The promises and limitations of codes of medical ethics as instruments of policy change.

Codes of medical ethics (codes) are part of a longstanding tradition in which physicians publicly state their core values and commitments to patients, peers, and the public. However, codes are not static. Using the historical evolution of the Canadian Medical Association's Code of Ethics as an illustrative case, we argue that codes are living, socio-historically situated documents that comprise a mix of prescriptive and aspirational content. Reflecting their socio-historical situation, we can expect the upheaval of the COVID-19 pandemic to prompt calls to revise codes. Indeed, Alex John London has argued in favour of specific modifications to the World Medical Association's International Code of Medical Ethics (which has since been revised) in light of moral and scientific failures that occurred during the COVID-19 pandemic. Responding to London, we address the more general question: should codes be modified to reflect lessons drawn from the COVID-19 pandemic or future such upheavals? We caution that codes face limitations as instruments of policy change because they are inherently interpretive and 'multivocal', that is, they usually underdetermine or provide more than one answer to the question, 'What should I do now?' Nonetheless, as both prescriptive and aspirational documents, codes also serve as tools for reflection and deliberation-collective practices that are necessary to engaging with and addressing the moral and scientific uncertainties inherent to medicine.

Epidemic dynamics in census-calibrated modular contact network.

Network-based models are apt for understanding epidemic dynamics due to their inherent ability to model the heterogeneity of interactions in the contemporary world of intense human connectivity. We propose a framework to create a wire-frame that mimics the social contact network of the population in a geography by lacing it with demographic information. The framework results in a modular network with small-world topology that accommodates density variations and emulates human interactions in family, social, and work spaces. When loaded with suitable economic, social, and urban data shaping patterns of human connectance, the network emerges as a potent decision-making instrument for urban planners, demographers, and social scientists. We employ synthetic networks to experiment in a controlled environment and study the impact of zoning, density variations, and population mobility on the epidemic variables using a variant of the SEIR model. Our results reveal that these demographic factors have a characteristic influence on social contact patterns, manifesting as distinct epidemic dynamics. Subsequently, we present a real-world COVID-19 case study for three Indian states by creating corresponding surrogate social contact networks using available census data. The case study validates that the demography-laced modular contact network reduces errors in the estimates of epidemic variables.

Sweeteners World Trade and Behaviour in the Pandemic. Evidence from Honey Remedies Nexus Mountain Apis Mellifera Product.

The paper proposes a construct for sweeteners (SMH-sugar, molasses, and honey class) consumer behavior, focusing on the mountain Apis Mellifera healing effects and its market. The paper develops three research dimensions, respectively, the importance of the healing properties of SMH products, the consumer behavior of SMH clients, and the world trade of SMH. Apis Mellifera product is considered one of the primary natural prevention and treatment for COVID-19. Presented empirical and experimental studies, respectively, qualitative analysis for Apis Mellifera product, reveal that honey, especially dark honey, presents healing effects. People understand the healing effects of honey in the COVID-19 context, and consequently, honey consumption increased. The forecasting model of the export value, for the 2021-2040 period, takes into consideration the descriptive statistics analysis based on 2001-2020 data. The paper contains relevant data about the SMH class related to statistics of the World Bank, United Nations, Eurostat, International Trade Center, and other sources presented in the paper. Data have been processed into SPSS and Excel, according to ANOVA (descriptive statistics with a focus on frequency analysis) and forecasting analysis. Supplementary Information: The online version contains supplementary material available at 10.1007/s12355-023-01243-6.

Economical synthesis of oxygen to combat the COVID-19 pandemic.

