THE CONTAGION NUMBER: HOW FAST CAN A DISEASE SPREAD?

The overall purpose of this paper is to define a new metric on the spreadability of a disease. Herein, we define a variant of the well-known graph-theoretic burning number (BN) metric that we coin the contagion number (CN). We aver that the CN is a better metric to model disease spread than the BN as the CN concentrates on first time infections. This is important because the Centers for Disease Control and Prevention report that COVID-19 reinfections are rare. This paper delineates a novel methodology to solve for the CN of any tree, in polynomial time, which addresses how fast a disease could spread (i.e., a worst-cast analysis). We then employ Monte Carlo simulation to determine the average contagion number (ACN) (i.e., a most-likely analysis) of how fast a disease would spread. The latter is analyzed on scale-free graphs, which are specifically designed to model human social networks (sociograms). We test our method on some randomly generated scale-free graphs and our findings indicate the CN to be a robust, tractable (the BN is NP-hard even for a tree), and effective disease spread metric for decision makers. The contributions herein advance disease spread understanding and reveal the importance of the underlying network structure. Understanding disease spreadability informs public policy and the associated managerial allocation decisions. © 2023 by the authors.

Courts Effective Response during Covid-19 Pandemic Unaltering the Rule of Law - a Critical Study

Corona is a new type of virus that emerged from China country in Asian continent which have created a strong death fear among the people. This Corona has occupied the entire globe within a short span of time many died for want of oxygen. It is the responsibility of the government to extend the constitutional rights such as the right to education, distribution of food, security, etc. Online classes started for all, and measurement is taken to supply food for everybody through the distribution process. To take care of safety and security lockdown imposed everything has come to a total stop in the transportation, shops, offices other than hospitals and related units,judiciary has made a vital role during this period. The police officials have a tough time moving with people as they are not listening to the words of police who advised, politely, requested them to follow, and pleaded. There afterward, taken action and arrested them later produced to the court. We shall keep our environment very clean to avoid the spread of Corona, health is wealth. Indian constitution is very powerful in India and the Disaster Management Act is also laid down in the constitutions. Without violating the constitutions that means unaltering the rule of law right to education has provided to the children through online mode, right to stay, all the citizens can choose the place of stay according to their choice hence during the period of COVID-19 even though they are in abroad they wanted to come back and stay in India for which court permitted and asked the government to follow the rules carefully so that Corona cannot spread due to them The Prime Minister being the head of constitution has to care for equal distribution of food even though in crisis. First survival then rules at that particular point of time court effectively responded means when the country is in crisis due to ACT OF GOD, COVID-19 pandemic by directing the government unaltering the rule of law provides education for all, that is possible only through online mode of education. Even though a lot of problems persist, the net is not available, people are not aware of the technology don't you think that it is a tough task we salute the nation for handling the situation with utmost care effectively and excellently.Copyright © 2020 Ubiquity Press. All rights reserved.

Online Examination System with Measures for Prevention of Cheating along with Rapid Assessment and Automatic Grading

The requirement for remote examination had emerged along with remote learning during the COVID-19 pandemic as the unprecedented situation had brought the world to halt. The pandemic had forced many educational institutions to move towards the online mode of assessment to assess the caliber of the students. This paper focuses on the ways that an online examination system can be prepared and can be used for conducting exams remotely in a secure way. It also emphasizes on various test cases that are essential for an efficient and useful examination system that can benefit both students and faculty by saving them time and effort. Due to the challenges in the existing mode of online assessment such as the use of digital forms that are usually used for conducting surveys, scanning and uploading answer sheets using phone with poor camera quality, the problem of engaging in the different kinds of misconduct, it was important to understand the user requirements at an examiner and examinee level and prepare a web application that addresses them and makes it convenient to conduct and attempt. We propose different methodologies that can be implemented in a Python based web application with the help of JavaScript such as switching the browser window to full-screen in order to restrict access to other applications, limited exits from full-screen, easy management of examiner and candidate data along with visualization of exam data that help to better understand and draw quick conclusions at the time of exam. It is also focused on the continuously evolving distance education system and finding the best software solution possible for online examinations. Additionally, an automated grading system may help to reduce human error and declare results easily reducing fatigue. © 2022 IEEE.

