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Optimizing COVID-19 surveillance in long-term care facilities: a modelling study

BACKGROUND: Long-term care facilities (LTCFs) are vulnerable to outbreaks of coronavirus disease 2019 (COVID-19). Timely epidemiological surveillance is essential for outbreak response, but is complicated by a high proportion of silent (non-symptomatic) infections and limited testing resources. METH...

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Autores principales: Smith, David R. M., Duval, Audrey, Pouwels, Koen B., Guillemot, Didier, Fernandes, Jérôme, Huynh, Bich-Tram, Temime, Laura, Opatowski, Lulla
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7721547/
https://www.ncbi.nlm.nih.gov/pubmed/33287821
http://dx.doi.org/10.1186/s12916-020-01866-6
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author Smith, David R. M.
Duval, Audrey
Pouwels, Koen B.
Guillemot, Didier
Fernandes, Jérôme
Huynh, Bich-Tram
Temime, Laura
Opatowski, Lulla
author_facet Smith, David R. M.
Duval, Audrey
Pouwels, Koen B.
Guillemot, Didier
Fernandes, Jérôme
Huynh, Bich-Tram
Temime, Laura
Opatowski, Lulla
author_sort Smith, David R. M.
collection PubMed
description BACKGROUND: Long-term care facilities (LTCFs) are vulnerable to outbreaks of coronavirus disease 2019 (COVID-19). Timely epidemiological surveillance is essential for outbreak response, but is complicated by a high proportion of silent (non-symptomatic) infections and limited testing resources. METHODS: We used a stochastic, individual-based model to simulate transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) along detailed inter-individual contact networks describing patient-staff interactions in a real LTCF setting. We simulated distribution of nasopharyngeal swabs and reverse transcriptase polymerase chain reaction (RT-PCR) tests using clinical and demographic indications and evaluated the efficacy and resource-efficiency of a range of surveillance strategies, including group testing (sample pooling) and testing cascades, which couple (i) testing for multiple indications (symptoms, admission) with (ii) random daily testing. RESULTS: In the baseline scenario, randomly introducing a silent SARS-CoV-2 infection into a 170-bed LTCF led to large outbreaks, with a cumulative 86 (95% uncertainty interval 6–224) infections after 3 weeks of unmitigated transmission. Efficacy of symptom-based screening was limited by lags to symptom onset and silent asymptomatic and pre-symptomatic transmission. Across scenarios, testing upon admission detected just 34–66% of patients infected upon LTCF entry, and also missed potential introductions from staff. Random daily testing was more effective when targeting patients than staff, but was overall an inefficient use of limited resources. At high testing capacity (> 10 tests/100 beds/day), cascades were most effective, with a 19–36% probability of detecting outbreaks prior to any nosocomial transmission, and 26–46% prior to first onset of COVID-19 symptoms. Conversely, at low capacity (< 2 tests/100 beds/day), group testing strategies detected outbreaks earliest. Pooling randomly selected patients in a daily group test was most likely to detect outbreaks prior to first symptom onset (16–27%), while pooling patients and staff expressing any COVID-like symptoms was the most efficient means to improve surveillance given resource limitations, compared to the reference requiring only 6–9 additional tests and 11–28 additional swabs to detect outbreaks 1–6 days earlier, prior to an additional 11–22 infections. CONCLUSIONS: COVID-19 surveillance is challenged by delayed or absent clinical symptoms and imperfect diagnostic sensitivity of standard RT-PCR tests. In our analysis, group testing was the most effective and efficient COVID-19 surveillance strategy for resource-limited LTCFs. Testing cascades were even more effective given ample testing resources. Increasing testing capacity and updating surveillance protocols accordingly could facilitate earlier detection of emerging outbreaks, informing a need for urgent intervention in settings with ongoing nosocomial transmission. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12916-020-01866-6.