The whole world has been affected by the COVID-19 pandemic and oxygen demand is greater than ever, but the supply is expectedly short. People in need of this oxygen are not able to receive it, especially those who cannot afford it. In addition to these issues, the oxygen from production plants is not getting delivered to hospitals on a timely basis due to insufficient availability of tankers and cylinders. It is therefore crucial to enable access of oxygen beds and cylinders to the public by developing economical methods for medical oxygen generation. Conventional methods like oxygen concentrators, the Pressure Swing Adsorption (PSA) Technique and Air Separation Units (ASUs) are either too expensive, energy intensive or feasible only on a small scale. This indicates the need to exploit methods that have not been utilized fully yet, such as Integrated Energy Systems (IES). However, reducing the cost of a process is not enough. It needs to be scaled up to have a real impact on the situation at hand. Ion Transport Membranes (ITM) are promising in this aspect as they can produce large volumes of extremely high-purity oxygen at low costs. All these methods along with their economic aspects have been discussed and then compared to identify the most feasible one.

Sharing real-world data for public benefit: a qualitative exploration of stakeholder views and perceptions.

BACKGROUND: There has been an increasing interest in the use of "real-world" data to inform care decision making that could lead to public health benefit. Routinely collected service and activity data associated with the administration of care services and service-users (such as electronic health records or electronic social care records), hold potential to better inform effective and responsive decision-making about health and care services provided to national and local populations. This study sought to gain an in-depth understanding regarding the potential to unlock real world data that was held in individual organisations, to better inform public health decision-making. This included sharing data between and within health service providers and local governing authorities, but also with university researchers to inform the evidence base. METHODS: We used qualitative methods and carried out a series of online workshops and interviews with stakeholders (senior-level decision-makers and service leads, researchers, data analysts, those with a legal and governance role, and members of the public). We identified recurring themes in initial workshops, and explored these with participants in subsequent workshops. By this iterative process we further refined the themes identified, compared views and perceptions amongst different stakeholder groups, and developed recommendations for action. RESULTS: Our study identified key elements of context and timing, the need for a different approach, and obstacles including governmental and legal, organisational features, and process factors which adversely affect the sharing of real world data. The findings also highlighted a need for improved communication about data for secondary uses to members of the public. CONCLUSION: The Covid-19 pandemic context and changes to organisational structures in the health service in England have provided opportunities to address data sharing challenges. Change at national and local level is required, within current job roles and generating new jobs roles focused on the use and sharing of real-world data. The study suggests that actions can be taken to unlock the potential of real-world data for public health benefit, and provides a series of recommendations at a national level, for organisational leaders, those in data roles and those in public engagement roles.

Epidemiology and Disease Burden of Alcohol Associated Liver Disease.

Consumption of alcohol in excess leads to substantial medical, economic, and societal burdens. Approximately 5.3% of all global deaths may be attributed to alcohol consumption. Moreover, the burden of alcohol associated liver disease (ALD) accounts for 5.1% of all disease and injury worldwide. Alcohol use disorder (AUD) affects men more than women globally with significant years of life loss to disability in low, middle and well-developed countries. Precise data on global estimates of alcohol related steatosis, alcohol related hepatitis, and alcohol related cirrhosis have been challenging to obtain. In the United States (US), alcohol related steatosis has been estimated at 4.3% based on NHANES data which has remained stable over 14 years. However, alcohol-related fibrotic liver disease has increased over the same period. In those with AUD, the prevalence of alcohol related hepatitis has been estimated at 10-35%. Globally, the prevalence of alcohol-associated cirrhosis has been estimated at 23.6 million individuals for compensated cirrhosis and 2.46 million for those with decompensated cirrhosis. The contribution of ALD to global mortality and disease burden of liver related deaths is substantial. In 2016 liver disease related to AUD contributed to 50% of the estimated liver disease deaths for age groups 15 years and above. Data from the US report high cost burdens associated with those admitted with alcohol-related liver complications. Finally, the recent COVID-19 pandemic has been associated with marked increase in alcohol consumption worldwide and will likely increase the burden of ALD.

A scoping review of the risk factors and strategies followed for the prevention of COVID-19 and other infectious diseases during sports mass gatherings: Recommendations for future FIFA World Cups.

