Large-Scale Urban Heating and Pollution Domes over the Indian Subcontinent

The unique geographical diversity and rapid urbanization across the Indian subcontinent give rise to large-scale spatiotemporal variations in urban heating and air emissions. The complex relationship between geophysical parameters and anthropogenic activity is vital in understanding the urban environment. This study analyses the characteristics of heating events using aerosol optical depth (AOD) level variability, across 43 urban agglomerations (UAs) with populations of a million or more, along with 13 industrial districts (IDs), and 14 biosphere reserves (BRs) in the Indian sub-continent. Pre-monsoon average surface heating was highest in the urban areas of the western (42 degrees C), central (41.9 degrees C), and southern parts (40 degrees C) of the Indian subcontinent. High concentration of AOD in the eastern part of the Indo-Gangetic Plain including the megacity: Kolkata (decadal average 0.708) was noted relative to other UAs over time. The statistically significant negative correlation (-0.51) between land surface temperature (LST) and AOD in urban areas during pre-monsoon time illustrates how aerosol loading impacts the surface radiation and has a net effect of reducing surface temperatures. Notable interannual variability was noted with, the pre-monsoon LST dropping in 2020 across most of the selected urban regions (approx. 89% urban clusters) while it was high in 2019 (for approx. 92% urban clusters) in the pre-monsoon season. The results indicate complex variability and correlations between LST and urban aerosol at large scales across the Indian subcontinent. These large-scale observations suggest a need for more in-depth analysis at city scales to understand the interplay and combined variability between physical and anthropogenic atmospheric parameters in mesoscale and microscale climates.

Emotional Labour During the COVID-19 Pandemic—Current Inquiry and Suggested Future Research Directions

The objective of this article is to examine the impact of macro-extreme emotional experience (MEEE) and the new societal norms during the COVID-19 pandemic on health and well-being and their situational consequences on emotional labour of frontline employees. The vast literature on emotional labour in the past has focused on several situational cues, and individual and organizational factors as antecedents. We did a systematic review of available literature on emotional labour, literature on sentiment analysis and emotional experience during the pandemic and analysed COVID-19 related blogs using Natural Language Processing (NLP) in RStudio. At the same time, we attempted to look at the possible intervention of individual factors of MEEEs and social aspects of the new societal norms as antecedents on emotion regulation process and its outcome and propose a conceptual framework for future research on emotional labour under the ‘new normal'. It was concluded that perceived risk, fear and anxiety are extreme emotions that individuals are experiencing during the pandemic. © 2023 XLRI Jamshedpur, School of Business Management & Human Resources.

