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Objectives: To describe the operational model, epidemiology and outcomes of COVID-19 cases managed by the first decentralised Victorian Public Health Unit (PHU) in the Barwon South-West (BSW) region in 2020., Methods: The Barwon Health team used a clinician-led, locally-based interprofessional model of care, combining clinical care and monitoring, contact tracing and public health measures., Results: From 7th March to 5th October 2020, 575 confirmed COVID-19 cases (82 in Wave 1; 493 in Wave 2) were identified in residents of the BSW region. Overall, 4.7% were admitted to local hospitals (0.7% to intensive care units) and 1.7% died. COVID-19 incidence in the region was 129 cases/100,000. Wave 2 in the region featured community transmission in high-risk settings and among culturally and linguistically diverse and mobile populations. Within 3 months of the initial local case in Wave 2, SARS-COV-2 was eliminated from the community., Conclusions: A local interprofessional model of care was key to the containment of community transmission and complex outbreaks with the elimination of COVID-19 in the community., Implications for Public Health: Key successes and learnings from the BSW PHU contributed to the improvement of statewide systems and responses and provided an impetus for the implementation of a decentralised public health model for Victoria., Competing Interests: Conflicts of interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: Human immunodeficiency virus (HIV) is a sexually transmitted infection impacting populations worldwide. While there have been major improvements in controlling HIV over recent years, the COVID-19 pandemic may have potentially resulted in major interruptions to this control of HIV. Bharat (India) is a country that has been greatly impacted by the COVID-19 pandemic, and we aimed to analyse the trends in HIV control since the start of the pandemic., Methods: In this study we evaluated changes in rates of HIV incidence and mortality across Bharat for the years both before, and after, the start of the COVID-19 pandemic. Percent and absolute changes were determined, and thereafter, both bivariate and multi linear regression was conducted to evaluate the relationship between COVID-19 burden and changes in HIV epidemiology across the nation., Results: It was shown that, despite the COVID-19 pandemic, annual incidence and deaths of HIV/AIDS have both decreased across Bharat. From 2019-2021, in Bharat, the total number of new HIV cases annually decreased by 9.03%, and the total number of HIV/AIDS deaths annually decreased by 28.82%. A similar trend was shown across most states/union territories; however, there were notable exceptions (such as Karnataka, Bihar, and Assam) where the rates have instead increased., Conclusions: Our analysis has demonstrated that government efforts to control the HIV/AIDS epidemic have not been greatly impacted across the majority of Bharat since the emergence of COVID-19. The reduction in annual HIV/AIDS deaths in the country has been better than the world average, and the improvements from the period of 2019 to 2021 were greater than those from 2017 to 2019. Regardless, there are regions in the nation where the epidemic has instead worsened during this period., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Objective: This article presents a longitudinal analysis of COVID-19 infection and vaccination coverage in Melbourne metropolitan local government areas (LGAs) during the 2021 Delta wave., Methods: COVID-19 vaccination and infection data from 12 July to 27 November 2021 were sourced from government websites. Summary statistics and associated 95% confidence intervals (95% CI) were compared by LGA ranked according to socioeconomic status: total "burden" (total infections per thousand), "peak" (highest weekly infection rate), "lag" (interval between peak and 70% double vaccination)., Results: LGAs in the bottom five deciles for social advantage experienced higher infection rates (39.0 per thousand [95% CI: 38.5, 39.5] vs. 14.8 [14.7, 14.9]), and had lower two-dose vaccination coverage (23.8% [23.6, 23.9] vs. 32.7% [32.6, 32.7]) compared with LGAs in the top five deciles. LGAs that achieved 70% coverage two weeks or more after the infection peak experienced nearly twice the total infection burden (27.7 per 1000 [27.3, 28.0] compared with 14.9 [14.7, 15.0]) than LGAs with a shorter lag., Conclusions: Exposure and transmission risk factors cluster within disadvantaged LGAs. The potential for large local outbreaks is heightened if vaccination uptake trails in these communities., Implications for Public Health: In a pandemic, decision-makers must prioritise disease control and harm reduction interventions for at-risk LGAs., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Background: This study investigated cause-specific mortality rates in 12 countries during the COVID-19 pandemic in 2020 and 2021., Methods: We collected weekly cause-specific mortality data from