Monitoring, Tracking and Fighting Pandemics using Drone-based Artificial Intelligence in IoT

The world has altered since the World Health Organization (WHO) designated (COVID-19) a worldwide epidemic. Everything in society, from professions to routines, has shifted to accommodate the new reality. The World Health Organization warns that future pandemics of infectious diseases are likely and that people should be ready for the worst. Therefore, this study presents a framework for tracking and monitoring COVID-19 using a Deep Learning (DL) perfect. The suggested framework utilises UAVs (such as a quadcopter or drone) equipped with artificial intelligence (AI) and the Internet of Things (IoT) to keep an eye on and combat the spread of COVID-19. AI/IoT for COVID-19 nursing and a drone-based IoT scheme for sterilisation make up the bulk of the infrastructure. The proposed solution is based on the use of a current camera installed in a face-shield or helmet for use in emergency situations like pandemics. The developed AI algorithm processes the thermal images that have been detected using multi-scale similar convolution blocks (MPCs) and Res blocks that are trained using residual learning. When infected cases are detected, the helmet's embedded Internet of Things system can trigger the drone system to intervene. The infected population is eradicated with the help of the drone's sterilisation process. The developed system undergoes experimental evaluation, and the findings are presented. The developed outline delivers a novel and well-organized arrangement for monitoring and combating COVID-19 and additional future epidemics, as evidenced by the results. © 2023 IEEE.

Attention-augmented U-Net (AA-U-Net) for semantic segmentation.

Deep learning-based image segmentation models rely strongly on capturing sufficient spatial context without requiring complex models that are hard to train with limited labeled data. For COVID-19 infection segmentation on CT images, training data are currently scarce. Attention models, in particular the most recent self-attention methods, have shown to help gather contextual information within deep networks and benefit semantic segmentation tasks. The recent attention-augmented convolution model aims to capture long range interactions by concatenating self-attention and convolution feature maps. This work proposes a novel attention-augmented convolution U-Net (AA-U-Net) that enables a more accurate spatial aggregation of contextual information by integrating attention-augmented convolution in the bottleneck of an encoder-decoder segmentation architecture. A deep segmentation network (U-Net) with this attention mechanism significantly improves the performance of semantic segmentation tasks on challenging COVID-19 lesion segmentation. The validation experiments show that the performance gain of the attention-augmented U-Net comes from their ability to capture dynamic and precise (wider) attention context. The AA-U-Net achieves Dice scores of 72.3% and 61.4% for ground-glass opacity and consolidation lesions for COVID-19 segmentation and improves the accuracy by 4.2% points against a baseline U-Net and 3.09% points compared to a baseline U-Net with matched parameters. Supplementary Information: The online version contains supplementary material available at 10.1007/s11760-022-02302-3.

Adaptive 3D and VFX Films Virtual Learning Environments

In this study, the analysis of the topic 'Adaptive 3D and VFX Films Virtual Learning' has been provided. As virtual learning and 3D technologies use are increasing, the interest in their learning in academic discussion is increasing daily. However, there are various drawbacks to the use of3D for learning environments. To solve this drawback, the use of adaptive learning environments is increasing more, such as an environment that can dynamically adapt to the learner and the activities that can be performed by that specific learner. As the new ways of learning have been increasing over the past years (in the times of the COVID-19 Pandemic) through the use of computers in the educational sector. The learning environment has been widely adopted by the educational sectors in the case of obtaining promising outcomes. In recent years, these environments have evolved into more advanced environments with the implication of3D technology. With the help of 3D, these adaptive environments are helping learners according to their preferences. © 2022 IEEE.

Impact of Mobile Technology and Use of Big Data in Physics Education During Coronavirus Lockdown

The speed of spread of Coronavirus Disease 2019 led to global lockdowns and disruptions in the academic sector. The study examined the impact of mobile technology on physics education during lockdowns. Data were collected through an online survey and later evaluated using regression tools, frequency, and an analysis of variance (ANOVA). The findings revealed that the usage of mobile technology had statistically significant effects on physics instructors' and students' academics during the coronavirus lockdown. Most of the participants admitted that the use of mobile technologies such as smartphones, laptops, PDAs, Zoom, mobile apps, etc. were very useful and helpful for continued education amid the pandemic restrictions. Online teaching is very effective during lock-down with smartphones and laptops on different platforms. The paper brings the limelight to the growing power of mobile technology solutions in physics education. © 2018 Tsinghua University Press.