Preface

International Conference on Geospatial Science for Digital Earth Observation (GSDEO 2021)The international conference on "Geospatial Science for Digital Earth Observation” (GSDEO) 2021 was successfully held on a virtual platform of Zoom on March 26th and 27th, 2021. The conference was jointly organized by the Indian Society of Remote Sensing (ISRS), Kolkata chapter, and the Department of Geography, School of Basic and Applied Sciences, Adamas University. Due to the non-predictable behaviour of the COVID-19 second wave, which imposed restrictions on organizing offline events, the GSDEO (2021) organizing committee decided to organize the conference online, instead of postponing the event.Remotely sensed data and geographic information systems have been increasingly used together for a vast range of applications, which include land use/land cover mapping, water resource management, weather forecasting, environmental monitoring, agriculture, disaster management, etc. Currently, intensive research is being carried out using remotely sensed data on the geoinformatics platform. New developments have led to dynamic advances in recent years. The objective of the international conference on Geospatial Science for Digital Earth Observation (GSDEO 2021) was to bring the scientists, academicians, and researchers, in the field of geo-environmental sciences on a common platform to exchange ideas and their recent findings related to the latest advances and applications of geospatial science. The call for papers received an enthusiastic response from the academic community, and over 100+ participants from 50+ colleges, universities, and institutions participated in the conference. In total 50+ research papers had been presented through the virtual Zoom conference platform in GSDEO 2021.The conference witnessed the presentation of research papers from diverse applied fields of geospatial sciences, which include the application of geoinformatics in geomorphology, hydrology, urban science, land use planning, climate, and environmental studies. There were four sessions namely, TS 1: Geomorphology and Hydrology, TS 2: Urban Science, TS 3: Social Sustainability and Land Use Planning, and TS 4: Climate and Environment. Each session was further subdivided, into two parts, namely Technical Session 1-A and 1-B. Each sub-session had been designed with one keynote speech and 5 oral presentations. Oral sessions were organized in two parts and offered through live and pre-recorded components based on the preference of the presenters. The presentation session was followed by a live Q&A session. The session chairs moderated the discussions. Similarly, poster sessions were organized in three parts and offered e-poster, live, and pre-recorded components. The best presenter of each sub-session received the best paper award.Dr. Prithvish Nag, Ex-Director of NATMO & Ex Surveyor General of India delivered the inaugural speech, and Dr. P. Chakrabarti, Former Chief Scientist of the DST&B, Govt. of West Bengal delivered a special lecture after the inaugural session. Eight eminent keynote speakers, Prof. S.P. Agarwal from the Indian Institute of Remote Sensing, Prof. Ashis Kumar Paul from Vidyasagar University, Prof. Soumya Kanti Ghosh from the Indian Institute of Technology, Kharagpur, Prof. L. N. Satpati from the University of Calcutta, Prof. R.B. Singh from the University of Delhi, Dr. A.K. Raha, IFS (Retd), Prof. Gerald Mills from the University College Dublin and Prof. Sugata Hazra from Jadavpur University enriched the knowledge of participants in the field of geoinformatics by their informative lectures. The presentations and discussions widely covered the various spectrums of geoinformatics and its application in monitoring natural resources like vegetation mapping, agricultural resource monitoring, forest health assessment, water, and ocean resource management, disaster management, land resource management, water and climate studies, drought vulnerability assessment, groundwater quality monitoring, accretion mapping and the use of geospatial sci nce in studying morphological, hydrological, and other biophysical characteristics of a region etc. Application of geoinformatics in predicting urban expansion, urban climate, disaster management, healthcare accessibility, anthropogenic resource monitoring, spatial-interaction mapping, and, sustainable regional planning were well-discussed topics of the conference.List of Committees, photos are available in the pdf.

Irrawaddy dolphin in Asia's largest brackish water lagoon: A perspective from SWOT and sentiment analysis for sustainable ecotourism

Chilika, the largest brackish water lagoon in Asia, is a habitat of the Irrawaddy Dolphin (IRD) and a popular tourist destination for dolphin watching. However, this dolphin-based tourism has turned unsustainable due to the adverse impacts of tourism on IRD. This study uses SWOT (strengths, weaknesses, opportunities, threats) to analyze various internal and external factors that control IRD-based tourism in Chilika. Content analysis and sentiment analysis are also used to know the tourists' views regarding dolphin tourism in Chilika lagoon, and telephonic interviews with boat owners to determine the impact of tourism on local communities before and after the COVID-19 pandemic. SWOT analysis highlights existing opportunities and strengths of IRD tourism in Chilika, such as prevalent ‘positive' perception (48%) among the tourists and stable IRD population. 38% of tourists' perceptions were ‘negative,' corroborating inherent weaknesses and threats of IRD tourism, such as faulty marketing strategy, excessive tourist pressure, and the ongoing COVID-19 pandemic. Interviews with boat operators reveal that the pandemic lockdown laid the financial situation of local tourist boat operators down. This study suggests multipronged solution approaches, for example ensuring the integrity of IRD habitat, optimal resource utilization, high service quality, and necessary infrastructure development to facilitate a sustainable ecotourism model in the lagoon. © 2023 Elsevier B.V.

Impact of COVID-19 on the World Economy

Coronavirus is impacting the world we live in for the most vanquished way and all regions of the planet are left with hung economic loss. This pandemic has not just negatively affected medical care frameworks and communities' lives yet additionally influenced world economies and brought about employment misfortunes, and business disturbances, and made us head towards one of the most awful times ever for individuals on the planet. Nearly, every one of the enterprises is going through huge decreases in their business, and the effect is so much tremendous of this pandemic, that they are extending more terrible times ahead. The outburst of the Covid-19 pandemic has a remarkable shock to the Indian economy. The economy was at that point in a dreadful state before COVID-19 struck. With the persistent far-reaching lockdown, worldwide financial crisis, and related interruption of interest and supply chains, the economy has likely confronted an extended time of stand stillness. The extent of the financial effect is presently subject to the span and seriousness of the well-being emergency, the period of the lockdown, and how the circumstance unfurls once the lockdown is lifted. This study depicts the condition of the global economy in the pre-COVID-19 period, surveys the possible effect of the shock on different portions of the economy, and dissects the strategies that have been declared such a long way by the central government and the international banks to improve the financial shock and set forward a bunch of strategy proposals for explicit areas.

