Cost-effective proactive testing strategies during COVID-19 mass vaccination: A modelling study.

COVID-19 Cost-effectiveness Modelling SARS-CoV-2 Testing Vaccination

Journal

Lancet regional health. Americas
ISSN: 2667-193X
Titre abrégé: Lancet Reg Health Am
Pays: England
ID NLM: 9918232503006676

Informations de publication

Date de publication:
Apr 2022
Historique:
entrez: 24 1 2022
pubmed: 25 1 2022
medline: 25 1 2022
Statut: ppublish

Résumé

As SARS-CoV-2 vaccines are administered worldwide, the COVID-19 pandemic continues to exact significant human and economic costs. Mass testing of unvaccinated individuals followed by isolation of positive cases can substantially mitigate risks and be tailored to local epidemiological conditions to ensure cost effectiveness. Using a multi-scale model that incorporates population-level SARS-CoV-2 transmission and individual-level viral load kinetics, we identify the optimal frequency of proactive SARS-CoV-2 testing, depending on the local transmission rate and proportion immunized. Assuming a willingness-to-pay of US$100,000 per averted year of life lost (YLL) and a price of $10 per test, the optimal strategy under a rapid transmission scenario ( Mass proactive testing and case isolation is a cost effective strategy for mitigating the COVID-19 pandemic in the initial stages of the global SARS-CoV-2 vaccination campaign and in response to resurgences of vaccine-evasive variants. US National Institutes of Health, US Centers for Disease Control and Prevention, HK Innovation and Technology Commission, China National Natural Science Foundation, European Research Council, and EPSRC Impact Acceleration Grant.

Sections du résumé

BACKGROUND BACKGROUND
As SARS-CoV-2 vaccines are administered worldwide, the COVID-19 pandemic continues to exact significant human and economic costs. Mass testing of unvaccinated individuals followed by isolation of positive cases can substantially mitigate risks and be tailored to local epidemiological conditions to ensure cost effectiveness.
METHODS METHODS
Using a multi-scale model that incorporates population-level SARS-CoV-2 transmission and individual-level viral load kinetics, we identify the optimal frequency of proactive SARS-CoV-2 testing, depending on the local transmission rate and proportion immunized.
FINDINGS RESULTS
Assuming a willingness-to-pay of US$100,000 per averted year of life lost (YLL) and a price of $10 per test, the optimal strategy under a rapid transmission scenario (
INTERPRETATION CONCLUSIONS
Mass proactive testing and case isolation is a cost effective strategy for mitigating the COVID-19 pandemic in the initial stages of the global SARS-CoV-2 vaccination campaign and in response to resurgences of vaccine-evasive variants.
FUNDING BACKGROUND
US National Institutes of Health, US Centers for Disease Control and Prevention, HK Innovation and Technology Commission, China National Natural Science Foundation, European Research Council, and EPSRC Impact Acceleration Grant.

Identifiants

DOI: 10.1016/j.lana.2021.100182 PMID: 35072146 PMC: PMC8759769
pubmed: 35072146
doi: 10.1016/j.lana.2021.100182
pii: S2667-193X(21)00178-2
pmc: PMC8759769
doi:

Types de publication

Journal Article

Langues

eng

Pagination

100182

Subventions

Organisme : NIGMS NIH HHS
ID : U01 GM087719
Pays : United States
Organisme : ACL HHS
ID : U01IP001137
Pays : United States
Organisme : NIAID NIH HHS
ID : K01 AI141576
Pays : United States
Organisme : NCIRD CDC HHS
ID : U01 IP001136
Pays : United States
Organisme : NCIRD CDC HHS
ID : U01 IP001137
Pays : United States

Informations de copyright

© 2022 The Author(s).

Déclaration de conflit d'intérêts

We declare no competing interests. Dr Chinazzi, Dr Pastore y Piontti and Prof. Alessandro Vespignani report grants from Metabiota Inc, outside the submitted work. Prof. Benjamin J. Cowling reports honoraria from AstraZeneca, GSK, Moderna, Pfizer, Roche and Sanofi Pasteur. The authors report no other potential conflicts of interest.

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Auteurs

Zhanwei Du (Z)

WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.
Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China.
The University of Texas at Austin, Austin, Texas, USA.

Lin Wang (L)

Department of Genetics, University of Cambridge, Cambridge, UK.

Yuan Bai (Y)

WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.
Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China.

Xutong Wang (X)

The University of Texas at Austin, Austin, Texas, USA.

Abhishek Pandey (A)

Center for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, CT, USA.

Meagan C Fitzpatrick (MC)

Center for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, CT, USA.
Center for Vaccine Development and Global Health, University of Maryland School of Medicine, Baltimore, MD, USA.

Matteo Chinazzi (M)

Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, USA.

Ana Pastore Y Piontti (A)

Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, USA.

Nathaniel Hupert (N)

Population Health Sciences, Weill Cornell Medicine and Cornell Institute for Disease and Disaster Preparedness, New York, NY, USA.

Michael Lachmann (M)

Santa Fe Institute, Santa Fe, NM, USA.

Alessandro Vespignani (A)

Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, USA.

Alison P Galvani (AP)

Center for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, CT, USA.

Benjamin J Cowling (BJ)

WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.
Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China.

Lauren Ancel Meyers (LA)

The University of Texas at Austin, Austin, Texas, USA.
Santa Fe Institute, Santa Fe, NM, USA.

Classifications MeSH