Human Body Performance with COVID-19 Affectation According to Virus Specification Based on Biosensor Techniques.

Life was once normal before the first announcement of COVID-19's first case in Wuhan, China, and what was slowly spreading became an overnight worldwide pandemic. Ever since the virus spread at the end of 2019, it has been morphing and rapidly adapting to human nature changes which cause difficult conundrums in the efforts of fighting it. Thus, researchers were steered to investigate the virus in order to contain the outbreak considering its novelty and there being no known cure. In contribution to that, this paper extensively reviewed, compared, and analyzed two main points; SARS-CoV-2 virus transmission in humans and detection methods of COVID-19 in the human body. SARS-CoV-2 human exchange transmission methods reviewed four modes of transmission which are Respiratory Transmission, Fecal-Oral Transmission, Ocular transmission, and Vertical Transmission. The latter point particularly sheds light on the latest discoveries and advancements in the aim of COVID-19 diagnosis and detection of SARS-CoV-2 virus associated with this disease in the human body. The methods in this review paper were classified into two categories which are RNA-based detection including RT-PCR, LAMP, CRISPR, and NGS and secondly, biosensors detection including, electrochemical biosensors, electronic biosensors, piezoelectric biosensors, and optical biosensors.

An Innovative Reinforcement Learning-Based Framework for Quality of Service Provisioning Over Multimedia-Based SDN Environments

Within the current global context, the coronavirus pandemic has led to an unprecedented surge in the Internet traffic, with most of the traffic represented by video. The improved wired and guided network infrastructure along with the emerging 5G networks enables the provisioning of increased bandwidth support while the virtualization introduced by the integration of Software Defined Networks (SDN) enables traffic management and remote orchestration of networking devices. However, the popularity and variety of multimedia-rich applications along with the increased number of users has led to an ever increasing pressure that these multimedia-rich content applications are placing on the underlying networks. Consequently, a simple increase in the system capacity will not be enough and an intelligent traffic management solution is required to enable the Quality of Service (QoS) provisioning. In this context, this paper proposes a Reinforcement Learning (RL)-based framework within a multimedia-based SDN environment, that decides on the most suitable routing algorithm to be applied on the QoS-based traffic flows to improve QoS provisioning. The proposed RL-based solution was implemented and evaluated using an experimental setup under a realistic SDN environment and compared against other state-of-the-art solutions from the literature in terms of throughput, packet loss, latency, peak signal-to-noise ratio (PSNR) and mean opinion score (MOS). The proposed RL-based framework finds the best trade-off between QoS vs. Quality of User Experience (QoE) when compared to other state-of-the-art approaches.

Visual Analytic Tools and Techniques in Population Health and Health Services Research: Scoping Review.

BACKGROUND: Visual analytics (VA) promotes the understanding of data with visual, interactive techniques, using analytic and visual engines. The analytic engine includes automated techniques, whereas common visual outputs include flow maps and spatiotemporal hot spots. OBJECTIVE: This scoping review aims to address a gap in the literature, with the specific objective to synthesize literature on the use of VA tools, techniques, and frameworks in interrelated health care areas of population health and health services research (HSR). METHODS: Using the 2018 PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, the review focuses on peer-reviewed journal articles and full conference papers from 2005 to March 2019. Two researchers were involved at each step, and another researcher arbitrated disagreements. A comprehensive abstraction platform captured data from diverse bodies of the literature, primarily from the computer and health sciences. RESULTS: After screening 11,310 articles, findings from 55 articles were synthesized under the major headings of visual and analytic engines, visual presentation characteristics, tools used and their capabilities, application to health care areas, data types and sources, VA frameworks, frameworks used for VA applications, availability and innovation, and co-design initiatives. We found extensive application of VA methods used in areas of epidemiology, surveillance and modeling, health services access, use, and cost analyses. All articles included a distinct analytic and visualization engine, with varying levels of detail provided. Most tools were prototypes, with 5 in use at the time of publication. Seven articles presented methodological frameworks. Toward consistent reporting, we present a checklist, with an expanded definition for VA applications in health care, to assist researchers in sharing research for greater replicability. We summarized the results in a Tableau dashboard. CONCLUSIONS: With the increasing availability and generation of big health care data, VA is a fast-growing method applied to complex health care data. What makes VA innovative is its capability to process multiple, varied data sources to demonstrate trends and patterns for exploratory analysis, leading to knowledge generation and decision support. This is the first review to bridge a critical gap in the literature on VA methods applied to the areas of population health and HSR, which further indicates possible avenues for the adoption of these methods in the future. This review is especially important in the wake of COVID-19 surveillance and response initiatives, where many VA products have taken center stage. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/14019.

Seroprevalence and Risk Factors of SARS CoV-2 in Health Care Workers of Tertiary-Care Hospitals in the Province of Khyber Pakhtunkhwa, Pakistan (preprint)

Background: High number of SARS CoV2 infected patients has overburdened healthcare delivery system, particularly in low-income countries. In the recent past many studies from the developed countries have been published on the prevalence of SARS CoV2 antibodies and the risk factors of COVID 19 in healthcare-workers but little is known from developing countries. Methods: This cross-sectional study was conducted on prevalence of SARS CoV2 antibody and risk factors for seropositivity in HCWs in tertiary care hospitals of Peshawar city, Khyber Pakhtunkhwa province Pakistan. Findings: The overall seroprevalence of SARS CoV2 antibodies was 30.7% (CI, 27.8 to 33.6) in 1011 HCWs. Laboratory technicians had the highest seropositivity (50.0%, CI, 31.8 to 68.1). Risk analysis revealed that wearing face-mask and observing social-distancing within a family could reduce the risk (OR:0.67. p<0.05) and (OR:0.73. p<0.05) while the odds of seropositivity were higher among those attending funeral and visiting local-markets (OR:1.83. p<0.05) and (OR:1.66. p<0.01). In Univariable analysis, being a nursing staff and a paramedical staff led to higher risk of seropositivity (OR:1.58. p< 0.05), (OR:1.79. p< 0.05). Fever (OR:2.36, CI, 1.52 to 3.68) and loss of smell (OR:2.95, CI: 1.46 to 5.98) were significantly associated with increased risk of seropositivity (p<0.01). Among the seropositive HCWs, 165 (53.2%) had no symptoms at all while 145 (46.8%) had one or more symptoms. Interpretation: The high prevalence of SARS CoV2 antibodies in HCWs warrants for better training and use of protective measure to reduce their risk. Early detection of asymptomatic HCWs may be of special importance because they are likely to be potential threat to others during the active phase of viremia. Funding: Prime Foundation Pakistan.