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1.
Nat Commun ; 13(1): 1155, 2022 03 03.
Article in English | MEDLINE | ID: covidwho-1730286

ABSTRACT

Many locations around the world have used real-time estimates of the time-varying effective reproductive number ([Formula: see text]) of COVID-19 to provide evidence of transmission intensity to inform control strategies. Estimates of [Formula: see text] are typically based on statistical models applied to case counts and typically suffer lags of more than a week because of the latent period and reporting delays. Noting that viral loads tend to decline over time since illness onset, analysis of the distribution of viral loads among confirmed cases can provide insights into epidemic trajectory. Here, we analyzed viral load data on confirmed cases during two local epidemics in Hong Kong, identifying a strong correlation between temporal changes in the distribution of viral loads (measured by RT-qPCR cycle threshold values) and estimates of [Formula: see text] based on case counts. We demonstrate that cycle threshold values could be used to improve real-time [Formula: see text] estimation, enabling more timely tracking of epidemic dynamics.


Subject(s)
COVID-19/transmission , SARS-CoV-2 , Viral Load , Basic Reproduction Number/statistics & numerical data , COVID-19/epidemiology , COVID-19/virology , Computer Simulation , Computer Systems , Epidemics , Hong Kong/epidemiology , Humans , Models, Statistical , Pandemics , Viral Load/statistics & numerical data
2.
Sensors (Basel) ; 21(23)2021 Nov 25.
Article in English | MEDLINE | ID: covidwho-1580512

ABSTRACT

The smart grid (SG) is a contemporary electrical network that enhances the network's performance, reliability, stability, and energy efficiency. The integration of cloud and fog computing with SG can increase its efficiency. The combination of SG with cloud computing enhances resource allocation. To minimise the burden on the Cloud and optimise resource allocation, the concept of fog computing integration with cloud computing is presented. Fog has three essential functionalities: location awareness, low latency, and mobility. We offer a cloud and fog-based architecture for information management in this study. By allocating virtual machines using a load-balancing mechanism, fog computing makes the system more efficient (VMs). We proposed a novel approach based on binary particle swarm optimisation with inertia weight adjusted using simulated annealing. The technique is named BPSOSA. Inertia weight is an important factor in BPSOSA which adjusts the size of the search space for finding the optimal solution. The BPSOSA technique is compared against the round robin, odds algorithm, and ant colony optimisation. In terms of response time, BPSOSA outperforms round robin, odds algorithm, and ant colony optimisation by 53.99 ms, 82.08 ms, and 81.58 ms, respectively. In terms of processing time, BPSOSA outperforms round robin, odds algorithm, and ant colony optimisation by 52.94 ms, 81.20 ms, and 80.56 ms, respectively. Compared to BPSOSA, ant colony optimisation has slightly better cost efficiency, however, the difference is insignificant.


Subject(s)
Cloud Computing , Computer Systems , Algorithms , Reproducibility of Results
3.
Comput Math Methods Med ; 2021: 8591036, 2021.
Article in English | MEDLINE | ID: covidwho-1523094

ABSTRACT

During the ongoing COVID-19 pandemic, Internet of Things- (IoT-) based health monitoring systems are potentially immensely beneficial for COVID-19 patients. This study presents an IoT-based system that is a real-time health monitoring system utilizing the measured values of body temperature, pulse rate, and oxygen saturation of the patients, which are the most important measurements required for critical care. This system has a liquid crystal display (LCD) that shows the measured temperature, pulse rate, and oxygen saturation level and can be easily synchronized with a mobile application for instant access. The proposed IoT-based method uses an Arduino Uno-based system, and it was tested and verified for five human test subjects. The results obtained from the system were promising: the data acquired from the system are stored very quickly. The results obtained from the system were found to be accurate when compared to other commercially available devices. IoT-based tools may potentially be valuable during the COVID-19 pandemic for saving people's lives.


