Predicting COVID-19 vaccinators based on machine learning and sentiment analysis

In the past two years, the world witnessed the spread of the coronavirus (COVID-19) pandemic that disrupted the entire world, the only solution to this epidemic was health isolation, and with it everything stopped. When announcing the availability of a vaccine, the world was divided over the effectiveness and harms of this vaccine. This article provides an analysis of vaccinators and analysis of people's opinions of the vaccine's efficacy and whether negative or positive. Then a model is built to predict the future numbers of vaccinators and a model that predicts the number of negative opinions or tweets. The model consists of three stages: first, converting data sets into a synchronized time series, that is, the same place and time for vaccination and tweets. The second stage is building a prediction model and the third stage was descripting analysis of the prediction results. The autoregressive integrated moving averages (ARIMA) method was used after decomposing the components of ARIMA and choosing the optimal model, the best results obtained from seasonal ARIMA (SARIMA) for both predictions, the last stage is the descriptive analysis of the results and linking them together to obtain an analysis describing the change in the number of vaccinators and the number of negative tweets. © 2023, Institute of Advanced Engineering and Science. All rights reserved.

Did all Emerging Equity Markets get Equally Affected by Covid-19? Role of Market Characteristics, Economic Conditions, and Government Policies

The Covid-19 pandemic wreaked havoc on global economies. Emerging markets were hit particularly harder during the Covid19 pandemic due to their reliance on exports, tourism, and weaker fiscal policies. This paper aims to analyze the performance of the equity markets of 22 developing countries based on their average firm-related characteristics, macroeconomic conditions, and freedom variables during the early outbreak of Covid-19. Our results show that leverage, the fiscal health of the country, and financial freedom were the most important variables for emerging market countries as they provided resilience during the Pandemic. These findings have clear policy implications and are important for the sustainability of emerging stock markets. © 2022, AfricaGrowth Institute. All rights reserved.

Novel COVID-19 Based Optimization Algorithm (C-19BOA) for Performance Improvement of Power Systems

The ongoing pandemic due to novel coronavirus disease-2019 (COVID-19) has rapidly unsettled the health sector with a considerable fatality rate. The main factors that help minimize the spread of this deadly virus are the proper use of masks, social distancing and antibody growth rate in a person. Based on these factors, we propose a new nature-inspired meta-heuristic algorithm named COVID-19 Based Optimization Algorithm (C-19BOA). The proposed C-19BOA mimics the spread and control behavior of coronavirus disease centered on three containment factors: (1) social distancing, (2) use of masks, and (3) antibody rate. Initially, the mathematical models of containment factors are presented, and further, the proposed C-19BOA is developed. To ascertain the effectiveness of the developed C-19BOA, its performance is verified on standard IEEE mathematical benchmark functions for the minimization of these benchmark functions and convergence to the optimal values. These performances are compared with established bio-inspired optimization algorithms available in the literature. Finally, the developed C-19BOA is applied on an electrical power system load–frequency–control model to test its effectiveness in optimizing the power system parameters and to check its applicability in solving modern engineering problems. A performance comparison of the proposed C-19BOA and other optimization algorithms is validated based on optimizing the controller gains for reducing the steady-state errors by comparing the effective frequency and tie-line power regulation ability of an industrially applied Proportional–Integral–Derivative controller (PID) and Active Disturbance Rejection controller (ADRC). Moreover, the robustness of C-19BOA optimized PID and ADRC gains is tested by varying the system parameters from their nominal values.

COVID-19 Vaccination: The Extent of Knowledge and Perception in Health Care Workers of Rural Tertiary Health Center in India

BACKGROUND: Mass vaccination is a key preventive approach against COVID-19 pandemic and governments have prioritized health care workers (HCWs) for vaccination. HCWs are the frontline army of the COVID-19 pandemic and are at a high risk of infection. Some studies have documented that not the entire HCWS are ready to accept COVID-19 vaccines, when offered in their country. HCW acceptance or rejection, can influence the general population's perception towards COVID-19 vaccines. Thus the study was planned to determine the COVID‐19 vaccine perception and to improve vaccine awareness among HCWS of Maharashtra, India MATERIAL AND METHODS: This cross-sectional study was conducted among HCWS of Noor hospital from 1 June - 14 June 2021 through self-reported, structured questionnaire prepared from prior evidence from studies on vaccine perception among HCWS and general population. RESULTS: Out of 392, 300 HCWS (response rate 76.53%) had completely filled the online survey questionnaire. In present study 87% of the participant were agreed to take COVID-19 vaccine and 13% were reluctant to take it. Acceptance for vaccine was more in doctors (94.80%) and nurses (89.92%) than pharmacist (80.76%) and laboratory technicians (75%). CONCLUSION: Vaccine acceptance is more in doctors and nurses as compared to pharmacist and technical staff. Vaccine acceptance is influenced by academic level, exposure to infection in family and inadequate information regarding vaccine.

