Optimizing COVID-19 surveillance in long-term care facilities: a modelling study
Abstract 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 resourc...
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2020-12-01
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Online Access: | https://doi.org/10.1186/s12916-020-01866-6 |
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Article |
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DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
David R. M. Smith Audrey Duval Koen B. Pouwels Didier Guillemot Jérôme Fernandes Bich-Tram Huynh Laura Temime Lulla Opatowski on behalf of the AP-HP/Universities/Inserm COVID-19 research collaboration |
spellingShingle |
David R. M. Smith Audrey Duval Koen B. Pouwels Didier Guillemot Jérôme Fernandes Bich-Tram Huynh Laura Temime Lulla Opatowski on behalf of the AP-HP/Universities/Inserm COVID-19 research collaboration Optimizing COVID-19 surveillance in long-term care facilities: a modelling study BMC Medicine SARS-CoV-2 COVID-19 Testing Infectious disease surveillance Long-term care Transmission dynamics |
author_facet |
David R. M. Smith Audrey Duval Koen B. Pouwels Didier Guillemot Jérôme Fernandes Bich-Tram Huynh Laura Temime Lulla Opatowski on behalf of the AP-HP/Universities/Inserm COVID-19 research collaboration |
author_sort |
David R. M. Smith |
title |
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_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_sort |
optimizing covid-19 surveillance in long-term care facilities: a modelling study |
publisher |
BMC |
series |
BMC Medicine |
issn |
1741-7015 |
publishDate |
2020-12-01 |
description |
Abstract 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. |
topic |
SARS-CoV-2 COVID-19 Testing Infectious disease surveillance Long-term care Transmission dynamics |
url |
https://doi.org/10.1186/s12916-020-01866-6 |
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doaj-51e4d2017de647b48830a1d97551259c2020-12-13T12:23:21ZengBMCBMC Medicine1741-70152020-12-0118111610.1186/s12916-020-01866-6Optimizing COVID-19 surveillance in long-term care facilities: a modelling studyDavid R. M. Smith0Audrey Duval1Koen B. Pouwels2Didier Guillemot3Jérôme Fernandes4Bich-Tram Huynh5Laura Temime6Lulla Opatowski7on behalf of the AP-HP/Universities/Inserm COVID-19 research collaborationInstitut Pasteur, Epidemiology and Modelling of Antibiotic Evasion (EMAE)Institut Pasteur, Epidemiology and Modelling of Antibiotic Evasion (EMAE)Health Economics Research Centre, Nuffield Department of Population Health, University of OxfordInstitut Pasteur, Epidemiology and Modelling of Antibiotic Evasion (EMAE)Clinique de soins de suite et réadaptationInstitut Pasteur, Epidemiology and Modelling of Antibiotic Evasion (EMAE)Modélisation, épidémiologie et surveillance des risques sanitaires (MESuRS), Conservatoire national des arts et métiersInstitut Pasteur, Epidemiology and Modelling of Antibiotic Evasion (EMAE)Abstract 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.https://doi.org/10.1186/s12916-020-01866-6SARS-CoV-2COVID-19TestingInfectious disease surveillanceLong-term careTransmission dynamics |