The Chaotic Behavior of the Spread of Infection During the COVID-19 Pandemic in the United States and Globally

Sapkota, Nabin; Karwowski, Waldemar; Davahli, Mohammad Reza; Aljuaid, Awad M.; Taı̈ar, Redha; Murata, Atsuo; Wróbel, Grzegorz; Marek, Tadeusz

doi:10.1109/access.2021.3085240

Cited by 20 publications

(19 citation statements)

References 33 publications

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“…This can be attributed to the fact that doctors were given an important role in the COVID-19 vaccine incentive programme in our country and that the recommendations received from doctors are more convincing, as shown in our study. Sapkota et al showed that the COVID-19 epidemic demonstrates chaotic behaviour, and it is difficult to predict the dynamics of the pandemic in the long term [ 24 ]. Based on these data, health policies should be constructed based on the effects of the physician awareness effect on population behaviour.…”

Section: Discussionmentioning

confidence: 99%

Awareness of Vaccination against Respiratory Tract Diseases, Including Pneumonia, Influenza, and COVID-19 in Patients with Diabetes Mellitus

Karagun

Evran

Odabaş

et al. 2022

International Journal of Clinical Practice

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Patients with diabetes have an increased risk of severe acute respiratory infections, and vaccination is their life-saving option. This study aimed to investigate the interest and knowledge of patients about influenza, pneumonia, and COVID-19(coronavirus disease 2019) vaccines. Materials. We handed out a questionnaire to patients with diabetes who were admitted to the endocrinology clinic between April and August 2021. The questionnaire collected information on demographic data, knowledge about respiratory tract disease vaccines, and hesitancy about vaccines. Results. Four hundred twenty-four patients (female = 256, male = 168) enrolled in the study. In this study, 148 (34.9%) participants were vaccinated against pneumonia, 155 (36.6%) against flu, and 312 (73.6%) against COVID-19. In addition, antivaccination sentiment was recorded in 8.7% of patients with diabetes. We found that participants in the study primarily rely on doctors as the source of information about vaccines (doctor (46.7%), nurse (1.2%), television (8.7%), friend/neighbour (8.7%), and others (2.6%)). The rate of vaccination was statistically higher than the presence of comorbid diseases. Conclusions. We examined the vaccine awareness of patients with diabetes and investigated factors affecting it. İt was determined that vaccination awareness is affected by many factors, especially comorbid diseases and educational status. The study showed that patients primarily relied on doctors as their source of information for vaccination. Doctor-centered vaccination promotion programmes can increase the rate of vaccination.

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Section: Discussionmentioning

confidence: 99%

Awareness of Vaccination against Respiratory Tract Diseases, Including Pneumonia, Influenza, and COVID-19 in Patients with Diabetes Mellitus

Karagun

Evran

Odabaş

et al. 2022

International Journal of Clinical Practice

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“…With increasing incidences of emerging diseases such as COVID-19, for which immediate treatments were not available, the need for drugs to treat the disease became indispensable. With the SARS-CoV virus, mutating at a fast pace and affecting millions of people globally, there was a pressing need to address this issue by using all alternative approaches to discovering a drug [ 36 , 37 ]. In this light, drug repurposing came into the picture.…”

Section: Need For Repurposed Molecules and The Latest Developments In...mentioning

confidence: 99%

Scope of repurposed drugs against the potential targets of the latest variants of SARS-CoV-2

Niranjan¹,

Setlur²,

Karunakaran³

et al. 2022

Struct Chem

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The unprecedented outbreak of the severe acute respiratory syndrome (SARS) Coronavirus-2, across the globe, triggered a worldwide uproar in the search for immediate treatment strategies. With no specific drug and not much data available, alternative approaches such as drug repurposing came to the limelight. To date, extensive research on the repositioning of drugs has led to the identification of numerous drugs against various important protein targets of the coronavirus strains, with hopes of the drugs working against the major variants of concerns (alpha, beta, gamma, delta, omicron) of the virus. Advancements in computational sciences have led to improved scope of repurposing via techniques such as structure-based approaches including molecular docking, molecular dynamic simulations and quantitative structure activity relationships, network-based approaches, and artificial intelligence-based approaches with other core machine and deep learning algorithms. This review highlights the various approaches to repurposing drugs from a computational biological perspective, with various mechanisms of action of the drugs against some of the major protein targets of SARS-CoV-2. Additionally, clinical trials data on potential COVID-19 repurposed drugs are also highlighted with stress on the major SARS-CoV-2 targets and the structural effect of variants on these targets. The interaction modelling of some important repurposed drugs has also been elucidated. Furthermore, the merits and demerits of drug repurposing are also discussed, with a focus on the scope and applications of the latest advancements in repurposing.

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“…The quality of life due to COVID-19 has gotten worse, including increased anxiety and depression [119]. The chaotic behavior of COVID-19 has caused it to spread as a nonlinear infection throughout different parts of the world [120]. Certain places of the world have a larger number of infections, while some have lower numbers.…”

Section: Discussionmentioning

confidence: 99%

Four Types of Multiclass Frameworks for Pneumonia Classification and Its Validation in X-ray Scans Using Seven Types of Deep Learning Artificial Intelligence Models

et al. 2022

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Background and Motivation: The novel coronavirus causing COVID-19 is exceptionally contagious, highly mutative, decimating human health and life, as well as the global economy, by consistent evolution of new pernicious variants and outbreaks. The reverse transcriptase polymerase chain reaction currently used for diagnosis has major limitations. Furthermore, the multiclass lung classification X-ray systems having viral, bacterial, and tubercular classes—including COVID-19—are not reliable. Thus, there is a need for a robust, fast, cost-effective, and easily available diagnostic method. Method: Artificial intelligence (AI) has been shown to revolutionize all walks of life, particularly medical imaging. This study proposes a deep learning AI-based automatic multiclass detection and classification of pneumonia from chest X-ray images that are readily available and highly cost-effective. The study has designed and applied seven highly efficient pre-trained convolutional neural networks—namely, VGG16, VGG19, DenseNet201, Xception, InceptionV3, NasnetMobile, and ResNet152—for classification of up to five classes of pneumonia. Results: The database consisted of 18,603 scans with two, three, and five classes. The best results were using DenseNet201, VGG16, and VGG16, respectively having accuracies of 99.84%, 96.7%, 92.67%; sensitivity of 99.84%, 96.63%, 92.70%; specificity of 99.84, 96.63%, 92.41%; and AUC of 1.0, 0.97, 0.92 (p < 0.0001 for all), respectively. Our system outperformed existing methods by 1.2% for the five-class model. The online system takes <1 s while demonstrating reliability and stability. Conclusions: Deep learning AI is a powerful paradigm for multiclass pneumonia classification.

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The Chaotic Behavior of the Spread of Infection During the COVID-19 Pandemic in the United States and Globally

Cited by 20 publications

References 33 publications

Awareness of Vaccination against Respiratory Tract Diseases, Including Pneumonia, Influenza, and COVID-19 in Patients with Diabetes Mellitus

Awareness of Vaccination against Respiratory Tract Diseases, Including Pneumonia, Influenza, and COVID-19 in Patients with Diabetes Mellitus

Scope of repurposed drugs against the potential targets of the latest variants of SARS-CoV-2

Four Types of Multiclass Frameworks for Pneumonia Classification and Its Validation in X-ray Scans Using Seven Types of Deep Learning Artificial Intelligence Models

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