Forecasting of COVID-19 Outbreak in India: A Time Series Analysis

The World Health Organization (WHO) declared the status of coronavirus disease 2019 (COVID-19) to a global pandemic on March 11, 2020. Since then, numerous statistical, epidemiological and mathematical models have been used and investigated by researchers across the world to predict the spread of this pandemic in different geographical locations. The data for COVID-19 outbreak in India has been collated on daily new confirmed cases from March 12, 2020 to April 10, 2021. A time series analysis using Auto Regressive Integrated Moving Average (ARIMA) model was used to investigate the dataset and then forecast for the next 30-day time-period from April 11, 2021, to May 10, 2021. The selected model predicts a surge in the number of daily new cases and number of deaths. An investigation into the daily infection rate for India has also been done. © 2023 The authors and IOS Press.

Bioinspired CNN Approach for Diagnosing COVID-19 Using Images of Chest X-Ray

Introduction: The provision of medical facilities needed for COVID-19 diagnosis is a global concern. They must be a powerful tool for detecting and diagnosing the virus quickly using a variety of tests, as well as low-cost advancements. Whereas a chest X-ray image is an effective screening technique, the image acquisition by the instruments must be read appropriately and quickly if multiple tests are performed. Objectives: COVID-19 causes continuous respiratory parenchymal ground glass and integrates respiratory opacity, with a curved shape and peripheral pulmonary dissemination in some cases, which is difficult to anticipate earlier on. In this chapter, we intend to construct a good platform to identify COVID-19 characteristics from the image of chest X-ray to aid in early analysis. Methods: In particular, based on the Cuckoo search method, this chapter provides a bioinspired CNN-based model for COVID-19 diagnosis. This method identifies different deep learning strategies of COVID-19 patients' chest X-ray images for accurate infection identification. The suggested model's performance is estimated using the Cuckoo search approach. Furthermore, the bioinspired CNN characteristics are fine-tuned using optimization algorithm. Rigorous testing reveals that suggested method may accurately categorize chest X-ray images with high performance, remembrance, and sensitivity. Results: As a result, the suggested approach can be used to classify COVID-19 diseases from chest X-ray images in real time and also accuracy will be validated. Conclusion: Finally, the investigation of comparison results illustrates the Cuckoo algorithm is realized to determine the interested regions of the COVID-19 x-ray images. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Indian Agricultural Sector Present and Post Pandemic Condition

The COVID-19 influenced global pandemic severely affected the market of small industries and had a deep impact on the agri economic of the farmer community across the globe. The main objective of this article is to emphasize on the influence of global pandemic with agriculture and food sector. The lockdown made ambivalent in agriculture, the point of concern is that, at the first phase of lockdown in India, Rabi crops are at harvest stage, due to the lockdown the breakdown of supply chain has been interrupted and left a noticeable impact on the marketability of agriculture crops even though it has registered moderate growth in terms of yield. At present globally mankind is experiencing the waves of pandemic and it caused significant loss to the yield of crops. If the situation continuous, the world is going to experience the hunger deaths. To overcome the issue discussed, agriculture sector needs to adapt new technologies, right from the cultivation, harvest and supply chain with marketing to bring the new normal life back to mankind. This is the right time to have transition from conventional agri practices to the technology invented smart agriculture. Indian agriculture sector should adapt and the former community need to be educated in applying ICT based smart agriculture practices such as utilization of automated machinery, AI (artificial intelligence) enabled cultivation methods, Internet of Things (IoT) and Wireless Sensor Networks based monitoring and maintenance of the agriculture practice. The application ICTs methods in agriculture practices facilitate to choose good quality seeds, optimum quantity of manures required for the enhanced crop yield and direct monetary of the agriculture firm in order to show resilience to the global pandemic impact on agriculture sector. In the present review authors emphasised on various smart agriculture methods and their importance in promoting the agriculture practice as profitable venture and also how this ICT methods helps the sector to overcome the impact of global pandemic and to bring back the new normal life. © 2023,International Journal of Advanced Computer Science and Applications. All Rights Reserved.

