A Review on Challenges in Telerehabilitation and Human Activity Recognition Approaches during Covid-19 Pandemic

Impact of COVID-19 pandemic is widespread imposing limitations on the healthcare services all over the world. Due to this pandemic, governments around the world have imposed restrictions that limit individual freedom and have enforced social distance to prevent the collapse of national health care systems. In such situation, to offer medical care and rehabilitation to the patients, Telerehabilitation (TR) is a promising way of delivering healthcare facilities remotely using telecommunication and internet. Technological advancement has played the vital role to establish this TR technology to remotely assess patient's physical condition and act accordingly during this pandemic. Likewise, Human Activity Recognition (HAR) is a key part of the recovery process for a wide variety of conditions, such as stroke, arthritis, brain injury, musculoskeletal injuries, Parkinson's disease, and others. Different approaches of human activity recognition can be utilized to monitor the health and activity levels of such a patient effectively and TR allows to do this remotely. Therefore, in situations where conventional care is inadequate, combination of telerehabilitation and HAR approaches can be an effective means of providing treatment and these opportunities have become patently apparent during the COVID-19 outbreak. However, this new era of technical progress has significant limitations, and in this paper, our main focus is on the challenges of telerehabilitation and the various human activity recognition approaches. This study will help researchers identify a good activity detection platform for a TR system during and after COVID-19, considering TR and HAR challenges.

Artificial Intelligence- assisted Security Analysis of Smart Healthcare Systems

The healthcare sector has been overburdened with the massive outbreak of COVID-19 for almost two years and has created an urgent need for remote patient monitoring and treatment with minimal human involvement. Adopting artificial-intelligence- (AI)-based techniques for patient treatment has brought a dramatic revolution into the modern smart healthcare system (SHS). Modern healthcare is leveraging the internet of medical things (IoMT) network comprised of wireless body sensor devices (WBSDs) and implantable medical devices (IMDs) to reduce treatment latency and cost drastically. However, the open network communication of the less secured IoMT devices and increasing growth of adversarial capability give rise to several vulnerabilities, which need to be taken into consideration while designing SHS. We propose a novel and comprehensive framework with machine learning (ML) and formal analysis capability to build a secure and attack-resilient SHS. Our framework uses a novel ensemble of unsupervised ML-based patient status classification and anomaly detection models with bio-inspired computing (BIC)-based ML models' hyperparameter optimization techniques. The proposed anomaly detection model (ADM) can detect zero-day attacks and uses a novel fitness function calculation technique for BIC-based SHS ADM's hyperparameter optimization. Moreover, the framework leverages novel formal attack analytics to assess the robustness of the underlying classification and abnormality detection models. Our framework is evaluated using the University of Queensland Vital Signs dataset and a realistic synthetic dataset. © 2023 selection and editorial matter, Fei Hu and Xiali Hei;individual chapters, the contributors.

Modeling the epidemic trend of middle eastern respiratory syndrome coronavirus with optimal control

Since the outbreak of the Middle East Respiratory Syndrome Coronavirus (MERS-CoV) in 2012 in the Middle East, we have proposed a deterministic theoretical model to understand its transmission between individuals and MERS-CoV reservoirs such as camels. We aim to calculate the basic reproduction number (R0) of the model to examine its airborne transmission. By applying stability theory, we can analyze and visualize the local and global features of the model to determine its stability. We also study the sensitivity of R0 to determine the impact of each parameter on the transmission of the disease. Our model is designed with optimal control in mind to minimize the number of infected individuals while keeping intervention costs low. The model includes time -dependent control variables such as supportive care, the use of surgical masks, government campaigns promoting the importance of masks, and treatment. To support our analytical work, we present numerical simulation results for the proposed model.

