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Title
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Assessing the feasibility and effectiveness of household-pooled universal testing to control COVID-19 epidemics
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Author
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Abstract
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| Outbreaks of SARS-CoV-2 are threatening the health care systems of several countries around the world. The initial control of SARS-CoV-2 epidemics relied on non-pharmaceutical interventions, such as social distancing, teleworking, mouth masks and contact tracing. However, as pre-symptomatic transmission remains an important driver of the epidemic, contact tracing efforts struggle to fully control SARS-CoV-2 epidemics. Therefore, in this work, we investigate to what extent the use of universal testing, i.e., an approach in which we screen the entire population, can be utilized to mitigate this epidemic. To this end, we rely on PCR test pooling of individuals that belong to the same households, to allow for a universal testing procedure that is feasible with the limited testing capacity. We evaluate two isolation strategies: on the one hand pool isolation , where we isolate all individuals that belong to a positive PCR test pool, and on the other hand individual isolation , where we determine which of the individuals that belong to the positive PCR pool are positive, through an additional testing step. We evaluate this universal testing approach in the STRIDE individual-based epidemiological model in the context of the Belgian COVID-19 epidemic. As the organisation of universal testing will be challenging, we discuss the different aspects related to sample extraction and PCR testing, to demonstrate the feasibility of universal testing when a decentralized testing approach is used. We show through simulation, that weekly universal testing is able to control the epidemic, even when many of the contact reductions are relieved. Finally, our model shows that the use of universal testing in combination with stringent contact reductions could be considered as a strategy to eradicate the virus. |
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Language
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English
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Source (journal)
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PLoS computational biology. - San Francisco, Calif.
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Related dataset(s)
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Publication
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San Francisco, Calif.
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2021
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ISSN
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1553-734X
1553-7358
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DOI
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10.1371/JOURNAL.PCBI.1008688
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Volume/pages
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17
:3
(2021)
, 22 p.
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Article Reference
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e1008688
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ISI
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000627463800001
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Pubmed ID
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33690626
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Medium
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E-only publicatie
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Full text (Publisher's DOI)
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Full text (open access)
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