Data Visualization for Corona Patients Globally using Real-Time APIs

All the nations' administrative units are concerned about the COVID-19 outbreak in different regions of the world. India is also trying to control the spread of the virus with strict measures and has managed to slow down its growth rate. The administration of each country faces the challenge of maintaining records of corona patients. To address this challenge, this work builds a website from scratch using real-time APIs for data visualization. The website provides information on the number of active cases, death cases, recovery cases, and total cases of COVID-19 patients in each country. The data can be visualized using graphs, making it easier to compare the situation in different countries. The website allows for monitoring which country has a higher number of deaths, patients, favorable recovery rates, and a large number of active cases. The COVID-19 status regarding patients can be analyzed through graphical representation using real-time data. © 2023 IEEE.

Control and Monitoring of Thermographic Chambers by Means of PLC and HMI

Covid-19 disease affects the individual's body in different ways. Most of the infected people present various symptoms of complexity. This article develops the design of a system of control and monitoring of people through the use of thermographic cameras, which includes an intelligent control system for the detection of people with symptoms of Covid-19, which at the same time allows estimating a reading of parameters obtained from the thermographic camera, the possible suspected cases of people entering the Continental University. The development of the proposed system will allow obtaining real-time data of each user entering the Continental University, these parameters obtained will be stored in a SQL database that is linked to an HMI screen where the temperature of each person is displayed, if in case they exceed the established temperature ranges, instant access to the facility is restricted. The results of the research showed that the system design contributes to the prevention and mass propagation of Covid-19. © 2022 IEEE.

COVID-19 Prediction Using Time Series Models

Real-time data has evolved to become an integral part of understanding events across different timelines. Machine Learning uses different varieties of algorithms to determine the relationship between sets of data spread across timelines, visualize the current situation, and forecast the future, which is the most important aspect. Due to the breakout of COVID-19, a novel coronavirus, the entire planet is currently experiencing a disastrous crisis. At this time, the SARS-CoV-2 virus has proven to be a possible hazard to human life. The ARIMA Model i.e., Autoregressive Integrated Moving Average is compared with Facebook's Prophet and VARMAX model to foretell the future. The dataset is divided into the training and testing set. The size of the COVID-19 dataset is relatively small as it is a pandemic that occurred recently, due to which much of the data is used for training purposes and the last twelve days have been used for testing and validating the model. The model is trained and fits on the training data set. The algorithms are now ready to anticipate future forecasts after it has been tested and trained. The models also record the predicted and actual values, allowing them to improve their accuracy in the future. In this paper, the results of the ARIMA model are compared against Prophet and VARMAX which are other popular machine learning time series models. For the ease of visualization of covid trends, a dashboard is built using Python's Plotly and Dash and has been deployed using Voila. © 2022 IEEE.

Nowcasting Japan's GDP.

This paper backtests a nowcast of Japan's real GDP growth. It has three contributions: (i) use of genuine real-time data, (ii) implementation of a new method for the revision analysis that relates the revision of the nowcast to not only new observations but also data revisions, and (iii) a benchmarking of the nowcast to a market consensus forecast at monthly forecasting horizons. Our nowcast's forecast accuracy is comparable to that of the consensus at most, but not all, monthly horizons. Our revision analysis of the March 2011 earthquake finds the nowcast reacting to a steep post-quake decline in car production. In contrast, the consensus hardly budged, most likely because the decline was correctly viewed as temporary. The onset of COVID-19 triggers the consensus to take a precipitous descent. The nowcast, despite timely red flags from "soft" (i.e., survey-based) indicators, does not respond immediately in full, because it took a month or more for "hard" (i.e., non-survey-based) indicators to register sharply reduced economic activities.

