Scalable RT-LAMP-based SARS-CoV-2 testing for infection surveillance with applications in pandemic preparedness.

Throughout the SARS-CoV-2 pandemic, limited diagnostic capacities prevented sentinel testing, demonstrating the need for novel testing infrastructures. Here, we describe the setup of a cost-effective platform that can be employed in a high-throughput manner, which allows surveillance testing as an acute pandemic control and preparedness tool, exemplified by SARS-CoV-2 diagnostics in an academic environment. The strategy involves self-sampling based on gargling saline, pseudonymized sample handling, automated RNA extraction, and viral RNA detection using a semiquantitative multiplexed colorimetric reverse transcription loop-mediated isothermal amplification (RT-LAMP) assay with an analytical sensitivity comparable with RT-qPCR. We provide standard operating procedures and an integrated software solution for all workflows, including sample logistics, analysis by colorimetry or sequencing, and communication of results. We evaluated factors affecting the viral load and the stability of gargling samples as well as the diagnostic sensitivity of the RT-LAMP assay. In parallel, we estimated the economic costs of setting up and running the test station. We performed > 35,000 tests, with an average turnover time of < 6 h from sample arrival to result announcement. Altogether, our work provides a blueprint for fast, sensitive, scalable, cost- and labor-efficient RT-LAMP diagnostics, which is independent of potentially limiting clinical diagnostics supply chains.

Surveillance testing using salivary RT-PCR for SARS-CoV-2 in managed quarantine facilities in Australia: A laboratory validation and implementation study.

Background: Regular repeat surveillance testing is a strategy to identify asymptomatic individuals with SARS-CoV-2 infections in high-risk work settings to prevent onward community transmission. Saliva sampling is less invasive compared to nasal/oropharyngeal sampling, thus making it suitable for regular testing. In this multi-centre evaluation, we aimed to validate RT-PCR using salivary swab testing of SARS-CoV-2 for large-scale surveillance testing and assess implementation amongst staff working in the hotel quarantine system in Victoria, Australia. Methods: A multi-centre laboratory evaluation study was conducted to systematically validate the in vitro and clinical performance of salivary swab RT-PCR for implementation of SARS-CoV-2 surveillance testing. Analytical sensitivity for multiple RT-PCR platforms was assessed using a dilution series of known SARS-CoV-2 viral loads, and assay specificity was examined using a panel of viral pathogens other than SARS-CoV-2. In addition, we tested capacity for large-scale saliva testing using a four-sample pooling approach, where positive pools were subsequently decoupled and retested. Regular, frequent self-collected saliva swab RT-PCR testing was implemented for staff across fourteen quarantine hotels. Samples were tested at three diagnostic laboratories validated in this study, and results were provided back to staff in real-time. Findings: The agreement of self-collected saliva swabs for RT-PCR was 84.5% (95% CI 68.6 to 93.8) compared to RT-PCR using nasal/oropharyngeal swab samples collected by a healthcare practitioner, when saliva samples were collected within seven days of symptom onset. Between 7th December 2020 and 17th December 2021, almost 500,000 RT-PCR tests were performed on saliva swabs self-collected by 102 staff working in quarantine hotels in Melbourne. Of these, 20 positive saliva swabs were produced by 13 staff (0.004%). The majority of staff that tested positive occurred during periods of community transmission of the SARS-CoV-2 Delta variant. Interpretation: Salivary RT-PCR had an acceptable level of agreement compared to standard nasal/oropharyngeal swab RT-PCR within early symptom onset. The scalability, tolerability and ease of self-collection highlights utility for frequent or repeated testing in high-risk settings, such as quarantine or healthcare environments where regular monitoring of staff is critical for public health, and protection of vulnerable populations. Funding: This work was funded by the Victorian Department of Health.

An Agent-Based Model to Support Infection Control Strategies at School

Many governments enforced physical distancing measures during the COVID-19 pandemic to avoid the collapse of often fragile and overloaded health care systems. Following the physical distancing measures, school closures seemed unavoidable to keep the transmission of the pathogen under control, given the potentially high-risk of these environments. Nevertheless, closing schools was considered an extreme and the last resort of governments, and so various non-pharmaceutical interventions in schools were implemented to reduce the risk of transmission. By means of an agent-based model, we studied the efficacy of active surveillance strategies in the school environment. Simulations settings provided hypothetical although realistic scenarios which allowed us to identify the most suitable control strategy to avoid massive school closures while adapting to contagion dynamics. Reducing risk by means of public policies explored in our study is essential for both health authorities and school administrators. © 2022, University of Surrey. All rights reserved.

Comparison of high-frequency in-pipe SARS-CoV-2 wastewater-based surveillance to concurrent COVID-19 random clinical testing on a public U.S. university campus.