Objective: Sports mass gatherings of people pose particular concerns and place an additional burden on the host countries and the countries of origin of the travelers. It is imperative to identify how countries dealt with various communicable diseases in the context of previous world cups and identify possible advice for protection from outbreaks. Methods: A scoping review was employed in this study and a PRISMA extension for scoping reviews was employed to guide the reporting of this study. A systematic search was performed using PubMed, Embase, Web of Science, SCOPUS, SportDiscus, and Google scholar. The search strategy included two main strings viz "communicable disease" AND "sport" AND "setting" as keywords for each string. A total of 34 studies were included in this review. Results: Information on risk factors for infectious diseases during FIFA, and recommendations for disease prevention in various stages of the event: pre-event, during, and post-event were charted. These strategies can be achieved with the empowerment of the public by enhancing their social responsibility and the coordination between the healthcare system, the ministry of public health, and other stakeholders. Conclusion: The findings will support planning for protection strategies to prevent any outbreak while having the FIFA World Cup or any other sports gatherings. A model was constructed to present the findings and recommendations from this review.

Cost-effectiveness of Favipiravir in moderately to severely ill COVID-19 patients in the real-world setting of Saudi Arabian pandemic referral hospitals.

Purpose: We aimed to evaluate the cost effectiveness of Favipiravir treatment versus SC in moderately to severely ill COVID-19 patients from the Saudi healthcare payer perspective. Methods: We used the patient-level simulation method to simulate a cohort of 415 patients with moderate to severe COVID-19 disease who were admitted to two Saudi COVID-19 referral hospitals: 220 patients on Favipiravir and 195 patients on SC. We estimated the incremental cost-effectiveness ratio (ICER) of Favipiravir versus SC in terms of the probability to be discharged alive from hospital and the mean time in days to discharge one patient alive. The model was performed twice: first, using unweighted, and second, using weighted clinical and economic data. Weighting using the inverse weight probability method was performed to achieve balance in baseline characteristics. Results: In the unweighted model, base case (probabilistic) ICER estimates favored Favipiravir at savings of Saudi Riyal (SAR)1,611,511 (SAR1,998,948) per 1% increase in the probability of being discharged alive. As to mean time to discharging one patient alive, ICERs favored Favipiravir at savings of SAR11,498 (SAR11,125). Similar results were observed in the weighted model with savings using Favipiravir of SAR1,514,893 (SAR2,453,551) per 1% increase in the probability of being discharged alive, and savings of SAR11,989 (SAR11,277) for each day a patient is discharged alive. Conclusion: From the payer perspective, the addition of Favipiravir in moderately to severely ill COVID-19 patients was cost-savings over SC. Favipiravir was associated with a higher probability of discharging patients alive and lower daily spending on hospitalization than SC.

Are government bonds still safe havens in the context of COVID-19?

This study analyzes whether government bonds can act as safe havens in the context of COVID-19. Using a panel fixed effect model, data were collected for both advanced and emerging market economies from March 11, 2020, to June 30, 2021. Robustness tests were used to add to the credibility of the findings. Our evidence supports that government bonds maintained their safe haven status during the COVID-19 pandemic. Hence, investors can still use government bonds to hedge financial market risks in the uncertain environment associated with this pandemic. Additionally, the negative effects of the COVID-19 pandemic on government bond yields in emerging economies are larger than in advanced economies. Therefore, policymakers' measures should focus on reducing COVID-19 cases to alleviate panic and diminish economic fluctuations, especially for emerging economies. Regulators can also use short-term interest rates to guide market capital flow to avoid a liquidity crisis, reducing financial stress and market uncertainty. © 2021 Informa UK Limited, trading as Taylor & Francis Group.

