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Cost-effective proactive testing strategies during COVID-19 mass vaccination: A modelling study.
Du, Zhanwei; Wang, Lin; Bai, Yuan; Wang, Xutong; Pandey, Abhishek; Fitzpatrick, Meagan C; Chinazzi, Matteo; Pastore Y Piontti, Ana; Hupert, Nathaniel; Lachmann, Michael; Vespignani, Alessandro; Galvani, Alison P; Cowling, Benjamin J; Meyers, Lauren Ancel.
  • 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.
  • Wang L; Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China.
  • Bai Y; The University of Texas at Austin, Austin, Texas, USA.
  • Wang X; Department of Genetics, University of Cambridge, Cambridge, UK.
  • Pandey A; 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.
  • Fitzpatrick MC; Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China.
  • Chinazzi M; The University of Texas at Austin, Austin, Texas, USA.
  • Pastore Y Piontti A; Center for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, CT, USA.
  • Hupert N; Center for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, CT, USA.
  • Lachmann M; Center for Vaccine Development and Global Health, University of Maryland School of Medicine, Baltimore, MD, USA.
  • Vespignani A; Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, USA.
  • Galvani AP; Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, USA.
  • Cowling BJ; Population Health Sciences, Weill Cornell Medicine and Cornell Institute for Disease and Disaster Preparedness, New York, NY, USA.
  • Meyers LA; Santa Fe Institute, Santa Fe, NM, USA.
Lancet Reg Health Am ; 8: 100182, 2022 Apr.
Article in English | MEDLINE | ID: covidwho-1620909
ABSTRACT

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:

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:

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 (Re ∼ 2.5) is daily testing until one third of the population is immunized and then weekly testing until half the population is immunized, combined with a 10-day isolation period of positive cases and their households. Under a low transmission scenario (Re ∼ 1.2), the optimal sequence is weekly testing until the population reaches 10% partial immunity, followed by monthly testing until 20% partial immunity, and no testing thereafter.

INTERPRETATION:

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:

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.
Keywords

Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies / Observational study / Prognostic study Topics: Vaccines / Variants Language: English Journal: Lancet Reg Health Am Year: 2022 Document Type: Article Affiliation country: J.lana.2021.100182

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies / Observational study / Prognostic study Topics: Vaccines / Variants Language: English Journal: Lancet Reg Health Am Year: 2022 Document Type: Article Affiliation country: J.lana.2021.100182