Examining COVID-19 Vaccine Access Profiles through Spatial Microsimulation and Activity Allocation
Creators: name: Tuccillo, Joseph V.
name: Gaboardi, James D.
Publisher: name: Zenodo
Year: 2022
Subjects: synthetic population
activity allocation
vaccine access
Python

description: Equitable access to COVID-19 vaccines posed a major challenge for their rollout during the early months of 2021 in the United States [<em>1</em>]. In this paper, we explore the degree to which access disparities during this period occurred with respect to patterns of life and differences in individual and neighborhood socioeconomic status. We apply a novel microsimulation toolkit, <strong>Likeness</strong> [2<em>,3</em>], that is specialized to perform dynamic microsimulations of human activities on high-fidelity synthetic population data. This approach enables us to 1) examine the intersection between transportation equity and socioeconomic status, and accessibility at an atomic (e.g., person and household) level, that then 2) scale to wider concerns surrounding access to COVID-19 vaccines. <strong>Likeness</strong> features two key components: a population synthesizer that generates individual agents linked to a variety of characteristics provided by the American Community Survey’s (ACS) Public-Use Microdata Sample (PUMS), and an activity allocation model that simulates agent travel to regular daytime destinations (e.g., work and school) on real-world transportation networks (e.g., road, bus, and walk/bike). Here, we create synthetic populations from the 2020 ACS PUMS 5-year estimates, then simulate work commutes for essential workers (employees), with destinations set by points of interest (POIs) curated from location service platforms [<em>4</em>]. We evaluate access to vaccination sites (e.g., medical clinics, pharmacies) from home and work locations, as well as along workers’ optimal commute routes based on buffered distances from street intersections and transit stops. We then combine these results into access profiles, which are in turn related to individual and neighborhood socioeconomic characteristics. Our initial case study is performed on the Lincoln, Nebraska Metropolitan Statistical Area, chosen for its 1) compact size and relative isolation, 2) mixture of urban and rural populations, 3) availability of multi-modal transportation data via OpenStreetMap, and 4) presence of service sector jobs linked to a large public university. We develop this case study as an initial conceptualization for larger scale analyses of accessibility, equity, and public health interventions. References Jean-Jacques, M. and Bauchner, H. (2021). Vaccine distribution—equity left behind?. <em>JAMA</em>, <em>325</em>(9), pp. 829-830. DOI: 10.1001/jama.2021.1205. Tuccillo, J.V. and Gaboardi, J.D. (2022, June). Connecting Vivid Population Data to Human Dynamics. <em>Distilling Diversity by Tapping High-Resolution Population and Survey Data</em>. DOI: 10.5281/zenodo.6607533. Tuccillo, J.V. and Gaboardi, J.D. (2022, June). Likeness: a Python toolkit for connecting the social fabric of place to human dynamics. <em>GeoPython 2022</em>. DOI: 10.5281/zenodo.6685086. Thakur, G.S., Sparks, K., Li, R., Stewart, R.N. and Urban, M.L. (2016, October). Demonstrating PlanetSense: gathering geo-spatial intelligence from crowd-sourced and social-media data. In <em>Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems</em> (pp. 1-4). DOI: 10.1145/2996913.2996975.