respiratory disease, pneumonia, cardiovascular disease (CVD) and cancer from national vital statistic databases. We calculated excess mortality for respiratory disease (excluding COVID-19 codes), pneumonia, and CVD in 2020 and 2021 by comparing observed weekly against expected mortality based on historical data (2015-2019), accounting for seasonal trends. We used multilevel regression models to investigate the association between country-level pandemic-related variables and cause-specific mortality., Results: Significant reductions in cumulative mortality from respiratory disease and pneumonia were observed in 2020 and/or 2021, except for Georgia, Northern Ireland, Kazakhstan, and Ukraine, which exhibited excess mortality for one or both causes. Australia, Austria, Cyprus, Georgia, and Northern Ireland experienced excess cumulative CVD mortality in 2020 and/or 2021. Australia, Austria, Brazil, Cyprus, Georgia, Northern Ireland, Scotland and Slovenia, experienced increased crude cumulative cancer mortality during 2020 and/or 2021 compared to previous years. Among pandemic-related variables, reported COVID-19 incidence was negatively associated with increased cancer mortality, excess respiratory, (2020) and pneumonia (2021) mortality, and positively associated with respiratory and CVD mortality (2021). Stringency of control measures were negatively associated with excess respiratory disease, CVD, and increased cancer mortality (2021)., Conclusions: This study provides evidence of substantial excess mortality from CVD, and notable reductions in respiratory disease and pneumonia in both years across most countries investigated. Our study also highlights the beneficial impact of stringent control measures in mitigating excess mortality from most causes in 2021., (© 2024. The Author(s).)

The worldwide COVID-19 pandemic has profoundly influenced the health and everyday experiences of individuals across the planet. It is a highly contagious respiratory disease requiring early and accurate detection to curb its rapid transmission. Initial testing methods primarily revolved around identifying the genetic composition of the coronavirus, exhibiting a relatively low detection rate and requiring a time-intensive procedure. To address this challenge, experts have suggested using radiological imagery, particularly chest X-rays, as a valuable approach within the diagnostic protocol. This study investigates the potential of leveraging radiographic imaging (X-rays) with deep learning algorithms to swiftly and precisely identify COVID-19 patients. The proposed approach elevates the detection accuracy by fine-tuning with appropriate layers on various established transfer learning models. The experimentation was conducted on a COVID-19 X-ray dataset containing 2000 images. The accuracy rates achieved were impressive of 99.55%, 97.32%, 99.11%, 99.55%, 99.11% and 100% for Xception, InceptionResNetV2, ResNet50 , ResNet50V2, EfficientNetB0 and EfficientNetB4 respectively. The fine-tuned EfficientNetB4 achieved an excellent accuracy score, showcasing its potential as a robust COVID-19 detection model. Furthermore, EfficientNetB4 excelled in identifying Lung disease using Chest X-ray dataset containing 4,350 Images, achieving remarkable performance with an accuracy of 99.17%, precision of 99.13%, recall of 99.16%, and f1-score of 99.14%. These results highlight the promise of fine-tuned transfer learning for efficient lung detection through medical imaging, especially with X-ray images. This research offers radiologists an effective means of aiding rapid and precise COVID-19 diagnosis and contributes valuable assistance for healthcare professionals in accurately identifying affected patients., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Nurses, often considered the backbone of global health services, are disproportionately vulnerable to COVID-19 due to their front-line roles. They conduct essential patient tests, including blood pressure, temperature, and complete blood counts. The pandemic-induced loss of nursing staff has resulted in critical shortages. To address this, robotic solutions offer promising avenues. To solve this problem, we developed an ensemble deep learning (DL) model that uses seven different models to detect patients. Detected images are then used as input for the soft robot, which performs basic assessment tests. In this study, we introduce a deep learning-based approach for nursing soft robots, and propose a novel deep learning model named Deep Ensemble of Adaptive Architectures. Our method is twofold: firstly, an ensemble deep learning technique detects COVID-19 patients; secondly, a soft robot performs basic assessment tests on the identified patients. We evaluate the performance of various deep learning-based object detectors for patient detection, examining implementations of You Only Look Once (YOLO), Single Shot MultiBox Detector (SSD), Region-Based Convolutional Neural Network (RCNN), and Region-Based Fully Convolutional Network (R-FCN) on a proprietary dataset comprising 