Preventing COVID-19 outbreaks through surveillance testing in healthcare facilities: a modelling study

Information-guided adaptive learning approach for active surveillance of infectious diseases

The infectious disease surveillance system is a key support tool for public health decision making. Current research concentrates on optimizing static sentinel deployment to address the problem of incomplete data due to the lack of sufficient surveillance resources. In this study, we introduce an information-guided adaptive learning strategy for the dynamic surveillance of infectious diseases. The goal is to improve monitoring effectiveness in situations where it is possible to adjust the focus of surveillance, such as serial surveys and allocation of testing tools. Specifically, we develop a probabilistic neural network model to learn spatio-temporal correlations among the numbers of infections. Based on a probabilistic model, we evaluate the information gain of monitoring a spatio-temporal target and design a greedy selection algorithm for monitoring targets selection. Moreover, we integrate two major surveillance objectives, i.e., informativeness and coverage, in the monitoring target selection. The experimental results on the synthetic dataset and two real-world datasets demonstrate the effectiveness of our approach, showcasing the promise of further exploration and application of dynamic adaptive active surveillance.

Hospital population density and risk of respiratory infection: Is close contact density dependent?

Respiratory infections acquired in hospital depend on close contact, which may be affected by hospital population density. Models of infectious disease transmission typically assume that contact rates are independent of density (frequency dependence) or proportional to it (linear density dependence), without justification. We evaluate these assumptions by measuring contact rates in hospitals under different population densities. We analysed data from a study in 15 wards in which staff, patients and visitors carried wearable sensors which detected close contacts. We proposed a general model, non-linear density dependence, and fit this to data on several types of interactions. Finally, we projected the fitted models to predict the effect of increasing population density on epidemic risk. We identified considerable heterogeneity in density dependence between wards, even those with the same medical specialty. Interactions between all persons present usually depended little on the population density. However, increasing patient density was associated with higher rates of patient contact for staff and for other patients. Simulations suggested that a 10 % increase in patient population density would carry a markedly increased risk in many wards. This study highlights the variance in density dependent dynamics and the complexity of predicting contact rates.

A model-based assessment of social isolation practices for COVID-19 outbreak response in residential care facilities

BACKGROUND

Residential aged-care facilities (RACFs, also called long-term care facilities, aged care homes, or nursing homes) have elevated risks of respiratory infection outbreaks and associated disease burden. During the COVID-19 pandemic, social isolation policies were commonly used in these facilities to prevent and mitigate outbreaks. We refer specifically to general isolation policies that were intended to reduce contact between residents, without regard to confirmed infection status. Such policies are controversial because of their association with adverse mental and physical health indicators and there is a lack of modelling that assesses their effectiveness.

METHODS

In consultation with the Australian Government Department of Health and Aged Care, we developed an agent-based model of COVID-19 transmission in a structured population, intended to represent the salient characteristics of a residential care environment. Using our model, we generated stochastic ensembles of simulated outbreaks and compared summary statistics of outbreaks simulated under different mitigation conditions. Our study focuses on the marginal impact of general isolation (reducing social contact between residents), regardless of confirmed infection. For a realistic assessment, our model included other generic interventions consistent with the Australian Government's recommendations released during the COVID-19 pandemic: isolation of confirmed resident cases, furlough (mandatory paid leave) of staff members with confirmed infection, and deployment of personal protective equipment (PPE) after outbreak declaration.

RESULTS

In the absence of any asymptomatic screening, general isolation of residents to their rooms reduced median cumulative cases by approximately 27%. However, when conducted concurrently with asymptomatic screening and isolation of confirmed cases, general isolation reduced the median number of cumulative infections by only 12% in our simulations.

CONCLUSIONS

Under realistic sets of assumptions, our simulations showed that general isolation of residents did not provide substantial benefits beyond those achieved through screening, isolation of confirmed cases, and deployment of PPE. Our results also highlight the importance of effective case isolation, and indicate that asymptomatic screening of residents and staff may be warranted, especially if importation risk from the outside community is high. Our conclusions are sensitive to assumptions about the proportion of total contacts in a facility accounted for by casual interactions between residents.

An epidemiological modeling framework to inform institutional-level response to infectious disease outbreaks: a Covid-19 case study

Institutions have an enhanced ability to implement tailored mitigation measures during infectious disease outbreaks. However, macro-level predictive models are inefficient for guiding institutional decision-making due to uncertainty in local-level model input parameters. We present an institutional-level modeling toolkit used to inform prediction, resource procurement and allocation, and policy implementation at Clemson University throughout the Covid-19 pandemic. Through incorporating real-time estimation of disease surveillance and epidemiological measures based on institutional data, we argue this approach helps minimize uncertainties in input parameters presented in the broader literature and increases prediction accuracy. We demonstrate this through case studies at Clemson and other university settings during the Omicron BA.1 and BA.4/BA.5 variant surges. The input parameters of our toolkit are easily adaptable to other institutional settings during future health emergencies. This methodological approach has potential to improve public health response through increasing the capability of institutions to make data-informed decisions that better prioritize the health and safety of their communities while minimizing operational disruptions.