A Framework for Real-time Remote ECG Monitoring and Diagnoses

Enabled by the fast development of Internet of Things (IoT) technologies in recent years, the healthcare domain has witnessed significant advancements in wearable devices that seamlessly collect vital medical information. With the availability of IoT devices serving the healthcare domain, extraordinary amounts of sensory data are generated in real-time, requiring immediate diagnoses and attention in critical medical conditions. The provision of remote patient monitoring (RPM) and analytics infrastructure proved to be fundamental components of the healthcare domain during the Coronavirus pandemic. Traditional healthcare services are digitized and offered virtually, where patients are monitored and managed remotely without the need to go to hospitals. This paper presents a comprehensive RPM framework for real-time telehealth operations with scalable data monitoring, real-time analytics and decision-making, fine-grained data access and robust notification mechanisms in emergencies and critical health conditions. We focus on the overall framework architecture, enabling technologies integration, various system-level integrations and deployment options. Furthermore, we provide a use case application for patients with chronic heart conditions for real-time electrocardiogram (ECG) monitoring. We are releasing the framework as open-source software to the active research community. © 2022 IEEE.

Does working from home work? A natural experiment from lockdowns

Using tracked changes from a large open-source software platform, this paper studies how working from home affected the output of individuals working in tech. The basis of the natural experiment comes from idiosyncratic and state-imposed workplace closures during the COVID-19 pandemic. I find a negative but almost-negligible change in individual-level output of 0.5 percent (standard error of 0.091 percent). Overall, and based on descriptive analyses of the time-stamped data, tracked changes in software development cadences approximate regular work activity and provide a useful avenue for future studies of work. © 2022 Elsevier B.V.

Assessing the informational content of card transactions for nowcasting retail trade: Evidence for Latvia

We use a novel card transaction data maintained at the Central Bank of Latvia to assess their informational content for nowcasting retail trade in Latvia. During the COVID-19 pandemic in Latvia, the retail trade turnover dynamics underwent drastic changes reflecting the various virus containment measures introduced during three separate waves of the pandemic. We show that the nowcasting model augmented with card transaction data successfully captures the turbulence in retail trade turnover induced by the COVID-19 pandemic. The model with card transaction data outperforms all benchmark models in the out-of-sample nowcasting exercise and yields a notable improvement in forecasting metrics. We conduct our nowcasting exercise in forecast-as-you-go manner or in real-time squared;that is, we use real-time data vintages, and we make our nowcasts in real time as soon as card transaction data become available for the target month. © 2023 The Authors. Journal of Forecasting published by John Wiley & Sons Ltd.

Improved Gentry–Halevi's fully homomorphic encryption-based lightweight privacy preserving scheme for securing medical Internet of Things

Internet of Medical Things (IoMT) solutions have proliferated rapidly in the COVID-19 pandemic era. The smart medical sensors capture real-time data from remote patients and communicate it to medical servers in a secure and privacy-preserving manner. It is a herculean challenge to guarantee security and privacy in Medical IoT applications. Hence, an improved Gentry–Halevi's fully homomorphic encryption-based (IGHFHE) lightweight privacy preserving user authentication scheme is proposed in this work. The scheme is proposed with an integer matrix computation strategy for securing data computation with privacy protection. It adopts the translation process of Gentry–Halevi's fully homomorphic encryption process for performing homomorphic addition and multiplication, then encrypt an integer matrix modulo that represents a positive integer. Extensive informal investigation and simulation of the proposed IGHFHE scheme shows that it is more resistant to well-known attacks for preventing authentication breaches. Also, the proposed IGHFHE scheme reduced computational and storage overhead by 4.98% and 5.78% respectively on average in comparison to other prevailing schemes. © 2023 John Wiley & Sons Ltd.

Development a Teleconsultation Platform for Outpatients during the COVID-19 Pandemic based on Cloud Firestore and Realtime Databases

Due to the global epidemic situation of the Coronavirus Disease 2019 (Covid-19), in addition to serving patients with suspected symptoms and sickness from COVID-19, the hospital also provides services to patients outside requiring a lot of treatment causing a large number of queues in patients. It takes a long time to wait to see the doctor. The researcher therefore developed a teleconsultation platform. Hence, that patients can talk or seek advice from a doctor without the need to go to the hospital, allow patients to schedule appointments to see a doctor. Also, the patient can talk to the doctor via video calling developed in the system. Moreover, doctors can dispense medicines to patients by mail. To increase the efficiency of the system more and to support a wide range of applications, any devices, real-Time data updates, appointment notification via chatbot using Cloud Firestore and Realtime Databases, a NoSQL database, and study the performance gained. The results obtained from the test were satisfactory, with an average tracing server response of 107 ms + 0.14%, and an average handling latency in Thailand at 108 ms. © 2022 IEEE.