Wastewater-based epidemiology (WBE) is utilized globally as a tool for quantifying the amount of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) within communities, yet the efficacy of community-level wastewater monitoring has yet to be directly compared to random Coronavirus Disease of 2019 (COVID-19) clinical testing; the best-supported method of virus surveillance within a single population. This study evaluated the relationship between SARS-CoV-2 RNA in raw wastewater and random COVID-19 clinical testing on a large university campus in the Southwestern United States during the Fall 2020 semester. Daily composites of wastewater (24-hour samples) were collected three times per week at two campus locations from 16 August 2020 to 1 January 2021 (n = 95) and analyzed by reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR) targeting the SARS-CoV-2 E gene. Campus populations were estimated using campus resident information and anonymized, unique user Wi-Fi connections. Resultant trends of SARS-CoV-2 RNA levels in wastewater were consistent with local and nationwide pandemic trends showing peaks in infections at the start of the Fall semester in mid-August 2020 and mid-to-late December 2020. A strong positive correlation (r = 0.71 (p < 0.01); n = 15) was identified between random COVID-19 clinical testing and WBE surveillance methods, suggesting that wastewater surveillance has a predictive power similar to that of random clinical testing. Additionally, a comparative cost analysis between wastewater and clinical methods conducted here show that WBE was more cost effective, providing data at 1.7% of the total cost of clinical testing ($6042 versus $338,000, respectively). We conclude that wastewater monitoring of SARS-CoV-2 performed in tandem with random clinical testing can strengthen campus health surveillance, and its economic advantages are maximized when performed routinely as a primary surveillance method, with random clinical testing reserved for an active outbreak situation.

Pooled testing efficiency increases with test frequency.

Pooled testing increases efficiency by grouping individual samples and testing the combined sample, such that many individuals can be cleared with one negative test. This short paper demonstrates that pooled testing is particularly advantageous in the setting of pandemics, given repeated testing, rapid spread, and uncertain risk. Repeated testing mechanically lowers the infection probability at the time of the next test by removing positives from the population. This effect alone means that increasing frequency by x times only increases expected tests by around [Formula: see text] However, this calculation omits a further benefit of frequent testing: Removing infections from the population lowers intragroup transmission, which lowers infection probability and generates further efficiency. For this reason, increasing testing frequency can paradoxically reduce total testing cost. Our calculations are based on the assumption that infection rates are known, but predicting these rates is challenging in a fast-moving pandemic. However, given that frequent testing naturally suppresses the mean and variance of infection rates, we show that our results are very robust to uncertainty and misprediction. Finally, we note that efficiency further increases given natural sampling pools (e.g., workplaces, classrooms) that induce correlated risk via local transmission. We conclude that frequent pooled testing using natural groupings is a cost-effective way to provide consistent testing of a population to suppress infection risk in a pandemic.

Optimizing COVID-19 control with asymptomatic surveillance testing in a university environment.

The high proportion of transmission events derived from asymptomatic or presymptomatic infections make SARS-CoV-2, the causative agent in COVID-19, difficult to control through the traditional non-pharmaceutical interventions (NPIs) of symptom-based isolation and contact tracing. As a consequence, many US universities developed asymptomatic surveillance testing labs, to augment NPIs and control outbreaks on campus throughout the 2020-2021 academic year (AY); several of those labs continue to support asymptomatic surveillance efforts on campus in AY2021-2022. At the height of the pandemic, we built a stochastic branching process model of COVID-19 dynamics at UC Berkeley to advise optimal control strategies in a university environment. Our model combines behavioral interventions in the form of group size limits to deter superspreading, symptom-based isolation, and contact tracing, with asymptomatic surveillance testing. We found that behavioral interventions offer a cost-effective means of epidemic control: group size limits of six or fewer greatly reduce superspreading, and rapid isolation of symptomatic infections can halt rising epidemics, depending on the frequency of asymptomatic transmission in the population. Surveillance testing can overcome uncertainty surrounding asymptomatic infections, with the most effective approaches prioritizing frequent testing with rapid turnaround time to isolation over test sensitivity. Importantly, contact tracing amplifies population-level impacts of all infection isolations, making even delayed interventions effective. Combination of behavior-based NPIs and asymptomatic surveillance also reduces variation in daily case counts to produce more predictable epidemics. Furthermore, targeted, intensive testing of a minority of high transmission risk individuals can effectively control the COVID-19 epidemic for the surrounding population. Even in some highly vaccinated university settings in AY2021-2022, asymptomatic surveillance testing offers an effective means of identifying breakthrough infections, halting onward transmission, and reducing total caseload. We offer this blueprint and easy-to-implement modeling tool to other academic or professional communities navigating optimal return-to-work strategies.

University COVID -19 Surveillance Testing Center: Challenges and Opportunities for Schools of Nursing.

