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Background: Infection Prevention and Control (IPC) is critical in controlling the COVID-19 pandemic and is one of the pillars of the WHO COVID-19 Strategic Preparedness and Response Plan 2020. We conducted an Intra-Action Review (IAR) of IPC response efforts to the COVID-19 pandemic in Cox's Bazar, Bangladesh, to identify best practices, challenges, and recommendations for improvement of the current and future responses., Methods: We conducted two meetings with 54 participants purposively selected from different organizations and agencies involved in the frontline implementation of IPC in Cox's Bazar district, Bangladesh. We used the IPC trigger questions from the WHO country COVID-19 IAR: trigger question database to guide the discussions. Meeting notes and transcripts were then analyzed manually using content analysis, and results were presented in text and quotes., Results: Best practices included: assessments, a response plan, a working group, trainings, early case identification and isolation, hand hygiene in Health Facilities (HFs), monitoring and feedback, general masking in HFs, supportive supervision, design, infrastructure and environmental controls in Severe Acute Respiratory Infection Isolation and Treatment Centers (SARI ITCs) and HFs and waste management. Challenges included: frequent breakdown of incinerators, limited PPE supply, inconsistent adherence to IPC, lack of availability of uniforms for health workers, in particular cultural and gender appropriate uniforms and Personal Protective Equipment (PPE). Recommendations from the IAR were: (1) to promote the institutionalization of IPC, programs in HFs (2) establishment of IPC monitoring mechanisms in all HCFs, (3) strengthening IPC education and training in health care facilities, and (4) strengthen public health and social measures in communities., Conclusion: Establishing IPC programmes that include monitoring and continuous training are critical in promoting consistent and adaptive IPC practices. Response to a pandemic crisis combined with concurrent emergencies, such as protracted displacement of populations with many diverse actors, can only be successful with highly coordinated planning, leadership, resource mobilization, and close supervision., (© 2023. The Author(s).)

The next big step in combating the COVID-19 pandemic will be gaining widespread acceptance of a vaccination campaign for SARS-CoV-2. This study aims to report detailed Spatiotemporal analysis and result-oriented storytelling of the COVID-19 vaccination campaign across the globe. An exploratory data analysis (EDA) with interactive data visualization using various python libraries was conducted. The results show that, globally, with the rapid vaccine development and distribution, people from the different regions are also getting vaccinated and revealing their positive intent toward the COVID-19 vaccination. The outcomes of this exploration also established that mass vaccination campaigns in populated countries including Brazil, China, India, and the US reduced the number of daily COVID-19 deaths and confirmed cases. Overall, our findings contribute to current policy-relevant research by establishing a link between increasing immunization rates and lowering COVID-19's rising curve.

COVID-19 has exacerbated existing health inequalities globally. Guided by the culture-centered approach, this study examined perspectives and experiences of healthcare during two lockdowns in four marginalized contexts in Aotearoa New Zealand. The participants' narratives depicted dissatisfaction with the new modes of healthcare delivery, reporting longer waiting times, a preference for face-to-face delivery, language barriers, and issues with the limitations in support people attending appointments. This resulted in healthcare being delivered in a way that was not in keeping with the localized cultural norms of communication and collective support, further exacerbating existing health inequalities. Our findings suggest that public health interventions in response to COVID-19 within the context of healthcare delivery have the potential to further reify and reproduce exclusions and experiences of marginalization, with cultural marginalization reifying structural marginalization.