Deep Learning and IoT Based Assistance System for Autism Spectrum Disorder People

During the Covid-19 pandemic situation, the economic and social disruption is devastating. People could not get out of their homes and lead their normal life. Schools across the country have switched to remote learning, which is also inevitable. Though there are some advantages in the online classes, the fact that many of the children suffered mental stress due to these classes could not be denied. Especially, when speaking about the autism people, they could not handle the stress like normal children. The caretaker is necessary to assist them all the time. Therefore, an assistance system is needed to monitor the stress level. In this research, prototype of deep learning and Internet of Things (IoT) based assistance system has been proposed. It monitors the stress parameters namely body temperature, pulse rate, skin conductivity and the facial emotion of the autism disorder people. Further, the hardware model (Raspberry Pi) has been developed to measure the stress level. The processed data from the model has been stored in the Thingspeak cloud platform for monitoring the autism people remotely. From the threshold stress parameters, the level of the stress can be predicted by the proposed stress management algorithm. © 2022 IEEE.

Prevalence of anxiety and depressive symptoms during COVID-19 pandemic among the general population in India: A systematic review and meta-analysis

Objective: The novel coronavirus (n COVID-19) has affected every walk of life across the world including India. Several studies have been available on the COVID-19-related anxiety and depressive symptoms in the public health context. However, there is a dearth of evidence of a meta-analysis regarding the pooled estimates of anxiety and depressive symptoms related to this pandemic based on the existing studies conducted among the general population of India. The aim of the study was to estimate the pooled prevalence of COVID-19-related anxiety and depressive symptoms among the general population in India. Material and Methods: We searched the following electronic bibliographic databases: PubMed, Ovid, Science Direct, and Wiley online library for studies conducted from the onset of the COVID-19 pandemic and until September 25, 2021. We separately analyzed the outcome measures based on the risk of bias assessment. The publication bias was evaluated by funnel plots and Egger's test. Results: We used a random-effect model due to the significant heterogeneity between the studies (Anxiety symptoms – I2 = 99.40% and Depressive symptoms – I2 = 95.3%). According to the index meta-analysis, the pooled estimates of anxiety and depressive symptoms among general population of India during COVID-19 pandemic are 23.5% (95% CI: 17.4–29.6%;n = 21 studies) and 20.2% (95% CI: 17.2–23.2%;n = 17 studies), respectively. In subgroup analyses, good-quality studies (Score ≥7/9) had a significant effect on the pooled prevalence. Conclusion: About one-fifth of the general population of India reported having anxiety and depressive symptoms during the COVID-19 pandemic. The pooled estimates varied with the methodological quality of included studies. The present study provides a comprehensive picture of the overall magnitude of anxiety and depressive symptoms due to the COVID-19 outbreak which will guide the policy makers to measure the burden of similar pandemics more judiciously in the future.

Population Density Prediction Using Surveillance Cameras in Enclosed Public Spaces

People have always disliked offline shopping due to its tough and stressful nature. However, after the COVID epidemic, people now have no choice but to avoid it as much as possible. With the help of this project, users will be able to plan their visits to avoid rush hour and crowd and learn when there will be the least amount of consumer traffic. The store owner will also profit from knowing how customers are distributed throughout the store according to a heat map because this information will eventually allow him to ascertain which areas of the store customers are more drawn to and which areas receive the fewest customers, allowing him to more efficiently plan staffing and advertising. As the e-commerce industry grows, more people are choosing to purchase online in order to avoid crowds and noise. Thanks to this initiative, people may also enjoy offline shopping just as much, which will benefit offline business owners and enhance the entire shopping experience. © 2022 IEEE.

General Adversarial Networks: A Tool to Detect the Novel Coronavirus from CT Scans

Detection of the novel Corona virus in the early stages is crucial, since no known vaccines exist. Artificial Intelligence- aided prognosis using CT scans can be used as an effective method to identify symptoms of the virus and can thus significantly reduce the workload on the radiologists, who have to perform this task using their eyes. Among the most widely used deep learning convolutional neural networks, research shows that the Xception, Inception and the ResNet50 provide the best accuracy in detecting Covid-19. This paper proposes that using General Adversarial Network (GAN) as a data augmentation technique, in combination with these models will significantly improve the accuracy and thereby increase the chances of detecting the same. The paper also compares and contrasts how each of the three GANs namely DCGAN, LSGAN, CoGAN, perform in association with the aforementioned models. The main aim of this paper is to determine the most credible GAN network to carry out the task of data augmentation as well to prove that involving GANs would improve the existing accuracy of our model, paving way for an effective approach to train the model. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Detection of COVID-19 Using Denoising Autoencoders and Gabor Filters

