ABSTRACT
The transmission risk of SARS-CoV-2 within hospitals can exceed that in the general community because of more frequent close proximity interactions (CPIs). Heterogeneity of risk across wards is still poorly described. We measured CPIs in 15 clinical wards across three hospitals using wearable sensors over 36 hours in spring 2020. This data was combined with a transmission model to estimate and compare transmission risks across wards. We found a four-fold range of epidemic risk between wards, with patients frequently presenting high risk to patients and healthcare workers (HCWs). Using a simulation study, we then assessed the potential impact on global risk of targeting individuals for prevention based on their contact patterns. We found that targeting individuals with the highest cumulative contact hours was most impactful. This study reveals patterns of interactions between individuals in hospital during a pandemic and opens new routes for research into airborne nosocomial risk. One Sentence SummaryWe measured contacts between staff, patients and visitors in 15 hospital wards, and used models to predict epidemic risk and evaluate interventions.
ABSTRACT
Circulation of multidrug-resistant bacteria (MRB) in healthcare facilities is a major public health problem. These settings have been greatly impacted by the COVID-19 pandemic, notably due to surges in COVID-19 caseloads and the implementation of infection control measures. Yet collateral impacts of pandemic COVID-19 on MRB epidemiology remain poorly understood. Here, we present a dynamic transmission model in which SARS-CoV-2 and MRB co-circulate among patients and staff in a hospital population in an early pandemic context. Responses to SARS-CoV-2 outbreaks are captured mechanistically, reflecting impacts on factors relevant for MRB transmission, including contact behaviour, hand hygiene compliance, antibiotic prescribing and population structure. In a first set of simulations, broad parameter ranges are accounted for, representative of diverse bacterial species and hospital settings. On average, COVID-19 control measures coincide with MRB prevention, including fewer incident cases and fewer cumulative person-days of patient MRB colonization. However, surges in COVID-19 caseloads favour MRB transmission and lead to increased rates of antibiotic resistance, especially in the absence of concomitant control measures. In a second set of simulations, methicillin-resistant Staphylococcus aureus and extended-spectrum beta-lactamase-producing Escherichia coli are simulated in specific hospital wards and pandemic response scenarios. Antibiotic resistance dynamics are highly context-specific in these cases, and SARS-CoV-2 outbreaks significantly impact bacterial epidemiology only in facilities with high underlying risk of bacterial transmission. Crucially, antibiotic resistance burden is reduced in facilities with timelier, more effective implementation of COVID-19 control measures. This highlights the control of antibiotic resistance as an important collateral benefit of robust pandemic preparedness. Significance StatementImpacts of COVID-19 on the spread of antibiotic resistance are poorly understood. Here, an epidemiological model accounting for the simultaneous spread of SARS-CoV-2 and antibiotic-resistant bacteria is presented. The model is tailored to healthcare settings during the first wave of the COVID-19 pandemic, and accounts for hand hygiene, inter-individual contact behaviour, and other factors relevant for pathogen spread. Simulations demonstrate that public health policies enacted to slow the spread of COVID-19 also tend to limit bacterial transmission. However, surges in COVID-19 cases simultaneously select for higher rates of antibiotic resistance. Selection for resistance is thus mitigated by prompt implementation of effective COVID-19 prevention policies. This highlights the control of antibiotic resistance as an important collateral benefit of pandemic preparedness.