Utility of routine post-admission testing for SARS-CoV-2 in a rehabilitation facility

Asymptomatic screening for SARS-CoV-2 is recommended in healthcare settings during periods of increased incidence, yet studies in rehabilitation settings are lacking. Routine weekly post-admission asymptomatic testing in a rehabilitation facility offered marginal gain beyond syndromic and targeted unit testing and was not associated with a reduced risk of healthcare-associated COVID-19.

COVID-19 monitoring of school personnel through molecular salivary test and dried blood spot analysis

Background

When the coronavirus disease 2019 (COVID-19) pandemic broke out, most countries enforced school closures as a precautionary measure. Although COVID-19 is still present three years later, schools have been reopened. We aimed to test the association of molecular salivary testing (MST) and dried blood spot (DBS) analysis for community surveillance by investigating the immunological profile of a group of school staff during and following COVID-19 vaccination.

Methods

We conducted the study in a school in Milan from April 2021, when school staff were administered the first dose of vaccine against SARS-CoV-2, until the school year ended in June 2022. Each participant provided samples for MST and DBS one month (T1, W1) after receiving their first dose of vaccine. Subsequently, they collected weekly MST samples for five weeks (W2-W6), plus a DBS sample in the last week (T2). Both samples were collected one (T3), four (T4), and seven months (T5) after the administration of the second vaccine dose in May 2021. A final DBS sample was collected one year (T6) after T3.

Results

Sixty participants provided 327 MSTs and 251 DBSs. None of the MST samples tested positive for SARS-CoV-2 RNA during the study period. A total of 201 DBS samples tested positive for the IgG semiquantitative analysis. Negative samples were found only at T1 (20.45%) and T2 (7.32%). We observed borderline results at T1 (4.55%), T2 (7.32%), and T4 (2.70%). The anti-SARS-CoV-2 average antibody ratio increased after the second dose between T2 and T3, and the trend peaked after the third dose between T4 and T6. We performed an immunoenzymatic assay of antibodies against nucleocapsid protein on samples collected at T1 from five participants who reported having been infected before the study and from four subjects with an abnormal increase in the antibody values at T4. Two samples tested positive in the first group and two in the second one.

Conclusions

Our findings show that MST and DBS could be effective tools in the active surveillance of school personnel and that schools could be considered safe settings in view of SARS-CoV-2 infection. Vaccines might have contributed to case and/or symptom reduction.

How have mathematical models contributed to understanding the transmission and control of SARS-CoV-2 in healthcare settings? A systematic search and review

Since the onset of the COVID-19 pandemic, mathematical models have been widely used to inform public health recommendations regarding COVID-19 control in healthcare settings. The objective of this study was to systematically review SARS-CoV-2 transmission models in healthcare settings, and to summarize their contributions to understanding nosocomial COVID-19. A systematic search and review of published articles indexed in PubMed was carried out. Modelling studies describing dynamic inter-individual transmission of SARS-CoV-2 in healthcare settings, published by mid-February 2022 were included. Models have mostly focused on acute-care and long-term-care facilities in high-income countries. Models have quantified outbreak risk, showing great variation across settings and pandemic periods. Regarding surveillance, routine testing rather than symptom-based was highlighted as essential for COVID-19 prevention due to high rates of silent transmission. Surveillance impacts depended critically on testing frequency, diagnostic sensitivity, and turn-around time. Healthcare re-organization also proved to have large epidemiological impacts: beyond obvious benefits of isolating cases and limiting inter-individual contact, more complex strategies (staggered staff scheduling, immune-based cohorting) reduced infection risk. Finally, vaccination impact, while highly effective for limiting COVID-19 burden, varied substantially depending on assumed mechanistic impacts on infection acquisition, symptom onset and transmission. Modelling results form an extensive evidence base that may inform control strategies for future waves of SARS-CoV-2 and other viral respiratory pathogens. We propose new avenues for future models of healthcare-associated outbreaks, with the aim of enhancing their efficiency and contributions to decision-making.

