A Deep Learning-Based Preventive Measures of COVID-19 in a crowd using Reinforcement Model over GAN for Enhanced efficiency

The fast human-to-human spread of COVID-19 has caused significant lifestyle changes for many individuals. At the end of January 2020, the pandemic began, and many nations responded with varying degrees of testing, sanitation, lockdown, and quarantine centers. New normals of testing, sanitization, social separation, and lockdown are being implemented, and people are gradually returning to work and other daily routines. The COVID-19 infected population is monitored by testing individuals regularly. But it's a resource-heavy endeavor to test everyone without good reason. An optimum strategy is required to efficiently identify persons who are most likely to test positive for COVID-19. Sanitation is utilized for both persons and public spaces to eliminate germs. However, the disruption of governmental operations and economic development makes the use of lockdown and quarantine centers a resource-intensive endeavor. Conversely, it degrades the standard of living across a society. Furthermore, keeping people inside their houses or quarantine centers for an unlimited amount of time would not allow the government to care for everyone. These variables impact virus propagation, human health and happiness, available resources, and the economy's health, making their management resource-intensive. counting and density estimation are both attempts to create clever and efficient algorithms that can interpret the data provided by images to carry out Efficiency. GANs have been proven to have promising applications in overcoming the data dearth problem in COVID-19 lung image analysis. The Convolutional Neural Network (CNN) models built for the diagnosis of COVID-19 have benefited from the GAN-generated data used to refine their training. Moreover, GANs have helped improve the performance of CNNs by super-resolving pictures and performing segmentation. This work highlights the Reinforcement deep learning model over the fundamental constraints of the possible transformation of GANs-based approaches. This work proposes the model be developed with a new intelligent approach using RL to quantify these different types of testing considered for social distancing, face mask detection, limiting the gathering, and locking the location using the Q Learning technique. Different RL algorithms are implemented, and agents are equipped with these algorithms so that they may interact with the environment and learn the optimum method for doing so. © 2023 IEEE.

Impacts of COVID-19-Related Non-Pharmaceutical Interventions on Mobility and Accidents in Bangladesh

Transport plays a major role in spreading contagious diseases such as COVID-19 by facilitating social contacts. The standard response to fighting COVID-19 in most countries has been imposing a lockdown—including on the transport sector—to slow down the spread. Though the Government of Bangladesh also imposed a lockdown quite early, it was forced to relax the lockdown for economic reasons. This motivates this study to assess the interaction between various non-pharmaceutical intervention (NPI) policies and transport sector outcomes, such as mobility and accidents, in Bangladesh. The study explores the effect of NPIs on both intra-and inter-regional mobility. Intra-regional mobility is captured using Google mobility reports which provide information about the number of visitors at different activity locations. Inter-regional, or long-distance, mobility is captured using vehicle count information from toll booths on a major bridge. Modeling shows that, in most cases, the policy interventions had the desired impact on people's mobility patterns. Closure of education institutes, offices, public transport, and shopping malls reduced mobility at most locations. The closure of garment factories reduced mobility for work and at transit stations only. Mobility was increased at all places except at residential locations, after the wearing of masks was made mandatory. Reduced traffic because of policy interventions resulted in a lower number of accidents (crashes) and related fatalities. However, mobility-normalized crashes and fatalities increased nationally. The outcomes of the study are especially useful in understanding the differential impacts of various policy measures on transport, and thus would help future evidence-based decision-making. © National Academy of Sciences: Transportation Research Board 2021.

Impact of COVID-19 Lockdown on Ambient Air Quality in the Southwest Coastal Urban Regions of India