ABSTRACT
COVID-19 pandemic responses have dramatically modified the global ecological and epidemiological landscape of many infectious diseases. However, the pandemics impacts on antimicrobial resistance (AMR) are currently poorly understood and lack data. While surges in COVID-19 cases during the first wave of the pandemic may have exacerbated AMR, decreases in antibiotic use may have had the opposite effect. To disentangle how pandemic impacts such as lockdowns and modified antibiotic prescribing may affect AMR, we developed a mathematical model formalizing simultaneous transmission of SARS-CoV-2 and colonization with a bacterial pathogen across six pandemic scenarios. We used simulation to assess the effect of each scenario on the bacterial carriage prevalence, antibiotic resistance rate, and the invasive bacterial disease (IBD) incidence, using parameters based on the commensal community bacterium Streptococcus pneumoniae. Pandemic scenarios without community-wide lockdowns all resulted in a decrease in carriage prevalence of antibiotic-sensitive bacteria and an increase in the prevalence of antibiotic-resistant bacteria, while the addition of a population-wide lockdown resulted in a large reduction in colonization prevalence and IBD incidence for both strains (>70%). This translated to an increase in the antibiotic resistance rate across all scenarios to varying degrees, with lingering effects after the cessation of COVID-19 response measures. In the absence of lockdown, a population-wide surge in antibiotic prescribing coincident with the peak in SARS-CoV-2 infection resulted in the greatest increases in resistance rate (23%) and resistant IBD incidence (6%). Within-host interactions, SARS-CoV-2 variants, and population immunity are found to further drive the magnitude of pandemic impacts on resistant IBD incidence. Sensitivity analyses suggest that the extent of such impacts likely varies across different bacterial species. Although real-life scenarios are significantly more complicated, our findings suggest that COVID-19 pandemic responses may significantly impact antibiotic resistance in the community and support the need for monitoring resistance during pandemic waves. Author SummaryAntimicrobial resistance (AMR) is a leading threat to global health. The ongoing COVID-19 pandemic has occurred during global efforts to combat AMR, and pandemic responses implemented to slow SARS-CoV-2 transmission have dramatically affected the incidence of common viral and bacterial respiratory infections at a global scale. However, impacts of the COVID-19 pandemic on AMR and the incidence of invasive bacterial disease (IBD) caused by antibiotic-resistant bacteria are still being uncovered. Here, we use mathematical modelling to explore how different pandemic scenarios accounting for variation in lockdown implementation and antibiotic use in the community may affect antibiotic-resistant bacteria. We found that lockdown implementation substantially reduces the number of annual cases of resistant IBD (over 70%), although the proportion of resistant bacteria among carriers is expected to increase when prophylactic antibiotics are prescribed in response to SARS-CoV-2 infection. We also found that a population-wide surge in antibiotic prescribing in the absence of lockdown may contribute to a large increase (6%) in the number of IBD cases caused by antibiotic-resistant bacteria, while the effect of reduced antibiotic use is negligible. However, such impacts likely vary depending on the bacterial species considered, within-host interactions, SARS-CoV-2 variants, and population immunization levels.
ABSTRACT
Covid-19 poses significant risk of nosocomial transmission, and preventing this requires good estimates of the basic reproduction number R0 in hospitals and care facilities, but these are currently lacking. Such estimates are challenging due to small population sizes in these facilities and inconsistent testing practices. We estimate the patient-to-patient R0 and daily transmission rate of SARS-CoV-2 using data from a closely monitored hospital outbreak in Paris 2020 during the first wave. We use a realistic epidemic model which accounts for progressive stages of infection, stochastic effects and a large proportion of asymptomatic infections. Innovatively, we explicitly include changes in testing capacity over time, as well as the evolving sensitivity of PCR testing at different stages of infection. We conduct rigorous statistical inference using iterative particle filtering to fit the model to the observed patient data and validate this methodology using simulation. We provide estimates for R0 across the entire hospital (2.6) and in individual wards (from 3 to 15), possibly reflecting heterogeneity in contact patterns or control measures. An obligatory mask-wearing policy introduced during the outbreak is likely to have changed the R0, and we estimate values before (8.7) and after (1.3) its introduction, corresponding to a policy efficacy of 85%.
