The Moderation Role of Innovation and Infrastructure on the Relationship between COVID-19 Crises and Health Care Performance: Evidence from Jordan

This study examines the moderation role of innovation and infrastructure on the relationship between covid-19 crisis and healthcare performance in Jordan. The methodology of this study includes a literature review to identify relevant studies and theories related to the topic and identify gaps in the existing research. Attention is paid to human behavior and personnel interaction in the hospitals that receive Covid-19 cases. The findings of this study will be used to identify areas where improvements can be made in healthcare infrastructure and innovative practices to support healthcare performance during future crises better. This paper identifies specific innovative and most effective infrastructure supporting healthcare performance during a crisis, such as telemedicine, remote monitoring, or emergency medical service (EMS) systems. Also, the paper informs policy-making by providing insights into the impact of innovative practices and infrastructure on healthcare performance and how these factors can mitigate future crises' impact on healthcare systems. The healthcare industry needs ideas and strategies as the Covid-19 pandemic grips the world. These strategies will help the industry deal with the unstable and continuously changing environment. © 2023 WITPress. All rights reserved.

A Study of Factors that Impact the Production of Anti-SARS-CoV-2 Antibodies in Patients with Covid-19

Objectives: The aim of this paper was to investigate the impact of different variables on the production of antibodies in patients who were infected with Covid-19.Methods: This cross-sectional study was conducted in Duhok City, Kurdistan Region of Iraq. The study was conducted between January 2021 and March 2022. Demographic data were collected via face-to-face interview. Antibody levels were determined using Elecsys Anti-SARS-CoV-2.Results: The levels of antibody were studied in 138 patients. A significant association was found between antibody levels and the age of the participants (r = 0.175;P = 0.04). Besides, a significant correlation was found between antibody levels and the duration of symptoms (r = 0.206;P = 0.015). The antibody levels were not associated with gender;history of chronic diseases;marital status or time interval before testing.Conclusion: Different variables that may impact the levels of antibody were studied. Significant associations were found between antibody levels and both age and duration of symptoms. Our results can be used by healthcare providers to focus on patients who are at risk of low antibody production.

The Quandary in Data Protection and Rights to Privacy of AI Technology Adoption in Malaysia

Artificial Intelligence (AI) is increasingly taking hold throughout worldwide businesses. Since 2017, anticipated AI usage in Malaysia has increased by 32 percent due to innovative city projects and public safety and intelligent transportation applications. However, such AI development is afflicted by privacy and data breaches as the existing data protection regulation safeguards against unlawful commercial use of personal data. Recent developments reflect the failure of present legal mechanisms to address such infringements and effective data management and protection deficiencies. This study examines the legal concerns of privacy and data protection and problems associated with the use of AI in Malaysia. This research adopts a methodology of doctrinal research, a systematic method of legal reasoning, including analyzing primary and secondary resources. The findings imply that AI evolves, magnifying the ability to use personal information that may affect privacy. Thus, infringement of privacy and use of personal data without legal oversight or effective governance could result in privacy deprivation and abuse of personal data that might be used to perpetrate crimes © 2021 IEEE.

Impact of climate indicators on the COVID-19 pandemic in Saudi Arabia.

The novel coronavirus (COVID-19) outbreak has left a major impact on daily lifestyle and human activities. Many recent studies confirmed that the COVID-19 pandemic has human-to-human transmissibility. Additional studies claimed that other factors affect the viability, transmissibility, and propagation range of COVID-19. The effect of weather factors on the spread of COVID-19 has gained much attention among researchers. The current study investigates the relationship between climate indicators and daily detected COVID-19 cases in Saudi Arabia, focusing on the top five cities with confirmed cases. The examined climate indicators were temperature (°F), dew point (°F), humidity (%), wind speed (mph), and pressure (Hg). Using data from Spring 2020 and 2021, we conducted spatio-temporal correlation, regression, and time series analyses. The results provide preliminary evidence that the COVID-19 pandemic spread in most of the considered cities is significantly correlated with temperature (positive correlation) and pressure (negative correlation). The discrepancies in the results from different cites addressed in this study suggest that non-meteorological factors need to be explored in conjunction with weather attributes in a sufficiently long-term analysis to provide meaningful policy measures for the future.

Data Analytics for Predicting COVID-19 Cases in Top Affected Countries: Observations and Recommendations.

The outbreak of the 2019 novel coronavirus disease (COVID-19) has adversely affected many countries in the world. The unexpected large number of COVID-19 cases has disrupted the healthcare system in many countries and resulted in a shortage of bed spaces in the hospitals. Consequently, predicting the number of COVID-19 cases is imperative for governments to take appropriate actions. The number of COVID-19 cases can be accurately predicted by considering historical data of reported cases alongside some external factors that affect the spread of the virus. In the literature, most of the existing prediction methods focus only on the historical data and overlook most of the external factors. Hence, the number of COVID-19 cases is inaccurately predicted. Therefore, the main objective of this study is to simultaneously consider historical data and the external factors. This can be accomplished by adopting data analytics, which include developing a nonlinear autoregressive exogenous input (NARX) neural network-based algorithm. The viability and superiority of the developed algorithm are demonstrated by conducting experiments using data collected for top five affected countries in each continent. The results show an improved accuracy when compared with existing methods. Moreover, the experiments are extended to make future prediction for the number of patients afflicted with COVID-19 during the period from August 2020 until September 2020. By using such predictions, both the government and people in the affected countries can take appropriate measures to resume pre-epidemic activities.

COVID-19 and thrombosis: From bench to bedside.

Coronavirus disease of 2019 (COVID-19) is the respiratory viral infection caused by the coronavirus SARS-CoV2 (Severe Acute Respiratory Syndrome Coronavirus 2). Despite being a respiratory illness, COVID-19 is found to increase the risk of venous and arterial thromboembolic events. Indeed, the link between COVID-19 and thrombosis is attracting attention from the broad scientific community. In this review we will analyze the current available knowledge of the association between COVID-19 and thrombosis. We will highlight mechanisms at both molecular and cellular levels that may explain this association. In addition, the article will review the antithrombotic properties of agents currently utilized or being studied in COVID-19 management. Finally, we will discuss current professional association guidance on prevention and treatment of thromboembolism associated with COVID-19.