An Integrated Artificial Intelligence Model for Efficiency Assessment in Pharmaceutical Companies During the COVID-19 Pandemic

The spread of coronavirus disease around the world has had an immense impact on most economic sectors. Yet amid the turmoil and chaos from the worldwide pandemic, one industry is thriving noticeably. The coronavirus disease is a once in a lifetime business opportunity for pharmaceutical companies. This study presents an artificial intelligence method composed of optimization and machine learning. Data envelopment analysis (DEA) is used to measure productivities and efficiencies of pharmaceutical companies during the COVID-19 pandemic using the additive model in window analysis, the BCC (Banker-Charnes-Cooper) model, and the CCR (Charnes-Cooper-Rhodes) model. The three models are assessed using DataStream financial information with research and development (R&D) investment. The results indicated the additive model's superiority in window analysis, followed by the BCC and CCR models. In the end, some of well-known data mining algorithms, based on the suggested data, have been evaluated in various tools to find the most efficient tool and algorithm.

A Novel Hybrid Parametric and Non-Parametric Optimisation Model for Average Technical Efficiency Assessment in Public Hospitals during and Post-COVID-19 Pandemic.

The COVID-19 pandemic has had a significant impact on hospitals and healthcare systems around the world. The cost of business disruption combined with lingering COVID-19 costs has placed many public hospitals on a course to insolvency. To quickly return to financial stability, hospitals should implement efficiency measure. An average technical efficiency (ATE) model made up of data envelopment analysis (DEA) and stochastic frontier analysis (SFA) for assessing efficiency in public hospitals during and after the COVID-19 pandemic is offered. The DEA method is a non-parametric method that requires no information other than the input and output quantities. SFA is a parametric method that considers stochastic noise in data and allows statistical testing of hypotheses about production structure and degree of inefficiency. The rationale for using these two competing approaches is to balance each method's strengths, weaknesses and introduce a novel integrated approach. To show the applicability and efficacy of the proposed hybrid VRS-CRS-SFA (VCS) model, a case study is presented.

Transient increased immunoglobulin levels in a hyper-IgM syndrome patient with COVID-19 infection.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has affected millions of people around the world. This zoonotic-enveloped virus is primarily transmitted through inhalation. Infected people are commonly asymptomatic or manifest mild symptoms, including fever, cough, diarrhea, and fatigue. However, it may lead to severe patterns associated with multiple organ failure in individuals with an impaired immune system. OBJECTIVE: Here we report a 7-year-old girl with hyper-immunoglobulin M (IgM) (HIgM) phenotype, admitted to the hospital emergency department with fever, cough, and pneumonia symptoms because of the COVID-19 infection. Coronavirus infection was confirmed by a positive real-time polymerase chain reaction test. Surprisingly, serum levels of both IgG and IgA of the patient were transiently normalized during the COVID-19 infection when tested prior to the monthly injection of intravenous immunoglobulin. After she recovered from the COVID infection, her immunoglobulin levels returned to the primary stage and she demonstrated HIgM phenotype. CONCLUSION: Since this transient increase in the levels of immunoglobulins was solely observed during the COVID-19 infection, and no other infectious episodes were diagnosed in the patient, clarifying the exact cause would help to understand in a better manner the implications and specification of humoral immunity in patients with primary antibody deficiencies.

Impact of SARS-CoV-2 Pandemic on Patients with Primary Immunodeficiency.

Although it is estimated that COVID-19 life-threatening conditions may be diagnosed in less than 1:1000 infected individuals below the age of 50, but the real impact of this pandemic on pediatric patients with different types of primary immunodeficiency (PID) is not elucidated. The current prospective study on a national registry of PID patients showed that with only 1.23 folds higher incidence of infections, these patients present a 10-folds higher mortality rate compared to population mainly in patients with combined immunodeficiency and immune dysregulation. Therefore, further management modalities against COVID-19 should be considered to improve the survival rate in these two PID entities using hematopoietic stem cell transplantation and immunomodulatory agents.