Evidence map›Paper›PMID 41714058›Full record

ArticleSpatial and spatio-temporal epidemiology2026

Spatial and spatio-temporal county-level trends in COVID-19 mortality and emergency department visits in U.S. with R-INLA.

Diba Khan, Mark J Panaggio, Molly E Gallagher, Philip Graff, Lance Broeker, Mark Weng, Farida Ahmad, Michael Sheppard

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PubMed Full text at PMC Publisher
In one paragraph

Article in Spatial and spatio-temporal epidemiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Diba KhanCenters for Disease Control and Prevention, Atlanta, Georgia. Electronic address: ild1@cdc.gov.
Mark J PanaggioCenters for Disease Control and Prevention, Atlanta, Georgia; The Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA.
Molly E GallagherCenters for Disease Control and Prevention, Atlanta, Georgia; The Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA.
Philip GraffCenters for Disease Control and Prevention, Atlanta, Georgia; The Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA.
Lance BroekerThe Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA.
Mark WengCenters for Disease Control and Prevention, Atlanta, Georgia.
Farida AhmadCenters for Disease Control and Prevention, Atlanta, Georgia.
Michael SheppardCenters for Disease Control and Prevention, Atlanta, Georgia.

Funding

Intramural CDC HHS CC999999
6 · The paper itself

Abstract

backgroundWeekly county-level COVID-19 mortality and emergency department (ED) visits data are critical data sources for understanding COVID-19 trends, but subject to reporting delays, sampling variability, potential instability and concerns due to statistical reliability as well as data suppression due to small numbers and the need to protect personally identifiable information. Such suppression limits meaningful examination of county-level variation in COVID-19 mortality rates and ED visits.

methodsIn this study, we use Bayesian inference on latent Gaussian models in the software R-INLA (Integrated Nested Laplace Approximation) to generate reliable weekly estimates of COVID-19 ED visits and mortality rates at the county level in order to examine spatiotemporal variation.

resultsThe results demonstrate that weekly county-level COVID-19 mortality rates and ED visits can be accurately modeled using the INLA method. Model-based estimates reflect marked geographic variability for the years 2020-2025.

conclusionsEffective public health interventions rely on access to timely and detailed spatiotemporal data. Granular estimates that are subject to less reporting noise, such as those produced via INLA modeling, can be used to guide surveillance, improve response strategies, enhance preparedness, and inform public health policy.

Indexed as

COVID-19Emergency Room VisitsEmergency Service, HospitalBayes TheoremHumansSARS-CoV-2Spatio-Temporal AnalysisUnited StatesCountyCOVID-19Emergency department visitsINLAModelingMortalitySurveillance

Identifiers

PMID41714058
PMCPMC12968716

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