Android Based Smart Appointment System (SAS) for Booking and Interacting with Teacher for Counselling

An appointment system is going to be popular nowadays. The necessity of these types of systems is increasing day by day specially in education sector. Worldwide COVID-19 pandemic provoke the demand of these types of application. In this research paper, an Android-based appointment is built for booking an appointment and communicating with the teacher. To use this system both student and teacher have to an android device with connection of the internet. A single android application will be used for both types of users. Students can get the information of all teachers and book an appointment with teachers and teachers can accept or decline this appointment. Java programming language is used for this system and Google's Firebase is used for the database. In addition, the modern coding Architecture pattern MVVM (Model- View-View Model) followed to build this system. Hopefully, this system saves valuable time and makes the teacher-student interaction journey easier. © 2023 IEEE.

Study on Tea Consumption Behavior During the Covid-19 Pandemic and the Analysis of the Antioxidant Activity and Bioactive Compounds of Flavoring Ingredients of Tea

Tea is one of the most popular and oldest beverages available in many varieties and the use of different flavoring ingredients is becoming more common. The present study aimed to examine tea consumption behavior during the COVID-19 pandemic and analyzed the bioactive compounds of tea flavoring ingredients. At first, a cross-sectional study was carried out with 140 randomly selected participants to determine tea consumption patterns and data was collected through face-to-face interviews. Then 2,2-diphenyl-1-picrylhydrazyl (DPPH) test, the Folin-Ciocalteu technique, and the quercetin method were used to assess antioxidant activity, total phenolic content (TPC), and total flavonoid content (TFC) of tea flavoring ingredients. The study found that 57.86% of the participants increased their tea consumption during the COVID-19 pandemic, whereas 22.80% increased their tea consumption by at least one more cup per day. It was also found that ginger was the most popular (29.5%) among fifteen tea flavoring agents. By analyzing tea flavoring ingredients, the maximum antioxidant activity found in cinnamon was 87%, and lemon leaves had the lowest, which was 60%. On a dry weight basis, the TPC of the tea flavoring components ranged from 36.52 mg GAE/g for cloves to 9.62 mg GAE/g for ginger. The maximum TFC was also found in clove with 13.68 mg QE/g, and moringa was the second highest with 12.26 mg GAE/g. The antioxidant activity of flavoring compounds has a significant correlation (p<0.05) with TPC and TFC. Overall, tea consumption behavior with tea flavoring ingredients increased during the COVID-19 pandemic situation. Tea with flavoring ingredients may be one of the best dietary sources of antioxidants, TPC, and TFC which are important for strengthening the immune system and controlling different physiological and metabolic disorders. © 2023 The Author(s). Published by Enviro Research Publishers.

A Hybrid Method for Analyzing the Situation Based on Cumulative Fully Vaccinated and Confirmed Cases of Covid-19 in Malaysia

SARS-CoV-2 is an infection that affects several organs and has a wide range of symptoms in addition to producing severe acute respiratory syndrome. Millions of individuals were infected when it first started because of how quickly it travelled from its starting location to nearby countries. Anticipating positive Covid-19 incidences is required in order to better understand future risk and take the proper preventative and precautionary measures. As a result, it is critical to create mathematical models that are durable and have as few prediction errors as possible. This study suggests a unique hybrid strategy for examining the status of Covid-19 confirmed patients in conjunction with complete vaccination. First, the selective opposition technique is initially included into the Grey Wolf Optimizer (GWO) in this study to improve the exploration and exploitation capacity for the given challenge. Second, to execute the prediction task with the optimized hyper-parameter values, the Least Squares Support Vector Machines (LSSVM) method is integrated with Selective Opposition based GWO as an objective function. The data source includes daily occurrences of confirmed cases in Malaysia from February 24, 2021 to July 27, 2022. Based on the experimental results, this paper shows that SOGWO-LSSVM outperforms a few other hybrid techniques with ideally adjusted parameters. © 2022 IEEE.

