Browsing by Author "Mohapatra, Ranjan K."

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    Global emerging Omicron variant of SARS-CoV-2: Impacts, challenges and strategies
    (Elsevier, 2023) Dhama, Kuldeep; Nainu, Firzan; Frediansyah, Andri; Yatoo, Mohd Iqbal; Mohapatra, Ranjan K.; Chakraborty, Sandip; Zhou, Hao; Islam, Md Rabiul; Mamada, Sukamto S.; Kusuma, Hendrix Indra; Rabaan, Ali A.; Alhumaid, Saad; Al Mutair, Abbas; Iqhrammullah, Muhammad; Al-Tawfiq, Jaffar A.; Al Mohaini, Mohammed; Alsalman, Abdulkhaliq J.; Tuli, Hardeep Singh; Chakraborty, Chiranjib; Harapan, Harapan; Medicine, School of Medicine
    Newly emerging variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are continuously posing high global public health concerns and panic resulting in waves of coronavirus disease 2019 (COVID-19) pandemic. Depending on the extent of genomic variations, mutations and adaptation, few of the variants gain the ability to spread quickly across many countries, acquire higher virulency and ability to cause severe disease, morbidity and mortality. These variants have been implicated in lessening the efficacy of the current COVID-19 vaccines and immunotherapies resulting in break-through viral infections in vaccinated individuals and recovered patients. Altogether, these could hinder the protective herd immunity to be achieved through the ongoing progressive COVID-19 vaccination. Currently, the only variant of interest of SARS-CoV-2 is Omicron that was first identified in South Africa. In this review, we present the overview on the emerging SARS-CoV-2 variants with a special focus on the Omicron variant, its lineages and hybrid variants. We discuss the hypotheses of the origin, genetic change and underlying molecular mechanism behind higher transmissibility and immune escape of Omicron variant. Major concerns related to Omicron including the efficacy of the current available immunotherapeutics and vaccines, transmissibility, disease severity, and mortality are discussed. In the last part, challenges and strategies to counter Omicron variant, its lineages and hybrid variants amid the ongoing COVID-19 pandemic are presented.
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    Symptom-Based COVID-19 Prognosis through AI-Based IoT: A Bioinformatics Approach
    (Hindawi, 2022-07-23) Pal, Madhumita; Parija, Smita; Mohapatra, Ranjan K.; Mishra, Snehasish; Rabaan, Ali A.; Al Mutair, Abbas; Alhumaid, Saad; Al-Tawfiq, Jaffar A.; Dhama, Kuldeep; Medicine, School of Medicine
    Objective: Internet of Things (IoT) integrates several technologies where devices learn from the experience of each other thereby reducing human-intervened likely errors. Modern technologies like IoT and machine learning enable the conventional to patient-specific approach transition in healthcare. In conventional approach, the biggest challenge faced by healthcare professionals is to predict a disease by observing the symptoms, monitoring the remote area patient, and also attending to the patient all the time after being hospitalised. IoT provides real-time data, makes decision-making smarter, and provides far superior analytics, and all these to help improve the quality of healthcare. The main objective of the work was to create an IoT-based automated system using machine learning models for symptom-based COVID-19 prognosis. Methods: Comparative analysis of predictive microbiology of COVID-19 from case symptoms using various machine learning classifiers like logistics regression, k-nearest neighbor, support vector machine, random forest, decision trees, Naïve Bayes, and gradient booster is reported here. For the sake of the validation and verification of the models, performance of each model based on the retrieved cloud-stored data was measured for accuracy. Results: From the accuracy plot, it was concluded that k-NN was more accurate (97.97%) followed by decision tree (97.79), support vector machine (97.42), logistics regression (96.50), random forest (90.66), gradient boosting classifier (87.77), and Naïve Bayes (73.50) in COVID-19 prognosis. Conclusion: The paper presents a health monitoring IoT framework having high clinical significance in real-time and remote healthcare monitoring. The findings reported here and the lessons learnt shall enable the healthcare system worldwide to counter not only this ongoing COVID but many other such global pandemics the humanity may suffer from time to come.