Driving Operations Excellence During Covid-19 Pandemic by Capitalizing on Digitalization

The COVID-19 pandemic has caused significant disturbances to the ways businesses operate, and has driven the entire industry to re-evaluate its operations. Although the full impact of the disruption will not be understood for some time, however, many companies are now beginning to re-examine and use lessons learned to become more resilient moving forward. The Fourth Industrial Revolution (4.0) in Oil & Gas Industry creates a dynamic landscape where Operational Excellence (OE) strives for stability, quality, and efficiency while continuing to serve an increasingly demanding customer. Operational excellence is a journey, not a sole destination. Operational Excellence is a key strategic enabler to mitigate the impact of COVID-19 on shape the business of tomorrow. In line with the company vision of digitalization, a number of initiatives were considered for studies and further evaluations to facilitate remote operations through digitization. Below is list of initiatives those were successfully implemented to promote remote field operations. • Critical equipment like compressors and pumps startup sequences development. • Establish communication of wells subsurface (downhole) sensors with the control room for real time data monitoring. • Development of wells start up sequences to facilities wells startup from control room. • Real time monitoring of wells annulus pressure from control room compared to traditional field monitoring. • Reset of Emergency Shutdown Valves (ESD) from control room instead of field. All of the above proposals have already been implemented. In-house field operations implementation resulted in huge Capital as well as Operating Expenditure saving. By enhancing remote operations, essential benefits are achieved including capability to faster and more effective decision-making and improved HSE measures. • Enhance production and reservoir optimization by real time data monitoring and troubleshooting. • Enhanced well integrity by real time annulus pressure monitoring. • Enhanced HSE by eliminating Confined Space Entries (CSEs) and exposure to toxic H2S. • Reduction and operating expenditure (OPEX) • Reduced dependency on human leading to less human error. • Reduction in capital expenditure (CAPEX) • Enhanced life of critical equipment Operational Excellence played its role with a value improvement objective. Rather than replacing successful practices and programs, Operational Excellence knitted them into a larger, fully integrated tapestry woven to increase value produced within the overall business strategy which is very evident in this scenario. This case study is blend of Digitalization, Operations Excellence and innovation representing Management support to employee to solve current issues during COVID-19 pandemic. Such support is always beneficial for the company and employees. Copyright © 2022, Society of Petroleum Engineers.

Does working from home work? A natural experiment from lockdowns

Using tracked changes from a large open-source software platform, this paper studies how working from home affected the output of individuals working in tech. The basis of the natural experiment comes from idiosyncratic and state-imposed workplace closures during the COVID-19 pandemic. I find a negative but almost-negligible change in individual-level output of 0.5 percent (standard error of 0.091 percent). Overall, and based on descriptive analyses of the time-stamped data, tracked changes in software development cadences approximate regular work activity and provide a useful avenue for future studies of work.

Design of an AMR Using Image Processing and Deep Learning for Monitoring Safety Aspects in Warehouse

The latest spinoffs in the field of Autonomous Vehicles have paved way for a revolution in mobility and transportation;particularly in the warehousing and distribution sector. AMRs, Autonomous Mobile Robots, are being deployed to assist in warehousing activities as they present multiple advantages. In this paper, an AMR coupled with image processing and deep learning is introduced as a novel approach to solve a two-fold problem: surveillance and disinfection. Deep learning will make use of real-time data collected by the AMR's camera as a smart surveillance method for abnormal event detection. YOLOv4 is used to train a custom dataset for object detection on five different classes. The latter obtained a 74.40% accuracy. The vehicle will also be used to diffuse disinfecting agents as a mean to sanitize the stores and stocks against Covid-19. Moreover, autonomous navigation of the AMR will be based on image processing techniques for path track detection. © 2022 IST-Africa Institute and Authors.