The rapid growth of urban areas and population as well as associated development over recent decades have been a major factor controlling ambient air quality of the urban environment in Kerala (India). Being located at the southwestern fringe of the Indian peninsula, Kerala is one of the regions that has been significantly influenced by the activities in the Indian Ocean. The present study focuses on the effect of the COVID-19 lockdown (in 2021) on ambient air quality in the selected coastal metropolitan areas of Kerala. Although previous research studies reported improvement in ambient air quality in Kerala during the lockdown period, this study demonstrates the potential of onshore transport of air pollutants in controlling the air quality of coastal urban regions during the lockdown period. Data from the ambient air quality monitoring stations of the Kerala State Pollution Control Board in the urban areas of Thiruvananthapuram (TM), Kollam (KL), Kozhikode (KZ), and Kannur (KN) are used for the analysis. Temporal variation in the concentration of air pollutants during the pre-lockdown (PRLD), lockdown (LD), and post-lockdown (PTLD) periods (i.e., 1 March to 31 July) of 2021 is examined to assess the effect of lockdown measures on the National Air Quality Index (AQI). Results indicate a significant decline in the levels of air pollutants and subsequent improvement in air quality in the coastal urban areas. All the effect of lockdown measures has been evident in the AQI, an increase in the concentration of different pollutants including CO, SO2, and NH3 during the LD period suggests contributions from multiple sources including onshore transport due to marine traffic and transboundary transport. © 2023, The Author(s) under exclusive licence to Institute of Earth Environment, Chinese Academy Sciences.

Optimal interventions of infectious disease

The recent outbreak of novel coronavirus has highlighted the need for a benefit-cost framework to guide unconventional public health interventions aimed at reducing close contact between infected and susceptible individuals. In this paper, we propose an optimal control problem for an infectious disease model, wherein the social planner can control the transmission rate by implementing or lifting lockdown measures. The objective is to minimize total costs, which comprise infection costs, as well as fixed and variable costs associated with lockdown measures. We establish conditions concerning model primitives that guarantee the existence of a straightforward optimal policy. The policy specifies two switching points (Formula presented.), whereby the social planner institutes a lockdown when the percentage of infected individuals exceeds (Formula presented.), and reopens the economy when the percentage of infected individuals drops below (Formula presented.). We subsequently extend the model to cases where the social planner may implement multiple lockdown levels. Finally, numerical studies are conducted to gain additional insights into the value of these controls. © 2023 Wiley Periodicals LLC.

Don't Be a Victim during a Pandemic! Analysing Security and Privacy Threats in Twitter during COVID-19

There has been a huge spike in the usage of social media platforms during the COVID-19 lockdowns. These lockdown periods have resulted in a set of new cybercrimes, thereby allowing attackers to victimise social media users with a range of threats. This paper performs a large-scale study to investigate the impact of a pandemic and the lockdown periods on the security and privacy of social media users. We analyse 10.6 Million COVID-related tweets from 533 days of data crawling and investigate users' security and privacy behaviour in three different periods (i.e., before, during, and after the lockdown). Our study shows that users unintentionally share more personal identifiable information when writing about the pandemic situation (e.g., sharing nearby coronavirus testing locations) in their tweets. The privacy risk reaches 100% if a user posts three or more sensitive tweets about the pandemic. We investigate the number of suspicious domains shared on social media during different phases of the pandemic. Our analysis reveals an increase in the number of suspicious domains during the lockdown compared to other lockdown phases. We observe that IT, Search Engines, and Businesses are the top three categories that contain suspicious domains. Our analysis reveals that adversaries' strategies to instigate malicious activities change with the country's pandemic situation. © 2013 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.

Statistical analysis of crash data and impact of COVID-19 on road crashes in Maharashtra, India

This study analyzes crash data from 2016 to 2020 on a National Highway in Maharashtra, India. The impact of the COVID-19 lockdown on the road crashes of the study area is presented, and recommendations to improve road safety are proposed. The crash data is collected from the "National Highways Authority of India, Kolhapur” from 2016 to 2020, and the information is classified into three scenarios: Before Lockdown, After Lockdown, and Strict Lockdown. The crash data is analyzed under three scenarios for seven different classifications followed by their sub-classifications. The time-wise analysis of crash data is performed in four-time slots, namely 00:00–05:59 AM, 06:00–11:59 AM, 12:00–17:59 PM, and 18:00–23:59 PM. The season-wise analysis of crash data is performed in three seasons: Summer, Monsoon, and Winter. The crashes that occurred on 2-lane-straight roads having T-junction are more than 90% in all three scenarios. The significant factors responsible for crashes are "Head-on collision,” "Vehicle out of control,” and "Overspeeding.” Most crashes (more than 36%) occurred between 12:00 and 17:59 PM and in the Summer season (more than 42%) in all three scenarios. The crashes in the COVID-19 "Strict Lockdown” scenario witnessed a fall of 254.55% compared to 2019 and 2018. Surprisingly, there was a rise of 137.5% and a fall of 127.27% in crashes of the COVID-19 2020 "Strict Lockdown” scenario, compared to 2017 and 2016, respectively. The crashes under the sub-classifications "Right angle collision” and "Fatal” increased in 2020 compared to the previous 4 years due to the impact of COVID-19. © 2023, The Author(s).