As of 2019, COVID-19 is the most difficult issue that we are facing. Till now, it has reached over 30 million deaths. Since SARS-CoV-2 is the new virus, it took time to investigate and examine the influence of Coronavirus in human. After analyzing the spreading and infection of COVID-19, researchers applied Artificial Intelligence (AI) techniques to detect COVID-19 quickly to balance the rapid spreading of the virus. Image segmentation is a critical first step in clinical implementations, is a vital role in computer - aided diagnosis that relies heavily on image recognition. Image segmentation is used in medical MRI research to determine the proportions of different anatomical areas of the tissue, as well as how they change as the disease progresses. CT scans are often used to aid with diagnoses. Computer-assisted therapy (CAD) using AI is a particularly significant research area in intelligent healthcare. This paper presents the detection of COVID-19 at an early stage using autoencoders algorithm and Generative Adversarial Networks (GAN) using deep learning approach with more accurate results. The images of Chest Radiograph (CRG) and Chest Computed Tomography (CCT) are used as a trained dataset to detect since SARS-CoV-2 first affect the respiratory system in humans. We achieved a ratio of 1.0, 0.99, and 0.96, the combined dataset was randomly divided into the train, validation, and test sets. Although the early detection of Coronavirus is still a question since the accuracy of the deep learning approach is still under research. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Assessment of Potential Risk Factors, Characteristics, and Outcome of Pneumothorax and Pneumomediastinum in Patients with COVID-19: A Retrospective Case-control Study

Introduction: Pneumothorax (PTX) and/or pneumomediastinum (PMD) are rare complications of Coronavirus Disease-2019 (COVID-19) and are linked to high mortality. Incidence rates vary between 0.56-2.01% in the reported literature. With clinical examination being hampered in the current pandemic setting, there is a delay in the diagnosis. There is a need to identify and establish potential predictive factors, that may aid in identifying patients with a high-risk of developing PTX and/or PMD. Aim: To identify potential risk factors and thus, explore their association with PTX and/or PMD among patients with COVID-19. Materials and Methods: A retrospective case-control study was conducted at MS Ramaiah Medical College and Hospital, Bangalore, South India over a six-month period. A total of 130 patients diagnosed with COVID-19 were recruited in a 1:3 ratio as cases and controls respectively. Cases were patients, diagnosed radiologically with PTX and/or PMD, and controls were, matched individuals without PTX and/or PMD. Patient's clinical and laboratory parameters (complete blood count, renal and liver function tests, serum levels of inflammatory markers such as C-reactive protein (CRP), lactate dehydrogenase (LDH), and D-Dimer were tested for potential association with PTX and/or PMD. Student's t-test, Chi-square test, multivariate and univariate logistic regression analysis were performed. Results: During the study period, there was a total of 3,251 COVID-19 admissions at the centre with 976 patients requiring Intensive Care Unit (ICU) admission. The overall incidence of PTX and/or PMD during the study period was (31/3251) 0.95%. The previous history of COVID-19, non vaccination with COVID-19 vaccine, cough as a predominant symptom, high values of baseline CRP, total bilirubin, Aspartate Transaminase (AST), and total leukocyte counts had a positive association. In-hospital mortality (54.8% vs 33.30%) and mortality 28 days (35.7% vs 7.6%) following discharge, were higher among those with PTX and/or PMD. Conclusion: Patients with a history of previous infection with COVID-19, non vaccination/incomplete-vaccination with COVID-19 vaccines, and patients with increasing total leukocyte counts and AST levels, high baseline total serum bilirubin were at increased risk of a detrimental clinical course and may indicate, the possibility of development of PTX and/or PMD in COVID-19 disease.

Peptide-based direct electrochemical detection of receptor binding domains of SARS-CoV-2 spike protein in pristine samples.

