Concentric regulatory zones failed to halt surging COVID-19: Brooklyn 2020

MethodsWe relied on reports of confirmed case incidence and test positivity, along with data on the movements of devices with location-tracking software, to evaluate a novel scheme of three concentric regulatory zones introduced by then New York Governor Cuomo to address an outbreak of COVID-19 in S...

Full description

Saved in:
Bibliographic Details
Main Author: Jeffrey E. Harris (Author)
Format: Book
Published: Frontiers Media S.A., 2022-12-01T00:00:00Z.
Subjects:
Online Access:Connect to this object online.
Tags: Add Tag
No Tags, Be the first to tag this record!

MARC

LEADER 00000 am a22000003u 4500
001 doaj_a945f8a96ab749b2b44b8d7ab3a082aa
042 |a dc 
100 1 0 |a Jeffrey E. Harris  |e author 
700 1 0 |a Jeffrey E. Harris  |e author 
245 0 0 |a Concentric regulatory zones failed to halt surging COVID-19: Brooklyn 2020 
260 |b Frontiers Media S.A.,   |c 2022-12-01T00:00:00Z. 
500 |a 2296-2565 
500 |a 10.3389/fpubh.2022.970363 
520 |a MethodsWe relied on reports of confirmed case incidence and test positivity, along with data on the movements of devices with location-tracking software, to evaluate a novel scheme of three concentric regulatory zones introduced by then New York Governor Cuomo to address an outbreak of COVID-19 in South Brooklyn in the fall of 2020. The regulatory scheme imposed differential controls on access to eating places, schools, houses of worship, large gatherings and other businesses within the three zones, but without restrictions on mobility.ResultsWithin the central red zone, COVID-19 incidence temporarily declined from 131.2 per 100,000 population during the week ending October 3 to 62.5 per 100,000 by the week ending October 31, but then rebounded to 153.6 per 100,000 by the week ending November 28. Within the intermediate orange and peripheral yellow zones combined, incidence steadily rose from 28.8 per 100,000 during the week ending October 3 to 109.9 per 100,000 by the week ending November 28. Data on device visits to pairs of eating establishments straddling the red-orange boundary confirmed compliance with access controls. More general analysis of device movements showed stable patterns of movement between and beyond zones unaffected by the Governor's orders. A geospatial regression model of COVID-19 incidence in relation to device movements across zip code tabulation areas identified a cluster of five high-movement ZCTAs with estimated reproduction number 1.91 (95% confidence interval, 1.27-2.55).DiscussionIn the highly populous area of South Brooklyn, controls on access alone, without restrictions on movement, were inadequate to halt an advancing COVID-19 outbreak. 
546 |a EN 
690 |a SARS-CoV-2 
690 |a mobility 
690 |a mobile device tracking 
690 |a geospatial regression 
690 |a paired point-of-interest analysis 
690 |a Public aspects of medicine 
690 |a RA1-1270 
655 7 |a article  |2 local 
786 0 |n Frontiers in Public Health, Vol 10 (2022) 
787 0 |n https://www.frontiersin.org/articles/10.3389/fpubh.2022.970363/full 
787 0 |n https://doaj.org/toc/2296-2565 
856 4 1 |u https://doaj.org/article/a945f8a96ab749b2b44b8d7ab3a082aa  |z Connect to this object online.