FedCare: Federated Learning for Resource-Constrained Healthcare Devices in IoMT System

In social IoMT systems, resource-constrained devices face the challenges of limited computation, bandwidth, and privacy in the deployment of deep learning models. Federated learning (FL) is one of the solutions to user privacy and provides distributed training among several local devices. In addition, it reduces the computation and bandwidth of transferring videos to the central server in camera-based IoMT devices. In this work, we design an edge-based federated framework for such devices. In contrast to traditional methods that drop the resource-constrained stragglers in a federated round, our system provides a methodology to incorporate them. We propose a new phase in the FL algorithm, known as split learning. The stragglers train collaboratively with the nearest edge node using split learning. We test the implementation using heterogeneous computing devices that extract vital signs from videos. The results show a reduction of 3.6 h in the training time of videos using the split learning phase with respect to the traditional approach. We also evaluate the performance of the devices and system with key parameters, CPU utilization, memory consumption, and data rate. Furthermore, we achieve 87.29% and 60.26% test accuracy at the nonstragglers and stragglers, respectively, with a global accuracy of 90.32% at the server. Therefore, FedCare provides a straggler-resistant federated method for a heterogeneous system for social IoMT devices.

Effect of Mutations in the SARS-CoV-2 Spike RBD Region of Delta and Delta-Plus Variants on its Interaction with ACE2 Receptor Protein

The outbreak of severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) has undergone multiple significant mutations since its detection in 2019 in Wuhan, China. The emergence of new SARS-CoV-2 variants that can spread rapidly and undermine vaccine-induced immunity threatens the end of the COVID-19 pandemic. The delta variant (B.1.617.2) that emerged in India challenges efforts to control the COVID-19 pandemic. In addition to Delta, so-called Delta Plus sub-variants (B.1.617.2.1 and B.1.617.2.2) have become a new cause of global concern. Here we compare the interaction profile of RBD of the spike protein of the Delta and Delta-Plus variant of SARS-CoV-2 with the ACE2 receptor. From the molecular dynamics simulation, we observed the spike protein of Delta and Delta-Plus variant of SARS-CoV-2 utilizes unique strategies to have stable binding with ACE2. Using MM-GBSA/MM-PBSA algorithms, we found the binding affinity of spike protein of the Delta-variant-ACE2 complex is indeed high (GBTOT =-39.36 kcal mol-1, PBTOT=-17.52 kcal mol-1) in comparison with spike protein of Delta-Plus variant-ACE2 Complex (GBTOT =-36.83 kcal mol-1, PBTOT =-16.03 kcal mol-1). Stable binding of spike protein to ACE2 is essential for virus entry, and the interactions between them should be understood well for the treatment modalities. © 2022 by the authors.

Fungal infection: An unrecognized threat

The fungi are eukaryotes and of great interest to microbiologist. Fungi are heterotrophic organism that require organic compounds for nutrition. According to Hawksworth, only around 100 fungi cause diseases in humans and animals out of around 1.5 million existing in the universe. Fungal pathogenic infection may cause allergies, superficial infection, as well as invasive mycosis in severe cases. Public health can be significantly affected by zoonotic fungi that transmit naturally between animals and humans. Prevention of fungal infection arising out of zoonotes has received insufficient attention as it lacks mass awareness. A number of different fungal infections, their signs and symptoms, preventive measures, and treatment protocol are demonstrated in this chapter. Regarding the treatment of various fungal infections, azoles, fluoropyrimidines, polyenes, and echinocandins are the only four molecular classes of drugs available as on date to target fungal metabolic pathways despite years of drug discovery research. Few other promising molecules like morpholines and allylamines are useful antifungal but with poor efficacy and severe side effects when administered systematically. Development of resistance against most common antifungal drugs further aggravates the situation. Fungal infection like mucormycosis is observed in some parts of the world after a patient gets infected with COVID-19 as there is impairment in the immunity system. There is an urgent need to control this fungal infection as it poses serious threat silently. We can limit the spread of fungal infection by protecting susceptible population from being exposed. More efforts are needed from a global health perspective to aware the people regarding neglected fungal infection and its problem so that socioeconomic consequences and mortality can be better explained. An integrated platform of prevention and control strategies for the spread of fungal infection is the need of the hour. © 2023 Elsevier Inc. All rights reserved.