Improving city infrastructure resilience capacity: resilience management guidelines in mobile network

In Republic of Croatia ways of rescue and protection of citizens, materials, and other goods in catastrophes such as earthquakes, flooding or ongoing global pandemic of coronavirus disease are regulated by Law on Protection and Rescue. Some of the most important parts are ways of controlling, handling, and coordinating tasks, constitutions of bodies for administration and ways of alarming and informing in activities of protection and rescue. Water supply network is very important part of the city infrastructure. Hence, it is necessary to ensure high-quality and reliable water supply network management process. In our previous research network model and relevant real-time data are integrated into the complete system that will cover all city infrastructure, acting as infrastructure data interchange portal usable especially in the case of disasters. All citizens are communicated with this platform through different user roles or services using telecommunications infrastructure. Main goal of research in this paper is to improve ways of controlling, coordinating tasks and informing citizens. Platform is used to collect whole relevant data from different sources such as water supply network operating center, electricity network operating center, gas transmission network operating center, telecommunication network operating center, citizens, 112 service and so on in emergency. Different behavioral scenarios have been implemented in the platform. According to the collected relevant data, the platform activates a certain scenario that best suits the current situation. According to the scenario, the platform automatically generates tasks to relevant people via a mobile application. In addition, other citizens receive information about acting through social networks notifications. © 2022 Croatian Society MIPRO.

Inaccurate recording of routinely collected data items influences identification of COVID-19 patients.

BACKGROUND: During the Coronavirus disease 2019 (COVID-19) pandemic it became apparent that it is difficult to extract standardized Electronic Health Record (EHR) data for secondary purposes like public health decision-making. Accurate recording of, for example, standardized diagnosis codes and test results is required to identify all COVID-19 patients. This study aimed to investigate if specific combinations of routinely collected data items for COVID-19 can be used to identify an accurate set of intensive care unit (ICU)-admitted COVID-19 patients. METHODS: The following routinely collected EHR data items to identify COVID-19 patients were evaluated: positive reverse transcription polymerase chain reaction (RT-PCR) test results; problem list codes for COVID-19 registered by healthcare professionals and COVID-19 infection labels. COVID-19 codes registered by clinical coders retrospectively after discharge were also evaluated. A gold standard dataset was created by evaluating two datasets of suspected and confirmed COVID-19-patients admitted to the ICU at a Dutch university hospital between February 2020 and December 2020, of which one set was manually maintained by intensivists and one set was extracted from the EHR by a research data management department. Patients were labeled 'COVID-19' if their EHR record showed diagnosing COVID-19 during or right before an ICU-admission. Patients were labeled 'non-COVID-19' if the record indicated no COVID-19, exclusion or only suspicion during or right before an ICU-admission or if COVID-19 was diagnosed and cured during non-ICU episodes of the hospitalization in which an ICU-admission took place. Performance was determined for 37 queries including real-time and retrospective data items. We used the F1 score, which is the harmonic mean between precision and recall. The gold standard dataset was split into one subset including admissions between February and April and one subset including admissions between May and December to determine accuracy differences. RESULTS: The total dataset consisted of 402 patients: 196 'COVID-19' and 206 'non-COVID-19' patients. F1 scores of search queries including EHR data items that can be extracted real-time ranged between 0.68 and 0.97 and for search queries including the data item that was retrospectively registered by clinical coders F1 scores ranged between 0.73 and 0.99. F1 scores showed no clear pattern in variability between the two time periods. CONCLUSIONS: Our study showed that one cannot rely on individual routinely collected data items such as coded COVID-19 on problem lists to identify all COVID-19 patients. If information is not required real-time, medical coding from clinical coders is most reliable. Researchers should be transparent about their methods used to extract data. To maximize the ability to completely identify all COVID-19 cases alerts for inconsistent data and policies for standardized data capture could enable reliable data reuse.