COVID-19 Contact Tracing Using Low Calibrated Transmission Power from BLE—Approach and Algorithm Experimentation

Within a short period of time, the highly infectious COVID-19 virus has progressed into a pandemic which has forced countries to develop contact tracing solutions for closer monitoring of its further spread into the society. Bluetooth low energy (BLE) has been extensively adopted to implement contact tracing focusing mainly on utilizing received signal strength indicator (RSSI) for its distance estimation toward close contact identification (CCI). Nevertheless, when observed closely, many of these solutions were not able to accurately carry out the contact tracing as required by Centers for Disease Control (CDC) and Prevention. The provisions set were distance of within 6-ft (~ 2 m) and period of no less than 15 min for close contact identification. This is mainly because usage of RSSI is highly unstable and volatile. In closing the gap, we proposed a novel approach that utilizes low calibrated transmission power (Tx) employing nRF52832 BLE chipset as wearables, in which, at a distance of greater than 2 m, no close contact will be detected making the accuracy to high and low error distance estimation under ideal condition. Algorithm in establishing close contacts is also demonstrated with complete experimentation. Results show that our proposed solution has maximum error of 0.3209 m in distance estimation of 2 m and 71.43% accuracy in CCI with 4 devices and distance of 2 ± 0.3 m consideration. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A Mini Review: Origin, Treatments, Preventions, Real Facts and Viability of the Recent Pandemic of Novel Coronavirus-2019

Background: Outbreak of Coronavirus Disease-2019 (COVID-19) has sent billions of people into lockdown. It has a negative impact on daily life, physical and mental health. Never before was seen such a type of pandemic sparked by a coronavirus. It increased anxiety in the community. Impacts of this disruption affect every sector such as health, finance, education, transport, agriculture, and economical growth of countries. Most of the countries experience insecurity in these sectors. Objective(s): To reduce the spread of the novel Coronavirus-2019 and to bridge the knowledge gap of the research community, frontline health workers as well as those persons who are working in this regard to improve critical health challenges so that the community can plan effective prevention. In the present mini-review, we summarized the origin, route of transmission, current therapies of treatment, preventions, viability and real facts of fatal disease novel Coronavirus-2019 (2019-nCoV). Result(s): Achieving division of a large population into small-small groups and take RT-PCR tests on a very large scale. It will help to identify and isolate an accurate infected person. Isolation of infected cases and quarantine reduce the transmissibility of COVID-19. Conclusion(s): Knowledge about real-time evolution and transmission of the emerging pathogens helps to prevent its infection at all stages. To improve understanding of the risk, mechanism, and treatment in response to COVID-19 is required encouraging case studies, effective treatment therapies, drug discovery and developments. Make awareness in society about sanitation and avoid close contact to escape COVID-19 infection are the best ways of protection.Copyright © 2021 Bentham Science Publishers.

A Mathematical Model of the Risk Assessment for Airborne Transmission in a Classroom with a Ventilation System and Nine Distinct Face Mask Efficiency

TB, COVID-19, MERS, and SARS are all serious infectious diseases that are transmitted by the air or aerosol via coughing, spitting, sneezing, speaking, or wounds. When restaurants and bars reopen and continue operations in some parts of the United States, the Centers for Disease Control and Prevention (CDC) gives the following suggestions for how operators can reduce risk for employees, customers, and communities while also restricting the spread of COVID-19. The more and longer a person interacts with others, the greater the risk of COVID-19 spreading. Therefore, we need to be informed of its management and treatment. As a result, for the control and reduction of potentially polluted air, such as CO2 levels, good air quality management is required. They investigated the protective effectiveness of face masks against airborne transmission of infectious SARS-CoV-2 droplets and aerosols in response to the World Health Organization's recommendation to wear face masks to prevent the spread of COVID-19. Using nine different forms of mask efficiency, this research provides a mathematical model for calculating the chance of airborne transmission in a classroom. The fourth-order Runge-Kutta approach is used to approximate the model solution. The proposed strategy strikes a balance between the number of students allowed to stay in the classroom and the effectiveness of nine different masks. We can see how utilizing nine different masks and a well-ventilated system in the classroom can help to reduce the risk of airborne infection. © 2023, IAENG International Journal of Applied Mathematics. All Rights Reserved.