As the impact of the COVID-19 pandemic became clear, it was evident that higher education schools and Universities, including schools of nursing were facing enormous challenges to create a safe environment for educational instruction to continue. Clinical education in particular was affected as clinical sites were increasingly unable to accommodate student clinical rotations due to crushing volumes and overwhelming care needs of COVID patients. This article outlines the innovative efforts of one university that set up a robust surveillance testing program that required and provided weekly COVID-19 testing of all students, faculty and staff that were on-campus. The testing center is nurse led and nurse managed, providing a clinical experience for over 50 nursing students each semester, allowing them to accrue community clinical hours so that they can progress through their nursing program. Clinical quality and patient experience outcomes are shared, and lessons learned described.

Vaccination and testing of the border workforce for COVID-19 and risk of community outbreaks: a modelling study.

Throughout 2020 and the first part of 2021, Australia and New Zealand have followed a COVID-19 elimination strategy. Both countries require overseas arrivals to quarantine in government-managed facilities at the border. In both countries, community outbreaks of COVID-19 have been started via infection of a border worker. This workforce is rightly being prioritized for vaccination. However, although vaccines are highly effective in preventing disease, their effectiveness in preventing infection with and transmission of SARS-CoV-2 is less certain. There is a danger that vaccination could prevent symptoms of COVID-19 but not prevent transmission. Here, we use a stochastic model of SARS-CoV-2 transmission and testing to investigate the effect that vaccination of border workers has on the risk of an outbreak in an unvaccinated community. We simulate the model starting with a single infected border worker and measure the number of people who are infected before the first case is detected by testing. We show that if a vaccine reduces transmission by 50%, vaccination of border workers increases the risk of a major outbreak from around 7% per seed case to around 9% per seed case. The lower the vaccine effectiveness against transmission, the higher the risk. The increase in risk as a result of vaccination can be mitigated by increasing the frequency of routine testing for high-exposure vaccinated groups.

Mathematical modelling of the spread of COVID-19 on a university campus.

In this paper we present a deterministic transmission dynamic compartmental model for the spread of the novel coronavirus on a college campus for the purpose of analyzing strategies to mitigate an outbreak. The goal of this project is to determine and compare the utility of certain containment strategies including gateway testing, surveillance testing, and contact tracing as well as individual level control measures such as mask wearing and social distancing. We modify a standard SEIR-type model to reflect what is currently known about COVID-19. We also modify the model to reflect the population present on a college campus, separating it into students and faculty. This is done in order to capture the expected different contact rates between groups as well as the expected difference in outcomes based on age known for COVID-19. We aim to provide insight into which strategies are most effective, rather than predict exact numbers of infections. We analyze effectiveness by looking at relative changes in the total number of cases as well as the effect a measure has on the estimated basic reproductive number. We find that the total number of infections is most sensitive to parameters relating to student behaviors. We also find that contact tracing can be an effective control strategy when surveillance testing is unavailable. Lastly, we validate the model using data from Villanova University's online COVID-19 Dashboard from Fall 2020 and find good agreement between model and data when superspreader events are incorporated in the model as shocks to the number of infected individuals approximately two weeks after each superspreader event.

A simple model for control of COVID-19 infections on an urban campus.

A customized susceptible, exposed, infected, and recovered compartmental model is presented for describing the control of asymptomatic spread of COVID-19 infections on a residential, urban college campus embedded in a large urban community by using public health protocols, founded on surveillance testing, contact tracing, isolation, and quarantine. Analysis in the limit of low infection rates-a necessary condition for successful operation of the campus-yields expressions for controlling the infection and understanding the dynamics of infection spread. The number of expected cases on campus is proportional to the exogenous infection rate in the community and is decreased by more frequent testing and effective contact tracing. Simple expressions are presented for the dynamics of superspreader events and the impact of partial vaccination. The model results compare well with residential data from Boston University's undergraduate population for fall 2020.

When intuition falters: repeated testing accuracy during an epidemic.

Widespread, repeated testing using rapid antigen tests to proactively detect asymptomatic SARS-CoV-2 infections has been a promising yet controversial topic during the COVID-19 pandemic. Concerns have been raised over whether currently authorized lateral flow tests are sufficiently sensitive and specific to detect enough infections to impact transmission whilst minimizing unnecessary isolation of false positives. These concerns have often been illustrated using simple, textbook calculations of positivity rates and positive predictive value assuming fixed values for sensitivity, specificity and prevalence. However, we argue that evaluating repeated testing strategies requires the consideration of three additional factors: new infections continue to arise depending on the incidence rate, isolating positive individuals reduces prevalence in the tested population, and each infected individual is tested multiple times during their infection course. We provide a simple mathematical model with an online interface to illustrate how these three factors impact test positivity rates and the number of isolating individuals over time. These results highlight the potential pitfalls of using inappropriate textbook-style calculations to evaluate statistics arising from repeated testing strategies during an epidemic.

