SARS-CoV-2 Screening Testing Program for Safe In-person Learning in K-12 Schools.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) screening testing is a recommended mitigation strategy for schools, although few descriptions of program implementation are available. METHODS: Kindergarten through 12th grade (K-12) students and staff practicing universal masking during the delta and omicron variant waves from 5 schools in Durham, North Carolina and 8 in Kansas City, Missouri participated; Durham's program was structured as a public health initiative facilitated by school staff, and Kansas City's as a research study facilitated by a research team. Tests included school-based rapid antigen or polymerase chain reaction testing, at-home rapid antigen testing, and off-site nucleic acid amplification testing. RESULTS: We performed nearly 5,700 screening tests on more than 1,600 K-12 school students and staff members. The total cost for the Durham testing program in 5 public charter K-12 schools, each with 500-1000 students, was $246,587 and approximately 752 hours per semester; cost per test was $70 and cost per positive result was $7,076. The total cost for the Kansas City program in 8 public K-12 schools was $292,591 and required approximately 537 hours in personnel time for school-based testing; cost per test was $132 and cost per positive result was $4,818. SARS-CoV-2 positivity rates were generally lower (0-16.16%) than rates in the community (2.7-36.47%) throughout all testing weeks. CONCLUSIONS AND RELEVANCE: Voluntary screening testing programs in K-12 schools are costly and rarely detect asymptomatic positive persons, particularly in universally masked settings.

Modeling the Effectiveness of Healthcare Personnel Reactive Testing and Screening for the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Omicron Variant Within Nursing Homes.

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Omicron variant has been hypothesized to exhibit faster clearance (time from peak viral concentration to clearance of acute infection), decreased sensitivity of antigen tests, and increased immune escape (the ability of the variant to evade immunity conferred by past infection or vaccination) compared to prior variants. These factors necessitate reevaluation of prevention and control strategies, particularly in high-risk, congregate settings like nursing homes that have been heavily impacted by other coronavirus disease 2019 (COVID-19) variants. We used a simple model representing individual-level viral shedding dynamics to estimate the optimal strategy for testing nursing home healthcare personnel and quantify potential reduction in transmission of COVID-19. This provides a framework for prospectively evaluating testing strategies in emerging variant scenarios when data are limited. We find that case-initiated testing prevents 38% of transmission within a facility if implemented within a day of an index case testing positive, and screening testing strategies could prevent 30% to 78% of transmission within a facility if implemented daily, depending on test sensitivity.

Under or Absent Reporting of Light Stimuli in Testing of Anxiety-Like Behaviors in Rodents: The Need for Standardization.

Behavioral neuroscience tests such as the Light/Dark Test, the Open Field Test, the Elevated Plus Maze Test, and the Three Chamber Social Interaction Test have become both essential and widely used behavioral tests for transgenic and pre-clinical models for drug screening and testing. However, as fast as the field has evolved and the contemporaneous involvement of technology, little assessment of the literature has been done to ensure that these behavioral neuroscience tests that are crucial to pre-clinical testing have well-controlled ethological motivation by the use of lighting (i.e., Lux). In the present review paper, N = 420 manuscripts were examined from 2015 to 2019 as a sample set (i.e., n = ~20-22 publications per year) and it was found that only a meager n = 50 publications (i.e., 11.9% of the publications sampled) met the criteria for proper anxiogenic and anxiolytic Lux reported. These findings illustrate a serious concern that behavioral neuroscience papers are not being vetted properly at the journal review level and are being released into the literature and public domain making it difficult to assess the quality of the science being reported. This creates a real need for standardizing the use of Lux in all publications on behavioral neuroscience techniques within the field to ensure that contributions are meaningful, avoid unnecessary duplication, and ultimately would serve to create a more efficient process within the pre-clinical screening/testing for drugs that serve as anxiolytic compounds that would prove more useful than what prior decades of work have produced. It is suggested that improving the standardization of the use and reporting of Lux in behavioral neuroscience tests and the standardization of peer-review processes overseeing the proper documentation of these methodological approaches in manuscripts could serve to advance pre-clinical testing for effective anxiolytic drugs. This report serves to highlight this concern and proposes strategies to proactively remedy them as the field moves forward for decades to come.

A hybrid stochastic model and its Bayesian identification for infectious disease screening in a university campus with application to massive COVID-19 screening at the University of Liège.