Privacy-Preserving Publishing of Individual-Level Pandemic Data Based on a Game Theoretic Model

Sharing individual-level pandemic data is essential for accelerating the understanding of a disease. For example, COVID-19 data have been widely collected to support public health surveillance and research. In the United States, these data need to be de-identified before being released to the public due to privacy concerns. However, current data publishing approaches for individual-level pandemic data, such as those adopted by the U.S. Centers for Disease Control and Prevention (CDC), have not flexed over time to account for the dynamic nature of infection rates. Thus, the policies generated by these strategies may either raise privacy risks or impair the data utility (or usability). To optimize the tradeoff between privacy risk and data utility, we introduce a game theoretic model that adaptively generates policies to publish individual-level COVID-19 data according to infection dynamics. We model the data publishing process as a two-player Stackelberg game between a data publisher and a data recipient and then search for the best strategy for the publisher. In this game, we consider 1) the average accuracy of predicting future case counts for all demographic groups, and 2) the mutual information between the original data and the released data. We use COVID-19 case data from Vanderbilt University Medical Center from March 2020 to December 2021 to demonstrate our model and evaluate its effectiveness. The experimental results show that our game theoretic model outperforms all baseline approaches, including those adopted by CDC, while maintaining low privacy risk. © 2022 IEEE.

Perceptions about Nonpharmacological Interventions in the Prevention of COVID-19 in the Field Practice Area of Jawaharlal Nehru Medical College

Introduction: COVID-19 pandemic is a major global public health threat. Coronavirus includes a large group of viruses, which infects both humans and animals. China reported the outbreak on December 31, 2019, to World Health Organization. Center for Disease Control and Prevention, USA, has published nonpharmacological interventions such as social distancing, zonal lockdown, rolling lockdown, wearing masks, and washing hands to combat the spread of COVID-19. The present study was conducted to assess the perceptions of people about nonpharmacological interventions in the prevention of COVID-19. Materials and Methods: A facility-based study was conducted among 220 participants from December 01, 2020, to February 28, 2021, among outpatients in the field practice area of urban primary health care Rukmini Nagar, under the administrative control of J. N. Medical College in Belagavi district, Karnataka. Results: A total of 220 participants were interviewed and analyzed for the study. Out of which, 36 (16.4%) were male and 184 (83.6%) were female. One hundred and seventy (77.2%) of the participants practiced good hand hygiene and personal hygiene. One hundred and forty-five (65.9%) of the participants always wore a face mask, when they were going outside. One hundred and eighty-one (82.2%) of the participants started drinking more fluids in the form of water compared with normal days. Conclusion: There was a lack of awareness about face protection and the use of hand sanitizer among the common public. Grassroots level health-care workers such as Accredited Social Health Activist, Anganwadi workers, and community volunteers should be trained for giving health education about nonpharmacological interventions to the public for COVID-19 prevention.

Comparing Twitter Sentiment and United States COVID-19 Vaccination Rates

As more people use social media as a source of news and information, it is important to understand its impact on individual health decisions. This article compares the sentiment expressed in COVID-19 related tweets with national rates for first dose vaccinations as recorded by the Centers for Disease Control and Prevention. To conduct the study, the text from over 570,000 COVID-related tweets from January 2021 to December 2021 was captured. The tweets were segregated by month and Google Cloud's Natural Language API was used determine the sentiment in each tweet, with each post labeled as having positive, negative, or neutral sentiment. Overall, there was greater prevalence of negative sentiment as compared with positive sentiment during the period of review, with 45% of tweets negative, 33% positive and 22% neutral. The number of positive and negative tweets was more balanced in the early months of 2021 (when the vaccine was first available) and became decidedly more negative in the later part of the year, as misinformation about the vaccines spread prolifically on social media. This comparison of the tweet sentiment to first-time vaccine doses in the US shows that misinformation about vaccines on social media appears to have had an impact on behavior. Vaccine adoption declined significantly in the latter half of 2021, even as vaccines and information from public health officials regarding their efficacy became more available to the general public. These findings are validated by subsequent analysis of word usage by month, with positive comments about vaccines and vaccination in January through May coinciding with high vaccination rates, and a negative conversational shift to variants, increased deaths and suspicion about vaccine safety and effectiveness later in the year during a stagnation period in vaccinations. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Are the Technological Tools used in Virtual and Hybrid Classrooms Still Useful in a Fully In-Person Setting? An Assessment of the Effectiveness of the Technological Tools in Enhancing the Pedagogy in the New Normal