Implementation of a pooled surveillance testing program for asymptomatic SARS-CoV-2 infections in K-12 schools and universities.

BACKGROUND: The negative impact of continued school closures during the height of the COVID-19 pandemic warrants the establishment of cost-effective strategies for surveillance and screening to safely reopen and monitor for potential in-school transmission. Here, we present a novel approach to increase the availability of repetitive and routine COVID-19 testing that may ultimately reduce the overall viral burden in the community. METHODS: We implemented a testing program using the SalivaClear࣪ pooled surveillance method that included students, faculty and staff from K-12 schools (student age range 5-18 years) and universities (student age range >18 years) across the country (Mirimus Clinical Labs, Brooklyn, NY). The data analysis was performed using descriptive statistics, kappa agreement, and outlier detection analysis. FINDINGS: From August 27, 2020 until January 13, 2021, 253,406 saliva specimens were self-collected from students, faculty and staff from 93 K-12 schools and 18 universities. Pool sizes of up to 24 samples were tested over a 20-week period. Pooled testing did not significantly alter the sensitivity of the molecular assay in terms of both qualitative (100% detection rate on both pooled and individual samples) and quantitative (comparable cycle threshold (Ct) values between pooled and individual samples) measures. The detection of SARS-CoV-2 in saliva was comparable to the nasopharyngeal swab. Pooling samples substantially reduced the costs associated with PCR testing and allowed schools to rapidly assess transmission and adjust prevention protocols as necessary. In one instance, in-school transmission of the virus was determined within the main office and led to review and revision of heating, ventilating and air-conditioning systems. INTERPRETATION: By establishing low-cost, weekly testing of students and faculty, pooled saliva analysis for the presence of SARS-CoV-2 enabled schools to determine whether transmission had occurred, make data-driven decisions, and adjust safety protocols. We provide strong evidence that pooled testing may be a fundamental component to the reopening of schools by minimizing the risk of in-school transmission among students and faculty. FUNDING: Skoll Foundation generously provided funding to Mobilizing Foundation and Mirimus for these studies.

Assessing school-based policy actions for COVID-19: An agent-based analysis of incremental infection risk.

Many schools and universities have seen a significant increase in the spread of COVID-19. As such, a number of non-pharmaceutical interventions have been proposed including distancing requirements, surveillance testing, and updating ventilation systems. Unfortunately, there is limited guidance for which policy or set of policies are most effective for a specific school system. We develop a novel approach to model the spread of SARS-CoV-2 quanta in a closed classroom environment that extends traditional transmission models that assume uniform mixing through air recirculation by including the local spread of quanta from a contagious source. In addition, the behavior of students with respect to guideline compliance was modeled through an agent-based simulation. Estimated infection rates were on average lower using traditional transmission models compared to our approach. Further, we found that although ventilation changes were effective at reducing mean transmission risk, it had much less impact than distancing practices. Duration of the class was an important factor in determining the transmission risk. For the same total number of semester hours for a class, delivering lectures more frequently for shorter durations was preferable to less frequently with longer durations. Finally, as expected, as the contact tracing level increased, more infectious students were identified and removed from the environment and the spread slowed, though there were diminishing returns. These findings can help provide guidance as to which school-based policies would be most effective at reducing risk and can be used in a cost/comparative effectiveness estimation study given local costs and constraints.

COVID-19 screening test by using random oropharyngeal saliva.

An optimal clinical specimen for accurate detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by minimizing the usage of consumables and reduce hazard exposure to healthcare workers is an urgent priority. The diagnostic performance of SARS-CoV-2 detection between healthcare worker-collected nasopharyngeal and oropharyngeal (NP + OP) swabs and patient performed self-collected random saliva was assessed. Paired NP + OP swabs and random saliva were collected and processed within 48 h of specimen collection from two cohort studies which recruited 562 asymptomatic adult candidates. Real-time reverse-transcription polymerase chain reaction targeting Open reading frame 1a (ORF1a) and nucleocapsid (N) genes was performed and the results were compared. Overall, 65 of 562 (28.1%) candidates tested positive for COVID-19 based on random saliva, NP + OP swabs, or both testing techniques. The detection rate of SARS-CoV-2 was higher in random saliva compared to NP + OP testing (92.3%; 60/65 vs. 73.8%; 48/65; p < .05). The estimated sensitivity and specificity of random saliva were higher than NP + OP swabs (95.0; 99.9 vs. 72.2; 99.4). The Ct  values of ORF1a and N genes were significantly lower in random saliva compared to NP + OP swabs specimens. Our findings demonstrate that random saliva is an alternative diagnostic specimen for the detection of SARS-CoV-2. Self-collected random oropharyngeal saliva is a valuable specimen that provides accurate SARS-CoV-2 surveillance testing of a community.