The Impacts of Lockdown on Open Source Software Contributions during the COVID-19 Outbreak

We leverage the lockdown of Wuhan, China in January 2020 in response to COVID-19 as a natural experiment to study its impacts on individuals' contributions to open source software (OSS) on GitHub - the world's largest OSS platform. We find that Wuhan developers' contributions decreased by 10.2% relative to those in Hong Kong, Macau, and Taiwan (HMT) regions in the five weeks after the lockdown. Moreover, the contributions of Wuhan developers who interacted more with local developers on GitHub were reduced more after the lockdown. We conjecture that the lack of face-to-face (F2F) collaboration for Wuhan developers is the main driver of their reduced contributions, providing important insights for OSS platforms and stakeholders. © 2022 IEEE Computer Society. All rights reserved.

Impact of the UK's COVID-19 lockdown on the residential electricity consumption

Extreme disruptive scenarios such as pandemic lockdown force people to alter regular daily routines, impacting their energy consumption pattern. The implication of such a disruptive scenario for a more extended period on energy consumption is uncertain. This study aimed to investigate the impact of COVID-19 lockdown on residential electricity consumption in 100 houses from the southwestern UK. For the study, we analysed highly granular (1-minutely) electricity consumption data for April-September 2020 compared to the same months in 2019 for the same houses. Our study showed statistically significant differences during the lockdown period (the analysed six months) in energy demand. The minutely average electricity demand was 1.4-10% lower during April-September 2020 than in 2019. Our analysis showed that not all houses had similar type of changes during the lockdown. Some houses demonstrated a 38% increase in electricity demand, whereas some houses showed a 54% reduction during the lockdown period compared to 2019. Some houses showed significantly higher electricity use during the morning and afternoon than in 2019, which might be due to working and schooling from homes during the lockdown. © International Building Performance Simulation Association, 2022

Sacred Spaces Enduring Pro-environmental Behavior: A Case Study from Kerala Temple

In this Anthropocene era, it is relevant to understand how culture influences humans' ecological behavior. This research aims to understand the impact of the COVID-19 lockdown on temple-led eco-conservation strategies from the Sasthamcotta Shri Dharma Sastha temple, known for its natural landscapes and pro-environmental activities. A qualitative study based on semi-structured interviews was conducted virtually among the people who regularly interact with the temple. The data was collected directly from the field and is analyzed systematically based on grounded theory. The findings indicate that temples can generate eco-conservation approaches that are socio-psychologically relevant to humans, such as (a) connectedness to nature, (b) sense of place, (c) values, beliefs, and norms, and (d) general awareness. The temple also serves as a management hub for (e) pro-social activities and (f) environmental decision-making. Managerial factors such as pro-social activities and environmental decision-making were curtailed during the COVID-19 lockdown, and the strategies based on socio-psychological factors remain unchanged. According to our findings, new environmental conservation strategies should be based on socio-psychological aspects that are more in line with the mental model of the community. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A Novel Machine Learning Classification Model to Analyze the COVID-19 Pandemic's Impact on Academics: Sentiment Analysis Approach

The COVID-19 Pandemic is considered as the worst situation for human beings;it affected people's lives worldwide. Due to this pandemic, the respective government authority announced the lockdown to break the coronavirus chain. The lockdown impacted people's mental health, leading to many psychological issues as well as hampered students' academics. In this chapter we have studied the impacts on students' academics due to lockdown effect. The data has been collected via a google form questionnaire circulated to various educational institutes. Further, we have developed a novel machine learning classifier model called Naïve Bayes-Support Vector Machine for analyzing the data, which utilizes the properties of both classifiers by using a deep learning framework. We have used natural language processing (TextBlob, Stanza and Vader) libraries to label the dataset and applied in the proposed NBSVM method and other machine learning models and classified the sentiments into two categories (Positive vs Negative). We also applied the natural language processing libraries used a topic-modelling technique called Latent Dirichlet Allocation to know the essential topics words of both classes from students' feedback data. The study revealed 83% and 86% accuracy for unigram and bigram, respectively, whereas the precision was 79% and recall 81%. According to NLP libraries' result, approximately 71% of the feedback's sentiment is negative, and only 16% of feedbacks are positive. The proposed model shown that (Naïve Bayes-Support Vector Machine) outperforms the other variants of the Naïve Bayes and support vector machine. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Effect on the Competencies Development and Collaborative Learning During the COVID-19 Lock Down from a Student Perception