ABSTRACT
extensive protective measures, SARS-CoV-2 widely circulates within healthcare facilities, posing a significant risk to both patients and healthcare workers. Several control strategies have been proposed; however, the global efficacy of local measures implemented at the ward level may depend on hospital-level organizational factors. We aimed at better understanding the role of between-ward interactions on nosocomial outbreaks and their control in a multiward psychiatric hospital in Western France. We built a stochastic compartmental transmission model of SARS-CoV-2 in the 24-wards hospital, accounting for the various infection states among patients and staff, and between-ward connections resulting from staff sharing. We first evaluated the potential of hospital-wide diffusion of local outbreaks, depending on the ward they started in. We then assessed control strategies, including a screening area upon patient admission, an isolation ward for COVID-19 positive patients and changes in staff schedules to limit between-ward mixing. Much larger and more frequent outbreaks occurred when the index case originated in one of the most connected wards with up to four times more transmissions when compared to the more isolated ones. The number of wards where infection spreads was brought down by up to 53 % after reducing staff sharing. Finally, we found that setting up an isolation ward reduced the number of transmissions by up to 70 %, while adding a screening area before admission seemed ineffective. Significance StatementHospital acquired COVID-19 poses a major problem to many countries. Despite extensive protective measures, transmission within hospitals still occurs regularly and threatens those essential to the fight against the pandemic while putting patients at risk. Using a stochastic compartmental model, we simulate the spread of SARS-CoV-2 in a multi-ward hospital, assessing the effect of different scenarios and infection control strategies. The novelty of our method resides in the consideration of staff sharing data to better reflect the field reality. Our results highlight the poor efficiency of implementing a screening area before hospital admission, while the setting up of an isolation ward dedicated to COVID-19 patients and the restriction of healthcare workers movements between wards significantly reduce epidemic spread.
ABSTRACT
Healthcare facilities are vulnerable to SARS-CoV-2 introductions and subsequent nosocomial outbreaks. Antigen rapid diagnostic testing (Ag-RDT) is widely used for population screening, but its health and economic benefits as a reactive response to local surges in outbreak risk are unclear. We simulate SARS-CoV-2 transmission in a long-term care hospital with varying COVID-19 containment measures in place (social distancing, face masks, vaccination). Across scenarios, nosocomial incidence is reduced by up to 40-47% (range of means) with routine symptomatic RT-PCR testing, 59-63% with the addition of a timely round of Ag-RDT screening, and 69-75% with well-timed two-round screening. For the latter, a delay of 4-5 days between the two screening rounds is optimal for transmission prevention. Screening efficacy varies depending on test sensitivity, test type, subpopulations targeted, and community incidence. Efficiency, however, varies primarily depending on underlying outbreak risk, with health-economic benefits scaling by orders of magnitude depending on the COVID-19 containment measures in place.
ABSTRACT
Festive gatherings this 2020 holiday season threaten to cause a surge in new cases of novel coronavirus disease 2019 (COVID-19). Hospitals and long-term care facilities are key hotspots for COVID-19 outbreaks, and may be at elevated risk as patients and staff return from holiday celebrations in the community. Some settings and institutions have proposed fortified post-holiday testing regimes to mitigate this risk. We use an existing model to assess whether implementing a single round of post-holiday screening is sufficient to detect and manage holiday-associated spikes in COVID-19 introductions to the long-term care setting. We show that while testing early helps to detect cases prior to potential onward transmission, it likely to miss a substantial share of introductions owing to false negative test results, which are more probable early in infection. We propose a two-stage post-holiday testing regime as a means to maximize case detection and mitigate the risk of nosocomial COVID-19 outbreaks into the start of the new year. Whether all patients and staff should be screened, or only community-exposed patients, depends on available testing capacity: the former will be more effective, but also more resource-intensive.