Vocabulary Index as a Sustainable Resource for Teaching Extended Writing in the Post-Pandemic Era

In the wake of the COVID-19 pandemic, Malaysian English teachers identified a pressing need to support upper primary school pupils, particularly those in the upper levels, in the effective composition of extended writing. Additionally, these educators required more innovative methodologies for teaching vocabulary in this context. Consequently, the current study aimed to develop a vocabulary index as a suggested resource for Malaysian English teachers instructing upper primary school pupils on extended writing. To achieve this, a quantitative computational research strategy and corpus-driven research design were employed. A purposive sampling technique was used to select 560 advanced upper primary school pupils from 28 schools, each with high English performance in the capital of each state and the federal territory of Malaysia, who produced a total of 152,187 words in extended writing for analysis. LancsBox, a primary computational linguistics application, was used for data processing. Given that the vocabulary index for extended writing necessitates a more comprehensive coverage of vocabulary, functional and content words were included, and keywords, raw and normalised frequencies were analysed and reported. Through the vocabulary index built in this study, the researchers found English teachers in Malaysia should utilise local issues in writing prompts, emphasise the use of both positive and negative adjectives, introduce complex sentence structures to enhance pupils‟ writing abilities and also train pupils to organise the ideas in their writing. Future linguistic studies could replicate the present investigation, so that it can respond to their classroom needs. © Annals of Translational Medicine. All rights reserved.

Islamic Social Funds to Foster Yunusian Social Business and Conventional Social Enterprises

This paper proposes an integrated, comprehensive financial model that can provide startup capital to socially committed business ventures, such as social enterprises and Yunus Social Business (YSB), by using Islamic social funds (ISFs), Zakat (almsgiving), Waqf (endowments), Sadaqat (charity), and Qard Hasan (interest-free benevolent loans). The literature review method was adopted to explain this model's architecture, applications, implications, and viability. On the basis of logical reasoning, it concludes that ISFs can yield greater social wellbeing if utilised in SEs and YSB than in unconditional charity because both business models work for social betterment in entrepreneurial ways while remaining operationally self-reliant and economically sustainable. Additionally, ISFs can complement Yunus Social Business's zero-return investment approach to make it more robust towards social contributions. The implementation of the model orchestrated in this paper would enhance societal business practices and, hence, scale up social wellbeing while helping rejuvenate pandemic-stricken economies. It paves the way for new research too. © 2023 by the authors.

Containing an Island: Coronavirus (Covid-19) Outbreak in Perhentian Islands, Terengganu State of Malaysia, in 2021

Background: The spread of COVID-19 was inevitable and has not spared small and isolated communities, including the community on Perhentian Island in Besut District, Terengganu. Managing clusters in small islands can be difficult, given the limited resources. This study explores the characteristics of COVID-19 cases and the experience of outbreak containment at Perhentian Island. Methodology: A retrospective study involving record review of COVID-19 cases and at-risk individuals registered under the Perhentian Cluster were retrieved from the Besut District Health Office COVID-19 online registry from the 16th August 2021 until 6th October 2021. All notified cases and close contacts who fulfilled the inclusion criteria were extracted and analysed using descriptive statistics. Result(s): A total of 1,093 out of 2,500 community members of Perhentian Island were screened of which 170 (15.5%) tested positive for COVID-19, while 923 (84.5%) tested negative. Among individuals who tested positive, the majority were adults (52.4%), males (51.8%), Malays (98.8%), and villagers (96.5%). Clinical characteristics were categorized into: asymptomatic (55.9%), had no known medical comorbidities (90.6%), low-risk groups (87.1%), vaccinated (57.6%), and admitted to PKRC (97.1%) for treatment. Multiple agencies were involved in the outbreak containment of the Perhentian Cluster, working collectively and in good coordination. Conclusion(s): The outbreak was attributed to community gatherings and close interactions among villagers. Prompt actions, targeted planning, and inter-agency collaboration were the key factors in successful containment of further spread of COVID-19 in Perhentian Island.Copyright © 2023, Faculty of Medicine, University of Malaya. All rights reserved.

