Intervention Serology and Interaction Substitution: Modeling the Role of 'Shield Immunity' in Reducing COVID-19 Epidemic Spread.


Journal

medRxiv : the preprint server for health sciences
Titre abrégé: medRxiv
Pays: United States
ID NLM: 101767986

Informations de publication

Date de publication:
03 Apr 2020
Historique:
entrez: 9 6 2020
pubmed: 9 6 2020
medline: 9 6 2020
Statut: epublish

Résumé

The COVID-19 pandemic has precipitated a global crisis, with more than 690,000 confirmed cases and more than 33,000 confirmed deaths globally as of March 30, 2020 [1-4]. At present two central public health control strategies have emerged: mitigation and suppression (e.g, [5]). Both strategies focus on reducing new infections by reducing interactions (and both raise questions of sustainability and long-term tactics). Complementary to those approaches, here we develop and analyze an epidemiological intervention model that leverages serological tests [6, 7] to identify and deploy recovered individuals as focal points for sustaining safer interactions via interaction substitution, i.e., to develop what we term 'shield immunity' at the population scale. Recovered individuals, in the present context, represent those who have developed protective, antibodies to SARS-CoV-2 and are no longer shedding virus [8]. The objective of a shield immunity strategy is to help sustain the interactions necessary for the functioning of essential goods and services (including but not limited to tending to the elderly [9], hospital care, schools, and food supply) while decreasing the probability of transmission during such essential interactions. We show that a shield immunity approach may significantly reduce the length and reduce the overall burden of an outbreak, and can work synergistically with social distancing. The present model highlights the value of serological testing as part of intervention strategies, in addition to its well recognized roles in estimating prevalence [10, 11] and in the potential development of plasma-based therapies [12-15].

Identifiants

pubmed: 32511605
doi: 10.1101/2020.04.01.20049767
pmc: PMC7276032
pii:
doi:

Types de publication

Preprint

Langues

eng

Commentaires et corrections

Type : UpdateIn

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Auteurs

Joshua S Weitz (JS)

School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA.
School of Physics, Georgia Institute of Technology, Atlanta, GA, USA.
Center for Microbial Dynamics and Infection, Georgia Institute of Technology, Atlanta, GA, USA.

Stephen J Beckett (SJ)

School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA.

Ashley R Coenen (AR)

School of Physics, Georgia Institute of Technology, Atlanta, GA, USA.

David Demory (D)

School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA.

Marian Dominguez-Mirazo (M)

Interdisciplinary Graduate Program in Quantitative Biosciences, Georgia Institute of Technology, Atlanta, GA, USA.
School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA.

Jonathan Dushoff (J)

Department of Biology, McMaster University, Hamilton, ON, Canada.
DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, ON, Canada.

Chung-Yin Leung (CY)

School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA.
School of Physics, Georgia Institute of Technology, Atlanta, GA, USA.

Guanlin Li (G)

Interdisciplinary Graduate Program in Quantitative Biosciences, Georgia Institute of Technology, Atlanta, GA, USA.
School of Physics, Georgia Institute of Technology, Atlanta, GA, USA.

Andreea Măgălie (A)

Interdisciplinary Graduate Program in Quantitative Biosciences, Georgia Institute of Technology, Atlanta, GA, USA.
School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA.

Sang Woo Park (SW)

Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.

Rogelio Rodriguez-Gonzalez (R)

Interdisciplinary Graduate Program in Quantitative Biosciences, Georgia Institute of Technology, Atlanta, GA, USA.
School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA.

Shashwat Shivam (S)

School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA.

Conan Zhao (C)

Interdisciplinary Graduate Program in Quantitative Biosciences, Georgia Institute of Technology, Atlanta, GA, USA.
School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA.

Classifications MeSH