ABSTRACT
In response to the COVID-19 epidemic, Egypt established a unique care model based on quarantine hospitals where only externally-referred confirmed COVID-19 patients were admitted, and healthcare workers resided continuously over 1-to 2-week working shifts. While the COVID-19 risk for HCWs has been widely reported in standard healthcare settings, it has not been evaluated yet in quarantine hospitals. Here, we relied on longitudinal data, including results of routine RT-PCR tests, collected within three quarantine hospitals located in Cairo and Fayoum, Egypt. Using a model-based approach that accounts for the time-since-exposure variation in false-negative rates of RT-PCR tests, we computed the incidence of SARS-CoV-2 infection among HCWs. Over a total follow-up of 6,064 person-days (PD), we estimated an incidence rate (per 100 PD) of 1.05 (95% CrI: 0.58-1.65) at Hospital 1, 1.92 (95% CrI: 0.93-3.28) at Hospital 2 and 7.62 (95% CrI: 3.47-13.70) at Hospital 3. The probability for an HCW to be infected at the end of a shift was 13.7% (95% CrI: 7.8%-20.8%) and 23.8% (95% CrI: 12.2%-37.3%) for a 2-week shift at Hospital 1 and Hospital 2, respectively, which lies within the range of risk levels previously documented in standard healthcare settings, whereas it was >3-fold higher for a 7-day shift at Hospital 2 (42.6%, 95%CrI: 21.9%-64.4%). Our model-based estimates unveil a proportion of undiagnosed infections among HCWs of 46.4% (95% CrI: 18.8%-66.7%), 45.0% (95% CrI: 5.6%-70.8%) and 59.2% (95% CrI: 34.8%-78.8%), for Hospitals 1 to 3, respectively. The large variation in SARS-CoV-2 incidence we document here suggests that HCWs from quarantine hospitals may face a high occupational risk of infection, but that, with sufficient anticipation and infection control measures, this risk can be brought down to levels similar to those observed in standard healthcare settings. WHAT THIS PAPER ADDSO_ST_ABSWhat is already known on this topicC_ST_ABSPrevious studies conducted in standard care settings have documented that frontline healthcare workers (HCWs) face high risk of COVID-19. Whether risk levels differ in alternative care models, such as COVID-19 quarantine hospitals in Egypt where HCWs resided in the hospital days and nights for various durations, is unknown. What this study addsCOVID-19 risk for HCWs in quarantine hospitals varies substantially between facilities, from risk levels that are in the range of those documented in standard healthcare settings to levels that were approximatively 3 times higher. How this study might affect research, practice or policyWith sufficient anticipation and infection control measures, occupational COVID-19 risk for HCWs working in quarantine hospitals can be brought down to levels similar to those observed in standard healthcare settings.
ABSTRACT
To date, no specific estimate of R0 for SARS-CoV-2 is available for healthcare settings. Using inter-individual contact data, we highlight that R0 estimates from the community cannot translate directly to healthcare settings, with pre-pandemic R0 values ranging 1.3-7.7 in three illustrative healthcare institutions. This has implications for nosocomial Covid-19 control.
ABSTRACT
BackgroundLong-term care facilities (LTCFs) are vulnerable to COVID-19 outbreaks. Timely epidemiological surveillance is essential for outbreak response, but is complicated by a high proportion of silent (non-symptomatic) infections and limited testing resources. MethodsWe used a stochastic, individual-based model to simulate SARS-CoV-2 transmission along detailed inter-individual contact networks describing patient-staff interactions in real LTCF settings. We distributed nasopharyngeal swabs and 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. ResultsIn the baseline scenario, randomly introducing SARS-CoV-2 into a 170-bed LTCF led to large outbreaks, with a cumulative 86 (6-224) infections after three weeks of unmitigated transmission. Efficacy of symptom-based screening was limited by (i) lags between infection and symptom onset, and (ii) silent transmission from asymptomatic and pre-symptomatic infections. Testing upon admission detected up to 66% of patients silently infected upon LTCF entry, but 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 (>1 test/10 beds/day), cascades were most effective, with a 22-52% probability of detecting outbreaks prior to any nosocomial transmission, and 38-63% prior to first onset of COVID-19 symptoms. Conversely, at low capacity (<1 test/85 beds/day), pooling randomly selected patients in a daily group test was most effective (9-15% probability of detecting outbreaks prior to transmission; 30-44% prior to symptoms). The most efficient strategy compared to the reference was to pool individuals with any COVID-like symptoms, requiring only 5-7 additional tests and 17-24 additional swabs to detect outbreaks 5-6 days earlier, prior to an additional 14-18 infections. ConclusionsGroup testing is an effective and efficient COVID-19 surveillance strategy for resource-limited LTCFs. Cascades are 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.