Convolutional Neural Network Based Feature Hybridization For Ct Scan Images: Covid-19 Detection Perspective

Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. Early diagnosis is only the proactive process to resist against the unwanted death. However, machine vision-based diagnosis systems show unparalleled success with higher accuracy and low false diagnosis rate. Working with the proposed method, this research has found that Computed Tomography (CT) provides more satisfactory outcomes regarding all the performance metrics. The proposed method uses a feature hybridization technique of concatenating the textural features with neural features. The literature review suggests that medical experts recommended chest CT in covid diagnosis rather than chest X-ray as well as RT-PCR. It is found that chest CT is more effective in diagnosis for being low false-negative rate. Moreover, the proposed method has used segmentation technique to dig the potential region of interest and obtain accurate features. Compared with different CNN classifier, such as, VGG-16, AlexNet, VGG-19 or ResNet50 and scratch model also. To obtain the satisfactory performance VGG-19 was used in this study. The Proposed machine learning based fusion technique achieves superior performance according to COVID-19 positive or negative with the accuracy of 98.63%, specificity of 99.08% and sensitivity of 98.18%. © 2022 IEEE.

Lung Disease Classification Using Deep Learning Models from Chest X-ray Images

In the very recent past, Infectious disease-related sickness has long posed a concern on a global scale. Each year, COVID-19, pneumonia, and tuberculosis cause a large number of deaths because they all affect the lungs. Early detection and diagnosis can increase the likelihood of receiving quality treatment in all circumstances. A low-cost, simple imaging approach called chest X-ray imaging enables to detection and screen lung abnormalities brought on by infectious diseases for example Covid-19, pneumonia, and tuberculosis. This paper provided a thorough analysis of current deep-learning methods for diagnosing Covid-19, pneumonia, and TB. According to the research papers reviewed, Deep Convolutional Neural Network is the most used deep learning method for identifying Covid-19, pneumonia, and TB from chest X-ray (CXR) images. We compared the proposed DNN to well-known DNNs like Efficient-NetB0, DenseNet169, and DenseNet201 in order to more accurately assess how well it performed. Our findings are equivalent to the state-of-the-art, and since the proposed CNN is lightweight, it may be employed for widespread screening in areas with limited resources. From three diverse publicly accessible datasets merged into one dataset, the suggested DNN generated the following precisions for that dataset: 99.15%, 98.89%, and 97.79% for EfficientNetB0, DenseNet169, and DenseNet201 respectively. The proposed network can help radiologists make quick and accurate diagnoses because it is effective at identifying COVID-19 and other lung contagious disorders utilizing chest X-ray images. This paper also gives young scientists a good insight into how to create CNN models that are highly efficient when used with medical images to identify diseases early. © 2023 IEEE.

A bioinformatics approach to characterize a hypothetical protein Q6S8D9_SARS of SARS-CoV

Characterization as well as prediction of the secondary and tertiary structure of hypothetical proteins from their amino acid sequences uploaded in databases by in silico approach are the critical issues in computational biology. Severe acute respiratory syndrome-associated coronavirus (SARS-CoV), which is responsible for pneumonia alike diseases, possesses a wide range of proteins of which many are still uncharacterized. The current study was conducted to reveal the physicochemical characteristics and structures of an uncharacterized protein Q6S8D9_SARS of SARS-CoV. Following the common flowchart of characterizing a hypothetical protein, several sophisticated computerized tools e.g., ExPASy Protparam, CD Search, SOPMA, PSIPRED, HHpred, etc. were employed to discover the functions and structures of Q6S8D9_SARS. After delineating the secondary and tertiary structures of the protein, some quality evaluating tools e.g., PROCHECK, ProSA-web etc. were performed to assess the structures and later the active site was identified also by CASTp v.3.0. The protein contains more negatively charged residues than positively charged residues and a high aliphatic index value which make the protein more stable. The 2D and 3D structures modeled by several bioinformatics tools ensured that the proteins had domain in it which indicated it was functional protein having the ability to trouble host antiviral inflammatory cytokine and interferon production pathways. Moreover, active site was found in the protein where ligand could bind. The study was aimed to unveil the features and structures of an uncharacterized protein of SARS-CoV which can be a therapeutic target for development of vaccines against the virus. Further research are needed to accomplish the task.

