Population surveillance approach to detect and respond to new clusters of COVID-19

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creativework.keywords - en

Surveillance

Detection

COVID-19

Mathematical approach

dc.contributor.author

Rees, Erin E.

Rodin, Rachel

Ogden, Nicholas H.

dc.date.accessioned

2024-10-02T13:34:35Z

dc.date.available

2024-10-02T13:34:35Z

dc.date.issued

2021-06

dc.description.abstract - en

BACKGROUND: To maintain control of the coronavirus disease 2019 (COVID-19) epidemic as lockdowns are lifted, it will be crucial to enhance alternative public health measures. For surveillance, it will be necessary to detect a high proportion of any new cases quickly so that they can be isolated, and people who have been exposed to them traced and quarantined. Here we introduce a mathematical approach that can be used to determine how many samples need to be collected per unit area and unit time to detect new clusters of COVID-19 cases at a stage early enough to control an outbreak. METHODS: We present a sample size determination method that uses a relative weighted approach. Given the contribution of COVID-19 test results from sub-populations to detect the disease at a threshold prevalence level to control the outbreak to 1) determine if the expected number of weekly samples provided from current healthcare-based surveillance for respiratory virus infections may provide a sample size that is already adequate to detect new clusters of COVID-19 and, if not, 2) to determine how many additional weekly samples were needed from volunteer sampling. RESULTS: In a demonstration of our method at the weekly and Canadian provincial and territorial (P/T) levels, we found that only the more populous P/T have sufficient testing numbers from healthcare visits for respiratory illness to detect COVID-19 at our target prevalence level—assumed to be high enough to identify and control new clusters. Furthermore, detection of COVID-19 is most efficient (fewer samples required) when surveillance focuses on healthcare symptomatic testing demand. In the volunteer populations: the higher the contact rates; the higher the expected prevalence level; and the fewer the samples were needed to detect COVID-19 at a predetermined threshold level. CONCLUSION: This study introduces a targeted surveillance strategy, combining both passive and active surveillance samples, to determine how many samples to collect per unit area and unit time to detect new clusters of COVID-19 cases. The goal of this strategy is to allow for early enough detection to control an outbreak.

dc.identifier.citation

Rees EE, Rodin R, Ogden NH. Population surveillance approach to detect and respond to new clusters of COVID-19. Can Commun Dis Rep. 2021 Jun 9;47(56):243-250. doi: 10.14745/ccdr.v47i56a01.

dc.identifier.doi

https://doi.org/10.14745/ccdr.v47i56a01

dc.identifier.issn

1481-8531

dc.identifier.pubmedID

34220348

dc.identifier.uri

https://open-science.canada.ca/handle/123456789/2996

dc.language.iso

en

dc.publisher

Public Health Agency of Canada

dc.relation.istranslationof

https://open-science.canada.ca/handle/123456789/2997

dc.rights - en

Creative Commons Attribution 4.0 International (CC BY 4.0)

dc.rights - fr

Creative Commons Attribution 4.0 International (CC BY 4.0)

dc.rights.uri - en

https://creativecommons.org/licenses/by/4.0/

dc.rights.uri - fr

https://creativecommons.org/licenses/by/4.0/deed.fr

dc.subject - en

Health

dc.subject - fr

Santé

dc.subject.en - en

Health

dc.subject.fr - fr

Santé

dc.title - en

Population surveillance approach to detect and respond to new clusters of COVID-19

dc.type - en

Article

dc.type - fr

Article

local.article.journalissue

5-6

local.article.journaltitle

Canada Communicable Disease Report

local.article.journalvolume

47

local.pagination

243-250

local.peerreview - en

Yes

local.peerreview - fr

Oui

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