Information retrieval and extraction on COVID-19 clinical articles using graph community detection and Bio-BERT embeddings

In this paper, we present an information retrieval system on a corpus of scientific articles related to COVID-19. We build a similarity network on the articles where similarity is determined via shared citations and biological domain-specific sentence embeddings. Ego-splitting community detection on the article network is employed to cluster the articles and then the queries are matched with the clusters. Extractive summarization using BERT and PageRank methods is used to provide responses to the query. We also provide a Question-Answer bot on a small set of intents to demonstrate the efficacy of our model for an information extraction module. © ACL 2020.All right reserved.

LiMS-Net: A Lightweight Multi-Scale CNN for COVID-19 Detection from Chest CT Scans

Recent years have witnessed a rise in employing deep learning methods, especially convolutional neural networks (CNNs) for detection of COVID-19 cases using chest CT scans. Most of the state-of-the-art models demand a huge amount of parameters which often suffer from overfitting in the presence of limited training samples such as chest CT data and thereby, reducing the detection performance. To handle these issues, in this paper, a lightweight multi-scale CNN called LiMS-Net is proposed. The LiMS-Net contains two feature learning blocks where, in each block, filters of different sizes are applied in parallel to derive multi-scale features from the suspicious regions and an additional filter is subsequently employed to capture discriminant features. The model has only 2.53M parameters and therefore, requires low computational cost and memory space when compared to pretrained CNN architectures. Comprehensive experiments are carried out using a publicly available COVID-19 CT dataset and the results demonstrate that the proposed model achieves higher performance than many pretrained CNN models and state-of-the-art methods even in the presence of limited CT data. Our model achieves an accuracy of 92.11% and an F1-score of 92.59% for detection of COVID-19 from CT scans. Further, the results on a relatively larger CT dataset indicate the effectiveness of the proposed model. © 2023 Association for Computing Machinery.

The Impact of Nutrients on Coronavirus (Covid-19): A Review

A properly balanced diet can improve the immunity system and also prevent various diseases including COVID-19 which is caused by severe acute respiratory syndrome coronavirus 2 (SARC-Co2). This review mainly describes dietary guidelines or approaches to build up our immunity as well as better health and protect from corona virus. As we all know one line "Health is Wealth". So this wealth can be established or secure by optimal diet. Basic food components that are carbohydrates, protein, fat, vitamin and minerals have various important functions to fight against diseases. Most of the documents encourage to consumption of fruits, green vegetables, proteins, whole grains and fluids. Vitamins such as C, E, D, A most important to boost up our immunity. Vitamin C and E also known as natural antioxidants because they protect our body from infection and vitamin C also helps to absorption of iron. Zinc selenium, amino acid and omega 3 fatty acids are necessary to fight COVID-19. Besides this good hygiene practice, proper physical practice or daily work out and proper amount of water intake can improve good health status and prevent chronic illness. Sleep is necessary to heal and rest our body especially during critical illness. Exercise helps to increase the level of white blood corpacell and antibody which helps to fight against infections. This paper discussed the role of these nutrients and specific functions related to improving COVID patients. These nutrients can protect our health from various infectious diseases as well as can decreases mortality and the morbidity rate of COVID-19 patients by improving immunity levels. Copyright © RJPT All right reserved.

Adoption of FinTech services amidst COVID-19 pandemic: empirical evidence from Assam

PurposeThe paper intends to comprehend the pattern of usage of FinTech services among bank customers during the COVID-19 pandemic. The paper also examines the factors influencing the adoption of FinTech services by using the constructs from the technology acceptance model (TAM) together with highlighting the issues faced in using FinTech services in Assam.Design/methodology/approachThe research is empirical in nature. Data have been collected from 1,066 prime earners of the households having a bank account.FindingsThere has been an upsurge in the use of FinTech services in the area of study. Apart from government and private service employees, businessmen, self-employed professionals, many daily-wage earners and agriculturists have also experienced an increase in their frequency of usage of FinTech services thereby making technology-based financial services an indispensable tool in enhancing access, improving inclusivity in the times of crisis and aftermath. Government support, trust, perceived usefulness (PU), attitude and social influence have a positive influence on FinTech adoption;however, perceived risks impact respondents' trust towards FinTech services thereby requiring necessary measures to evaluate organizations' preparedness to deal with cyber threats.Originality/valueThe paper provides insight into the factors impacting the adoption of FinTech services to stimulate superior connectivity infrastructure, robust security measures and maintaining financial stability with adequate supervisory and monitoring regulations to enhance trust towards FinTech services during the crisis and aftermath.