Human Lung Non-Invasive Anomaly Detection through UWB Microwave Imaging and Diagnosis of COVID-19: A Possible Application

This study shows a prototype for detecting lung effects using microwave imaging. Continuous monitoring of pulmonary fluid levels is one of the most successful approaches for detecting fluid in the lung;early Chest X-rays, computational tomography (CT)-scans, and magnetic resonance imaging (MRI) are the most commonly used instruments for fluid detection. Nonetheless, they lack sensitivity to ionizing radiation and are inaccessible to the general public. This research focuses on the development of a low-cost, portable, and noninvasive device for detecting Covid-19 or lung damage. The simulation of the system involved the antenna design, a 3D model of the human lung, the building of a COMSOL model, and image processing to estimate the lung damage percentage. The simulation consisted of three components. The primary element requires mode switching for four array antennas (transmit and receive). In the paper, microwave tomography was used. Using microwave near-field imaging, the second component of the simulation analyses the lung's bioheat and electromagnetic waves as well as examines the image creation under various conditions;many electromagnetic factors seen at the receiving device are investigated. The final phase of the simulation shows the affected area of the lung phantom and the extent of the damage. © 2023 IEEE.

Post-Covid-19 Irritable Bowel Syndrome

Background and aim: The long-term consequences of COVID- 19 infection on the gastrointestinal tract remain unclear. Here we aimed to evaluate the prevalence of gastrointestinal symptoms and post-COVID-19 disorders of gut-brain interaction (DGBI) after hospitalization for SARS-CoV-2 infection. Material(s) and Method(s): GI-COVID19 is a prospective, multicenter, controlled study. Patients with and without COVID-19 diagnosis were evaluated upon hospital admission and after 1, 6, and 12 months post-hospitalization. Gastrointestinal symptoms, anxiety, and depression were assessed using validated questionnaires, namely the Gastrointestinal Symptoms Rating Scale (GSRS), the Hanxiety and Depression Scale (HADS) and the Rome IV Diagnostic Questionnaire for Functional Gastrointestinal Disorders in Adults. Result(s): The study included 2183 hospitalized patients. The primary analysis included a total of 883 patients (614 COVID-19 patients and 269 controls) due to the exclusion of patients with pre-existing gastrointestinal symptoms and/or surgery. At enrollment, gastrointestinal symptoms were more frequent among COVID-19 patients than in the control group (59.3% vs. 39.7%, P<0.001). At the 12-month follow- up, constipation and hard stools were significantly more prevalent in controls than in COVID-19 patients (16% vs. 9.6%, P=0.019 and 17.7% vs. 10.9%, P=0.011, respectively). Compared to controls, COVID- 19 patients reported higher rates of irritable bowel syndrome (IBS) according to Rome IV criteria: 0.5% vs. 3.2%, P=0.045. Factors significantly associated with IBS diagnosis included history of allergies, chronic intake of proton pump inhibitors, and presence of dyspnea. [Table presented] At the 6-month follow-up, the rate of COVID-19 patients fulfilling the criteria for depression was higher than among controls. Conclusion(s): Compared to controls, hospitalized COVID-19 patients had fewer complaints of constipation and hard stools at 12 months after acute infection. COVID-19 patients had significantly higher rates of IBS than controls. ClinicalTrials.gov number, NCT04691895.Copyright © 2023. Editrice Gastroenterologica Italiana S.r.l.

