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A framework for discovering health disparities among cohorts in an influenza epidemic

  • Lijing Wang
  • Jiangzhuo Chen
  • Achla Marathe
Article
Part of the following topical collections:
  1. Special Issue on Social Computing and Big Data Applications

Abstract

Infectious diseases such as Influenza and Ebola pose a serious threat to everyone but certain demographics and cohorts face a higher risk of infection than others. This research provides 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. An agent-based model is used to simulate an influenza epidemic over a synthetic social contact network of the Montgomery County in Southwest Virginia to identify infected cases which are then labeled with a specific clinical outcome by using a predefined probability distribution based on age and risk level. We divide the population into age and income based cohorts and measure the direct and indirect economic impact of vaccination for each cohort. Simulation-based results find strong health disparities across age and income groups. Various vaccine distribution strategies are considered and outcomes are measured through metrics such as death count, total number of infections, net return per capita, net return per dollar spent and net return per vaccinated person. The results, framework, and methodology developed here can assist public health policy makers in efficiently allocating limited pharmaceutical resources.

Keywords

Agent based Computational framework Health disparity Simulation Vaccination 

Notes

Acknowledgments

This work has been partially supported by the National Institutes of Health (NIH) (grant number 1R01GM109718), NSF Research Traineeship (grant number NRT-DESE-154362), Defense Threat Reduction Agency (DTRA) (grant number HDTRA1-11-1-0016), and DTRA Comprehensive National Incident Management System contracts (grant number HDTRA1-11-D-0016-0001, grant number HDTRA1-17-D-0023).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Science, Virginia Tech, Network Dynamics and Simulation Science LaboratoryBiocomplexity Institute of Virginia TechBlacksburgUSA
  2. 2.Network Dynamics and Simulation Science LaboratoryBiocomplexity Institute of Virginia TechBlacksburgUSA
  3. 3.Department of Agricultural and Applied Economics, Virginia Tech Network Dynamics and Simulation Science LaboratoryBiocomplexity Institute of Virginia TechBlacksburgUSA

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