The impact on education of university students in COVID-19 pandemic period: A factor analysis

The educational sector of Bangladesh is severely affected due to the sudden outbreak of novel Corona virus (COVID-19). Bangladesh which is one of the densely populated countries has a significant improvement in the education sector along with the others in last some decades but this pandemic has played a serious setback to almost all the sectors of this small country. As all the educational institutes of Bangladesh are closed since 17th March 2020 till 30th June 14, 2021 and this may lead to many detrimental effects. To measure these, a survey was conducted and collected data was analyzed by SPSS Statistics v 25.0. This paper highlighted the mental stress, socio-economic crisis of the students that badly affected their education. It is observed in this study that around 80% of the students are going through mental stress particularly for internet facilities and financial crisis in pandemic period.

Immunogenetic perspective of inflammatory disorders

Understanding of the genetic basis underlying inflammatory disorders has progressed in recent years. Contribution of proinflammatory cytokines, human leukocyte antigen (HLA), and non-HLA polymorphisms in the pathogenesis of several autoimmune and immune-mediated inflammatory disorder is critical. HLA plays a central role in disease pathology. Harmful stimuli triggering the signaling mechanisms including nuclear factor-kappa B pathway, Janus kinase-signal transducer and activator of transcription pathway, and mitogen-activated protein kinase pathway results in the release of inflammatory mediators. From acute to chronic inflammation, the etiology of various inflammatory disorders is poorly understood. Inflammatory disorder such as COVID 19 is a devastating havoc to the world. As we reach the end of 2020, >1 million people have succumbed to death worldwide. Disease-manifesting clinical features include mild to severe pneumonia, loss of respiratory function progressing to acute respiratory distress syndrome with occasional multiorgan failure. Cytokine storm, decreased T cell count, and insufficient immune response are conducive issues to COVID 19 pandemic. Varied immune responses to the same antigen across different individuals determine the genetic perspective of disease susceptibility. Through genome-wide association studies, next-generation sequencing and other genetic techniques, several genetic risk loci associated with various inflammatory diseases such as inflammatory bowel disease, psoriasis, sclerosis, and systemic lupus erythematosus (SLE) have been identified. Dysregulated inflammatory pathways, gene mutation, or elevated cytokine level may lead to the disease progression. However, the production of autoantibodies against the nuclear antigens is a hallmark of diseases like SLE and rheumatoid arthritis. Moreover, environmental factors like smoking also increase the risk of inflammatory disorders. Understanding the functional aspects of casual genetic factors underlying the disease pathogenesis greatly facilitates the ability to identify the therapeutic targets relevant to disease. The current chapter deals with the idea of genetic perspective associated with various inflammatory disorders and their potential therapeutic targets along with the factors contributing to disease susceptibility. © 2022 Elsevier Inc. All rights reserved.

Demand Forecasting of a Multinational Retail Company using Deep Learning Frameworks

In this modern era of digitization, the competition is significantly increasing among retailers. One of the major challenges for them is demand prediction or sales forecasting. Especially in this Covid pandemic, retail sales forecasting became very crucial due to the employee shortage, and increasing online demand. In the modern era of digitization, competition is increasing. This research explores the application of an advanced deep learning approach in predicting the market demands in advance of individual products for the future seasons. This application aims to support an American Multinational Retail company in ordering, purchasing, and managing inventory. Accordingly, the company provides a real sales dataset to perform this study. This research proposes two sales forecasting strategies based on LSTM and LGBM models. We first execute data preprocessing techniques using statistical feature engineering on the raw sales data. Thereafter perform the LSTM and LGBM algorithms for training and prediction. LGBM takes past data from lag feature engineering for better forecasting. For that, we found that LGBM performs better than LSTM in forecasting. Copyright (C) 2022 The Authors.