During the last years, the way of teaching classes was impacted by modifying almost 100% of the university courses to digital mode, this modified the academic space for the learning and development of competencies of the students, including collaborative learning. Collaborative work helps students to exchange knowledge, solving doubts between them and complementing their skills when they are solving problems or challenges. This paper presents an analysis of the development of competencies through collaborative learning using virtual communication channels. For this purpose, the following transversal competencies were evaluated: critical thinking, scientific thinking, leadership and reasoning for complexity. The question that seeks to answer this work is from the perception of the students, if their development of competencies and their performance in collaborative learning, has been affected due to the pandemic and the permanent online connection. The statistical test used was the paired t-test, since the same population was evaluated at different times, a survey was carried out on students at the beginning of the semester and at the end of the semester. The results showed that the lockdown situation modified the compromise of most of the students to work on teams as well as their ability to stay focused. Therefore, only the competencies that involve self-work (scientific work, reasoning of complexity) or multidisciplinary work, were increased during the lockdown. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Policy Driven Epidemiological (PDE) Model for Prediction of COVID-19 in India

The fast spread of COVID-19 has made it a global issue. Despite various efforts, proper forecasting of COVID-19 spread is still in question. Government lockdown policies play a critical role in controlling the spread of coronavirus. However, existing prediction models have ignored lockdown policies and only focused on other features such as age, sex ratio, travel history, daily cases etc. This work proposes a Policy Driven Epidemiological (PDE) Model with Temporal, Structural, Profile, Policy and Interaction Features to forecast COVID-19 in India and its 6 states. PDE model integrates two models: Susceptible-Infected-Recovered-Deceased (SIRD) and Topical affinity propagation (TAP) model to predict the infection spread within a network for a given set of infected users. The performance of PDE model is assessed with respect to linear regression model, three epidemiological models (Susceptible-Infectious-Recovered-Model (SIR), Susceptible-Exposed-Infectious-Recovered-Model (SEIR) and SIRD) and two diffusion models (Time Constant Cascade Model and Time Decay Feature Cascade Model). Experimental evaluation for India and six Indian states with respect to different government policies from 15th June to 30th June, i.e., Maharashtra, Gujarat, Tamil Nadu, Delhi, Rajasthan and Uttar Pradesh divulge that prediction accuracy of PDE model is in close proximity with the real time for the considered time frame. Results illustrate that PDE model predicted the COVID-19 cases up to 94% accuracy and reduced the Normalize Mean Squared Error (NMSE) up to 50%, 35% and 42% with respect to linear regression, epidemiological models and diffusion models, respectively. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

CoviBand - Wearable Social Distancing and Contact Tracing Device Using Signal Strength Indicator Metrics

The Covid-19 pandemic has grown to be a highly hazardous threat to the survival of most of the human race. It has not only caused prolonged stay-at-home or lockdown policies in many countries but has also been eating away from the global economy. Staying at home for long durations has affected the lives of daily wage workers tremendously and has also had negative consequences on the mental health of many. This paper aims to reduce the risk of contracting the disease when people leave their homes for essential services and during the gradual lift of the lockdown restrictions. This is achieved through a wearable device (wristband) which constantly looks for other wristbands in the vicinity using a WiFi module. This WiFi module is inbuilt into a NodeMCU Amica board and the setup is used in addition to a buzzer which sounds an alarm when two wristbands are dangerously close. In addition to the warning feature using the buzzer, the device would also store the contact history and the duration of contact on a remote server which can then be used for contact tracing in case a person is found to test positive for Covid-19. The interface of the remote server would be such that it gives a detailed list of the other wristbands that came into contact with any particular wristband. This device would also have an edge over some of the contact tracing apps as many people fear that these apps are an invasion of privacy and drain their mobile batteries quickly. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Results from The Quiet Project- UK Acoustic Community's response to COVID19

