Detecting COVID-19 Clusters at High Spatiotemporal Resolution, New York City, New York, USA, June–July 2020

Greene, Sharon K.; Peterson, Eric R.; Balan, Dominique; Jones, Lucretia; Culp, Gretchen M.; Fine, Annie D.; Kulldorff, Martin

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Detecting COVID-19 Clusters at High Spatiotemporal Resolution, New York City, New York, USA, June–July 2020

Supporting Files Public Domain

2021
By Greene, Sharon K. ; Peterson, Eric R. ; Balan, Dominique ; ...

File Language:

English

Details

Alternative Title:

Emerg Infect Dis
Personal Author:

Greene, Sharon K. ; Peterson, Eric R. ; Balan, Dominique ; Jones, Lucretia ; Culp, Gretchen M. ; Fine, Annie D. ; Kulldorff, Martin
Description:

A surveillance system that uses census tract resolution and the SaTScan prospective space-time scan statistic detected clusters of increasing severe acute respiratory syndrome coronavirus 2 test percent positivity in New York City, NY, USA. Clusters included one in which patients attended the same social gathering and another that led to targeted testing and outreach.
Subjects:

Coronavirus Infections Covid-19 Humans Prospective Studies Sars-Cov-2
Source:

Emerg Infect Dis. 27(5):1500-1504
Pubmed ID:

33900181
Pubmed Central ID:

PMC8084513
Document Type:

Journal Article
Place as Subject:

New York City, NY
Location:

North America
Volume:

27
Issue:

5
Collection(s):

Emerging Infectious Diseases
Main Document Checksum:

urn:sha256:2873fd392372612c1303b31e3f314890474ef6956d80c7a5a16f1be9c6413c54
Download URL:

https://stacks.cdc.gov/view/cdc/106007/cdc_106007_DS1.pdf
File Type:

[PDF - 237.58 KB ]

File Language:

English

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