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. 2020 Dec 8;18(1):386.
doi: 10.1186/s12916-020-01866-6.

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

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

David R M Smith et al. BMC Med. .

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.

Keywords: COVID-19; Computational modelling; Contact network; Infectious disease surveillance; Long-term care; Mathematical modelling; Public health; SARS-CoV-2; Testing; Transmission dynamics.

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Conflict of interest statement

LO reports grants from Pfizer, outside the submitted work. All other authors report no competing interests.

Figures

Fig. 1
Fig. 1
Characteristics of the SARS-CoV-2 transmission model. a A diagram of the baseline LTCF, showing the average weekly number of patients and staff in each ward, including “Other” staff not primarily in any one specific ward. Below the LTCF is a description of the epidemiological scenarios considered for how SARS-CoV-2 was introduced into the LTCF. b A snapshot of the simulated dynamic contact network, showing all patients (PA, circles) and staff (PE, triangles) present in the baseline LTCF as nodes, and inter-individual contacts aggregated over one randomly selected day as edges. Nodes and edges are coloured by ward, with grey edges representing contacts across wards. c A diagram of the modified SEIR process used to characterize COVID-19 infection (S, susceptible; E, exposed; IP, infectious pre-symptomatic; IA, infectious asymptomatic; IM, infectious with mild symptoms; IS, infectious with severe symptoms; R, recovered), with transitions between states a to f (see Additional File 1: Table S1). Below, diagnostic sensitivity of RT-PCR for detecting SARS-CoV-2 in a true positive specimen was modelled as a function of time since infection
Fig. 2
Fig. 2
Epidemic curves of COVID-19 infection resulting from random introductions of SARS-CoV-2 into a 170-bed LTCF. Symptomatic cases represent just the “tip of the iceberg” in nascent outbreaks. a Two examples of epidemic simulations, demonstrating variation in outbreak velocity and lags until first onset of COVID-19 symptoms. b The median epidemic curve across all simulations for the baseline scenario, with dotted lines demarcating median time lags to selected events. Bars represent the median number of individuals in each infection class over time, and do not necessarily total to the median number infected (e.g. there is a median 1 infection at t = 0 but a median 0 infections in each class, as each index case had an equal 1/3 probability of being exposed, pre-symptomatic or asymptomatic). For the same simulation examples (c) and median (d), the probability of detecting outbreaks varied over time for different surveillance strategies (coloured lines), depending on how many, and which types of individuals became infected over time (vertical bars); here, testing capacity = 1 test/day
Fig. 3
Fig. 3
Test more to detect outbreaks sooner. a Median lags to outbreak detection (95% uncertainty interval) and b corresponding median outbreak sizes upon detection (95% uncertainty interval) are shown for each surveillance strategy (y-axis) as a function of the daily testing capacity (x-axis). Group testing strategies assume a maximum of 32 swabs per test. For both cascades and group testing, individual tests were always reserved for individuals with severe COVID-like symptoms; remaining tests were then distributed according to cascades or as a single group test. SS, severe symptoms; MS, mild symptoms; A, admission; R, random patients
Fig. 4
Fig. 4
Incremental efficiency plots for selected surveillance strategies relative to a reference strategy of only testing individuals with severe COVID-like symptoms. Here, improvement in COVID-19 surveillance (x-axis) is balanced against additional nasopharyngeal swabs used (y-axis for a) and additional RT-PCR tests conducted (y-axis for b) until outbreaks were detected. Both axes are log10-adjusted. For both panels, daily testing capacity is fixed at 1 test/day (for higher testing capacities, see Additional File 2: Fig. S7). Small translucent points represent median outcomes across 100 surveillance simulations for each simulated outbreak, and larger opaque points represent mean of medians across all outbreaks

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