Why Xi Jinping Reversed His Zero-Covid Policy in China

The rare display of public anger, with some protesters directly criticizing Mr. Xi and the Communist Party, alarmed Mr. Xi and his inner circle, the officials and advisers said. Keywords: leder;wsjworld;photo-news;Political/General News;Respiratory Tract Diseases;Global/World Issues;Health;Medical Conditions;Outbreaks/Epidemics;Politics/International Relations EN leder wsjworld photo-news Political/General News Respiratory Tract Diseases Global/World Issues Health Medical Conditions Outbreaks/Epidemics Politics/International Relations N.PAG N.PAG 1 01/11/23 20230105 NES 230105 A wave of protests coupled with urgent pleas from many corners of the government finally prodded the leader to scrap the strict lockdown system he had touted throughout the pandemic BEIJING - By the end of an otherwise triumphant Communist Party Congress for Xi Jinping in October, it was growing harder for China's leader to argue that his zero-Covid policy was working. [Extracted from the article] Copyright of Wall Street Journal (Online) is the property of Dow Jones & Company Inc and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Impact of Covid-19 on shipbuilding and shipping industry

Covid-19 has been affecting the world for more than two years. The maritime sector in general has been hit hard by the pandemic as well. Since most of the world trade is carried out by sea transportation, the sector has been suffering deeply. The supply and demand chain were broken due to preventive measures such as lock-downs, travel restrictions etc. have been imposed the governments. Passenger transportation by sea was affected as well. As a result of all these the demand for ships and in turn for new buildings was dipped. Some of the shipyards got into trouble completing their ongoing projects due to financial difficulties even bankrupted. This paper deals with the problems that were surfaced during pandemic in maritime industry and for possible remedies to get out of it. Along with the global review of the impact of the pandemic, the effects on Turkish shipbuilding was also taken into consideration. © 2023 the Author(s).

Selection of a SARS-CoV-2 antibody quantification method and development of an antibody reference standard for ELISA to test immunoglobulin preparations

The development of COVID-globulin, a COVID-19-specific human immunoglobulin preparation, involved choosing a method to quantify antibodies to SARS-CoV-2. Antibody titre determination by virus neutralisation (VN) is labour-intensive and unsuitable for large-scale application. To enable routine testing, it was necessary to develop a less demanding method;the enzyme-linked immunosorbent assay (ELISA) was the most appropriate of solutions. The lack of international and industry reference standards (RS) prompted the preparation and certification of an RS for COVID-globulin potency control. The aim of the study was to examine the possibility of substituting ELISA for VN and to develop an RS for SARS-CoV-2 antibody quantification in immunoglobulin preparations. Materials and methods: the authors used commercial ELISA kits by several manufacturers, COVID-globulin by Microgen (48 batches), and human plasma samples from multiple sources (1499 samples). The tests were performed by VN, ELISA, and chemiluminescent microparticle immunoassay. Results: the authors validated an ELISA method for SARSCoV-2 antibody quantification with the selected reagent kits by the National Medical Research Center for Hematology (NMRC for Hematology) and Euroimmun AG. The authors demonstrated the possibility of using ELISA instead of VN (with a correlation coefficient of more than 0.9). They developed and characterised an in-house RS for SARS-CoV-2 antibody content in human immunoglobulin preparations. The RS was certified in newly introduced anti-COVID units (ACU) and in international binding antibody units (BAU) using the World Health Organisation (WHO) international reference panel (NIBSC code: 20/268). The RS's potency was measured in terms of its neutralising activity in ACU (320 ACU/mL) and BAU (2234.8 BAU/mL). The authors established the relationship between ACU and BAU units. For the selected ELISA reagent kits, the conversion factors were 6.4 (NMRC for Hematology) and 7.0 (Euroimmun AG). Conclusions: the ELISA method for SARS-CoV-2 antibody quantification and the RS for SARS-CoV-2 antibody content can be applied to determine the potency of human anti-COVID-19 immunoglobulins.

