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1. Review: How Can Intelligent Robots and Smart Mechatronic Modules Facilitate Remote Assessment, Assistance, and Rehabilitation for Isolated Adults With Neuro-Musculoskeletal Conditions?

   https://doi.org/10.3389/frobt.2021.610529  

   Atashzar, S. Farokh ; Carriere, Jay ; Tavakoli, Mahdi ( April 2021 , Frontiers in Robotics and AI)

   null (Ed.)

   Worldwide, at the time this article was written, there are over 127 million cases of patients with a confirmed link to COVID-19 and about 2.78 million deaths reported. With limited access to vaccine or strong antiviral treatment for the novel coronavirus, actions in terms of prevention and containment of the virus transmission rely mostly on social distancing among susceptible and high-risk populations. Aside from the direct challenges posed by the novel coronavirus pandemic, there are serious and growing secondary consequences caused by the physical distancing and isolation guidelines, among vulnerable populations. Moreover, the healthcare system’s resources and capacity have been focused on addressing the COVID-19 pandemic, causing less urgent care, such as physical neurorehabilitation and assessment, to be paused, canceled, or delayed. Overall, this has left elderly adults, in particular those with neuromusculoskeletal (NMSK) conditions, without the required service support. However, in many cases, such as stroke, the available time window of recovery through rehabilitation is limited since neural plasticity decays quickly with time. Given that future waves of the outbreak are expected in the coming months worldwide, it is important to discuss the possibility of using available technologies to address this issue, as societies have a duty to protect the most vulnerable populations. In this perspective review article, we argue that intelligent robotics and wearable technologies can help with remote delivery of assessment, assistance, and rehabilitation services while physical distancing and isolation measures are in place to curtail the spread of the virus. By supporting patients and medical professionals during this pandemic, robots, and smart digital mechatronic systems can reduce the non-COVID-19 burden on healthcare systems. Digital health and cloud telehealth solutions that can complement remote delivery of assessment and physical rehabilitation services will be the subject of discussion in this article due to their potential in enabling more effective and safer NMSDK rehabilitation, assistance, and assessment service delivery. This article will hopefully lead to an interdisciplinary dialogue between the medical and engineering sectors, stake holders, and policy makers for a better delivery of care for those with NMSK conditions during a global health crisis including future pandemics. 

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2. Modeling and tracking Covid-19 cases using Big Data analytics on HPCC system platform

   https://doi.org/10.1186/s40537-021-00423-z  

   Villanustre, Flavio ; Chala, Arjuna ; Dev, Roger ; Xu, Lili ; LexisNexis, Jesse Shaw ; Furht, Borko ; Khoshgoftaar, Taghi ( December 2021 , Journal of Big Data)