Clinical Characteristics of Deceased with COVID-19 Infection: Data from the Largest COVID-19 Dedicated Hospital in Bangladesh

The death t toll of the coronavirus disease 2019 (COVID-19) has been considerable. Several risk factors have been linked to mortality due to COVID-19 in hospitals. This study aimed to describe the clinical characteristics of patients who either died from COVID-19 at Dhaka Medical College Hospital in Bangladesh. In this retrospective study, we reviewed the hospital records of patients who died or recovered and tested positive for COVID-19 from May 3 to August 31, 2020. All patients who died during the study period were included in the analysis. A comparison group of patients who survived COVID-19 at the same hospital during the same period was systematically sampled. All available information was retrieved from the records, including demographic, clinical, and laboratory variables. Of the 3115 patients with confirmed COVID-19 during the study period, 282 died.The mean age of patients who died was higher than that of those who survived (56.7 vs 52.6 years). Approximately three-fourths of deceased patients were male. History of smoking (risk ratio 2.3;95% confidence interval: 1.6-3.4), comorbidities (risk ratio: 1.5;95% confidence interal:1.1-2.1), chronic kidney disease (risk ratio: 3.2;95% confidence interval: 1.7-6.25), and ischemic heart disease (risk ratio:1.8;95% confidence interval: 1.1-2.9) were higher among the deceased than among those who survived. Mean C-reactive protein and D-dimer levels [mean (interquartile range), 34 (21-56) vs. 24 (12-48);and D-dimer [1.43 (1-2.4) vs. 0.8 (0.44-1.55)] were higher among those who died than among those who recovered. Older age, male sex, rural residence, history of smoking, and chronic kidney disease were found to be important predictors of mortality. Early hospitalization should be considered for patients with COVID-19 who are older, male, and have chronic kidney disease. Rapid referral to tertiary care facilities is necessary for high-risk patients in rural settings.Copyright © 2023 Hoque MM.

Student Attention Base Facial Emotion State Recognition Using Convolutional Neural Network

E-learning system advancements give students new opportunities to better their academic performance and access e-learning education. Because it provides benefits over traditional learning, e-learning is becoming more popular. The coronavirus disease pandemic situation has caused educational institution cancelations all across the world. Around all over the world, more than a billion students are not attending educational institutions. As a result, learning criteria have taken on significant growth in e-learning, such as online and digital platform-based instruction. This study focuses on this issue and provides learners with a facial emotion recognition model. The CNN model is trained to assess images and detect facial expressions. This research is working on an approach that can see real-time facial emotions by demonstrating students' expressions. The phases of our technique are face detection using Haar cascades and emotion identification using CNN with classification on the FER 2013 datasets with seven different emotions. This research is showing real-time facial expression recognition and help teachers adapt their presentations to their student's emotional state. As a result, this research detects that emotions' mood achieves 62% accuracy, higher than the state-of-the-art accuracy while requiring less processing. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

COVID-19 impact on tourism inflow in selected Asia-Pacific countries: a gravity model framework

Purpose: The main objective of this paper is to examine the impact of COVID-19 on the tourism flows of eight Asia-Pacific Countries: Australia, Hong Kong, Malaysia, New Zealand, the Philippines, Singapore, Taiwan and Thailand. Design/methodology/approach: Using monthly data from 2019M1 to 2021M10 and 48 origin and eight destination countries in a panel Poisson pseudo-maximum likelihood (PPML) estimation technique and gravity equation framework, this paper finds that after controlling for gravity determinants, COVID-19 periods have a 0.689% lower tourism inflow than in non-COVID-19 periods. The total observations in this paper are 12,138. Findings: A 1% increase in COVID-19 transmission in the origin country leads to a 0.037% decline in tourism flow in the destination country, while the reduction is just 0.011% from the destination. On the mortality side, the corresponding decline in tourism flows from origin countries is 0.030%, whereas it is 0.038% from destination countries. A 1% increase in vaccine intensity in the destination country leads to a 0.10% improvement in tourism flows, whereas vaccinations at the source have no statistically significant effect. The results are also robust at a 1% level in a pooled OLS and random-effects specification for the same model. Research limitations/implications: The findings provide insights into managing tourism flows concerning transmission, death and vaccination coverage in destination and origin countries. Practical implications: The COVID-19-induced tourism decline may also be considered another channel through which the global recession has been aggravated. If we convert this decline in terms of loss of GDP, the global figure will be huge, and airline industries will have to cut down many service products for a long time to recover from the COVID-19-induced tourism decline. Social implications: It is to be realized by the policymaker and politicians that infectious diseases have no national boundary, and the problem is not local or national. That's why it is to be faced globally with cooperation from all the countries. Originality/value: This is the first paper to address tourism disruption due to COVID-19 in eight Asia-Pacific countries using a gravity model framework. Highlights: Asia-Pacific countries are traditionally globalized through tourism channels This pattern was severely affected by COVID-19 transmission and mortality and improved through vaccination The gravity model can be used to quantify the loss in the tourism sector due to COVID-19 shocks Transmission and mortality should be controlled both at the origin and the destination countries Vaccinations in destination countries significantly raise tourism flows. © 2023, Emerald Publishing Limited.