32,668 hospital surveillance images. Our results indicate that while YOLO and VGG facilitate rapid detection, Faster-RCNN (Inception ResNet-v2) and our proposed Ensemble-DL achieve the highest accuracy. Ensemble-DL offers accurate results in a reasonable timeframe, making it apt for patient detection on embedded platforms. Through real-world experiments, our method outperforms baseline approaches (including Faster-RCNN, R-FCN variants, CNN+LSTM, etc.) in terms of both precision and recall. Achieving an impressive accuracy of 98.32%, our deep learning-based model for nursing soft robots presents a significant advancement in the identification and assessment of COVID-19 patients, ultimately enhancing healthcare efficiency and patient care., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Myocarditis is heart muscle inflammation that is becoming more prevalent these days, especially with the prevalence of COVID-19. Noninvasive imaging cardiac magnetic resonance (CMR) can be used to diagnose myocarditis, but the interpretation is time-consuming and requires expert physicians. Computer-aided diagnostic systems can facilitate the automatic screening of CMR images for triage. This paper presents an automatic model for myocarditis classification based on a deep reinforcement learning approach called as reinforcement learning-based myocarditis diagnosis combined with population-based algorithm (RLMD-PA) that we evaluated using the Z-Alizadeh Sani myocarditis dataset of CMR images prospectively acquired at Omid Hospital, Tehran. This model addresses the imbalanced classification problem inherent to the CMR dataset and formulates the classification problem as a sequential decision-making process. The policy of architecture is based on convolutional neural network (CNN). To implement this model, we first apply the artificial bee colony (ABC) algorithm to obtain initial values for RLMD-PA weights. Next, the agent receives a sample at each step and classifies it. For each classification act, the agent gets a reward from the environment in which the reward of the minority class is greater than the reward of the majority class. Eventually, the agent finds an optimal policy under the guidance of a particular reward function and a helpful learning environment. Experimental results based on standard performance metrics show that RLMD-PA has achieved high accuracy for myocarditis classification, indicating that the proposed model is suitable for myocarditis diagnosis., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Seyed Vahid Moravvej et al.)

In the pursuit of constructing a sustainable world for all through the instrumental seventeen Sustainable Development Goals, the COVID-19 pandemic emerged and affected the efforts concentrated on these goals. Therefore, there is a pressing need to analyze the extent of the impact that unfolded from the pandemic on each Sustainable Development Goal and further to direct the post-pandemic situation to accelerate the progress in every goal. Besides, there exists a knowledge gap in understanding the Sustainable Development Goals and its interaction with each goal through synergic and trade-off effects. To address the aforementioned imperative problems, this study is formulated to perform an impact assessment as well as to provide direction in the post-pandemic environment to effectively progress towards the Sustainable Development Goals by using a hybrid qualitative and quantitative framework. A detailed investigation is carried out to examine the pandemic impacts in every goal, and a quantified impact analysis is performed in terms of the targets of the Sustainable Development Goals with the aid of ranking methodology. The results indicate that SDG 1 and SDG 8 are the most impacted goal. To provide deeper perspectives into the Sustainable Development Goals, a critical analysis of the targets and indicators is performed to characterize the goals from their elemental point of view, such as nature of goals, depending factors, locus of the goal, and Sustainable Development Goal interactions. Further, a novel parameter, the degree of randomness, is proposed whose application in environmental research is immense. The impact on each goal and impact interaction between all the SDGs are also mapped, through which the dynamics of Sustainable Development Goal interactions is elaborated. In context with the post-pandemic scenario, the strategies to achieve the Sustainable Development Goals with environmental focus are presented with prioritization factor that supports quick recovery. The introduced prioritization factor is formulated by employing a multi-criteria analysis methodology. In addition, the fundamental elements of SDGs are built upon one another to frame an optimized and effective approach to achieving the SDGs in the post-pandemic environment. Despite the strategies, a conceptual framework to align the business practices with the SDGs is propounded. This study deep down would provide a unique perspective to the research community and would