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Optimal use of COVID19 Ag-RDT screening at border crossings to prevent community transmission: a modeling analysis
Preprint
in En
| PREPRINT-MEDRXIV
| ID: ppmedrxiv-21256154
ABSTRACT
BackgroundCountries around the world have implemented restrictions on mobility, especially cross-border travel to reduce or prevent SARS-CoV-2 community transmission. Rapid antigen testing (Ag-RDT), with on-site administration and rapid turnaround time may provide a valuable screening measure to ease cross-border travel while minimizing risk of local transmission. To maximize impact, we developed an optimal Ag-RDT screening algorithm for cross-border entry. MethodsUsing a previously developed mathematical model, we determined the daily number of imported COVID-19 cases that would generate no more than a relative 1% increase in cases over one month for different effective reproductive numbers (Rt) of the recipient country. We then developed an algorithm- for differing levels of Rt, arrivals per day, mode of travel, and SARS-CoV-2 prevalence amongst travelers-to determine the minimum proportion of people that would need Ag-RDT testing at border crossings to ensure no greater than the relative 1% community spread increase. FindingsWhen daily international arrivals and/or COVID-19 prevalence amongst arrivals increases, the proportion of arrivals required to test using Ag-RDT increases. At very high numbers of international arrivals/COVID-19 prevalence, Ag-RDT testing is not sufficient to prevent increased community spread, especially for lower levels of Rt. In these cases, Ag-RDT screening would need to be supplemented with other measures to prevent an increase in community transmission. InterpretationAn efficient Ag-RDT algorithm for SARS-CoV-2 testing depends strongly on Rt, volume of travel, proportion of land and air arrivals, test sensitivity, and COVID-19 prevalence among travelers. FundingUSAID, Government of the Netherlands
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Full text:
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Collection:
09-preprints
Database:
PREPRINT-MEDRXIV
Type of study:
Observational_studies
/
Prognostic_studies
/
Rct
Language:
En
Year:
2021
Document type:
Preprint