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 [ABSTRACT FROM AUTHOR]

Background: Coronavirus disease 2019 (COVID-19) is a global public health concern. Evidence shows that depression and anxiety are common among patients with COVID-19 after recovery. About one-third of the total COVID-19 cases in Bangladesh have been reported in Dhaka city. Therefore, the study aimed to evaluate the prevalence of depression and anxiety among COVID-19 survivors in Dhaka city as well as to identify the factors associated with these mental health conditions. Methods: A cross-sectional study was carried out among a total of 384 COVID-19 survivors aged 18 years or older. Data collection was done through face-toface and telephone interviews using a semi-structured questionnaire. Patient Health Questionnaire (PHQ-9) and Generalized Anxiety Disorder (GAD-7) scales were used to assess depression and anxiety, respectively. Binary logistic regression analysis was performed to identify the predictors of depression and anxiety among patients recovered from COVID-19. Results: The overall prevalence of depression and anxiety was 26.0% and 23.2%, respectively among COVID-19 survivors. The respondents who were =60 years were 2.62 and 3.02 times more likely to report depressive and anxiety symptoms, respectively than those aged 18 to 39 years. Hospitalised patients recovered from COVID-19 had a 2.18 times higher chance of developing anxiety than their nonhospitalised counterparts. COVID-19 recovered patients with comorbidities were at 3.35 and 2.97 times higher risk of depression and anxiety, respectively compared to those without comorbidities. Similarly, the respondents who had already passed a period of 15 days to 3 months after recovery showed 3.06 and 1.85 times higher odds of depression and anxiety, respectively than those who had already passed a period of above 3 to 6 months after recovery. Conclusion: The study reported a high prevalence of depression and anxiety among COVID-19 survivors living in Dhaka city. The findings suggest the need for appropriate interventions to reduce mental health complications in COVID-19 survivors. [ABSTRACT FROM AUTHOR]

Background. Coronavirus disease (COVID-19) is an infectious illness that spreads widely over a short period of time and finally causes a pandemic. Unfortunately, the lack of radiologists, improper COVID-19 diagnosing procedures, and insufficient medical supplies have all played roles in these devastating losses of life. Deep learning (DL) could be used to detect and classify COVID-19 for potential image-based diagnosis. Materials and Methods. This paper proposes an improved deep convolutional neural network (IDConv-Net) to detect and classify COVID-19 using X-ray and computed tomography (CT) images. Before the training phase, preprocessing methods such as filtering, data normalization, classification variable encoding, and data augmentation were used in conjunction with the proposed IDConv-Net to increase the effectiveness of the detection and classification processes. To extract essential features, deep CNN is then employed. As a result, the suggested model can identify patterns and relationships crucial to the image classification task, resulting in more precise and useful diagnoses. Python and Keras (with TensorFlow as a backend) were used to carry out the experiment. Results. The proposed IDConv-Net was tested using chest X-rays and CT images collected from hospitals in Sao Paulo, Brazil, and online databases. After evaluating the model, the proposed IDConv-Net achieved an accuracy of 99.53% and 98.41% in training and testing for CT images and 97.49% and 96.99% in training and testing for X-ray images, respectively. Further, the area under the curve (AUC) value is 0.954 and 0.996 for X-ray and CT images, respectively, indicating the excellent performance of the proposed model. Conclusion. The findings of our proposed IDConv-Net model confirm that the model outperformed compared to existing COVID-19 detection and classification models. The IDConv-Net outperforms current state-of-the-art models by 2.25% for X-rays and 2.81% for CT images. Additionally, the IDConv-Net training approach is significantly quicker than the current transfer learning models. [ABSTRACT FROM AUTHOR]

This essay draws upon a digital ethnography and in-depth interviews with Bangladeshi low-wage migrant workers in Singapore to theorize the COVID-19 outbreak in dormitories housing the workers as a crisis of the consuming city. Building on the concept of extreme neoliberalism, we examine the ways in which the erasure of worker voice as a technique of smart governmentality coded into the "Singapore model" underlies the pandemic outbreak. The in-depth interviews document the ways in which the poor infrastructures for migrant living are already always in crisis because of their exploitative design that maximizes profits while undermining the health, wellbeing, and dignity of workers. In this backdrop, the workers narrate strategies of resistance, enacted in articulations that resist and disrupt the smart propaganda crafted by the authoritarian state. [ABSTRACT FROM AUTHOR]