Robotic Systems Deployed to Combat COVID-19 Pandemic: A Review

The COVID-19 epidemic has been deemed a pandemic by the World Health Organization. It is triggered due to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It originated and spread from Wuhan, China, in December 2019. At present, the entire world is struggling from this virus due to large confirmed positive and death cases of COVID-19. People of every nation have been isolated, and lockdowns are instituted. Despite the introduction of several precautionary measures, the spread of the virus is still increasing at an alarming pace. Although promising development has been made for the development of vaccines for SARS-CoV-2, no vaccines have been reported to cure the infection. Different antiviral therapies have also been attempted but do not seem to be successful for every patient. To deter the dissemination and control the spread of virus, the frontline healthcare staff and police officers deployed numerous autonomous systems for an increased line of protection. Robots are deployed to conduct different operations including decontamination, package delivery, etc. It also acts as a mediator for two-way communication between the doctors and patients. Recent advancement in robotics for its application in healthcare facilities has been found very effective for the healthcare officials to communicate with the virus affected patients, and this literature has addressed it. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

COVID-19 and Lung Cancer: Strengthening the Prevention Component and Improving the Prognosis

The coronavirus disease-2019 (COVID-19) pandemic overwhelmed health-care delivery systems owing to the significant morbidity and mortality. Lung cancer in the year 2020 alone has accounted for more than 2.2 million new cases and 1.8 million deaths across the globe. The purpose of the current review is to explore the impact of COVID-19 on lung cancer and to identify measures that can improve the prognosis of cancer patients during the ongoing COVID-19 pandemic. An extensive search related to the topic was carried out in the PubMed and World Health Organization website. Relevant research articles focusing on COVID-19 and lung cancer published between April 2020 and June 2021 were included in the review. Forty-five studies similar to the current study objectives were identified initially. Among them, five were excluded due to unavailability of the complete version of the articles. Overall, forty articles were selected based on the suitability with current review objectives. Keywords used in the search include COVID-19 and Lung cancer in the title alone only. It has been estimated that patients with lung cancer will have a significantly higher risk of an adverse outcome, if they acquire COVID-19 infection. National bodies across multiple nations have released recommendations for both prevention and optimal management of COVID-19 infection among known lung cancer patients. To conclude, the COVID-19 pandemic has significantly affected patients with lung cancer. Owing to the emergence of evidence of poor prognosis of infection among lung cancer patients, there is an indispensable need to adopt a multidisciplinary treatment approach.

Hazmat Cooling Vest Temperature Using Heat Pipe and Thermoelectric

Corona Virus (COVID-19) is a virus that is endemic almost all over the world, including Indonesia. COVID-19 was first confirmed by the World Health Organization (WHO) on December 31, 2019, in Wuhan City, Hubei Province, China, and then rapidly expanded outside of China. To suppress the Covid-19 case, medical volunteers are needed as the main actors in efforts to handle Covid-19 patients. This makes health care facilities also need to focus on the principles of health worker safety, not only focus on the principles of patient safety. This also makes health care facilities also need to focus on the principles of health worker safety, not only focus on the principles of patient safety. The use of hazmat clothes is one of the efforts to protect health workers when in contact with Covid-19 patients. Hazmat clothes are technically referred to as "encapsulated waterproof protective clothing” which is PPE that must be used for officers from the risk of contracting the Covid-19 virus through airborne droplets and contact with patients and patient body fluids. Although hazmat clothing is an important PPE for health workers to stay protected, the use of hazmat clothing for a long time often makes medical personnel feel uncomfortable when providing services. Based on the problems above, the researchers conducted a study on the heat pipe - thermoelectric hazmat suit cooling vest. This technology can absorb more heat than other methods by simply applying the principle of capillarity to the wicks on the pipe walls. schematic of testing a cooling vest on a hazmat suit. The loading on the thermoelectric is given through the DC - Power supply. The temperature data read by the sensor will be detected by the computer system using the NI 9123 and C-DAQ 9174 modules. The test results can be viewed using the NI LabView 2017 software. The temperature used in this experiment is the result of tests carried out for 30 min. Based on the tests that have been carried out, the heat pipe-based thermoelectric hazmat suit cooling vest has been able to reach the lowest thermoelectric temperature of 24,42 ∘C, which is distributed through heat pipes to body parts. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Pandemic modelling: The impact of social distancing in Nordic countries