   Abstract This project is funded by the US National Science Foundation (NSF) through their NSF RAPID program under the title “Modeling Corona Spread Using Big Data Analytics.” The project is a joint effort between the Department of Computer & Electrical Engineering and Computer Science at FAU and a research group from LexisNexis Risk Solutions. The novel coronavirus Covid-19 originated in China in early December 2019 and has rapidly spread to many countries around the globe, with the number of confirmed cases increasing every day. Covid-19 is officially a pandemic. It is a novel infection with serious clinical manifestations, including death, and it has reached at least 124 countries and territories. Although the ultimate course and impact of Covid-19 are uncertain, it is not merely possible but likely that the disease will produce enough severe illness to overwhelm the worldwide health care infrastructure. Emerging viral pandemics can place extraordinary and sustained demands on public health and health systems and on providers of essential community services. Modeling the Covid-19 pandemic spread is challenging. But there are data that can be used to project resource demands. Estimates of the reproductive number (R) of SARS-CoV-2 show that at the beginning of the epidemic, each infected person spreads the virus to at least two others, on average (Emanuel et al. in N Engl J Med. 2020, Livingston and Bucher in JAMA 323(14):1335, 2020). A conservatively low estimate is that 5 % of the population could become infected within 3 months. Preliminary data from China and Italy regarding the distribution of case severity and fatality vary widely (Wu and McGoogan in JAMA 323(13):1239–42, 2020). A recent large-scale analysis from China suggests that 80 % of those infected either are asymptomatic or have mild symptoms; a finding that implies that demand for advanced medical services might apply to only 20 % of the total infected. Of patients infected with Covid-19, about 15 % have severe illness and 5 % have critical illness (Emanuel et al. in N Engl J Med. 2020). Overall, mortality ranges from 0.25 % to as high as 3.0 % (Emanuel et al. in N Engl J Med. 2020, Wilson et al. in Emerg Infect Dis 26(6):1339, 2020). Case fatality rates are much higher for vulnerable populations, such as persons over the age of 80 years (> 14 %) and those with coexisting conditions (10 % for those with cardiovascular disease and 7 % for those with diabetes) (Emanuel et al. in N Engl J Med. 2020). Overall, Covid-19 is substantially deadlier than seasonal influenza, which has a mortality of roughly 0.1 %. Public health efforts depend heavily on predicting how diseases such as those caused by Covid-19 spread across the globe. During the early days of a new outbreak, when reliable data are still scarce, researchers turn to mathematical models that can predict where people who could be infected are going and how likely they are to bring the disease with them. These computational methods use known statistical equations that calculate the probability of individuals transmitting the illness. Modern computational power allows these models to quickly incorporate multiple inputs, such as a given disease’s ability to pass from person to person and the movement patterns of potentially infected people traveling by air and land. This process sometimes involves making assumptions about unknown factors, such as an individual’s exact travel pattern. By plugging in different possible versions of each input, however, researchers can update the models as new information becomes available and compare their results to observed patterns for the illness. In this paper we describe the development a model of Corona spread by using innovative big data analytics techniques and tools. We leveraged our experience from research in modeling Ebola spread (Shaw et al. Modeling Ebola Spread and Using HPCC/KEL System. In: Big Data Technologies and Applications 2016 (pp. 347-385). Springer, Cham) to successfully model Corona spread, we will obtain new results, and help in reducing the number of Corona patients. We closely collaborated with LexisNexis, which is a leading US data analytics company and a member of our NSF I/UCRC for Advanced Knowledge Enablement. The lack of a comprehensive view and informative analysis of the status of the pandemic can also cause panic and instability within society. Our work proposes the HPCC Systems Covid-19 tracker, which provides a multi-level view of the pandemic with the informative virus spreading indicators in a timely manner. The system embeds a classical epidemiological model known as SIR and spreading indicators based on causal model. The data solution of the tracker is built on top of the Big Data processing platform HPCC Systems, from ingesting and tracking of various data sources to fast delivery of the data to the public. The HPCC Systems Covid-19 tracker presents the Covid-19 data on a daily, weekly, and cumulative basis up to global-level and down to the county-level. It also provides statistical analysis for each level such as new cases per 100,000 population. The primary analysis such as Contagion Risk and Infection State is based on causal model with a seven-day sliding window. Our work has been released as a publicly available website to the world and attracted a great volume of traffic. The project is open-sourced and available on GitHub. The system was developed on the LexisNexis HPCC Systems, which is briefly described in the paper. 

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3. Ensemble machine learning of factors influencing COVID-19 across US counties

   https://doi.org/10.1038/s41598-021-90827-x  

   McCoy, David ; Mgbara, Whitney ; Horvitz, Nir ; Getz, Wayne M. ; Hubbard, Alan ( December 2021 , Scientific Reports)

   Abstract Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) the causal agent for COVID-19, is a communicable disease spread through close contact. It is known to disproportionately impact certain communities due to both biological susceptibility and inequitable exposure. In this study, we investigate the most important health, social, and environmental factors impacting the early phases (before July, 2020) of per capita COVID-19 transmission and per capita all-cause mortality in US counties. We aggregate county-level physical and mental health, environmental pollution, access to health care, demographic characteristics, vulnerable population scores, and other epidemiological data to create a large feature set to analyze per capita COVID-19 outcomes. Because of the high-dimensionality, multicollinearity, and unknown interactions of the data, we use ensemble machine learning and marginal prediction methods to identify the most salient factors associated with several COVID-19 outbreak measure. Our variable importance results show that measures of ethnicity, public transportation and preventable diseases are the strongest predictors for both per capita COVID-19 incidence and mortality. Specifically, the CDC measures for minority populations, CDC measures for limited English, and proportion of Black- and/or African-American individuals in a county were the most important features for per capita COVID-19 cases within a month after the pandemic started in a county and also at the latest date examined. For per capita all-cause mortality at day 100 and total to date, we find that public transportation use and proportion of Black- and/or African-American individuals in a county are the strongest predictors. The methods predict that, keeping all other factors fixed, a 10% increase in public transportation use, all other factors remaining fixed at the observed values, is associated with increases mortality at day 100 of 2012 individuals (95% CI [1972, 2356]) and likewise a 10% increase in the proportion of Black- and/or African-American individuals in a county is associated with increases total deaths at end of study of 2067 (95% CI [1189, 2654]). Using data until the end of study, the same metric suggests ethnicity has double the association as the next most important factors, which are location, disease prevalence, and transit factors. Our findings shed light on societal patterns that have been reported and experienced in the U.S. by using robust methods to understand the features most responsible for transmission and sectors of society most vulnerable to infection and mortality. In particular, our results provide evidence of the disproportionate impact of the COVID-19 pandemic on minority populations. Our results suggest that mitigation measures, including how vaccines are distributed, could have the greatest impact if they are given with priority to the highest risk communities. 

