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Comparative Cost-Effectiveness of SARS-CoV-2 Testing Strategies

20 Pages Posted: 10 Nov 2020

See all articles by Zhanwei DU

Zhanwei DU

University of Texas at Austin

Abhishek Pandey

Yale University - Center for Infectious Disease Modeling and Analysis (CIDMA)

Yuan Bai

The University of Hong Kong

Meagan C. Fitzpatrick

Yale University - Center for Infectious Disease Modeling and Analysis (CIDMA)

Matteo Chinazzi

Northeastern University - Laboratory for the Modeling of Biological and Socio-Technical Systems (MOBS Lab)

Ana Pastore y Piontti

Northeastern University - Laboratory for the Modeling of Biological and Socio-technical Systems

Michael Lachmann

Santa Fe Institute

Alessandro Vespignani

Northeastern University - Laboratory for the Modeling of Biological and Socio-technical Systems

Benjamin J. Cowling

The University of Hong Kong

Alison P. Galvani

Yale University - Center for Infectious Disease Modeling and Analysis (CIDMA)

Lauren Ancel Meyers

University of Texas at Austin

More...

Abstract

Background: To mitigate the coronavirus pandemic that emerged in 2019 (COVID-19), countries worldwide have enacted unprecedented movement restrictions, social distancing measures, and face mask requirements. Until safe and efficacious vaccines or antiviral drugs become widely available, viral testing remains the primary mitigation measure for rapid identification and isolation of infected cases. 

Methods: We evaluate the economic tradeoffs of expanding and accelerating SARS-CoV-2 testing using a multi-scale model that incorporates SARS-CoV-2 transmission at the population level and daily viral load dynamics at the individual level. 

Findings: Assuming a willingness-to-pay of $100,000 per year of life lost (YLL) and a price of $5 per test, the strategy most likely to be cost-effective under a rapid transmission scenario (Re > 2) is daily testing followed by a one-week rather than two-week isolation period subsequent to test confirmation. Under lower transmission scenarios, weekly testing of the population is expected to be more cost effective. Expanded surveillance testing is expected to be cost effective if the price per test is less than $400 across all transmission rates considered. 

Interpretation: Extensive expansion of testing coupled with isolation of confirmed cases is essential for mitigating the COVID-19 pandemic. Further, resources recouped from shortened isolation duration could be cost-effectively allocated to more frequent testing.

Funding Statement: US National Institutes of Health and US Centers for Disease Control and Prevention.

Declaration of Interests: The authors declare no competing interests.

Ethics Approval Statement: Not applicable.

Keywords: COVID-19, SARS-CoV-2, Antigen test, Testing frequency, Epidemiological model, Cost effectiveness

Suggested Citation

DU, Zhanwei and Pandey, Abhishek and Bai, Yuan and C. Fitzpatrick, Meagan and Chinazzi, Matteo and Pastore y Piontti, Ana and Lachmann, Michael and Vespignani, Alessandro and J. Cowling, Benjamin and P. Galvani, Alison and Meyers, Lauren Ancel, Comparative Cost-Effectiveness of SARS-CoV-2 Testing Strategies. Available at SSRN: https://ssrn.com/abstract=3714642 or http://dx.doi.org/10.2139/ssrn.3714642

Zhanwei DU

University of Texas at Austin

2317 Speedway
Austin, TX 78712
United States

Abhishek Pandey

Yale University - Center for Infectious Disease Modeling and Analysis (CIDMA)

New Haven, CT
United States

Yuan Bai

The University of Hong Kong ( email )

Pokfulam Road
Hong Kong, Pokfulam HK
China

Meagan C. Fitzpatrick

Yale University - Center for Infectious Disease Modeling and Analysis (CIDMA) ( email )

New Haven, CT
United States

Matteo Chinazzi

Northeastern University - Laboratory for the Modeling of Biological and Socio-Technical Systems (MOBS Lab) ( email )

220 B RP
Boston, MA 02115
United States

Ana Pastore y Piontti

Northeastern University - Laboratory for the Modeling of Biological and Socio-technical Systems

Boston, MA
United States

Michael Lachmann

Santa Fe Institute

1399 Hyde Park Road
Santa Fe, NM 87501
United States

Alessandro Vespignani

Northeastern University - Laboratory for the Modeling of Biological and Socio-technical Systems

Boston, MA
United States

Benjamin J. Cowling

The University of Hong Kong ( email )

Pokfulam Road
Hong Kong, Pokfulam HK
China

Alison P. Galvani

Yale University - Center for Infectious Disease Modeling and Analysis (CIDMA) ( email )

New Haven, CT
United States

Lauren Ancel Meyers (Contact Author)

University of Texas at Austin

2317 Speedway
Austin, TX 78712
United States

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