Role and impact of disruptive technology in education and strategies harnessing it

India is a developing country, where education is given a high importance. However, the importance of updating the methods of educating the upcoming generation is not considered as seriously as it has to be. Covid-19 pandemic brought a sudden change around the globe in every field of service, including education sector. Mode of education has fully transformed into online learning atmosphere. Technology which is always a fascination to its users has taken over traditional method of teaching and learning. This study is done in the state of Kerala, collecting responses from college students of every district. This paper focuses on understanding the impact of disruptive technology in the field of education and to assess the attitude of students towards merging this technology with the traditional classroom learning system.

CoviFL: Edge-Assisted Federated Learning for Remote COVID-19 Detection in an AIoMT Framework

Detection of COVID-19 has been a global challenge due to the lack of proper resources across all regions. Recently, research has been conducted for non-invasive testing of COVID-19 using an individual's cough audio as input to deep learning models. However, these methods do not pay sufficient attention to resource and infrastructure constraints for real-life practical deployment and the lack of focus on maintaining user data privacy makes these solutions unsuitable for large-scale use. We propose a resource-efficient CoviFL framework using an AIoMT approach for remote COVID-19 detection while maintaining user data privacy. Federated learning has been used to decentralize the CoviFL CNN model training and test the COVID-19 status of users with an accuracy of 93.01 % on portable AIoMT edge devices. Experiments on real-world datasets suggest that the proposed CoviFL solution is promising for large-scale deployment even in resource and infrastructure-constrained environments making it suitable for remote COVID-19 detection. © 2022 IEEE.

An epidemiological study on body perception imaging and lifestyle behaviors among adolescents during COVID-19 pandemic in a rural area of West Bengal

Background: Adolescence is the intermediary linking phase from childhood to adulthood. Physical, sexual, psychological and social developmental changes during this period. Adolescence is a period of increased awareness of bodily cues and self-reflection, including evaluation of one's own body and appearance. Body misconception and body dissatisfaction, two very important potential causative factors of bad nutritional status of adolescents, have not been adequately investigated in rural India. Aims and Objectives: The present study on adolescents was conducted to assess the lifestyle factors and degree of dissatisfaction and misconception of their body. Material(s) and Method(s): An observational, descriptive, and community-based cross-sectional study was conducted by interviewing adolescents residing in the field practice area of the Department of Community Medicine, Medical College, Kolkata, during the months of March-April, 2022. The data were analyzed with the Statistical Package for the Social Sciences. Result(s): A total of 239 respondents were interviewed (74.1% male and 25.9% female). About 47.3% were early adolescents, 46.4% were middle adolescents, and rest 6.3% were late adolescents. About 56.5% was indulged in some kind of exercise. According to the total adolescent body image satisfaction scale score, 66.5% seemed to be satisfied with their body image. Body image dissatisfaction was associated with participants' age, gender, socio-economic status, and type of family. Conclusion(s): It is evident from the study that girl children, adolescents of the lower socio-economic status group, and middle, and late adolescents are more prone to be dissatisfied with their body image. Hence, they need to be given extra focus for restoration of their mental health. Copyright © 2022 Sudipta Das, et al.

A survival analysis approach for identifying the risk factors in time to recovery of COVID-19 patients using Cox proportional hazard model

The coronavirus pandemic was a global health crisis taking away millions of lives worldwide. People diseased by the virus, differ in the extent of severity of the infection. While it turns out to be fatal for some, for several others the extent of severity is as ordinary as common cold. These people are reported to have recovered from the disease without hospitalization and consuming some relevant medicine and home remedies. But people who have comorbidity like geriatric, high blood pressure, heart and lung problems, diabetes, cancer etc. are at high risk of developing serious illness from the infection. This study is an application of the Cox proportional hazard model with an aim to identify the risk factors that affect the recovery time of the COVID-19 patients. The model is an advanced regression technique that can be utilized to evaluate simultaneously the effect of several factors on the possibility of instantaneous failure in patients. The paper also uses the Mental-Heinzen test (Log-Rank test) to compare if the probability of survival of different treatment procedures or different groups of patients differ significantly. The information is collected from 129 respondents of Assam, India. The study identifies that the significant risk factors that prolong the recovery time from COVID-19 are pre-disease, location, and food habits.