Tools and Techniques Adapted for Teaching Software Engineering Topics Remotely during the COVID-19 Pandemic

To stop the spread of the COVID-19 virus, educational institutions abruptly switched from in-person to online, remote mode of teaching without giving educators the necessary tools and training. In this paper, we focus on the Software Engineering Education & Training (SEET) courses at the university levels and address questions like: What tools and techniques did they adapt to handle the modality transition challenges? What lessons they learned and what would they do differently the next time? What are the students' perspective on these, etc.? We interviewed 16 SEET educators from different countries around the world;followed by surveys of more than 300 educator and student participants. Our empirical study found some common themes of challenges, as well as suggestions on tools and techniques to overcome them. © 2022 IEEE Computer Society. All rights reserved.

Temporal dynamics and fatality of SARS-CoV-2 variants in Bangladesh.

Background and Aims: Since the beginning of the SARS-CoV-2 pandemic, multiple new variants have emerged posing an increased risk to global public health. This study aimed to investigate SARS-CoV-2 variants, their temporal dynamics, infection rate (IFR) and case fatality rate (CFR) in Bangladesh by analyzing the published genomes. Methods: We retrieved 6610 complete whole genome sequences of the SARS-CoV-2 from the GISAID (Global Initiative on Sharing all Influenza Data) platform from March 2020 to October 2022, and performed different in-silico bioinformatics analyses. The clade and Pango lineages were assigned by using Nextclade v2.8.1. SARS-CoV-2 infections and fatality data were collected from the Institute of Epidemiology Disease Control and Research (IEDCR), Bangladesh. The average IFR was calculated from the monthly COVID-19 cases and population size while average CFR was calculated from the number of monthly deaths and number of confirmed COVID-19 cases. Results: SARS-CoV-2 first emerged in Bangladesh on March 3, 2020 and created three pandemic waves so far. The phylogenetic analysis revealed multiple introductions of SARS-CoV-2 variant(s) into Bangladesh with at least 22 Nextstrain clades and 107 Pangolin lineages with respect to the SARS-CoV-2 reference genome of Wuhan/Hu-1/2019. The Delta variant was detected as the most predominant (48.06%) variant followed by Omicron (27.88%), Beta (7.65%), Alpha (1.56%), Eta (0.33%) and Gamma (0.03%) variant. The overall IFR and CFR from circulating variants were 13.59% and 1.45%, respectively. A time-dependent monthly analysis showed significant variations in the IFR (p = 0.012, Kruskal-Wallis test) and CFR (p = 0.032, Kruskal-Wallis test) throughout the study period. We found the highest IFR (14.35%) in 2020 while Delta (20A) and Beta (20H) variants were circulating in Bangladesh. Remarkably, the highest CFR (1.91%) from SARS-CoV-2 variants was recorded in 2021. Conclusion: Our findings highlight the importance of genomic surveillance for careful monitoring of variants of concern emergence to interpret correctly their relative IFR and CFR, and thus, for implementation of strengthened public health and social measures to control the spread of the virus. Furthermore, the results of the present study may provide important context for sequence-based inference in SARS-CoV-2 variant(s) evolution and clinical epidemiology beyond Bangladesh.