A novel coronavirus causing the severe and fatal respiratory syndrome was identified in China, is now producing outbreaks in more than 200 countries around the world, and became pandemic by the time. In this article, a modified version of the well‐known mathematical epidemic model susceptible‐infected‐recovered (SIR) is used to analyze the epidemic's course of COVID‐19 in eight different countries of the South Asian Association for Regional Cooperation (SAARC). To achieve this goal, the parameters of the SIR model are identified by using publicly available data for the corresponding countries: Afghanistan, Bangladesh, Bhutan, India, the Maldives, Nepal, Pakistan, and Sri Lanka. Based on the prediction model, we estimated the epidemic trend of COVID‐19 outbreak in SAARC countries for 20, 90, and 180 days, respectively. A short‐mid‐long term prediction model has been designed to understand the early dynamics of the COVID‐19 epidemic in the southeast Asian region. The maximum and minimum basic reproduction numbers (R0 = 1.33 and 1.07) for SAARC countries are predicted to be in Pakistan and Bhutan. We equate simulation results with real data in the SAARC countries on the COVID‐19 outbreak, and predicted different scenarios using the modified SIR prediction model. Our results should provide policymakers with a method for evaluating the impacts of possible interventions, including lockdown and social distancing, as well as testing and contact tracking. [ABSTRACT FROM AUTHOR]

Rapid and early diagnosis of lethal coronavirus disease-19 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an important issue considering global human health, economy, education, and other activities. The advancement of understanding of the chemistry/biochemistry and the structure of the SARS-CoV-2 virus has led to the development of low-cost, efficient, and reliable methods for COVID-19 diagnosis over "gold standard" real-time reverse transcription-polymerase chain reaction (RT-PCR) due to its several limitations. This led to the development of electrochemical sensors/biosensors for rapid, fast, and low-cost detection of the SARS-CoV-2 virus from the patient's biological fluids by detecting the components of the virus, including structural proteins (antigens), nucleic acid, and antibodies created after COVID-19 infection. This review comprehensively summarizes the state-of-the-art research progress of electrochemical biosensors for COVID-19 diagnosis. They include the detection of spike protein, nucleocapsid protein, whole virus, nucleic acid, and antibodies. The review also outlines the structure of the SARS-CoV-2 virus, different detection methods, and design strategies of electrochemical SARS-CoV-2 biosensors by highlighting the current challenges and future perspectives. [ABSTRACT FROM AUTHOR]

There is an obvious concern globally regarding the fact about the emerging coronavirus 2019 novel coronavirus (2019‐nCoV) as a worldwide public health threat. As the outbreak of COVID‐19 causes by the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) progresses within China and beyond, rapidly available epidemiological data are needed to guide strategies for situational awareness and intervention. The recent outbreak of pneumonia in Wuhan, China, caused by the SARS‐CoV‐2 emphasizes the importance of analyzing the epidemiological data of this novel virus and predicting their risks of infecting people all around the globe. In this study, we present an effort to compile and analyze epidemiological outbreak information on COVID‐19 based on the several open datasets on 2019‐nCoV provided by the Johns Hopkins University, World Health Organization, Chinese Center for Disease Control and Prevention, National Health Commission, and DXY. An exploratory data analysis with visualizations has been made to understand the number of different cases reported (confirmed, death, and recovered) in different provinces of China and outside of China. Overall, at the outset of an outbreak like this, it is highly important to readily provide information to begin the evaluation necessary to understand the risks and begin containment activities. Highlights: The outbreak of COVID‐19 has become a clinical threat worldwide.Knowledge about SARS‐CoV‐2 virus remains limited.Epidemiological data needs to be analyzed.A Visual Exploratory Data Analysis (V‐EDA) model helps to understand the consequences of the COVID‐19 outbreak. [ABSTRACT FROM AUTHOR]