impart deeper knowledge in connection with sustainability, while the solutions framed would steer the policy and decision-makers., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Chest X-ray (CXR) images have been one of the important diagnosis tools used in the COVID-19 disease diagnosis. Deep learning (DL)-based methods have been used heavily to analyze these images. Compared to other DL-based methods, the bag of deep visual words-based method (BoDVW) proposed recently is shown to be a prominent representation of CXR images for their better discriminability. However, single-scale BoDVW features are insufficient to capture the detailed semantic information of the infected regions in the lungs as the resolution of such images varies in real application. In this paper, we propose a new multi-scale bag of deep visual words (MBoDVW) features, which exploits three different scales of the 4th pooling layer's output feature map achieved from VGG-16 model. For MBoDVW-based features, we perform the Convolution with Max pooling operation over the 4th pooling layer using three different kernels: [Formula: see text], [Formula: see text], and [Formula: see text]. We evaluate our proposed features with the Support Vector Machine (SVM) classification algorithm on four CXR public datasets (CD1, CD2, CD3, and CD4) with over 5000 CXR images. Experimental results show that our method produces stable and prominent classification accuracy (84.37%, 88.88%, 90.29%, and 83.65% on CD1, CD2, CD3, and CD4, respectively)., (© 2021. The Author(s).)

In 2020, the COVID-19 pandemic led to government-enforced limits on activities worldwide, causing a marked reduction of human presence in outdoors environments, including in coastal areas that normally support substantial levels of boat traffic. These restrictions provided a unique opportunity to quantify the degree to which anthropogenic noise contributes to and impacts underwater soundscapes. In Guadeloupe, French West Indies, a significantly lower number of motor boats were recorded in the vicinity of the major urban marina during the peak of the first COVID-19 lockdown (April-May 2020), compared with the number recorded post-lockdown. The resumption of human activities at the end of May was correlated with a maximum increase of 6 decibels in the ambient noise level underwater. The change in noise level did not impact daily sound production patterns of vocal fishes, with increased activity at dusk seen both during and after the lockdown period. However, during the lockdown vocal activity was comprised of a reduced number of sounds, suggesting that anthropogenic noise has the potential to interfere with vocalization behaviours in fishes., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Significance: Optometric educators are constantly looking for learning and teaching approaches to improve clinical skills training. In addition, the COVID-19 pandemic has made educators scrutinize the time allocated to face-to-face teaching and practice. Simulation learning is an option, but its use must first be evaluated against traditional learning methods., Purpose: The purpose of this study was to compare the training of binocular indirect ophthalmoscopy skills achieved by students and optometrists through deliberate practice on the Eyesi Indirect Ophthalmoscope simulator with deliberate practice using a peer., Methods: Students and optometrists were randomly allocated to practice on either the simulator or a peer. Binocular indirect ophthalmoscopy performance was assessed using a peer and the simulator as the patients at different time points. Knowledge and confidence were examined before and following all practice sessions using a quiz and survey., Results: Significant improvements in binocular indirect ophthalmoscopy performance using either a peer or the simulator as the patient for assessment were seen after 8 hours of student practice (P < .001) and after a half-hour practice time for optometrists (P < .001). There was no significant difference in performance overall between those practicing on a simulator and those practicing on a peer (P > .05). Confidence in ability to perform the technique was lower for students who had practiced on the simulator., Conclusions: The simulator has similar efficacy to peer practice for teaching binocular indirect ophthalmoscopy to students and maintenance of this clinical skill for optometrists. Simulation does not replace the need for peer practice but may be a useful adjunct reducing the face-to-face hours required. These findings present a need for further research regarding diverse applications of the Eyesi Indirect Ophthalmoscope simulator in the curriculum for training optometry students and as a continuing professional development offering for optometrists, given the short exposure duration required to observe a significant improvement in skill., Competing Interests: Conflict of Interest Disclosure: None of the authors have reported a financial conflict of interest., (Copyright © 2021 American Academy of Optometry.)