Subject(s)
COVID-19/physiopathology , Computer Systems , Internet of Things , Monitoring, Physiologic/instrumentation , Adult , Body Temperature , COVID-19/diagnosis , COVID-19/epidemiology , Computational Biology , Computer Systems/statistics & numerical data , Equipment Design , Female , Heart Rate , Humans , Male , Middle Aged , Mobile Applications , Monitoring, Physiologic/statistics & numerical data , Pandemics , SARS-CoV-2 , User-Computer Interface , Young Adult
5.
Adv Mater ; 34(3): e2104608, 2022 Jan.
Article in English | MEDLINE | ID: covidwho-1499211

ABSTRACT

Solid-state transistor sensors that can detect biomolecules in real time are highly attractive for emerging bioanalytical applications. However, combining upscalable manufacturing with the required performance remains challenging. Here, an alternative biosensor transistor concept is developed, which relies on a solution-processed In2 O3 /ZnO semiconducting heterojunction featuring a geometrically engineered tri-channel architecture for the rapid, real-time detection of important biomolecules. The sensor combines a high electron mobility channel, attributed to the electronic properties of the In2 O3 /ZnO heterointerface, in close proximity to a sensing surface featuring tethered analyte receptors. The unusual tri-channel design enables strong coupling between the buried electron channel and electrostatic perturbations occurring during receptor-analyte interactions allowing for robust, real-time detection of biomolecules down to attomolar (am) concentrations. The experimental findings are corroborated by extensive device simulations, highlighting the unique advantages of the heterojunction tri-channel design. By functionalizing the surface of the geometrically engineered channel with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibody receptors, real-time detection of the SARS-CoV-2 spike S1 protein down to am concentrations is demonstrated in under 2 min in physiological relevant conditions.


Subject(s)
Biosensing Techniques/instrumentation , COVID-19/virology , SARS-CoV-2/chemistry , Spike Glycoprotein, Coronavirus/analysis , Transistors, Electronic , Angiotensin-Converting Enzyme 2/metabolism , Antibodies, Immobilized , Antibodies, Viral , Bioengineering , COVID-19/blood , COVID-19/diagnosis , COVID-19 Testing/instrumentation , COVID-19 Testing/methods , Computer Simulation , Computer Systems , DNA/analysis , Equipment Design , Humans , Indium , Microtechnology , Proof of Concept Study , SARS-CoV-2/immunology , Spike Glycoprotein, Coronavirus/immunology , Spike Glycoprotein, Coronavirus/metabolism , Zinc Oxide
6.
PLoS Comput Biol ; 17(9): e1009347, 2021 09.
Article in English | MEDLINE | ID: covidwho-1403289

ABSTRACT

We construct a recursive Bayesian smoother, termed EpiFilter, for estimating the effective reproduction number, R, from the incidence of an infectious disease in real time and retrospectively. Our approach borrows from Kalman filtering theory, is quick and easy to compute, generalisable, deterministic and unlike many current methods, requires no change-point or window size assumptions. We model R as a flexible, hidden Markov state process and exactly solve forward-backward algorithms, to derive R estimates that incorporate all available incidence information. This unifies and extends two popular methods, EpiEstim, which considers past incidence, and the Wallinga-Teunis method, which looks forward in time. We find that this combination of maximising information and minimising assumptions significantly reduces the bias and variance of R estimates. Moreover, these properties make EpiFilter more statistically robust in periods of low incidence, where several existing methods can become destabilised. As a result, EpiFilter offers improved inference of time-varying transmission patterns that are advantageous for assessing the risk of upcoming waves of infection or the influence of interventions, in real time and at various spatial scales.