Electricity consumption data of a middle-income household in Gauteng, South Africa: Pre and Post COVID-19 lockdown (2019-2021).

The real-time hourly electricity consumption data of a middle-income household in the Gauteng Province of South Africa was tracked for 30 months (i.e. 2019 to 2021) over three different residential properties. Layout diagram and physical characteristics of each of the residential properties are provided. An energy audit of all appliances at the residence was conducted at the beginning of the study and acquisition of new appliances was also captured. The aggregated electricity consumption throughout the study of all appliances at the family residence was captured from a single-phase electricity distribution sub-panel. The granularity of the captured data was at the hourly resolution level and presented as kilowatt-hour. A total of 20,852 hours of data points were captured. The data has not been processed further. In addition to the energy consumption data, 16 months of hourly data for wind speed, temperature, and humidity of the closest weather station to two of the residential properties has been provided. The energy consumption data will be useful for teaching and research in energy consumption prediction studies, and energy management strategy development. Considering the timing of the study that encompasses pre-COVID-19 and three peaks of COVID-19 in South Africa, the data can be useful in analysing the impact of COVID-19 on household electricity consumption.

An Electronic Data Capture Tool for Data Collection During Public Health Emergencies: Development and Usability Study.

BACKGROUND: The Discovery Critical Care Research Network Program for Resilience and Emergency Preparedness (Discovery PREP) partnered with a third-party technology vendor to design and implement an electronic data capture tool that addressed multisite data collection challenges during public health emergencies (PHE) in the United States. The basis of the work was to design an electronic data capture tool and to prospectively gather data on usability from bedside clinicians during national health system stress queries and influenza observational studies. OBJECTIVE: The aim of this paper is to describe the lessons learned in the design and implementation of a novel electronic data capture tool with the goal of significantly increasing the nation's capability to manage real-time data collection and analysis during PHE. METHODS: A multiyear and multiphase design approach was taken to create an electronic data capture tool, which was used to pilot rapid data capture during a simulated PHE. Following the pilot, the study team retrospectively assessed the feasibility of automating the data captured by the electronic data capture tool directly from the electronic health record. In addition to user feedback during semistructured interviews, the System Usability Scale (SUS) questionnaire was used as a basis to evaluate the usability and performance of the electronic data capture tool. RESULTS: Participants included Discovery PREP physicians, their local administrators, and data collectors from tertiary-level academic medical centers at 5 different institutions. User feedback indicated that the designed system had an intuitive user interface and could be used to automate study communication tasks making for more efficient management of multisite studies. SUS questionnaire results classified the system as highly usable (SUS score 82.5/100). Automation of 17 (61%) of the 28 variables in the influenza observational study was deemed feasible during the exploration of automated versus manual data abstraction. The creation and use of the Project Meridian electronic data capture tool identified 6 key design requirements for multisite data collection, including the need for the following: (1) scalability irrespective of the type of participant; (2) a common data set across sites; (3) automated back end administrative capability (eg, reminders and a self-service status board); (4) multimedia communication pathways (eg, email and SMS text messaging); (5) interoperability and integration with local site information technology infrastructure; and (6) natural language processing to extract nondiscrete data elements. CONCLUSIONS: The use of the electronic data capture tool in multiple multisite Discovery PREP clinical studies proved the feasibility of using the novel, cloud-based platform in practice. The lessons learned from this effort can be used to inform the improvement of ongoing global multisite data collection efforts during the COVID-19 pandemic and transform current manual data abstraction approaches into reliable, real time, and automated information exchange. Future research is needed to expand the ability to perform automated multisite data extraction during a PHE and beyond.

