SARS-CoV-2 transmission and impacts of unvaccinated-only screening in populations of mixed vaccination status

A Paper-Based Multiplexed Serological Test to Monitor Immunity against SARS-COV-2 Using Machine Learning

The rapid spread of SARS-CoV-2 caused the COVID-19 pandemic and accelerated vaccine development to prevent the spread of the virus and control the disease. Given the sustained high infectivity and evolution of SARS-CoV-2, there is an ongoing interest in developing COVID-19 serology tests to monitor population-level immunity. To address this critical need, we designed a paper-based multiplexed vertical flow assay (xVFA) using five structural proteins of SARS-CoV-2, detecting IgG and IgM antibodies to monitor changes in COVID-19 immunity levels. Our platform not only tracked longitudinal immunity levels but also categorized COVID-19 immunity into three groups: protected, unprotected, and infected, based on the levels of IgG and IgM antibodies. We operated two xVFAs in parallel to detect IgG and IgM antibodies using a total of 40 μL of human serum sample in <20 min per test. After the assay, images of the paper-based sensor panel were captured using a mobile phone-based custom-designed optical reader and then processed by a neural network-based serodiagnostic algorithm. The serodiagnostic algorithm was trained with 120 measurements/tests and 30 serum samples from 7 randomly selected individuals and was blindly tested with 31 serum samples from 8 different individuals, collected before vaccination as well as after vaccination or infection, achieving an accuracy of 89.5%. The competitive performance of the xVFA, along with its portability, cost-effectiveness, and rapid operation, makes it a promising computational point-of-care (POC) serology test for monitoring COVID-19 immunity, aiding in timely decisions on the administration of booster vaccines and general public health policies to protect vulnerable populations.

Modeling the transmission mitigation impact of testing for infectious diseases

A fundamental question of any program focused on the testing and timely diagnosis of a communicable disease is its effectiveness in reducing transmission. Here, we introduce testing effectiveness (TE)-the fraction by which testing and post-diagnosis isolation reduce transmission at the population scale-and a model that incorporates test specifications and usage, within-host pathogen dynamics, and human behaviors to estimate TE. Using TE to guide recommendations, we show that today's rapid diagnostics should be used immediately upon symptom onset to control influenza A and respiratory syncytial virus but delayed by up to two days to control omicron-era severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Furthermore, while rapid tests are superior to reverse transcription quantitative polymerase chain reaction (RT-qPCR) to control founder-strain SARS-CoV-2, omicron-era changes in viral kinetics and rapid test sensitivity cause a reversal, with higher TE for RT-qPCR despite longer turnaround times. Last, we illustrate the model's flexibility by quantifying trade-offs in the use of post-diagnosis testing to shorten isolation times.

Modeling the Transmission Mitigation Impact of Testing for Infectious Diseases

A fundamental question of any program focused on the testing and timely diagnosis of a communicable disease is its effectiveness in reducing transmission. Here, we introduce testing effectiveness (TE)-the fraction by which testing and post-diagnosis isolation reduce transmission at the population scale-and a model that incorporates test specifications and usage, within-host pathogen dynamics, and human behaviors to estimate TE. Using TE to guide recommendations, we show that today's rapid diagnostics should be used immediately upon symptom onset to control influenza A and respiratory syncytial virus (RSV), but delayed by up to 2d to control omicron-era SARS-CoV-2. Furthermore, while rapid tests are superior to RT-qPCR for control of founder-strain SARS-CoV-2, omicron-era changes in viral kinetics and rapid test sensitivity cause a reversal, with higher TE for RT-qPCR despite longer turnaround times. Finally, we illustrate the model's flexibility by quantifying tradeoffs in the use of post-diagnosis testing to shorten isolation times.

Optimizing COVID-19 testing strategies on college campuses: Evaluation of the health and economic costs

Colleges and universities in the US struggled to provide safe in-person education throughout the COVID-19 pandemic. Testing coupled with isolation is a nimble intervention strategy that can be tailored to mitigate the changing health and economic risks associated with SARS-CoV-2. We developed a decision-support tool to aid in the design of university-based screening strategies using a mathematical model of SARS-CoV-2 transmission. Applying this framework to a large public university reopening in the fall of 2021 with a 60% student vaccination rate, we find that the optimal strategy, in terms of health and economic costs, is twice weekly antigen testing of all students. This strategy provides a 95% guarantee that, throughout the fall semester, case counts would not exceed twice the CDC's original high transmission threshold of 100 cases per 100k persons over 7 days. As the virus and our medical armament continue to evolve, testing will remain a flexible tool for managing risks and keeping campuses open. We have implemented this model as an online tool to facilitate the design of testing strategies that adjust for COVID-19 conditions as well as campus-specific populations, resources, and priorities.