The COVID-19 pandemic has abruptly halted the Anthropocene's ever-expanding reign for the time being. The resulting global human confinement, dubbed as the Anthropause, has created an unprecedented opportunity for us to evaluate the environmental consequences of large-scale changes in anthropogenic activities. Based on a methodical and in-depth review of related literature, this study critically evaluates the positive and negative externalities of COVID-19 induced lockdown on environmental components including air, water, noise, waste, forest, wildlife, and biodiversity. Among adverse impacts of the lockdown, increased amount of healthcare waste (300-400%), increased level of atmospheric ozone (30-300%), elevated levels of illicit felling in forests and wildlife poaching were prominent. Compared to the negative impacts, significant positive changes in various quality parameters related to key environmental components were evident. Positive impacts on air quality, water quality, noise level, waste generation, and wildlife were apparent in varying degrees as evaluated in this study. By presenting a critical overview of the recommendations given in the major literature in light of these documented impacts, this paper alludes to potential policy reforms as a guideline for future sustainable environmental management planning. Some of the key recommendations are e.g., enhance remote working facilities, cleaner design, use of internet of things, automation, systematic lockdown, and inclusion of hazardous waste management in disaster planning. The summarized lessons of this review, pertinent to the dynamic relationship between anthropogenic activities and environmental degradation, amply bring home the need for policy reforms and prioritization of Sustainable Development Goals in the context of the planetary boundaries to the environmental sustainability for a new post-pandemic world., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

COVID-19 has caused many deaths worldwide. The automation of the diagnosis of this virus is highly desired. Convolutional neural networks (CNNs) have shown outstanding classification performance on image datasets. To date, it appears that COVID computer-aided diagnosis systems based on CNNs and clinical information have not yet been analysed or explored. We propose a novel method, named the CNN-AE, to predict the survival chance of COVID-19 patients using a CNN trained with clinical information. Notably, the required resources to prepare CT images are expensive and limited compared to those required to collect clinical data, such as blood pressure, liver disease, etc. We evaluated our method using a publicly available clinical dataset that we collected. The dataset properties were carefully analysed to extract important features and compute the correlations of features. A data augmentation procedure based on autoencoders (AEs) was proposed to balance the dataset. The experimental results revealed that the average accuracy of the CNN-AE (96.05%) was higher than that of the CNN (92.49%). To demonstrate the generality of our augmentation method, we trained some existing mortality risk prediction methods on our dataset (with and without data augmentation) and compared their performances. We also evaluated our method using another dataset for further generality verification. To show that clinical data can be used for COVID-19 survival chance prediction, the CNN-AE was compared with multiple pre-trained deep models that were tuned based on CT images., (© 2021. The Author(s).)

Optometry Australia's infection control guidelines 2020 deliver a revision of the paper: Infection control guidelines for optometrists 2016. A review of recent literature was undertaken, with information collected from peer-reviewed journal articles, guidelines from professional societies, government health departments and instructions from equipment manufacturers. This information was used to provide an update on current infection control best practice. The guidelines are presented in two sections: standard precautions and transmission-based precautions. The standard (routine) precautions section covers: hand hygiene; personal protective equipment; safe handling and disposal of sharps; routine environmental cleaning; reprocessing of reusable equipment, instruments and contact lenses; respiratory hygiene and cough etiquette; aseptic and clean technique; and waste management. The transmission-based precautions section covers considerations for optometry staff and/or patients when standard precautions alone may not be sufficient to prevent the spread of an infectious agent; particularly relevant during the COVID-19 pandemic. Finally, a comprehensive list of disinfection or sterilisation techniques to use on reusable devices, instruments or equipment in optometric practice is provided in an Appendix.