Subject(s)
Basic Reproduction Number/statistics & numerical data , Communicable Diseases/epidemiology , Communicable Diseases/transmission , Epidemics/statistics & numerical data , Algorithms , Basic Reproduction Number/prevention & control , Bayes Theorem , Bias , COVID-19/epidemiology , Communicable Disease Control/statistics & numerical data , Computational Biology , Computer Simulation , Computer Systems , Epidemics/prevention & control , Epidemiological Monitoring , Humans , Incidence , Influenza A Virus, H1N1 Subtype , Influenza, Human/epidemiology , Linear Models , Markov Chains , Models, Statistical , New Zealand/epidemiology , Retrospective Studies , SARS-CoV-2 , Time Factors , United States/epidemiology
8.
Cornea ; 40(12): 1639-1643, 2021 Dec 01.
Article in English | MEDLINE | ID: covidwho-1281892

ABSTRACT

PURPOSE: Proctored surgical instruction has traditionally been taught through in-person interactions in either the operating room or an improvised wet lab. Because of the COVID-19 pandemic, live in-person instruction was not feasible owing to social distancing protocols, so a virtual wet lab (VWL) was proposed and implemented. The purpose of this article is to describe our experience with a VWL as a Descemet membrane endothelial keratoplasty (DMEK) skills-transfer course. This is the first time that a VWL environment has been described for the instruction of ophthalmic surgery. METHODS: Thirteen participant surgeons took part in VWLs designed for DMEK skills transfer in September and October 2020. A smartphone camera adapter and a video conference software platform were the unique media for the VWL. After a didactic session, participants were divided into breakout rooms where their surgical scope view was broadcast live, allowing instructors to virtually proctor their participants in real time. Participants were surveyed to assess their satisfaction with the course. RESULTS: All (100%) participants successfully injected and unfolded their DMEK grafts. Ten of the 13 participants completed the survey. Respondents rated the experience highly favorably. CONCLUSIONS: With the use of readily available technology, VWLs can be successfully implemented in lieu of in-person skills-transfer courses. Further development catering to the needs of the participant might allow VWLs to serve as a viable option of surgical education, currently limited by geographical and social distancing boundaries.


Subject(s)
Descemet Stripping Endothelial Keratoplasty/education , Photography/instrumentation , SARS-CoV-2 , Smartphone/instrumentation , Video-Assisted Surgery/education , Videoconferencing/instrumentation , COVID-19/epidemiology , Computer Systems , Humans , Ophthalmologists/education , Software , Surveys and Questionnaires , User-Computer Interface
9.
J Healthc Eng ; 2021: 3277988, 2021.
Article in English | MEDLINE | ID: covidwho-1277006

ABSTRACT

The world has been facing the COVID-19 pandemic since December 2019. Timely and efficient diagnosis of COVID-19 suspected patients plays a significant role in medical treatment. The deep transfer learning-based automated COVID-19 diagnosis on chest X-ray is required to counter the COVID-19 outbreak. This work proposes a real-time Internet of Things (IoT) framework for early diagnosis of suspected COVID-19 patients by using ensemble deep transfer learning. The proposed framework offers real-time communication and diagnosis of COVID-19 suspected cases. The proposed IoT framework ensembles four deep learning models such as InceptionResNetV2, ResNet152V2, VGG16, and DenseNet201. The medical sensors are utilized to obtain the chest X-ray modalities and diagnose the infection by using the deep ensemble model stored on the cloud server. The proposed deep ensemble model is compared with six well-known transfer learning models over the chest X-ray dataset. Comparative analysis revealed that the proposed model can help radiologists to efficiently and timely diagnose the COVID-19 suspected patients.


Subject(s)
Artificial Intelligence , COVID-19 Testing , COVID-19/diagnosis , Internet of Things , SARS-CoV-2 , Brazil , China , Computer Simulation , Computer Systems , Databases, Factual , Deep Learning , Diagnosis, Computer-Assisted , Humans , Pattern Recognition, Automated , Radiography, Thoracic , United States , X-Rays
10.
Am J Emerg Med ; 49: 110-113, 2021 11.
Article in English | MEDLINE | ID: covidwho-1252388