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spelling pubmed-77215472020-12-08 Optimizing COVID-19 surveillance in long-term care facilities: a modelling study Smith, David R. M. Duval, Audrey Pouwels, Koen B. Guillemot, Didier Fernandes, Jérôme Huynh, Bich-Tram Temime, Laura Opatowski, Lulla BMC Med Research Article BACKGROUND: Long-term care facilities (LTCFs) are vulnerable to outbreaks of coronavirus disease 2019 (COVID-19). Timely epidemiological surveillance is essential for outbreak response, but is complicated by a high proportion of silent (non-symptomatic) infections and limited testing resources. METHODS: We used a stochastic, individual-based model to simulate transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) along detailed inter-individual contact networks describing patient-staff interactions in a real LTCF setting. We simulated distribution of nasopharyngeal swabs and reverse transcriptase polymerase chain reaction (RT-PCR) tests using clinical and demographic indications and evaluated the efficacy and resource-efficiency of a range of surveillance strategies, including group testing (sample pooling) and testing cascades, which couple (i) testing for multiple indications (symptoms, admission) with (ii) random daily testing. RESULTS: In the baseline scenario, randomly introducing a silent SARS-CoV-2 infection into a 170-bed LTCF led to large outbreaks, with a cumulative 86 (95% uncertainty interval 6–224) infections after 3 weeks of unmitigated transmission. Efficacy of symptom-based screening was limited by lags to symptom onset and silent asymptomatic and pre-symptomatic transmission. Across scenarios, testing upon admission detected just 34–66% of patients infected upon LTCF entry, and also missed potential introductions from staff. Random daily testing was more effective when targeting patients than staff, but was overall an inefficient use of limited resources. At high testing capacity (> 10 tests/100 beds/day), cascades were most effective, with a 19–36% probability of detecting outbreaks prior to any nosocomial transmission, and 26–46% prior to first onset of COVID-19 symptoms. Conversely, at low capacity (< 2 tests/100 beds/day), group testing strategies detected outbreaks earliest. Pooling randomly selected patients in a daily group test was most likely to detect outbreaks prior to first symptom onset (16–27%), while pooling patients and staff expressing any COVID-like symptoms was the most efficient means to improve surveillance given resource limitations, compared to the reference requiring only 6–9 additional tests and 11–28 additional swabs to detect outbreaks 1–6 days earlier, prior to an additional 11–22 infections. CONCLUSIONS: COVID-19 surveillance is challenged by delayed or absent clinical symptoms and imperfect diagnostic sensitivity of standard RT-PCR tests. In our analysis, group testing was the most effective and efficient COVID-19 surveillance strategy for resource-limited LTCFs. Testing cascades were even more effective given ample testing resources. Increasing testing capacity and updating surveillance protocols accordingly could facilitate earlier detection of emerging outbreaks, informing a need for urgent intervention in settings with ongoing nosocomial transmission. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12916-020-01866-6. BioMed Central 2020-12-08 /pmc/articles/PMC7721547/ /pubmed/33287821 http://dx.doi.org/10.1186/s12916-020-01866-6 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Smith, David R. M.
Duval, Audrey
Pouwels, Koen B.
Guillemot, Didier
Fernandes, Jérôme
Huynh, Bich-Tram
Temime, Laura
Opatowski, Lulla
Optimizing COVID-19 surveillance in long-term care facilities: a modelling study
title Optimizing COVID-19 surveillance in long-term care facilities: a modelling study
title_full Optimizing COVID-19 surveillance in long-term care facilities: a modelling study
title_fullStr Optimizing COVID-19 surveillance in long-term care facilities: a modelling study
title_full_unstemmed Optimizing COVID-19 surveillance in long-term care facilities: a modelling study
title_short Optimizing COVID-19 surveillance in long-term care facilities: a modelling study
title_sort optimizing covid-19 surveillance in long-term care facilities: a modelling study
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7721547/
https://www.ncbi.nlm.nih.gov/pubmed/33287821
http://dx.doi.org/10.1186/s12916-020-01866-6
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