SARS-CoV-2 self-test uptake and factors associated with self-testing during Omicron BA.1 and BA.2 waves in France, January to May 2022

BackgroundFollowing the SARS-CoV-2 Omicron variant spread, the use of unsupervised antigenic rapid diagnostic tests (self-tests) increased.AimThis study aimed to measure self-test uptake and factors associated with self-testing.MethodsIn this cross-sectional study from 20 January to 2 May 2022, the case series from a case-control study on factors associated with SARS-CoV-2 infection were used to analyse self-testing habits in France. A multivariable quasi-Poisson regression was used to explore the variables associated with self-testing among symptomatic cases who were not contacts of another infected individual. The control series from the same study was used as a proxy for the self-test background rate in the non-infected population of France.ResultsDuring the study period, 179,165 cases who tested positive through supervised tests were recruited. Of these, 64.7% had performed a self-test in the 3 days preceding this supervised test, of which 79,038 (68.2%) were positive. The most frequently reported reason for self-testing was the presence of symptoms (64.6%). Among symptomatic cases who were not aware of being contacts of another case, self-testing was positively associated with being female, higher education, household size, being a teacher and negatively associated with older age, not French by birth, healthcare-related work and immunosuppression. Among the control series, 12% self-tested during the 8 days preceding questionnaire filling, with temporal heterogeneity.ConclusionThe analysis showed high self-test uptake in France with some inequalities which must be addressed through education and facilitated access (cost and availability) for making it a more efficient epidemic control tool.

Optimized workplace risk mitigation measures for SARS-CoV-2 in 2022

596 million SARS-CoV-2 cases have been reported and over 12 billion vaccine doses have been administered. As vaccination rates increase, a gap in knowledge exists regarding appropriate thresholds for escalation and de-escalation of workplace COVID-19 preventative measures. We conducted 133,056 simulation experiments, evaluating the spread of SARS-CoV-2 virus in hypothesized working environments subject to COVID-19 infections from the community. We tested the rates of workplace-acquired infections based on applied isolation strategies, community infection rates, methods and scales of testing, non-pharmaceutical interventions, variant predominance, vaccination coverages, and vaccination efficacies. When 75% of a workforce is vaccinated with a 70% efficacious vaccine against infection, then no masking or routine testing + isolation strategies are needed to prevent workplace-acquired omicron variant infections when the community infection rate per 100,000 persons is ≤ 1. A CIR ≤ 30, and ≤ 120 would result in no workplace-acquired infections in this same scenario against the delta and alpha variants, respectively. Workforces with 100% worker vaccination can prevent workplace-acquired infections with higher community infection rates. Identifying and isolating workers with antigen-based SARS-CoV-2 testing methods results in the same or fewer workplace-acquired infections than testing with slower turnaround time polymerase chain reaction methods. Risk migration measures such as mask-wearing, testing, and isolation can be relaxed, or escalated, in commensurate with levels of community infections, workforce immunization, and risk tolerance. The interactive heatmap we provide can be used for immediate, parameter-based case count predictions to inform institutional policy making. The simulation approach we have described can be further used for future evaluation of strategies to mitigate COVID-19 spread.

Risk Factors Associated with the Spatiotemporal Spread of the SARS-CoV-2 Omicron BA.2 Variant — Shanghai Municipality, China, 2022

What is already known about this topic?

Previous studies have explored the spatial transmission patterns of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and have assessed the associated risk factors. However, none of these studies have quantitatively described the spatiotemporal transmission patterns and risk factors for Omicron BA.2 at the micro (within-city) scale.

What is added by this report?

This study highlights the heterogeneous spread of the 2022 Omicron BA.2 epidemic in Shanghai, and identifies associations between different metrics of spatial spread at the subdistrict level and demographic and socioeconomic characteristics of the population, human mobility patterns, and adopted interventions.

What are the implications for public health practice?

Disentangling different risk factors might contribute to a deeper understanding of the transmission dynamics and ecology of coronavirus disease 2019 and an effective design of monitoring and management strategies.

Optimizing COVID-19 testing strategies on college campuses: evaluation of the health and economic costs

Colleges and universities in the US struggled to provide safe in-person education throughout the COVID-19 pandemic. Testing coupled with isolation is a nimble intervention strategy that can be tailored to mitigate health and economic costs, as the virus and our arsenal of medical countermeasures continue to evolve. We developed a decision-support tool to aid in the design of university-based testing strategies using a mathematical model of SARS-CoV-2 transmission. Applying this framework to a large public university reopening in the fall of 2021 with a 60% student vaccination rate, we find that the optimal strategy, in terms of health and economic costs, is twice weekly antigen testing of all students. This strategy provides a 95% guarantee that, throughout the fall semester, case counts would not exceed the CDC's original high transmission threshold of 100 cases per 100k persons over 7 days. As the virus and our medical armament continue to evolve, testing will remain a flexible tool for managing risks and keeping campuses open. We have implemented this model as an online tool to facilitate the design of testing strategies that adjust for COVID-19 conditions, university-specific parameters, and institutional goals.

Update on COVID-19 and Effectiveness of a Vaccination Campaign in a Global Context

The COVID-19 pandemic caused by SARS-CoV-2 remains a significant issue for global health, the economy, and society. When SARS-CoV-2 began to spread, the most recent serious infectious disease of this century around the world, with its high morbidity and mortality rates, it is understandable why such infections have generally been spread in the past, mainly from international travel movements. This perspective review aimed to provide an update for clinicians on the recent developments related to the microbiological perspectives in pandemics, diagnostics, prevention (such as the spread of a virus), vaccination campaigns, treatment options, and health consequences for COVID-19 based on the current literature. In this way, the authors attempt to raise awareness on the transversal nature of these challenges by identifying the main risk/vulnerability factors that the scientific community must face including our current knowledge on the virus capacity of the mechanism of entry into the cells, the current classifications of viral variants, the knowledge of the mathematical model on the spread of viruses (the possible routes of transmission), and the effectiveness of vaccination campaigns in a global context of pandemic, particularly from COVID-19, with a look at new or future vaccines.