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Popul Health Manag ; 24(S1): S16-S25, 2021 02.
Article in English | MEDLINE | ID: covidwho-1236199

ABSTRACT

The coronavirus disease-2019 (COVID-19) pandemic is having a widespread impact on societies across the globe. As part of the effort to control transmission in the United States, many businesses either closed or instituted nonpharmaceutical control measures and allowed only essential workers on-site. During summer and fall of 2020, employers began formulating "return to work" strategies designed to mitigate the risk of transmission among employees. On a population level, several countries implemented national testing and surveillance strategies that proved effective in mitigating citizen-to-citizen transmission and contributed to suppressing COVID-19. A crucial component of many such strategies is population-based testing to identify and engage individuals with asymptomatic or presymptomatic infection, which also is relevant to return-to-work strategies. The authors describe an approach that multisite employers might use to help mitigate transmission of COVID-19 in the workplace. This approach leverages a bioinformatics platform informed by real-time PCR test data at the county and subcounty (eg, Public Use Microdata Area) level, allowing for population-based testing to be selectively targeted for employees in geographies with elevated SARS-CoV-2 positivity. A "Command Center" application integrates data from multiple sources (eg, local infection trends, employee symptom diaries, Bluetooth thermometers) in real time, which can be used to inform decisions regarding surveillance and employee self-isolation or quarantine; a mobile phone-based application provides for rapid, secure communication with employees. This overview is based on peer-reviewed literature and the early experience of a large employer with implementing bioinformatics tools to mitigate the impact of the pandemic on the workplace.


Subject(s)
COVID-19 , Models, Statistical , Occupational Health , COVID-19/epidemiology , COVID-19/prevention & control , COVID-19/transmission , COVID-19 Testing , Computational Biology , Humans , Pandemics , Public Health Surveillance , SARS-CoV-2 , United States , Workplace
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