RNA isolation and amplification-free user-friendly detection of SARS-CoV-2 is the need of hour especially at resource limited settings. Herein, we devised the peptides of human angiotensin converting enzyme-2 (hACE-2) as bioreceptor at electrode interface for selective targeting of receptor binding domains (RBD) of SARS-CoV-2 spike protein (SP). Disposable carbon-screen printed electrode modified with methylene blue (MB) electroadsorbed graphene oxide (GO) has been constructed as cost-efficient and scalable platform for hACE-2 peptide-based SARS-CoV-2 detection. In silico molecular docking of customized 25 mer peptides with RBD of SARS-CoV-2 SP were validated by AutoDock CrankPep. N-terminal region of ACE-2 showed higher binding affinity of - 20.6 kcal/mol with 15 H-bond, 9 of which were < 3 Å. Electrochemical biosensing of different concentrations of SPs were determined by cyclic voltammetry (CV) and chronoamperometry (CA), enabling a limit of detection (LOD) of 0.58 pg/mL and 0.71 pg/mL, respectively. MB-GO devised hACE-2 peptide platform exert an enhanced current sensitivity of 0.0105 mA/pg mL-1 cm-2 (R2 = 0.9792) (CV) and 0.45 nA/pg mL-1 (R2 = 0.9570) (CA) against SP in the range of 1 pg/mL to 1 µg/mL. For clinical feasibility, nasopharyngeal and oropharyngeal swab specimens in viral transport medium were directly tested with the prepared peptide biosensor and validated with RT-PCR, promising for point-of-need analysis.

Clinical profile and outcome of neonates born to covid positive mothers

Background: A new type of Corona virus that is SARS - Cov2 called COVID 19 had a huge pandemic worldwide. On January 30,2020 the World health organization (WHO) declared the outbreak of covid19 as a public health emergency of international concern1 Methods: Descriptive and retrospective study carried out at general hospital, Sapthagiri institute of medical science and research centre from September 2020 to September 2021 Results: Among 153 tested neonates, 91 were sars-cov2 positive. Out of 91 (59%), most common symptom reported is respiratory distress in the form of TTNB(43%) and require respiratory support for longer period compared to covid negative group. Conclusion: 55% of neonates were symptomatic and reported higher incidence of NICU admission rates in SARS-COV-2 positive neonates born to SARS-COV-2 infected mothers which is comparable to our study. [ FROM AUTHOR]

COVID-19: Containment, Life, Work and Restart Urban and Regional Studies

COVID-19 manifests as a viral respiratory disease that first was imported from Wuhan, Peoples Republic of China and then it spreads from human to human when they come in to contact everywhere in every continent. The response has been national and state governance with cooperation from the local government based on disaster management laws. The public health system became the frontline Corona Warriors and was respected by all for their services, but the system capacity was evaluated for its capability to have an unusually substantial number of patients. Many disciplines jointly must contribute a knowledge-based solution based on time-series data on infected, recovered and died as well as more reliable serum tests. When a nation declares one peak has reached the local data shows it has not and so local governance shall be the effective measure based on local data for COVID-19 governance. This book concentrates on local governance for COVID-19. This book believes that COVID-19 cannot be eliminated like smallpox or polio. It can appear and disappear seasonally like common cough and cold, with never-ending mutation of the virus, but it can cause deaths even after we had full vaccinations. The public health systems came out with preventive culture such as wearing masks, practising social distancing, washing hands with disinfectants etc. to combat this virus. The police were deployed to implement preventive measures enumerated above. In this process, both police and public health workers got infected and can even threaten the entire population with more deaths and collapse of the public health system. This book advocates concentrating on urban centres for COVID-19 because of high population density and public realms where the danger of COVID-19 spread from human contact is maximum. The use of humans for data collection and management involving surveys and analysis, policing and intervention of public health persons are all risky prepositions for the individuals involved. This book concentrates on the public realm for work and living and finds an alternate solution that can automate COVID-19 prevention methods with less human involvement. This book gives more importance to local governance based on local data and the use of tools available for local governance such as Master Plans, Zonal Plans, Public realm management using ICT-IoT systems, E-Democracy and E-government. These require modifications to the existing body of knowledge based on COVID-19 prevention capabilities. Hence zonal plans may get modified and non-human control of the public realm may be institutionalized. This chapter brings together the state of knowledge on all these discussed and the rest of the chapters use many of them to demonstrate locally based solutions based on locally generated data. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Collaborative Research: ‘COVID-19: Containment, Life, Work and Restart: Regional Studies’ and Conclusions

This chapter has two parts. In the first part, the goals and the organizational details of the international collaborative research project ‘COVID-19: Containment, Life, Work and Restart: Regional Studies’ are discussed. In the second part in consultation with the team leaders of the area studies including the city study, their general conclusions of the area study on COVID-19 are presented. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Collaborative Research: “COVID-19: Containment, Life, Work and Restart: Urban Studies” and Conclusions