An epidemiological modeling framework to inform institutional-level response to infectious disease outbreaks: A Covid-19 case study

Institutions have an enhanced ability to implement tailored mitigation measures during infectious disease outbreaks. However, macro-level predictive models are inefficient for guiding institutional decision-making due to uncertainty in local-level model input parameters. We present an institutional-level modeling toolkit used to inform prediction, resource procurement and allocation, and policy implementation at Clemson University throughout the Covid-19 pandemic. Through incorporating real-time estimation of disease surveillance and epidemiological measures based on institutional data, we argue this approach helps minimize uncertainties in input parameters presented in the broader literature and increases prediction accuracy. We demonstrate this through case studies at Clemson and other university settings during the Omicron BA.1 and BA.4/BA.5 variant surges. The input parameters of our toolkit are easily adaptable to other institutional settings during future health emergencies. This methodological approach has potential to improve public health response through increasing the capability of institutions to make data-informed decisions that better prioritize the health and safety of their communities while minimizing operational disruptions.

New insights into SARS-CoV-2 Lumipulse G salivary antigen testing: accuracy, safety and short TAT enhance surveillance

OBJECTIVES

The rapid, accurate and safe detection of SARS-CoV-2 is the key to improving surveillance and infection containment. The aim of the present study was to ascertain whether, after heat/chemical inactivation, SARS-CoV-2 N antigen chemiluminescence (CLEIA) assay in saliva remains a valid alternative to molecular testing.

METHODS

In 2022, 139 COVID-19 inpatients and 467 healthcare workers were enrolled. In 606 self-collected saliva samples (Salivette), SARS-CoV-2 was detected by molecular (TaqPath rRT-PCR) and chemiluminescent Ag assays (Lumipulse G). The effect of sample pre-treatment (extraction solution-ES or heating) on antigen recovery was verified.

RESULTS

Salivary SARS-CoV-2 antigen assay was highly accurate (AUC=0.959, 95% CI: 0.943-0.974), with 90% sensitivity and 92% specificity. Of the 254 antigen positive samples, 29 were false positives. We demonstrated that heterophilic antibodies could be a cause of false positive results. A significant antigen concentration decrease was observed after ES treatment (p=0.0026), with misclassification of 43 samples. Heat had a minimal impact, after treatment the correct classification of cases was maintained.

CONCLUSIONS

CLEIA SARS-CoV-2 salivary antigen provides accurate, timely and high-throughput results that remain accurate also after heat inactivation, thus ensuring a safer work environment. This supports the use of salivary antigen detection by CLEIA in surveillance programs.

A Robust, Highly Multiplexed Mass Spectrometry Assay to Identify SARS-CoV-2 Variants

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants are characterized by differences in transmissibility and response to therapeutics. Therefore, discriminating among them is vital for surveillance, infection prevention, and patient care. While whole-genome sequencing (WGS) is the "gold standard" for variant identification, molecular variant panels have become increasingly available. Most, however, are based on limited targets and have not undergone comprehensive evaluation. We assessed the diagnostic performance of the highly multiplexed Agena MassARRAY SARS-CoV-2 Variant Panel v3 to identify variants in a diverse set of 391 SARS-CoV-2 clinical RNA specimens collected across our health systems in New York City, USA and Bogotá, Colombia (September 2, 2020 to March 2, 2022). We demonstrated almost perfect levels of interrater agreement between this assay and WGS for 9 of 11 variant calls (κ ≥ 0.856) and 25 of 30 targets (κ ≥ 0.820) tested on the panel. The assay had a high diagnostic sensitivity (≥93.67%) for contemporary variants (e.g., Iota, Alpha, Delta, and Omicron [BA.1 sublineage]) and a high diagnostic specificity for all 11 variants (≥96.15%) and all 30 targets (≥94.34%) tested. Moreover, we highlighted distinct target patterns that could be utilized to identify variants not yet defined on the panel, including the Omicron BA.2 and other sublineages. These findings exemplified the power of highly multiplexed diagnostic panels to accurately call variants and the potential for target result signatures to elucidate new ones. IMPORTANCE The continued circulation of SARS-CoV-2 amid limited surveillance efforts and inconsistent vaccination of populations has resulted in the emergence of variants that uniquely impact public health systems. Thus, in conjunction with functional and clinical studies, continuous detection and identification are quintessential to informing diagnostic and public health measures. Furthermore, until WGS becomes more accessible in the clinical microbiology laboratory, the ideal assay for identifying variants must be robust, provide high resolution, and be adaptable to the evolving nature of viruses like SARS-CoV-2. Here, we highlighted the diagnostic capabilities of a highly multiplexed commercial assay to identify diverse SARS-CoV-2 lineages that circulated from September 2, 2020 to March 2, 2022 among patients seeking care in our health systems. This assay demonstrated variant-specific signatures of nucleotide/amino acid polymorphisms and underscored its utility for the detection of contemporary and emerging SARS-CoV-2 variants of concern.

