A Targeted Geospatial Approach to COVID-19 Vaccine Delivery: Findings from the Johns Hopkins Hospital Emergency Department
preprint
OA: gold
CC-BY-ND-4.0
Abstract
While COVID-19 vaccines have been shown to significantly decrease morbidity and mortality, there is still much debate about optimal strategies of vaccine rollout. We tested identity-unlinked stored remnant blood specimens of patients at least 18 years presenting to the Johns Hopkins Hospital emergency department (ED) between May to November 2020 for IgG to SARS-CoV-2. Data on SARS-CoV-2 RT PCR were available for patients who were tested due to suspected infection. SARS-CoV-2 infections was defined as either a positive IgG and/or RT-PCR. SARS-CoV-2 infection clustering by zipcode was analyzed by spatial analysis using the Bernoulli model (SaTScan software, Version 9.7). Median age of the 7,461 unique patients visiting the ED was 47 years and 50.8% were female; overall, 740 (9.9%) unique patients had evidence of SARS-CoV-2 infection. Prevalence of infection in ED patients by ZIP code ranged from 4.1% to 22.3%. The observed number of cases in ZIP code C was nearly double the expected (observed/expected ratio = 1.99; 95% CI: 1.62, 2.42). These data suggest a targeted geospatial approach to COVID vaccination should be considered to maximize vaccine rollout efficiency and include high-risk populations that may otherwise be subjected to delays, or missed.
My notes (saved in your browser only)
Citation neighborhood (no data yet)
We don't have any in-corpus citations linked to this paper yet. The paper's references may be in our DB but unresolved to ``paper_id`` (resolution happens at ingest when the cited DOI matches a row we already have). Run the cross-source citation reconcile pass to retry.
Source provenance
- europepmc
- last seen: 2026-05-19T01:45:01.086888+00:00
- unpaywall
- last seen: 2026-05-21T05:10:58.409756+00:00
License: CC-BY-ND-4.0