Analysis and Prognosis of Covid-19 using Machine Learning Algorithms and Visualization using Tableau

This paper uses prognosticative machine learning models that predict corona positives and deaths as a result of the crisis, and the recovery rate from the pandemic. This method aids in diagnosing the contours of an individual's presumption in data transmission based on medical knowledge and calculates the unfolding virus's socioeconomic impact. It examines the Covid-19's spread technique with the help of machine learning models. It also identifies the approaching prophecy and recessive presumption of the crisis at the same time, and as a result, this applicable analysis aids similar countries in making decisions. This paper also considers the global prevalence of the plague. Within the first phase of the irruption, eight supervised classification epidemiologic models are used to estimate the day-to-day and monomer incidents of coronavirus throughout the world, as well as the vital replica variety, growth rate, and increasing time. Calculations are also made for the more intricate efficacious replica variety, which reveals that since the predominant cases are confirmed to the specific countries, the severity has decreased. Machine learning models' prognosticative capabilities are found to provide an additional satisfactory match, and simple estimates of daily incidents around the world. © 2022 IEEE.

Financialisation of agricultural commodity and its trading during COVID-19 pandemic

The establishment of regularised trading exchanges for agricultural commodities attracted every market participant to benefit from their trade. The pandemic has created massive chaos in every asset class, and agri-commodities are no exception. However, the pandemic also taught lessons for the global traders to focus on food produce. Therefore, this paper intends to look at agricultural commodity trading behaviour during this COVID-19 pandemic by looking at the movement of the trade in the agri-futures index and other asset classes, including equity, exchange rates, bullion prices, etc. The results show that all the selected asset classes except the exchange rate are influencing Agridex. In addition, the Agridex returns are influenced by the severity of COVID-19 cases. Therefore, the policymakers should keep this in mind and work to prevent the price rise to an uncontrollable extent because this can lead to stagflation. Copyright © 2021 Inderscience Enterprises Ltd.

The impact of Lean Six Sigma practices on supply chain resilience during COVID 19 disruption: a conceptual framework

The purpose of this research is to explore the potential impact of Lean Six Sigma practices on supply chain resilience proposing a conceptual framework. A content analysis method was used to identify themes from the interview data conducted with (n = 21) participants who are involved within the healthcare sector. The first-order coding of interview data performed by the researchers with inter-reliability (k = 0.74) identified IT management, big data analytics, risk management, efficient process management, process reconfiguration and disruption readiness as the emerging themes. The second-order coding of interview data with inter-reliability (k = 0.84) analysed the relationship between the first-order themes exploring the impact of Lean Six Sigma practices on building supply chain resilience. As a result, a framework was developed for achieving resilience in the supply chain through the application of six sigma practices.

Lean Six Sigma as an organizational resilience mechanism in health care during the era of COVID-19

Purpose: The purpose of this study was to investigate how Lean Six Sigma (LSS) may help mitigate the impact of COVID-19 within health care environments. The goals of this study were to understand the current knowledge of LSS and COVID-19 through a systematic review of the current literature, identify the gap in the current knowledge of LSS in COVID-19 mitigation within health care environments and define the principles of LSS, within organizational resilience that support a health care organization’s ability to mitigate the impact of COVID-19. Design/methodology/approach: A narrative literature review was conducted to identify relevant research. A total of 21 subject matter experts (SMEs) meeting the inclusion criteria were approached through a guided interview process. Content analysis was conducted to describe how LSS principles contribute to supporting health care organizations operating in the era of COVID-19. Findings: Study results report that personal safety is the primary subject, followed by supporting dimensions of process redesign, and telemedicine. LSS topics that directly relate to COVID-19 are in four thematic areas: tools, applications, benefits and challenges. Particular areas of application, techniques, challenges and benefits are identified and discussed that could be applied proactively and reactively, to organizational and supply chain resilience to recover from COVID-19. Research limitations/implications: There were a number of limitations to the generalizability of this work. The sample size was small and purposeful, thus, external validity of the study results are not determined. The SMEs in this study have not implemented the practices noted in the results at the time of the study, and knowledge of results is limited to the study aims. Originality/value: This study of LSS principles and COVID-19 has implications for practitioners and offers specific guidance for areas of health care adoption of LSS techniques and tools that benefit patient safety, challenges for the user to be mindful of and potential benefits in resilience of operations in the era of COVID-19. © 2020, Emerald Publishing Limited.

Identification of a repurposed drug as an inhibitor of Spike protein of human coronavirus SARS-CoV-2 by computational methods

Severe acute respiratory syndrome coronavirus (SARS-CoV-2) is an emerging new viral pathogen that causes severe respiratory disease. SARS-CoV-2 is responsible for the outbreak of COVID-19 pandemic worldwide. As there are no confirmed antiviral drugs or vaccines currently available for the treatment of COVID-19, discovering potent inhibitors or vaccines are urgently required for the benefit of humanity. The glycosylated Spike protein (S-protein) directly interacts with human angiotensin-converting enzyme 2 (ACE2) receptor through the receptor-binding domain (RBD) of S-protein. As the S-protein is exposed to the surface and is essential for entry into the host, the S-protein can be considered as a first-line therapeutic target for antiviral therapy and vaccine development. In silico screening, docking, and molecular dynamics simulation studies were performed to identify repurposing drugs using DrugBank and PubChem library against the RBD of S-protein. The study identified a laxative drug, Bisoxatin (DB09219), which is used for the treatment of constipation and preparation of the colon for surgical procedures. It binds nicely at the S-protein–ACE2 interface by making substantial π-π interactions with Tyr505 in the ‘Site 1’ hook region of RBD and hydrophilic interactions with Glu406, Ser494, and Thr500. Bisoxatin consistently binds to the protein throughout the 100 ns simulation. Taken together, we propose that the discovered molecule, Bisoxatin may be a promising repurposable drug molecule to develop new chemical libraries for inhibiting SARS-CoV-2 entry into the host.