The development and advancement of technology during the COVID-19 pandemic have been a major contributor to the innovation in pedagogy. Teaching in virtual or hybrid classrooms brought challenges as well as opportunities, particularly for classes with large student enrollment. Many educators quickly learned to use the appropriate instructional technology to be able to not only teach in remote or hybrid mode, but also to keep the students engaged in the process. Keeping in mind the social distancing rules as prescribed by the Centers for Disease Control and Prevention (CDC) and personal preferences of both the students and the instructors alike, several large Civil and Environmental Engineering (CEE) courses at the University of Connecticut were offered either remotely or in a hybrid setting during the academic year (AY) 2020-21. This transition was feasible with the financial as well as instructional support from the university. This paper discusses three such courses taught by the authors: Applied Mechanics I and Soil Mechanics in Fall 2020, and Mechanics of Materials in Spring 2021. All these courses had large enrollment (over 100) and were taken primarily by upper-class students to fulfil the requirements of their majors. Several changes were made in the course delivery, method of student engagement, and assessment techniques to adjust for remote as well as hybrid teaching modes. To verify the effectiveness of those changes, both mid-semester surveys and annual student surveys were conducted in all three courses and the results are shared in this paper. With the availability of vaccines and by enforcing the mask mandate, most of the CEE courses were offered in person in the Fall of 2021. During this new normal, two of the large civil engineering courses (Principles of Construction I and Soil Mechanics) were taught by the authors in person. Based on the lessons learned during the pandemic (AY 2020-21), some of the virtual instructional tools were used in these in-person courses to improve student engagement. The purpose of this paper is to describe those instructional tools and their effectiveness in improving the pedagogy as well as the students' learning using the data collected during the mid-semester and annual student surveys. © American Society for Engineering Education, 2022.

Evaluation of high flow local extraction for controlling aerosol plumes in operating theaters

SARS-CoV-2 can be transmitted through contact with fomite, respiratory droplets, and aerosolized viruses. Recent evidence suggests that aerosol transmission represents a significant route of infection. In relation to healthcare workers (HCWs), much attention has been focused on personal protective equipment, yet this is the lowest level of the Centers for Disease Control and Prevention hierarchy of controls. Although engineering controls are prominent in the hierarchy, little attention has been given to developing effective interventions. This study aims to evaluate the performance of a simple extraction device in a clinical setting. This was accomplished by using a high flow local extraction (HFLE) that was connected to the existing ventilation system of the hospital on one end and to an intake nozzle near the patient's airway on the other end. Propylene glycol was aerosolized through a physiological test apparatus to simulate the breath of a patient. The field of interest was illuminated using a laser sheet in two planes from the model, namely, the sagittal plane and the transverse plane, and the movement of the simulated aerosol was recorded using a video camera to assess the dispersion of the aerosol qualitatively. In the meantime, the concentration of the aerosol particles was measured using a particle meter to evaluate the effectiveness of the extraction quantitatively. It was found that the HFLE device could effectively reduce the dispersion of the exhaled aerosols to undetectable levels when it was positioned within 250 mm from the mouth. This result has significance in the safety of HCWs involved in the management of patients with infectious diseases and may also have potential applications in other clinical areas with high airflow in the ventilation systems. © 2022 Author(s).

Pattern Formation Of The COVID-19 Pandemic Based On Lunar Calendar By Employing LSTM Autoencoder

The number of detected COVID-19 cases for the states Massachusetts, Colorado and Nevada are taken from the Centre for disease control and prevention dashboard from the period of March 2019 to December 2019. The above-mentioned raw dataset is used for the present work as a nonlinear time series signal. Furthermore, the raw dataset is pre-processed by classifying the same into eight categories depending on the lunar calendar, which in turn is based on the octet phases of the moon. The susceptible-exposed-infectious-removed model is developed from this pre-processed data by employing the Long Short Term Memory autoencoder. The ensuing pattern formation from the autoencoder is investigated. © The Electrochemical Society

IoT based Covid Patient Monitoring System

The Coronavirus outbreak has become massive in recent years. World Health organization warned about the COVID-19 pandemic in March 2020. The United States' Centers for Disease Control and Prevention (CDC) and the World Health Organization (WHO) kept tracking the pandemic effects and provided information on their websites. One such application is in healthcare, where COVID-19 patient health is tracked. The Internet of Things (IoT) increases medical equipment efficiency by allowing for practical tracking of health of affected patients, with wearable devices collecting information and reducing possible errors by humans. The collected details of a patient are transferred via a gateway from medical equipment to the Internet of Things, where they are stored and reviewed. One of the greatest roadblocks to the adoption of the could computing for medical applications is the tracking of each and every affected people from multiple places. Therefore, cloud computing in IoT offers a significant remedy for tracking people at minimum cost and improved disease treatment in the medical industry. The patient's body temperature and respiration are monitored. © 2022 IEEE.