The COVID-19 Lockdown created a new kind of environment both in the UK and globally, never experienced before or likely to occur again. A vital and time-critical working group was formed with the aim of gathering crowd-source high quality baseline noise levels and other supporting information. The acoustic community were mobilised through existing networks engaging private companies, public organisations, and academics to gather data in accessible places. A website was designed to advertise the project, provide instructions and to formalise the uploading of noise data, observations, and Soundscape feedback. The data was collected at 99 locations by 80 acousticians (64 male, 16 female) using professional grade calibrated instrumentation with 83% of measurements including spectral data. The locations covered 19 urban, 61 suburban, and 19 rural sites. The Lockdown 1 dataset consisted of a total of 1.6 GB of measurements and material (video, photos) covering 834 days between 1st April and 14th July 2020. This makes the award winning Quiet Project the largest ever noise and soundscape database ever recorded. The paper presents the quietest places in the UK and Ireland. As a government funded research project the databank will be made publicly available to assist future research. © 2022 Internoise 2022 - 51st International Congress and Exposition on Noise Control Engineering. All rights reserved.

Lockdown lifted: measuring spatial resilience from London's public transport demand recovery

The disruptive effects of the COVID-19 pandemic has rapidly shifted how individuals navigate in cities. Governments are concerned that travel behavior will shift toward a car-driven and homeworking future, shifting demand away from public transport use. These concerns place the recovery of public transport in a possible crisis. A resilience perspective may aid the discussion around recovery–particularly one that deviates from pre-pandemic behavior. This paper presents an empirical study of London's public transport demand and introduces a perspective of spatial resilience to the existing body of research on post-pandemic public transport demand. This study defines spatial resilience as the rate of recovery in public transport demand within census boundaries over a period after lockdown restrictions were lifted. The relationship between spatial resilience and urban socioeconomic factors was investigated by a global spatial regression model and a localized perspective through Geographically Weighted Regression (GWR) model. In this case study of London, the analysis focuses on the period after the first COVID-19 lockdown restrictions were lifted (June 2020) and before the new restrictions in mid-September 2020. The analysis shows that outer London generally recovered faster than inner London. Factors of income, car ownership and density of public transport infrastructure were found to have the greatest influence on spatial patterns in resilience. Furthermore, influential relationships vary locally, inviting future research to examine the drivers of this spatial heterogeneity. Thus, this research recommends transport policymakers capture the influences of homeworking, ensure funding for a minimum level of service, and advocate for a polycentric recovery post-pandemic. © 2023 Wuhan University. Published by Informa UK Limited, trading as Taylor & Francis Group.

Environmentally persistent free radicals in PM2.5 from a typical Chinese industrial city during COVID-19 lockdown: The unexpected contamination level variation

The outbreak of COVID-19 has caused concerns globally. To reduce the rapid transmission of the virus, strict city lockdown measures were conducted in different regions. China is the country that takes the earliest home-based quarantine for people. Although normal industrial and social activities were suspended, the spread of virus was efficiently controlled. Simultaneously, another merit of the city lockdown measure was noticed, which is the improvement of the air quality. Contamination levels of multiple atmospheric pollutants were decreased. However, in this work, 24 and 14 air fine particulate matter (PM2.5) samples were continuously collected before and during COVID-19 city lockdown in Linfen (a typical heavy industrial city in China), and intriguingly, the unreduced concentration was found for environmentally persistent free radicals (EPFRs) in PM2.5 after normal life suspension. The primary non-stopped coal combustion source and secondary Cu-related atmospheric reaction may have impacts on this phenomenon. The cigarette-based assessment model also indicated possible exposure risks of PM2.5-bound EPFRs during lockdown of Linfen. This study revealed not all the contaminants in the atmosphere had an apparent concentration decrease during city lockdown, suggesting the pollutants with complicated sources and formation mechanisms, like EPFRs in PM2.5, still should not be ignored. © 2022

"You are on mute”: the impact of indoor soundscape on sexual well-being during the COVID-19 lockdown