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4. PUFchain 3.0: Hardware-Assisted Distributed Ledger for Robust Authentication in Healthcare Cyber–Physical Systems

   https://doi.org/10.3390/s24030938  

   Bathalapalli, Venkata K. ; Mohanty, Saraju P. ; Kougianos, Elias ; Iyer, Vasanth ; Rout, Bibhudutta ( February 2024 , Sensors)

   This article presents a novel hardware-assisted distributed ledger-based solution for simultaneous device and data security in smart healthcare. This article presents a novel architecture that integrates PUF, blockchain, and Tangle for Security-by-Design (SbD) of healthcare cyber–physical systems (H-CPSs). Healthcare systems around the world have undergone massive technological transformation and have seen growing adoption with the advancement of Internet-of-Medical Things (IoMT). The technological transformation of healthcare systems to telemedicine, e-health, connected health, and remote health is being made possible with the sophisticated integration of IoMT with machine learning, big data, artificial intelligence (AI), and other technologies. As healthcare systems are becoming more accessible and advanced, security and privacy have become pivotal for the smooth integration and functioning of various systems in H-CPSs. In this work, we present a novel approach that integrates PUF with IOTA Tangle and blockchain and works by storing the PUF keys of a patient’s Body Area Network (BAN) inside blockchain to access, store, and share globally. Each patient has a network of smart wearables and a gateway to obtain the physiological sensor data securely. To facilitate communication among various stakeholders in healthcare systems, IOTA Tangle’s Masked Authentication Messaging (MAM) communication protocol has been used, which securely enables patients to communicate, share, and store data on Tangle. The MAM channel works in the restricted mode in the proposed architecture, which can be accessed using the patient’s gateway PUF key. Furthermore, the successful verification of PUF enables patients to securely send and share physiological sensor data from various wearable and implantable medical devices embedded with PUF. Finally, healthcare system entities like physicians, hospital admin networks, and remote monitoring systems can securely establish communication with patients using MAM and retrieve the patient’s BAN PUF keys from the blockchain securely. Our experimental analysis shows that the proposed approach successfully integrates three security primitives, PUF, blockchain, and Tangle, providing decentralized access control and security in H-CPS with minimal energy requirements, data storage, and response time. 

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5. Data-driven inferences of agency-level risk and response communication on COVID-19 through social media-based interactions

   https://doi.org/10.5055/jem.0589  

   Ahmed, PhD Candidate ; Sadri, PhD ; Amini, PhD ( October 2021 , Journal of Emergency Management)

   Risk perception and risk averting behaviors of public agencies in the emergence and spread of COVID-19 can be retrieved through online social media (Twitter), and such interactions can be echoed in other information outlets. This study collected time-sensitive online social media data and analyzed patterns of health risk communication of public health and emergency agencies in the emergence and spread of novel coronavirus using data-driven methods. The major focus is toward understanding how policy-making agencies communicate risk and response information through social media during a pandemic and influence community response—ie, timing of lockdown, timing of reopening, etc.—and disease outbreak indicators—ie, number of confirmed cases and number of deaths. Twitter data of six major public organizations (1,000-4,500 tweets per organization) are collected from February 21, 2020 to June 6, 2020. Several machine learning algorithms, including dynamic topic model and sentiment analysis, are applied over time to identify the topic dynamics over the specific timeline of the pandemic. Organizations emphasized on various topics—eg, importance of wearing face mask, home quarantine, understanding the symptoms, social distancing and contact tracing, emerging community transmission, lack of personal protective equipment, COVID-19 testing and medical supplies, effect of tobacco, pandemic stress management, increasing hospitalization rate, upcoming hurricane season, use of convalescent plasma for COVID-19 treatment, maintaining hygiene, and the role of healthcare podcast in different timeline. The findings can benefit emergency management, policymakers, and public health agencies to identify targeted information dissemination policies for public with diverse needs based on how local, federal, and international agencies reacted to COVID-19. 

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