Forecasting Time Series COVID-19 Statistical Data with Auto-Regressive Integrated Moving Average and Box-Jenkins' Models

The current epidemic situation due to COVID-19 is a public health disaster worldwide. Forecasting play's, a crucial role in determining the pandemic's hypothetical situation and economic situation. It provides the base for authorities, public health officials, management teams, and other stakeholders to plan for future preventive actions in their companies, citizens, and governments. This paper proposes Auto-Regressive Integrated Moving Average mathematical modeling in integration with Box-Jenkins' model-building approach examining the variation in pandemic severity through the Loess smoothed curves to forecast the COVID-19 pandemic situation. The time-plot and forecasting results show Chinese resilience to pact with pandemic situation effectively whereas India was severely affected by the pandemic. The future forecast for India shows the worst situation by the end of 2021. Pakistan and Bangladesh are the least affected among the specified countries while decline in weekly death cases has been observed in Iran till the end of 2021. We observed the Case Fatality Ratio (CFR) of 2.08% globally. © 2021 IEEE.

Analyzing and Battling the Emerging Variants of Covid-19 Using Artificial Neural Network and Blockchain

The fast expansion of the COVID-19 epidemic has revealed the shortcomings of current healthcare institutions in dealing with public emergency situations. One of the big reasons of Covid-19 spread is the lack of standard track and trace mechanisms in healthcare infrastructures. Furthermore, throughout the epidemic, the transmission of disinformation has accelerated, and existing platforms lacking capability of verifying the veracity of information, resulting to social unrest and illogical conduct. Therefore, building a track and trace system is critical to ensuring that data collected by the government and the public entities is accurate and dependable. It is obvious that implementing state-of-the-art predictive models like Artificial Neural Network and Blockchain-based traceable mechanisms can help to prevent the spreads of the new variants. In this paper, we proposed a Blockchain based traceable model to track and trace the infected cases so to help an effective planning to prevent the spread. © 2021 IEEE.

COVID-19 infection in kidney transplant recipients: A single-center experience

Introduction: The SARS-CoV-2 (COVID-19) pandemic presents significant challenges to kidney transplant recipients (KTRs) with risk for higher mortality yet sparse available data. We present our single center experience of COVID-19 infection in KTRs. Case Description: Methods We reviewed the electronic health records of KTRs with confirmed COVID-19 infection using rt-PCR testing via nasopharyngeal swabs, at our transplant center. Results We identified four KTRs with diagnosed COVID-19 infection in our institution. Details of individual cases are summarized in table 1. Patient A died of complications while patients B, C and D fully recovered. Patient A initially recovered from COVID-19 pneumonia but then was readmitted three weeks later and developed features closely resembling macrophage activation syndrome (MAS) and hemophagocytic lymphohistiocytosis (HLH) leading to death despite treatment with dexamethasone, IV immunoglobulin and anakinra. Patient C had minimal symptoms without cytokine storm possibly related to complement blockade from revulizumab, a C5 inhibitor, which was being used for treatment of aHUS. Discussion: Our single center case series of COVID-19 infections in KTRs is small but highlights two important aspects: 1) development of MAS/HLH like features in patient A which to our knowledge has not been described in the setting of COVID-19 infection in KTRs;2) development of minimal symptoms without cytokine storm in patient C likely related to use of C5 blocking agent revulizumab. Further studies are needed to shed light on these phenomenon.

The COVID-19 Infodemic: A Quantitative Analysis Through Facebook.

Background Social media is a crucial part of our daily life. Facebook, being the biggest social media platform, plays a significant role in the spread of information influencing the global response to the COVID-19 pandemic. Health care agencies like the World Health Organization (WHO) and Centers for Disease Control and Prevention (CDC) use social media as a platform to impart information regarding COVID-19; simultaneously, there is a spread of misinformation on social media, masking the credible sources of information. Our research aims to assess the utility of Facebook in providing misinformation and testing its "fact-check policy." Methods An online search was conducted on Facebook by a newly created account to eliminate bias. The Facebook search bar was used to investigate multiple keywords. Data were tabulated in Microsoft Excel (Microsoft Corporation, Redmond, WA). Descriptive statistical analysis of Facebook accounts and posts was done using the Statistical Package for the Social Sciences (SPSS) version 26 (IBM Corp., Armonk, NY) while statistical importance was set a priority at a p-value of 0.05. Results Our study consisted of 454 Facebook posts. Most (22.5%) were posted by verified accounts and 23.9% by informal individual/group accounts. The tone for most (40.4%) COVID-19 information was serious while the most common (43.9%) topic was medical/public health. In total, 22.3% included misinformation, 19.6% were unverifiable, and 27.5% included correct information verifiable by the WHO or CDC. Conclusions Misinformation/unverifiable information related to the COVID-19 crisis is spreading at a distressing rate on social media. We quantified the misinformation and tested Facebook's "fact-check policy." We advise strict initiatives to control this infodemic and advise future researches to evaluate the accuracy of content being circulated on other social media platforms.