SARS-CoV-2 (COVID-19): Zoonotic Origin and Susceptibility of Domestic and Wild Animals

Wildlife Consumption, Health, and Zoonotic Disease in China After the Emergence of COVID-19

There has been much discussion in the conservation and policy realms of COVID-19 as a zoonotic disease, or a disease transmitted from wildlife to humans. However, wildlife consumption in China is not only a potential source of disease but also a practice embedded in complex beliefs about health. This paper used survey data (N = 974) collected in China in June 2021 to examine attitudes and behaviors related to (a) wildlife consumption, (b) Traditional Chinese Medicine (TCM) and (c) zoonotic risk after the emergence of the COVID-19 pandemic. 40.1% of respondents self-reported that they are less likely to consume wild animals since the outbreak of COVID-19. Respondents who used wildlife supplements for TCM, who believed in the benefits of wild animal consumption and fresh slaughter of wildlife, and who had higher levels of agreement with the zoonotic origin of COVID-19 were more likely to report that they had decreased their wildlife consumption after the outbreak of COVID-19. Use of wildlife in TCM significantly increased the odds that a respondent believed that COVID-19 was very likely zoonotic. We discuss how situating wildlife consumption within complex beliefs about health and disease can assist with protecting wildlife and public health in the wake of COVID-19.

Emerging zoonotic diseases and COVID-19 pandemic: global Perspective and Indian Scenario

The current coronavirus disease 2019 (COVID-19) pandemic is one example of the scores of zoonotic diseases responsible for various outbreaks resulting in the deaths of millions of people for centuries. The COVID-19 pandemic has broken the age-old healthcare infrastructure and led to utter chaos. In the shadow of this pandemic, other zoonotic infections like the nipah virus, monkeypox, and langya virus, to name a few, have been neglected. Hence, outbreaks caused by such zoonotic viruses are rising in their endemic areas, like the Indian subcontinent. The mortality and morbidity due to such zoonoses are greater than usual due to the shortage of healthcare professionals caused by the COVID-19 crisis. Due to the lack of vaccines and therapeutics directed against this viral infection, treatment of patients is limited to supportive management and prevention, making preparedness for these potential zoonotic viral outbreaks essential. This paper highlights some of these zoonotic infections, which perpetuated and wreaked havoc while the world was occupied with containing the COVID-19 pandemic.

Non-uniform aspects of the SARS-CoV-2 intraspecies evolution reopen question of its origin

Several hypotheses have been presented on the origin of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) from its identification as the agent causing the current coronavirus disease 19 (COVID-19) pandemic. So far, no solid evidence has been found to support any hypothesis on the origin of this virus, and the issue continue to resurface over and over again. Here we have unfolded a pattern of distribution of several mutations in the SARS-CoV-2 proteins in 24 geo-locations across different continents. The results showed an evenly uneven distribution of the unique protein variants, distinct mutations, unique frequency of common conserved residues, and mutational residues across these 24 geo-locations. Furthermore, ample mutations were identified in the evolutionarily conserved invariant regions in the SARS-CoV-2 proteins across almost all geo-locations studied. This pattern of mutations potentially breaches the law of evolutionary conserved functional units of the beta-coronavirus genus. These mutations may lead to several novel SARS-CoV-2 variants with a high degree of transmissibility and virulence. A thorough investigation on the origin and characteristics of SARS-CoV-2 needs to be conducted in the interest of science and for the preparation of meeting the challenges of potential future pandemics.

Responses to COVID-19 in South Asian Association for Regional Cooperation (SAARC) countries in 2020, a data analysis during a world of crises

Coronavirus disease (COVID-19) caused by SARS-CoV-2 was notified from Wuhan city, Hubei province, China in the mid of December 2019. The disease is showing dynamic change in the pattern of confirmed cases and death toll in these low and middle-income countries (LMICs). In this study, exponential growth (EG) method was used to calculate the real-time reproductive number (R_t) for initial and later stage of epidemic in South Asian Association for Regional Cooperation (SAARC) member countries (April 2020 - December 2020). Time dependent (TD) method was used to calculate the weekly real -time reproduction number (R_t). We also presented the observations on COVID-19 epidemiology in relation with the health expenditure, poverty, BCG vaccination, literacy population density and R_t for understanding the current scenario, trends, and expected outcome of the disease in SAARC countries. A significant positive correlation was noticed between COVID-19 deaths and health expenditure (% GDP) (r = 0.58, P < 0.05). The other factors such as population density/sq km, literacy %, adult population %, and poverty % were not significantly correlated with number of COVID-19 cases and deaths. Among SAARC countries, the highest R_t was observed in India (R_t = 2.10; 95% CI 2.04-2.17) followed by Bangladesh (R_t = 1.62; 95% CI 1.59-1.64) in initial state of epidemic. A continuous monitoring is necessitated in all countries looking at the medical facilities, available infrastructure and healthcare manpower, constraints which may appear with increased number of critically ill patients if the situation persists longer.

Optimization and fine-tuning of DenseNet model for classification of COVID-19 cases in medical imaging

It’s been more than a year that the entire world is fighting against COVID-19 pandemic. Starting from the Wuhan city in China, COVID-19 has conquered the entire world with its rapid progression. But seeking the importance towards the human situation, it has become essential to build such an automated model to diagnose COVID-19 within less computational time easily. As the disease has spread, there is not enough data to implement an accurate COVID-19 predicting model. But technology is a boon, which makes it possible. Effective techniques based on medical imaging using artificial intelligence have approached to assist humans in needful time. It has become very essential to detect COVID-19 in humans at an early stage to prevent it from becoming more infectious. The neural networks have shown promising results in medical imaging. In this research, a deep learning-based approach is used for image classification to detect COVID-19 using chest X-ray images (CXR). A CNN classifier have been used to classify the normal-healthy images from the COVID-19 images, using transfer learning. The concept of early stopping is used to enhance the accuracy of the proposed DenseNet model. The results of the system have been evaluated using accuracy, precision, recall and F1-score metrics. An automated comparative analysis among multiple optimizers, LR Scheduler and Loss Function is performed to get the highest accuracy suitable for the proposed system. The Adamax optimizer with Cross Entropy loss function and StepLR scheduler have outperformed with 98.45% accuracy for normal-healthy CXR images and 98.32% accuracy for COVID-19 images.