Background: Without pharmacologic interventions, the preferred strategy to combat COVID-19 is to slow the virus' spread via social distancing measures. The components of social distancing include: school closure, restrictions on gatherings, non-essential business closure, stay at home orders and limitations on travel. Most countries have implemented many of these restrictions. Conversely, Sweden has not initiated these restrictions and instead has recommended that citizens avoid mass gatherings, which presents an opportunity to examine the effects of the components of social distancing on mortality in Nordic countries. Purpose: Investigate the impact of social distancing measures on fatalities associated with COVID-19. Method: COVID-19 fatalities, as reported by the World Health Organisation, were recorded for each of the Nordic countries from 6th February 2020 to 30th April 2020. The fatalities were compared using a Cox proportional hazard regression analysis. Results: The normalised fatalities ranged significantly (1.87 to 129 deaths/population/km2) in the Nordic countries. Sweden was found to have a significantly higher risk of COVID-19 related mortality at the α=0.05 level as compared to Finland (HR=0.15;p<0.001) and Norway and Denmark (HR=0.23;p=0.002). Conclusion: The population-density normalised mortality in Sweden was significantly greater than other Nordic countries, possibly due to differences in the implementation of social distancing policies.

Detection of human adenovirus F41 in wastewater and its relationship to clinical cases of acute hepatitis of unknown aetiology

As of 8 July 2022, the World Health Organization (WHO) have reported 1010 probable cases of acute hepatitis of unknown aetiology in children worldwide, including approximately 250 cases in the United Kingdom (UK). Clinical presentations have often been severe, with liver transplantation a frequent clinical outcome. Human adenovirus F41 (HAdV-F41) has been detected in most children with acute hepatitis, but its role in the pathogenesis of this infection has yet to be established. Wastewater-based epidemiology (WBE) has become a well-established tool for monitoring the community spread of SARS-CoV-2, as well as other pathogens and chemicals. In this study, we adopted a WBE approach to monitoring levels of HAdV-F40/41 in wastewater before and during an acute hepatitis outbreak in Northern Ireland. We report increasing detection of HAdV-F40/41 in wastewater, concomitant with increasing numbers of clinical cases. Amplicon whole genome sequencing further classified the wastewater-derived HAdV as belonging to the F41 genotype which in turn was homologous to clinically derived sequences. We propose that WBE has the potential to inform community surveillance of HAdV-F41 and can further contribute to the ongoing global discussion supporting HAdV-F41 involvement in acute hepatitis cases. © 2022 The Authors

Application of machine learning approaches to predict the impact of ambient air pollution on outpatient visits for acute respiratory infections

With a remarkable increase in industrialization among fast-developing countries, air pollution is rising at an alarming rate and has become a public health concern. The study aims to examine the effect of air pollution on patient's hospital visits for respiratory diseases, particularly Acute Respiratory Infections (ARI). Outpatient hospital visits, air pollution and meteorological parameters were collected from March 2018 to October 2021. Eight machine learning algorithms (Random Forest model, K-Nearest Neighbors regression model, Linear regression model, LASSO regression model, Decision Tree Regressor, Support Vector Regression, X.G. Boost and Deep Neural Network with 5-layers) were applied for the analysis of daily air pollutants and outpatient visits for ARI. The evaluation was done by using 5-cross-fold confirmations. The data was randomly divided into test and training data sets at a scale of 1:2, respectively. Results show that among the studied eight machine learning models, the Random Forest model has given the best performance with R2 = 0.606, 0.608 without lag and 1-day lag respectively on ARI patients and R2 = 0.872, 0.871 without lag and 1-day lag respectively on total patients. All eight models did not perform well with the lag effect on the ARI patient dataset but performed better on the total patient dataset. Thus, the study did not find any significant association between ARI patients and ambient air pollution due to the intermittent availability of data during the COVID-19 period. This study gives insight into developing machine learning programs for risk prediction that can be used to predict analytics for several other diseases apart from ARI, such as heart disease and other respiratory diseases. © 2022 Elsevier B.V.