Amid the COVID-19 pandemic, universities are implementing various prevention and mitigation measures. Identifying and isolating infectious individuals by using screening testing is one such a measure that can contribute to reducing spread. Here, we propose a hybrid stochastic model for infectious disease transmission in a university campus with screening testing and its surrounding community. Based on a compartmental modeling strategy, this hybrid stochastic model represents the evolution of the infectious disease and its transmission using continuous-time stochastic dynamics, and it represents the screening testing as discrete stochastic events. We also develop, in a Bayesian framework, the identification of parameters of this hybrid stochastic model, including transmission rates. These parameters were identified from the screening test data for the university population and observed incidence counts for the surrounding community. We implement the exploration of the Bayesian posterior using a machine-learning simulation-based inference approach. The proposed methodology was applied in a retrospective modeling study of a massive COVID-19 screening conducted at the University of Liège in Fall 2020. The emphasis of the paper is on the development of the hybrid stochastic model to assess the impact of screening testing as a measure to reduce spread. The hybrid stochastic model allows various factors to be represented and examined, such as interplay with the surrounding community, variability of the transmission dynamics, the rate of participation in the screening testing, the test sensitivity, the test frequency, the diagnosis delay, and compliance with isolation. The application in the retrospective modeling study suggests that a high rate of participation and a high test frequency are important factors to reduce spread.

Is Symptom Screening Useful for Identifying COVID-19 Infection in School Settings? Georgia, USA.

This study's goal was to characterize the utility of symptom screening in staff and students for COVID-19 identification and control of transmission in a school setting. We conducted a secondary analysis of cross-sectional data for staff, students and associated household members in a Georgia school district exposed to COVID-19 cases who received RT-PCR testing and symptom monitoring. Among positive contacts, 30/49 (61%) of students and 1/6 (17%) of staff reported no symptoms consistent with COVID-19. Symptom sensitivity was 30% in elementary students and 42% in middle/high students. Fifty-three percent (10/19) of symptomatic positive contacts had at least one household member test positive for SARS-CoV-2 compared with 50% (10/20) of asymptomatic positive contacts. The absence of symptoms in children is not indicative of a lack of SARS-CoV-2 infection or reduced risk of infection for associated household members. Testing all close contacts of people with COVID-19 in schools is needed to interrupt transmission networks.

Optimization in the Context of COVID-19 Prediction and Control: A Literature Review.

This paper presents an overview of some key results from a body of optimization studies that are specifically related to COVID-19, as reported in the literature during 2020-2021. As shown in this paper, optimization studies in the context of COVID-19 have been used for many aspects of the pandemic. From these studies, it is observed that since COVID-19 is a multifaceted problem, it cannot be studied from a single perspective or framework, and neither can the related optimization models. Four new and different frameworks are proposed that capture the essence of analyzing COVID-19 (or any pandemic for that matter) and the relevant optimization models. These are: (i) microscale vs. macroscale perspective; (ii) early stages vs. later stages perspective; (iii) aspects with direct vs. indirect relationship to COVID-19; and (iv) compartmentalized perspective. To limit the scope of the review, only optimization studies related to the prediction and control of COVID-19 are considered (public health focused), and which utilize formal optimization techniques or machine learning approaches. In this context and to the best of our knowledge, this survey paper is the first in the literature with a focus on the prediction and control related optimization studies. These studies include optimization of screening testing strategies, prediction, prevention and control, resource management, vaccination prioritization, and decision support tools. Upon reviewing the literature, this paper identifies current gaps and major challenges that hinder the closure of these gaps and provides some insights into future research directions.

From Easing Lockdowns to Scaling Up Community-based Coronavirus Disease 2019 Screening, Testing, and Contact Tracing in Africa-Shared Approaches, Innovations, and Challenges to Minimize Morbidity and Mortality.

The arrival of coronavirus disease 2019 (COVID-19) on the African continent resulted in a range of lockdown measures that curtailed the spread of the infection but caused economic hardship. African countries now face difficult choices regarding easing of lockdowns and sustaining effective public health control measures and surveillance. Pandemic control will require efficient community screening, testing, and contact tracing; behavioral change interventions; adequate resources; and well-supported, community-based teams of trained, protected personnel. We discuss COVID-19 control approaches in selected African countries and the need for shared, affordable, innovative methods to overcome challenges and minimize mortality. This crisis presents a unique opportunity to align COVID-19 services with those already in place for human immunodeficiency virus, tuberculosis, malaria, and non communicable diseases through mobilization of Africa's interprofessional healthcare workforce. By addressing the challenges, the detrimental effect of the COVID-19 pandemic on African citizens can be minimized.