Evidence map›Paper›PMID 38696456›Full record

ArticlePloS one2024

Spatial and temporal clustering of anti-SARS-CoV-2 antibodies in Illinois household cats, 2021-2023.

Chi Chen, Mathias Martins, Mohammed Nooruzzaman, Dipankar Yettapu, Diego G Diel, Jennifer M Reinhart, Ashlee Urbasic, Hannah Robinson, Csaba Varga, Ying Fang

Abstract read
In one paragraph

Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Spatiotemporal Analysis of Fentanyl-Associated Overdose Deaths in Chicago, IL, USA.Journal of urban health : bulletin of the New York Academy of Medicine · 2025
    Article
  5. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Chi ChenDepartment of Pathobiology, College of Veterinary Medicine, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America.ORCID 0009-0004-3662-0006
Mathias MartinsDepartment of Population Medicine and Diagnostic Sciences, College of Veterinary Medicine, Cornell University, Ithaca, New York, United States of America.
Mohammed NooruzzamanDepartment of Population Medicine and Diagnostic Sciences, College of Veterinary Medicine, Cornell University, Ithaca, New York, United States of America.ORCID 0000-0002-9358-1494
Dipankar YettapuDepartment of Pathobiology, College of Veterinary Medicine, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America.ORCID 0000-0003-0064-7501
Diego G DielDepartment of Population Medicine and Diagnostic Sciences, College of Veterinary Medicine, Cornell University, Ithaca, New York, United States of America.
Jennifer M ReinhartDepartment of Veterinary Clinical Medicine, College of Veterinary Medicine, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America.ORCID 0000-0002-3021-5075
Ashlee UrbasicVeterinary Diagnostic Laboratory at Veterinary Specialty Center, University of Illinois at Urbana-Champaign, Buffalo Grove, Illinois, United States of America.
Hannah RobinsonVeterinary Diagnostic Laboratory at Veterinary Specialty Center, University of Illinois at Urbana-Champaign, Buffalo Grove, Illinois, United States of America.
Csaba VargaDepartment of Pathobiology, College of Veterinary Medicine, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America.ORCID 0000-0003-2751-3677
Ying FangDepartment of Pathobiology, College of Veterinary Medicine, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America.

Funding

Novel diagnostic tools and animal model system for study human/animal interface of COVID-19R01AI166791 · NIAID · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · PI DIEL, DIEGO G, FANG, YING · 2021 to 2024
$3.1M
NIAID NIH HHS R01 AI166791
6 · The paper itself

Abstract

This study aimed to evaluate the seroprevalence and spatial and temporal clustering of SARS-CoV-2 antibodies in household cats within 63 counties in Illinois from October 2021 to May 2023. The analysis followed a stepwise approach. First, in a choropleth point map, we illustrated the distribution of county-level seroprevalence of SARS-CoV-2 antibodies. Next, spatial interpolation was used to predict the seroprevalence in counties without recorded data. Global and local clustering methods were used to identify the extent of clustering and the counties with high or low seroprevalence, respectively. Next, temporal, spatial, and space-time scan statistic was used to identify periods and counties with higher-than-expected seroprevalence. In the last step, to identify more distinct areas in counties with high seroprevalence, city-level analysis was conducted to identify temporal and space-time clusters. Among 1,715 samples tested by serological assays, 244 samples (14%) tested positive. Young cats had higher seropositivity than older cats, and the third quarter of the year had the highest odds of seropositivity. Three county-level space-time clusters with higher-than-expected seroprevalence were identified in the northeastern, central-east, and southwest regions of Illinois, occurring between June and October 2022. In the city-level analysis, 2 space-time clusters were identified in Chicago's downtown and the southwestern suburbs of Chicago between June and September 2022. Our results suggest that the high density of humans and cats in large cities such as Chicago, might play a role in the transmission and clustering of SARS-CoV-2. Our study provides an in-depth analysis of SARS-CoV-2 epidemiology in Illinois household cats, which will aid in COVID-19 control and prevention.

Indexed as

Antibodies, ViralCOVID-19SARS-CoV-2Spatio-Temporal AnalysisAnimalsCat DiseasesCatsCluster AnalysisFemaleHumansIllinoisMaleSeroepidemiologic StudiesAntibodies, Viral

Identifiers

PMID38696456
PMCPMC11065222

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.