Differential effects of COVID-19 lockdowns on well-being: interaction between age,gender and chronotype

The unprecedented restrictions imposed due to the COVID-19 pandemic altered our daily habits and severely affected our well-being and physiology. The effect of these changes is yet to be fully understood. Here, we analysed highly detailed data on 169 participants for two to six months, before and during the second COVID-19 lockdown in Israel. We extracted 12 well-being indicators from sensory data of smartwatches and from self-reported questionnaires, filled daily using a designated mobile application. We found that, in general, lockdowns resulted in significant changes in mood, sleep duration, sport duration, social encounters, resting heart rate and number of steps. Examining subpopulations, we found that younger participants (aged 20–40 years) suffered from a greater decline in mood and number of steps than older participants (aged 60–80 years). Likewise, women suffered from a higher increase in stress and reduction in social encounters than men. Younger early chronotypes did not increase their sleep duration and exhibited the highest drop in mood. Our findings underscore that while lockdowns severely impacted our well-being and physiology in general, greater damage has been identified in certain subpopulations. Accordingly, special attention should be given to younger people, who are usually not in the focus of social support, and to women.     

Overcoming the limitations of COVID-19 diagnostics with nanostructures,nucleic acid engineering,and additive manufacturing

The COVID-19 pandemic revealed fundamental limitations in the current model for infectious disease diagnosis and serology, based upon complex assay workflows, laboratory-based instrumentation, and expensive materials for managing samples and reagents. The lengthy time delays required to obtain test results, the high cost of gold-standard PCR tests, and poor sensitivity of rapid point-of-care tests contributed directly to society’s inability to efficiently identify COVID-19-positive individuals for quarantine, which in turn continues to impact return to normal activities throughout the economy. Over the past year, enormous resources have been invested to develop more effective rapid tests and laboratory tests with greater throughput, yet the vast majority of engineering and chemistry approaches are merely incremental improvements to existing methods for nucleic acid amplification, lateral flow test strips, and enzymatic amplification assays for protein-based biomarkers. Meanwhile, widespread commercial availability of new test kits continues to be hampered by the cost and time required to develop single-use disposable microfluidic plastic cartridges manufactured by injection molding. Through development of novel technologies for sensitive, selective, rapid, and robust viral detection and more efficient approaches for scalable manufacturing of microfluidic devices, we can be much better prepared for future management of infectious pathogen outbreaks. Here, we describe how photonic metamaterials, graphene nanomaterials, designer DNA nanostructures, and polymers amenable to scalable additive manufacturing are being applied towards overcoming the fundamental limitations of currently dominant COVID-19 diagnostic approaches. In this paper, we review how several distinct classes of nanomaterials and nanochemistry enable simple assay workflows, high sensitivity, inexpensive instrumentation, point-of-care sample-to-answer virus diagnosis, and rapidly scaled manufacturing.     

Cognitive impairment in a patient with COVID-19 on hemodialysis: Too dangerous to neglect!

Neurological manifestations of coronavirus disease 2019 (COVID-19) often have tragic repercussions. Although many reports of neurological complications of severe acute respiratory syndrome coronavirus 2 infection exist, none of them are of patients on hemodialysis, who have a fivefold greater risk of stroke than the general population. In this report, we emphasize the importance of being vigilant for mild stroke in high risk populations—such as patients on hemodialysis—with COVID-19, since these conditions have overlapping symptoms.     

A semi-parametric,state-space compartmental model with time-dependent parameters for forecasting COVID-19 cases,hospitalizations and deaths

Short-term forecasts of the dynamics of coronavirus disease 2019 (COVID-19) in the period up to its decline following mass vaccination was a task that received much attention but proved difficult to do with high accuracy. However, the availability of standardized forecasts and versioned datasets from this period allows for continued work in this area. Here, we introduce the Gaussian infection state space with time dependence (GISST) forecasting model. We evaluate its performance in one to four weeks ahead forecasts of COVID-19 cases, hospital admissions and deaths in the state of California made with official reports of COVID-19, Google’s mobility reports and vaccination data available each week. Evaluation of these forecasts with a weighted interval score shows them to consistently outperform a naive baseline forecast and often score closer to or better than a high-performing ensemble forecaster. The GISST model also provides parameter estimates for a compartmental model of COVID-19 dynamics, includes a regression submodel for the transmission rate and allows for parameters to vary over time according to a random walk. GISST provides a novel, balanced combination of computational efficiency, model interpretability and applicability to large multivariate datasets that may prove useful in improving the accuracy of infectious disease forecasts.     

Detection of an appropriate pharmaceutical company to get a suitable vaccine against COVID-19 with minimum cost under the quality control process

Some international pharmaceutical companies have succeeded in producing vaccines against COVID-19. Countries all over the world have aimed to obtain these vaccines with minimum cost. We consider a set of K-independent Markovian waiting lists. Each list contains a set of countries, where each one of them has an exponential service time and a Poisson arrival process. These companies differ in some characteristics such as the vaccine production cost and the speed of the required quantity delivery. We present a new detection model that helps in providing an appropriate decision to choose a suitable company. Moreover, the concept of balking and the retention of reneged countries is taken into consideration under the quality control process of each waiting list. Under steady state, we face an interesting and difficult discrete stochastic optimization problem. Its solution gives an optimal distribution of the searching effort, which is bounded by a known probability distribution. A simulation study has been derived to get the minimum value of the paid cost random values. The highest service rate, the total expected profit of each queuing system, and the optimum performance measures, which depend on this cost, have been obtained to show the effectiveness of this model.