A Hybrid Deep Fused Learning Approach to Segregate Infectious Diseases

Humankind is facing another deadliest pandemic of all times in history, caused by COVID-19. Apart from this challenging pandemic, World Health Organization (WHO) considers tuberculosis (TB) as a preeminent infectious disease due to its high infection rate. Generally, both TB and COVID-19 severely affect the lungs, thus hardening the job of medical practitioners who can often misidentify these diseases in the current situation. Therefore, the time of need calls for an immediate and meticulous automatic diagnostic tool that can accurately discriminate both diseases. As one of the preliminary smart health systems that examine three clinical states (COVID-19, TB, and normal cases), this study proposes an amalgam of image filtering, data-augmentation technique, transfer learning-based approach, and advanced deep-learning classifiers to effectively segregate these diseases. It first employed a generative adversarial network (GAN) and Crimmins speckle removal filter on X-ray images to overcome the issue of limited data and noise. Each pre-processed image is then converted into red, green, and blue (RGB) and Commission Internationale de l'Elcairage (CIE) color spaces from which deep fused features are formed by extracting relevant features using DenseNet121 and ResNet50. Each feature extractor extracts 1000 most useful features which are then fused and finally fed to two variants of recurrent neural network (RNN) classifiers for precise discrimination of three-clinical states. Comparative analysis showed that the proposed Bi-directional long-short-term-memory (Bi-LSTM) model dominated the long-short-term-memory (LSTM) network by attaining an overall accuracy of 98.22% for the three-class classification task, whereas LSTM hardly achieved 94.22% accuracy on the test dataset. © 2023 Tech Science Press. All rights reserved.

Mathematical modeling of vaccination as a control measure of stress to fight COVID-19 infections

The world experienced the life-threatening COVID-19 disease worldwide since its inversion. The whole world experienced difficult moments during the COVID-19 period, whereby most individual lives were affected by the disease socially and economically. The disease caused millions of illnesses and hundreds of thousands of deaths worldwide. To fight and control the COVID-19 disease intensity, mathematical modeling was an essential tool used to determine the potentiality and seriousness of the disease. Due to the effects of the COVID-19 disease, scientists observed that vaccination was the main option to fight against the disease for the betterment of human lives and the world economy. Unvaccinated individuals are more stressed with the disease, hence their body's immune system are affected by the disease. In this study, the SVEIHR deterministic model of COVID-19 with six compartments was proposed and analyzed. Analytically, the next-generation matrix method was used to determine the basic reproduction number (R0). Detailed stability analysis of the no-disease equilibrium (E0) of the proposed model to observe the dynamics of the system was carried out and the results showed that E0 is stable if R0<1 and unstable when R0>1. The Bayesian Markov Chain Monte Carlo (MCMC) method for the parameter identifiability was discussed. Moreover, the sensitivity analysis of R0 showed that vaccination was an essential method to control the disease. With the presence of a vaccine in our SVEIHR model, the results showed that R0=0.208, which means COVID-19 is fading out of the community and hence minimizes the transmission. Moreover, in the absence of a vaccine in our model, R0=1.7214, which means the disease is in the community and spread very fast. The numerical simulations demonstrated the importance of the proposed model because the numerical results agree with the sensitivity results of the system. The numerical simulations also focused on preventing the disease to spread in the community. © 2022 The Authors