In this study, we propose a novel approach called DCNN-ViT-GRU, which combines deep Convolutional Neural Networks (CNNs) with Gated Recurrent Units (GRUs) and the Vision Transformer (ViT) model for the accurate detection and classification of lung abnormalities. By leveraging the strengths of both CNNs and the ViT model, our architecture automatically extracts meaningful features from lung images, leading to improved diagnostic capabilities. The DCNN-ViT-GRU model utilizes a combination of deep CNN and GRU layers, allowing it to effectively capture local and global patterns. This comprehensive feature representation enhances the model's ability to identify various abnormalities in lung images, including lung cancer, COVID-19, and pneumonia. To further enhance the interpretability and transparency of our model, we integrate Explainable Artificial Intelligence (XAI) techniques, including LIME and SHAP. This integration provides valuable insights into the decision-making process of the DCNN-ViT-GRU model, enabling clinicians to understand and validate the predictions made by the model. We evaluated the performance of our proposed approach on diverse datasets containing cases of lung abnormalities. Through cross-validation, our DCNN-ViT-GRU model achieved impressive weighted mean accuracy of 99% and 99.86% for two distinct datasets, demonstrating its superior performance. Furthermore, in hold-out validation on separate datasets, the model achieved accuracies of 99.09% and 99.87%, respectively. Integrating the XAI techniques enhances the interpretability of the DCNN-ViT-GRU model and provides clinicians with valuable insights regarding the factors contributing to the diagnosis of lung abnormalities. This approach presents a promising solution for accurate and interpretable lung abnormalities detection and classification, with potential implications for improved patient care and treatment planning. • DCNN-ViT-GRU: Superior Fusion for Accurate Lung Abnormality Detection. • XAI Insights: LIME & SHAP Enhance Model Transparency for Clinicians. • Impressive Accuracy: DCNN-ViT-GRU Outperforms Transfer Learning Models. • Versatile Performance: DCNN-ViT-GRU Excels in Lung Cancer Detection Studies. • Comprehensive Validation: Model Accuracy and Robustness Confirmed in Diverse Datasets. [ABSTRACT FROM AUTHOR]

This paper reports on an ultrasensitive and label-free electrochemical immunosensor for monitoring the SARS-CoV-2 spike protein (SARS-CoV-2 SP). A self-supported electrode, which can simultaneously serve as an antibody immobilization matrix and electron transport channel, was initially fabricated by a controlled partial exfoliation of a flexible graphitic carbon foil (GCF). Mild acidic treatment enabled the partial oxidation and exfoliation (down to a few layers) of the flexible GCF; this also provided a high percentage of oxygen functionality and an enhanced surface roughness. The substrate electrode was further functionalized with ethylenediamine (EDA) to provide a suitable platform with even a higher surface roughness, for the covalent immobilization of an anti-SARS-CoV-2 antibody. The change in the current response for the [Fe(CN) 6 ]3−/4− redox couple, induced by the binding of SARS-CoV-2 SP to the antibody immobilized on the electrode surface, was used to determine the SARS-CoV-2 SP concentration. The immunosensor thus prepared could detect SARS-CoV-2 SP within 30 min with high reproducibility and specificity over a wide concentration range (0.2–100 ng/mL). Detection limits of 25 pg/mL and 27 pg/mL were found in a phosphate buffer solution (pH 7.4), and diluted blood plasma, respectively. The immunosensor was also employed to detect SARS-CoV-2 SP in artificial human saliva. A self-supported ethylene diamine (EDA) functionalized flexible graphitic carbon foil (GCF) is prepared by controlled and mild acidic oxidation and exfoliation of GCE surface of few layers with the subsequent EDA functionalization. The prepared self-supported EDA-GCF electrode provides an ultrasensitive immunoassay platform for label-free detection of SARS-CoV-2 spike protein at pg/mL levels within 30 min [Display omitted] • Flexible carbon-foils are functionalized to obtain electrochemical immunosensors. • The sensors are characterized by spectroscopic and electrochemical techniques. • An antibody-based electrochemical sensor is used for detecting COVID-19. • The immunosensor allows achieving a very low detection limit. [ABSTRACT FROM AUTHOR]