This chapter has two parts. In the first part, the goals and the organisational details of the international collaborative research project “COVID-19: Containment, Life, Work and Urban Restart” are discussed. In the second part in consultation with the team leaders of the area studies including the city study, their general conclusions of the area study on COVID-19 are presented. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

COVID-19: Containment, Life, Work and Restart: Urban and Regional Studies

COVID-19 manifests as a viral respiratory disease that first was imported from Wuhan, People’s Republic of China, and then it spreads from human to human when they come into contact everywhere in every continent. The response has been national and state governance with cooperation from the local government based on disaster management laws. The public health system became the frontline Corona Warriors and was respected by all for their services, but the system capacity was evaluated for its capability to have an unusually substantial number of patients. Many disciplines jointly must contribute a knowledge-based solution based on time series data on infected, recovered and died as well as more reliable serum tests. When a nation declares one peak has reached, the local data shows it has not and so local governance shall be the effective measure based on local data for COVID-19 governance. This book concentrates on local governance for COVID-19. This book believes that COVID-19 cannot be eliminated like smallpox or polio. It can appear and disappear seasonally like common cough and cold, with never-ending mutation of the virus, but it can cause deaths even after we had full vaccinations. The public health systems came out with preventive culture such as wearing masks, practising social distancing, washing hands with disinfectants to combat this virus. The police were deployed to implement preventive measures enumerated above. In this process, both police and public health workers got infected and can even threaten the entire population with more deaths and collapse of the public health system. This book advocates concentrating on urban centres for COVID-19 because of high population density and public realms where the danger of COVID-19 spread from human contact is maximum. The use of humans for data collection and management involving surveys and analysis, policing and intervention of public health persons is all risky prepositions for the individuals involved. This book concentrates on the public realm for work and living and finds an alternate solution that can automate COVID-19 prevention methods with less human involvement. This book gives more importance to local governance based on local data and the use of tools available for local governance such as Master Plans, zonal plans, public realm management using ICT-IoT systems, E-Democracy and E-government. These require modifications to the existing body of knowledge based on COVID-19 prevention capabilities. Hence, zonal plans may get modified and non-human control of the public realm may be institutionalised. This chapter brings together the state of knowledge on all these discussed, and the rest of the chapters use many of them to demonstrate locally based solutions based on locally generated data. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A study to evaluate the role of eosinophil count as a prognostic marker for assessing the outcome in patients with COVID-19 infection.

Background and Objectives: The triaging of COVID-19 patients is of paramount importance to plan further management. There are several clinical and laboratory parameters that help in categorizing the disease severity, triaging, and prognostication. Little is known about the prognostic significance of eosinopenia in predicting the severity of COVID-19 from large hospital data, especially from low- and middle-income countries. The objective of this study is to evaluate the level of eosinopenia as an early prognostic marker for assessing the outcomes in COVID-19 patients and to assess the superiority of eosinopenia as a prognostic marker for assessing the outcomes in COVID-19 patients compared to lymphopenia and neutrophil-to-lymphocyte ratio (NLR). Methods: The study was carried out in a tertiary care hospital. A retrospective longitudinal approach was adopted wherein the hospital records of COVID-19 patients were analyzed. In our study, two separate groups of patients were included for analysis to describe the association between initial eosinophil counts of the patients and the clinical outcomes. In the first group, the disease severity in terms of clinical and radiological parameters was compared in patients of COVID-19 presenting with and without the presence of initial eosinopenia. Commonly used markers for triage, namely lymphopenia and NLR, were compared with the presence of initial eosinopenia among the patients who progressed to moderate and severe disease. In the second group, an analysis of eosinopenia was made among the patients who succumbed to the illness. Results: It was seen that 29.6% of patients with eosinopenia had moderate and severe disease compared to those without eosinopenia where only 10.8% had moderate disease, none had severe disease. It was seen that 19.7% of patients with eosinopenia but no lymphopenia had more severe disease compared to patients with lymphopenia but no eosinopenia where 10.8% of the patients had moderate disease, none had severe disease. In patients younger than 60 years who died of COVID-19, it was found that initial eosinopenia was found in 86%, whereas a high NLR >17 was seen in only 25.6% of patients who died, thus implying that is eosinopenia is an important marker of disease severity in COVID-19. Conclusions: Eosinopenia is an important parameter in the evaluation of COVID-19 and the presence of it should alert the clinicians regarding the further progression of the disease. It is not only an important marker but also an early marker for severe disease.