Comparative analysis of public health information about Covid-19 for nursing homes

This paper describes a study that examined the extent to which COVID-19 information for nursing homes follows plain language guidelines. The study involved analysis of government information from the United States and Ireland, focusing on content from the Centers for Disease Control and Prevention (CDC) in the United States and the Health Service Executive (HSE) in Ireland. Preliminary findings suggest that most content incorporated some plain language guidelines, e.g. consistent terminology and use of headings. Some phrasing was ambiguous, however, and the majority of documents did not directly address nursing home residents, but rather visitors and staff. Documents did not include images, that might have helped to explain concepts. Further research is needed about how older populations process and use public health information, to ensure that content addresses them directly and in ways appropriate for their needs. © 2021 IEEE.

Advances in Nanomaterial-Based Platforms to Combat COVID-19: Diagnostics, Preventions, Therapeutics, and Vaccine Developments.

The COVID-19 pandemic caused by the SARS-CoV-2, a ribonucleic acid (RNA) virus that emerged less than two years ago but has caused nearly 6.1 million deaths to date. Recently developed variants of the SARS-CoV-2 virus have been shown to be more potent and expanded at a faster rate. Until now, there is no specific and effective treatment for SARS-CoV-2 in terms of reliable and sustainable recovery. Precaution, prevention, and vaccinations are the only ways to keep the pandemic situation under control. Medical and scientific professionals are now focusing on the repurposing of previous technology and trying to develop more fruitful methodologies to detect the presence of viruses, treat the patients, precautionary items, and vaccine developments. Nanomedicine or nanobased platforms can play a crucial role in these fronts. Researchers are working on many effective approaches by nanosized particles to combat SARS-CoV-2. The role of a nanobased platform to combat SARS-CoV-2 is extremely diverse (i.e., mark to personal protective suit, rapid diagnostic tool to targeted treatment, and vaccine developments). Although there are many theoretical possibilities of a nanobased platform to combat SARS-CoV-2, until now there is an inadequate number of research targeting SARS-CoV-2 to explore such scenarios. This unique mini-review aims to compile and elaborate on the recent advances of nanobased approaches from prevention, diagnostics, treatment to vaccine developments against SARS-CoV-2, and associated challenges.

Social Distance Monitoring Method with Deep Learning to prevent Contamination Spread of Coronavirus Disease

The ongoing COVID-19 virus pandemic has resulted in a global tragedy due to its lethal spread. The population's vulnerability grows as a result of a lack of effective helping agents and vaccines against the virus. The spread of viruses can be mitigated by minimizing close connections between people. Social distancing is a critical containment tool for COVID-19 prevention. In this paper, the social distancing violations that are being made by the people when they are in public places are detected. As per CDC (Centers for Disease Control and Prevention) minimum distance that should be maintained by people is 2-3 meters to prevent the spread of COVID- 19, the proposed tool will be used to detect the people who are maintaining less than 2-3 meters of distance between themselves and record them as a violation. As a result, the goal of this work is to develop a deep learning-based system for object detection and tracking models in social distancing detection. For object detection models, You Only Look Once, Version 3 (YOLO v3) is used in conjunction with deep sort algorithms to balance speed and accuracy. To recognize persons in video segments, the approach applies the YOLOv3 object recognition paradigm. An efficient computer vision-based approach centered on legitimate continuous tracking of individuals is presented to determine supportive social distancing in public locations by creating a model to generate a supportive climate that contributes to public safety and detect violations through camera. © 2022 IEEE.

Research on the Relationship between Mobile Network Data and COVID-19 Cases

During this nearly two-years-long pandemic period, the COVID-19 impacts people's lives dramatically, many people were forced to stay at home by the government's lockdown policy, and they also need to work and study at home. Therefore, there is an equivalent impact on networks as people are more dependent on them. But there are only a limited number of research has been done in this intersection area between the pandemic and networks. So, we want to fill this gap. In this paper, we will study the mobile network data from U.S. Federal Communications Commission (FCC) and COVID-19 cases data from the U.S. centers for disease control and prevention (CDC), then use machine learning to investigate the relationship between mobile network data and COVID-19 cases. We will discuss other related works, which used other methods or investigated this topic in other regions, then we will introduce our machine learning methods, experiments and give the conclusion. © 2022 IEEE.