Sexual well-being is a fundamental facet of the overall well-being of most individuals and implies the ability to have safe and pleasurable sexual experiences, beyond the absence of disease or disturbance. The extent to which people can achieve sexual well-being depends, among other aspects, on whether they live in an environment that promotes and support it. The present study focuses on the unexplored impacts of the perceived acoustic environment (i.e., the soundscape) on human sexual activity carried out in domestic settings. Verbal descriptions have been gathered from open-ended questions included in a survey administered to 848 respondents living in the UK (London area) and in Italy in January 2021 during the COVID-19 lockdown. Thematic analysis was used to extract a framework detailing the positive and negative impacts of the acoustic environment on sexual activity. The results show the mechanisms by which the acoustic features of the environment can impact on the sexual experience in terms of privacy, distraction, disruption or support, up to trigger coping strategies (e.g., controlling windows, playing music) and behavioural changes (e.g., lowering the volume of the voice) that can in turn limit or enhance the freedom of sexual behaviour, affect or foster sexual well-being. © 2022 Internoise 2022 - 51st International Congress and Exposition on Noise Control Engineering. All rights reserved.

Covid-19 Contactless Remedies for Students in Educational Institutes

As we all know fingerprint recognition is one of the secure and accurate Biometric Technologies. If think about it in deep even with the Biometric system the virus can be spread during these situations. To overcome this, we need to come up with a secure and contactless way of authentication. So, let's update to some contactless remedies like Iris authentication which are unique for every individual and they don't need to have any physical contact. So, we can use this Iris detection for a secure and contactless authentication system. The main aim of this research is to provide contactless remedies for students in Educational institutes like Smart Locking system, Attendance management system, and Library Transaction by using their Iris authentication and Face Recognition. Coming to the outline of the attendance management system, we will first collect the data from the Kaggle repository. Next, we split the data into training and testing, then we will train the data using transfer learning techniques and test the model by using test data. Finally, we integrated the trained model with the flask. If the Iris matches then the attendance of a particular person will be posted. If not matched then we train the model by adding new person's data. For the construction of modern electronic security systems, real-time face recognition is crucial. Face detection, feature extraction, and face recognition are the three procedures involved. After recognizing the face, it will check whether the person's face matches the collected database. If it matches it will show the person's name, the number of books he took, and what those books are for Library transactions and in the same way the locker will be open if the person's data is matched. The proposed methods are secure and unique contactless ways of authentication for every individual. So, we can use these detection and authentication systems for secure and contactless applications. It can be successfully used for students in Educational institutes like Smart Locking system, Attendance management system, and Library Transaction by using their Iris authentication and Face Recognition. The Covid-19 infection in society will undoubtedly decline if the proposed argument is implemented. © 2023 The authors and IOS Press.

Effects of Online Educational System on Personal Health of Students and Teachers in COVID-19 Crises

Healthful living is the central characteristic to live a wonderful life which is badly affected when people are physically inactive. Physically unfit people are restricted in several walks of life. Therefore, physical fitness and activeness is key to live a healthy life. COVID-19 restricted people due to lock-downs. Several physical activities were seized. Along with human lives, all businesses, import-export, economies of countries and many other areas were affected. Similarly, educational system was badly affected. Due to lockdowns, it was not possible to carry traditional educational system (TES) in COVID crises, therefore, everyone had to move towards online educational system (OES). By adopting OES, going to academies, attending physical classes, performing practical sessions, sports, face-to-face interactions with friends, being ready for outdoor and several other physical activities were eliminated from student's and teacher's routine. Whereas, excessive screen time, continuous sitting, increased laziness, weight gain, spine issues and other health problems were reported. This work focuses higher educational institutes – universities – of Pakistan, and attempts to investigate seriousness of health risks, diseases and chances of being unhealthy during such COVID times. The aim is to investigate the unsettled health issues by gathering the perceptions of students and teachers through surveys. With the help of different statistical techniques and comparisons, responses are analyzed to identify the health problems. To avoid and prevent students and teachers from recognized health issues in future, some suitable and relevant suggestions have been provided to audience with the help of existing studies and doctors' opinion, which will help academia to adopt OES smoothly and more effectively. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.