Impact of Prior Infection on SARS-CoV-2 Antibody Responses in Vaccinated Long-Term Care Facility Staff

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in 2019 and has resulted in millions of deaths worldwide. Certain populations are at higher risk for infection, especially staff and residents at long-term care facilities (LTCF), due to the congregant living setting and high proportions of residents with many comorbidities. Prior to vaccine availability, these populations represented large fractions of total coronavirus disease 2019 (COVID-19) cases and deaths in the United States. Due to the high-risk setting and outbreak potential, staff and residents were among the first groups to be vaccinated. To define the impact of prior infection on the response to vaccination, we measured antibody responses in a cohort of staff members at an LTCF, many of whom were previously infected by SARS-CoV-2. We found that neutralizing, receptor-binding domain (RBD)-binding, and nucleoprotein (NP)-binding antibody levels were significantly higher after the full vaccination course in individuals that were previously infected and that NP antibody levels could discriminate individuals with prior infection from vaccinated individuals. While an anticipated antibody titer increase was observed after a vaccine booster dose in naive individuals, a boost response was not observed in individuals with previous COVID-19 infection. We observed a strong relationship between neutralizing antibodies and RBD-binding antibodies postvaccination across all groups, whereas no relationship was observed between NP-binding and neutralizing antibodies. One individual with high levels of neutralizing and binding antibodies experienced a breakthrough infection (prior to the introduction of Omicron), demonstrating that the presence of antibodies is not always sufficient for complete protection against infection. These results highlight that a history of COVID-19 exposure significantly increases SARS-CoV-2 antibody responses following vaccination.

IMPORTANCE Long-term care facilities (LTCFs) have been disproportionately impacted by COVID-19, due to their communal nature, the high-risk profile of residents, and the vulnerability of residents to respiratory pathogens. In this study, we analyzed the role of prior natural immunity to SARS-CoV-2 in postvaccination antibody responses. The LTCF in our cohort experienced a large outbreak, with almost 40% of staff members becoming infected. We found that individuals that were infected prior to vaccination had higher levels of neutralizing and binding antibodies postvaccination. Importantly, the second vaccine dose significantly boosted antibody levels in those that were immunologically naive prior to vaccination, but not in those that had prior immunity. Regardless of the prevaccination immune status, the levels of binding and neutralizing antibodies were highly correlated. The presence of NP-binding antibodies could be used to identify individuals that were previously infected when prevaccination immune status was not known. Our results reveal that vaccination antibody responses differ depending on prior natural immunity.

A robust, highly multiplexed mass spectrometry assay to identify SARS-CoV-2 variants

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants are characterized by differences in transmissibility and response to therapeutics. Therefore, discriminating among them is vital for surveillance, infection prevention, and patient care. While whole viral genome sequencing (WGS) is the "gold standard" for variant identification, molecular variant panels have become increasingly available. Most, however, are based on limited targets and have not undergone comprehensive evaluation. We assessed the diagnostic performance of the highly multiplexed Agena MassARRAY ® SARS-CoV-2 Variant Panel v3 to identify variants in a diverse set of 391 SARS-CoV-2 clinical RNA specimens collected across our health systems in New York City, USA as well as in Bogotá, Colombia (September 2, 2020 - March 2, 2022). We demonstrate almost perfect levels of interrater agreement between this assay and WGS for 9 of 11 variant calls (κ ≥ 0.856) and 25 of 30 targets (κ ≥ 0.820) tested on the panel. The assay had a high diagnostic sensitivity (≥93.67%) for contemporary variants (e.g., Iota, Alpha, Delta, Omicron [BA.1 sublineage]) and a high diagnostic specificity for all 11 variants (≥96.15%) and all 30 targets (≥94.34%) tested. Moreover, we highlight distinct target patterns that can be utilized to identify variants not yet defined on the panel including the Omicron BA.2 and other sublineages. These findings exemplify the power of highly multiplexed diagnostic panels to accurately call variants and the potential for target result signatures to elucidate new ones.

Importance

The continued circulation of SARS-CoV-2 amidst limited surveillance efforts and inconsistent vaccination of populations has resulted in emergence of variants that uniquely impact public health systems. Thus, in conjunction with functional and clinical studies, continuous detection and identification are quintessential to inform diagnostic and public health measures. Furthermore, until WGS becomes more accessible in the clinical microbiology laboratory, the ideal assay for identifying variants must be robust, provide high resolution, and be adaptable to the evolving nature of viruses like SARS-CoV-2. Here, we highlight the diagnostic capabilities of a highly multiplexed commercial assay to identify diverse SARS-CoV-2 lineages that circulated at over September 2, 2020 - March 2, 2022 among patients seeking care at our health systems. This assay demonstrates variant-specific signatures of nucleotide/amino acid polymorphisms and underscores its utility for detection of contemporary and emerging SARS-CoV-2 variants of concern.