A Machine Learning Approach in Evaluating Symptom Screening in Predicting COVID-19

COVID-19 is a disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that, to date, has over 245 million confirmed cases and claimed almost 5 million lives. This disease attacks the respiratory system and comes with a number of symptoms. The US Center for Disease Control and Prevention presents a set of symptoms. However, these symptoms only begin to manifest after a number of days, which prevents early detection of this disease. This absence of symptoms during the early stages is what is considered by many to be the very factor that caused the virus into becoming a pandemic. Nonetheless, symptoms checking has been used in practice by commercial and business establishments as an initial screening for COVID-19. The bothersome process of symptom checking are still in place at the entrances of malls and airports. In this study, we determine whether or not symptom screening is an effective system to be employed to assess individuals for COVID-19. Specifically, it aims to determine whether or not one or a set of symptoms are effective predictors of the RT-PCR test results, the gold standard in Covid-19 testing, using machine learning. Using data from the Philippine Red Cross, classification models are developed using LightGBM, AdaBoost, Gaussian Naïve-Bayes, MultiLayer Perceptron, Quadratic Discriminant Analysis and Decision Tree. These models were evaluated using the following metrics: precision, sensitivity, specificity and the type II error rate. Furthermore, for explainability, symptoms are analyzed as to whether or not they are relatively influential on the predicting whether or not a patient has COVID-19. The high type II error rate, low sensitivity and low relative predictor scores of the most significant predictor symptoms clearly show that symptoms do not correlate with the RT-PCR testing results. Thus, we conclude that symptom screening is not a medically suitable process for determining whether an individual has COVID-19. In fact, it even exposes us to the risk of viral transmission as people congregate at the entrances and lobbies of establishments. © 2022 IEEE.

The current COVID-19 pandemic in China: An overview and corona data analysis

At the end of December 2019, the Wuhan Municipal Health Commission, revealed several cases of pneumonia of unknown etiology. Later, this etiology was called the coronavirus disease 2019 (COVID-19). COVID-19 disease is rapidly spreading around the globe, affected millions of people, compelling governments to take serious actions. Due to this deadly disease, a number of deaths have been occurred and still increasing exponentially. In the practice and application of big data sciences, it is always of interest to provide the best description of the data. In this present article, the event background, symptoms, and preventions from COVID-19 are discussed. The steps were taken by the Chinese government to control the COVID-19 has also been discussed. Up to date, details, and data of daily discovered cases, total discovered cases, daily deaths, and total deaths around the world are presented. Moreover, a new statistical distribution is introduced to provide the best characterization of the survival times of the patients affected by the COVID-19 in China. By analyzing the survival times of the COVID-19 patient's data, it is showed that the new model provides a closer fit to COVID-19 events. (c) 2021 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).

Positive Pressure Testing Booths Development and Deployment in Response to the COVID-19 Outbreak

The COVID-19 pandemic left an unprecedented impact on the general public health, resulting in hundreds of thousands of deaths in the U.S. alone. Nationwide testing plans were initiated with drive-through being the currently dominant testing approach, which, however, exhausts personal protective equipment supplies, and is unfriendly to individuals not owning a vehicle. Walkup positive pressure testing booths are a safe alternative, whereby a health care provider situated on the inside of an enclosed and positively pressurized booth swabs a patient on the outside through chemical resistant gloves. The booths, however, are too prohibitively priced on the market to allow for nationwide deployment. To mitigate this, we present in this paper a safe, accessible, mobile, and affordable design of positive-pressure COVID-19 testing booths. The booths have successfully passed the Centers for Disease Control and Prevention and Health care Infection Control Practices Advisory Committee pressure, air exchange, and air quality requirements for positive-pressure rooms, following the guidelines for environmental infection control in health care facilities. The booths are manufactured using primarily off-the-shelf components from U.S. vendors with minimized customization, and the final bill of materials does not surpass USD 3,900. Since August 2019, five booths were deployed and used at the Johns Hopkins University School of Nursing, Baltimore City Health Department, and two community health centers in Baltimore. No health care provider was infected when using our booths, which have shown to facilitate walkup testing with decreased personal protective equipment consumption, reduced risk of infection, and enhanced accessibility to lower-income communities and nondrivers. Copyright © 2022 by ASME.