Modeling the Influence of Vaccine Administration on COVID-19 Testing Strategies.
Viruses
; 13(12)2021 12 19.
Article
in English
| MEDLINE | ID: covidwho-1580421
Preprint
This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
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This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
See preprint
ABSTRACT
Vaccination is considered the best strategy for limiting and eliminating the COVID-19 pandemic. The success of this strategy relies on the rate of vaccine deployment and acceptance across the globe. As these efforts are being conducted, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is continuously mutating, which leads to the emergence of variants with increased transmissibility, virulence, and resistance to vaccines. One important question is whether surveillance testing is still needed in order to limit SARS-CoV-2 transmission in a vaccinated population. In this study, we developed a multi-scale mathematical model of SARS-CoV-2 transmission in a vaccinated population and used it to predict the role of testing in an outbreak with variants of increased transmissibility. We found that, for low transmissibility variants, testing was most effective when vaccination levels were low to moderate and its impact was diminished when vaccination levels were high. For high transmissibility variants, widespread vaccination was necessary in order for testing to have a significant impact on preventing outbreaks, with the impact of testing having maximum effects when focused on the non-vaccinated population.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Vaccination
/
COVID-19 Testing
/
COVID-19
/
Models, Theoretical
Type of study:
Diagnostic study
/
Prognostic study
Topics:
Vaccines
/
Variants
Limits:
Humans
Language:
English
Year:
2021
Document Type:
Article
Affiliation country:
V13122546
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