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Ever since the beginning of COVID-19 Pandemic, researchers from across the world have been investigating different options for the treatment of SARS-COV-2. This is critical even after two years from the start of pandemic due to different variants resulting in new waves of this virus spreading globally. In this regard, December 22 and 23rd, 2021 are landmark days, when FDA granted authorization to Pfizer’s Paxlovid and Merck’s Molnupiravir (COVID-19 Oral Antiviral products) for emergency use (EUA). The director of the FDA’s Center for Drug Evaluation and Research Patrizia Cavazzoni, M.D., described this an enormous step to tackle this global pandemic 

The Coronavirus disease 2019 (COVID-19) pandemic has impacted the world like no other pandemic in history. COVID-19 has progressed rapidly affecting health care in the global community. In this work, we look at the web user interface for elderly patients to easily keep track of their daily temperature and their pill consumption record and see if there is a need to visit a doctor. We are going to use Efficient Net, a Deep Learning Convolution Neural Network (CNN) architecture for systematical use of model scaling and balancing network depth, width, and resolution for better performance of our model. We are using angular components to develop user interface and expressJS to write backend services and MongoDB to store user records for the application which we learn in depth throughout the paper. we have trained and developed a model that could successfully detect people from the video and confirm the pill consumption by patient with high accuracy rate connected to a THT thermal camera. This study will help medical professionals, especially during the COVID-19 pandemic, in remotely monitoring the patient’s dosage and determining appropriate action in case of non-compliance. 

The underserved population could be at risk during the times of crisis, unless there is strong involvement from government agencies such as local and state Health departments and federal Center for Disease Control (CDC). The COVID-19 pandemic was a crisis of different proportion, creating a different type of burden on government agencies. Vulnerable communities including the elderly populations and communities of color have been especially hard hit by this pandemic. This forced these agencies to change their strategies and supply chains to support all populations receiving therapeutics. The National Science Foundation [National Science Foundation (NSF) Award Abstract # 2028612] funded RAID Labs to help federal agencies with strategies. This paper is based on a NSF funded grant to work on investigating supply chain strategies that would minimize the impact on underserved populations during pandemic. This NSF funded study identified the phenomena of last mile importance. The last mile transportation concept was critical in saving lives during the pandemic for underserved populations. The supply chain model then maximizes social goods by sending drugs or vaccines to the communities that need it the most regardless of ability to pay. The outcome of this study helped us prioritize the communities that need the vaccines the most. This informs our supply chain model to shift resources to these areas showing the value in real time prioritization of the COVID-19 supply chain. This paper provides information can be used in our healthcare supply chain model to ensure timely delivery of vaccines and supplies to COVID-19 patients that are the most vulnerable and hence the overall impact of COVID-19 can be minimized. The use of electrical vehicles for last mile transportation can help in significantly fighting the climate change. 

The Coronavirus disease 2019 (COVID-19) pandemic has impacted the world like no other pandemic in history. COVID-19 has progressed rapidly affecting health care in the global community. In this paper, we study the Image classification approach for confirming the consumption of medicine by people. The motivation for this research is to reduce contact with the people, without having them in physical observation and help minimize the spread of COVID-19. We used EfficientNet, a Deep Learning Convolution Neural Network (CNN) [7] architecture for systematical use of model scaling and balancing network depth, width, and resolution for better performance of our model. In this paper, we used the data augmentation technique to avoid overfitting. We used image hashing to compare and remove relatively similar images as data cleaning step and demonstrate the effectiveness of this approach by observing the classification accuracy. We used cloud computation to train the model. This trained model will be helpful to achieve the goal. In this study, we developed a model that could successfully detect the image and confirm the pill consumption by patient with high accuracy rate. This study will help medical professionals, especially during COVID-19 pandemic, in remotely monitoring the patient’s dosage and determine appropriate action in case of non-compliance. 

Social Distancing is the new normal. The key is to keep 6 feet of space between yourself and others. While this might seem like a daunting task, this can be achieved through smart wearables. Wearable technology impacts the daily life of its users. Our goal is to develop a smart band which will help in achieving that with sensing the opposite tagged- band within Six-feet and giving out signals to the end-user as a vibration and blinking of a Red LED. 

Due to the COVID-19 pandemic, a number of employees transitioned into virtually distributed supply chain project team environments for the first time in their careers due to shelter-in-place and social distancing mandates that impacted industries around the world. With project managers implementing processes and procedures remotely to employees fielding calls from home while caring for children, the world entered into an unprecedented time and an unknown unknown. As the pandemic required organizations to implement agile practices, project managers within various industries had to adjust operational processes, while at the same time had to ensure project team needs were addressed. In an effort to mitigate the risk of transitioning back to conventional management approaches, a qualitative survey method was performed in this study to understand supply chain project team member experiences during this transition period. The results of the survey uncovered that the creation of a supply chain uncertainty management plan, demonstrating the benefits of virtual supply chain project teams, and the continuous integration of collaborative technology tools may contribute positive outcomes from the COVID-19 pandemic.