Attribute based Double Bounded Rough Neutrosophic Sets in Facial Expression Detection

In this paper, a hybrid intelligent structure called “Double Bounded Rough Neutrosophic Sets” is defined, which is a combination of Neutrosophic sets theory and Rough sets theory. Further, the Attribute based Double Bounded Rough Neutrosophic Sets was implemented using this hybrid intelligent structure for Facial Expression Detection on real time data. Facial expression detection is becoming increasingly important to understand one's emotion automatically and efficiently and is rich in applications. This paper implements some of these applications of facial expression such as: differentiating between Genuine and Fake smiles, prediction of Depression, determining the Degree of Closeness to a particular Attribute/Expression and detection of fake expression during an examination. With the onset of COVID - 19 pandemic, majority of people are choosing to wear masks. A suitable method to detect Facial Expression with and without mask is also implemented. Double Bounded Rough Neutrosophic Sets proposed in this paper is found to yield better results as compared to that of individual structures (Neutrosophic sets theory or Rough sets theory) © 2022. All Rights Reserved.

Real Time Health Monitoring and Oxygen Distribution System for COVID-19 patients using IoT

In developing countries such as India, efficient use of resources and infrastructure is crucial in the light of healthcare crises such as the COVID-19 pandemic. Owing to overcrowded hospitals and inadequate medical infrastructure, traditional ways of examining and monitoring patients are ineffective. For the treatment of Chronic obstructive pulmonary diseases (COPDs) like COVID-19, monitoring a patient's SpO2 level along with the pulse rate is vital. This paper focuses on using IoT devices for documenting essential patient characteristics and performing data analytics on them for future predictions. Pulse oximeter sensor is used to obtain the patient's SpO2 level and pulse rate measurements. This sensor output is processed by Wi-Fi SoC NodeMCU. By unique identification of each patient, this data is displayed via a Mobile application to healthcare workers nearby. By analysing a patient's symptoms, a doctor can remotely regulate the supply of oxygen to the patient with the same mobile application. Machine learning algorithm is trained to analyse and predict a patient's future health conditions. With the adoption of such systems, the existing medical structure could improve vastly in its efficiency and capabilities during a healthcare crisis such as COVID-19. © 2021 IEEE.

Emergency Department and Intensive Care Unit Overcrowding and Ventilator Shortages in US Hospitals During the COVID-19 Pandemic, 2020-2021.

OBJECTIVE: In 2020, the COVID-19 pandemic overburdened the US health care system because of extended and unprecedented patient surges and supply shortages in hospitals. We investigated the extent to which several US hospitals experienced emergency department (ED) and intensive care unit (ICU) overcrowding and ventilator shortages during the COVID-19 pandemic. METHODS: We analyzed Health Pulse data to assess the extent to which US hospitals reported alerts when experiencing ED overcrowding, ICU overcrowding, and ventilator shortages from March 7, 2020, through April 30, 2021. RESULTS: Of 625 participating hospitals in 29 states, 393 (63%) reported at least 1 hospital alert during the study period: 246 (63%) reported ED overcrowding, 239 (61%) reported ICU overcrowding, and 48 (12%) reported ventilator shortages. The number of alerts for overcrowding in EDs and ICUs increased as the number of COVID-19 cases surged. CONCLUSIONS: Timely assessment and communication about critical factors such as ED and ICU overcrowding and ventilator shortages during public health emergencies can guide public health response efforts in supporting federal, state, and local public health agencies.

Real-time inflation forecasting using non-linear dimension reduction techniques

In this paper, we assess whether using non-linear dimension reduction techniques pays off for forecasting inflation in real-time. Several recent methods from the machine learning literature are adopted to map a large dimensional dataset into a lower-dimensional set of latent factors. We model the relationship between inflation and the latent factors using constant and time-varying parameter (TVP) regressions with shrinkage priors. Our models are then used to forecast monthly US inflation in real-time. The results suggest that sophisticated dimension reduction methods yield inflation forecasts that are highly competitive with linear approaches based on principal components. Among the techniques considered, the Autoencoder and squared principal components yield factors that have high predictive power for one-month- and one-quarter-ahead inflation. Zooming into model performance over time reveals that controlling for non-linear relations in the data is of particular importance during recessionary episodes of the business cycle or the current COVID-19 pandemic.