaluated the economic impact of vaccine-based interventions in response to influenza pandemic, for the city of Chicago, and compared the cost-benefit metrics between a dynamic agent-based network model and static Markov model. Simulated a base case scenario of no vaccine intervention with a basic reproductive number of 1.5 for a resultant attack rate of 58.1% and health care cost per capita of $1,124. Applying the vaccine intervention with efficacy of 40% and compliance rate of 40% was a cost saving intervention for both the dynamic agent-based network model and the static Markov model. The net return per capita at $21 per vaccine is $363 and $261 for the dynamic and static models respectively. We infer that higher number of cases of Influenza are averted in the dynamic agent-based network model in comparison to the static Markov model, as well as the vaccine-based interventions are comparatively more cost effective for all age and risk groups in the dynamic model (Dorratoltaj, Marathe et al. 2017).
Team

Professor

Professor of Public Health Sciences, School of Medicine

Research Associate Professor

Research Associate Professor

Professor

Professor of Public Health Sciences, School of Medicine

Research Associate Professor

Other Team Members

Kaja Abbas | Assistant Professor of Disease Modeling | London School of Hygiene and Tropical Medicine

Pamela Murray-Tuite | Professor of Civil Engineering | Clemson University

Publications
Network Systems Science and Advanced Computing
Wang, L; Chen, J; Marathe, A . World Wide Web. Springer US. 2019; 22(6):2997-3020
Network Systems Science and Advanced Computing
Singh M; Sarkhel P; Kang G; Marathe A; Boyle K; Murray-Tuite P; Abbas K; Swarup S . BMC infectious diseases. BioMed Central. 2019; 19(1):221
Network Systems Science and Advanced Computing
Adiga A; Chu S; Eubank S; Kuhlman C; Lewis B; Marathe A; Marathe M; Nordberg E; Swarup S; Vullikanti A; Wilson M . BMJ Open. British Medical Journal Publishing Group. 2018; 8(1):e017353
Network Systems Science and Advanced Computing
Chen J; Marathe A; Marathe M . Scientific reports. Nature Publishing Group. 2018; 8(1):12452
Network Systems Science and Advanced Computing
Dorratoltaj N; Marathe A; Lewis B; Swarup S; Eubank S; Abbas K . PLoS computational biology. Public Library of Science San Francisco, CA USA. 2017; 13(6):e1005521
Network Systems Science and Advanced Computing
Kang G; Ewing-Nelson S; Mackey L; Schlitt J; Marathe A; Abbas K; Swarup S . Vaccine. Elsevier. 2017; 35(29):3621-3638