ABSTRACT

INTRODUCTION: Staff-to-staff transmission of SARS-CoV-2 poses a significant risk to the Emergency Department (ED) workforce. We measured close (<6 ft), prolonged (>10 min) staff interactions in a busy pediatric Emergency Department in common work areas over time as the pandemic unfolded, measuring the effectiveness of interventions meant to discourage such close contact. METHODS: We used a Real-Time Locating System to measure staff groupings in crowded common work areas lasting ten or more minutes. We compared the number of these interactions pre-pandemic with those occurring early and then later in the pandemic, as distancing interventions were suggested and then formalized. Nearly all healthcare workers in the ED were included, and the duration of interactions over time were evaluated as well. RESULTS AND CONCLUSIONS: This study included a total of 12,386 pairs of staff-to-staff encounters over three time periods including just prior to the pandemic, early in the pandemic response, and later in the steady-state pandemic response. Pairs of staff averaged 0.89 high-risk interactions hourly prior to the pandemic, and this continued early in the pandemic with informal recommendations (0.80 high-risk pairs hourly). High-risk staff encounters fell significantly to 0.47 interactions per hour in the steady-state pandemic with formal distancing guidelines in place and decreased patient and staffing volumes. The duration of these encounters remained stable, near 16 min. Close contact between healthcare staff workers did significantly decrease with formal distancing guidelines, though some high-risk interactions remained, warranting additive protective measures such as universal masking.


Subject(s)
COVID-19/epidemiology , Computer Systems , Contact Tracing , Physical Distancing , COVID-19/prevention & control , Emergency Service, Hospital , Health Personnel , Humans , Longitudinal Studies , Ohio , Retrospective Studies , SARS-CoV-2
11.
Nature ; 593(7860): 502-505, 2021 05.
Article in English | MEDLINE | ID: covidwho-1246332
13.
J Biomed Inform ; 117: 103770, 2021 05.
Article in English | MEDLINE | ID: covidwho-1163987

ABSTRACT

Health information exchange (HIE) has mostly emerged as centralized data hubs that can pass data requests from one subscribing healthcare institution to another. Using traditional health information systems (HISs) with different technologies in hospitals leads to usability and incompatibility issues because of islands of information. This paper discusses shifting from HIE into an integrated universal health information infrastructure. Migration to such integrated universal electronic health records architecture could support real-time HIE and advanced modern big data analytics. However, there are various standards and technologies to facilitate HIS integration, a significant amount of efforts is still needed.


Subject(s)
Health Information Exchange , Health Information Systems , Computer Systems , Electronic Health Records , Hospitals
14.
Math Biosci Eng ; 18(2): 1513-1528, 2021 01 28.
Article in English | MEDLINE | ID: covidwho-1150821

ABSTRACT

The internet of things (IoT) and deep learning are emerging technologies in diverse research fields, including the provision of IT services in medical domains. In the COVID-19 era, intelligent medication behavior monitoring systems for stable patient monitoring are further required, because many patients cannot easily visit hospitals. Several previous studies made use of wearable devices to detect medication behaviors of patients. However, the wearable devices cause inconvenience while equipping the devices. In addition, they suffer from inconsistency problems due to errors of measured values. We devise a medication behavior monitoring system that uses the IoT and deep learning to avoid sensing errors and improve user experiences by effectively detecting various activities of patients. Based on the real-time operation of our proposed IoT device, the proposed solution processes captured images of patents via OpenPose to check medication situations. The proposed system identifies medication status on time by using a human activity recognition scheme and provides various notifications to patients' mobile devices. To support reliable communication between our system and doctors, we employ MQTT protocol with periodic data transmissions. Thus, the measured information of patient's medication status is transmitted to the doctors so that they can periodically perform remote treatments. Experimental results show that all medication behaviors are accurately detected and notified to the doctor efficiently, improving the accuracy of monitoring the patient's medication behavior.


Subject(s)
COVID-19/drug therapy , Deep Learning , Medication Adherence , Monitoring, Physiologic/methods , SARS-CoV-2 , Biomedical Engineering , Computer Systems , Directly Observed Therapy , Equipment Design , Humans , Internet of Things , Medication Adherence/psychology , Medication Adherence/statistics & numerical data , Monitoring, Physiologic/instrumentation , Monitoring, Physiologic/statistics & numerical data , Neural Networks, Computer , Pandemics , Software , Wearable Electronic Devices
15.
Biosens Bioelectron ; 181: 113160, 2021 Jun 01.
Article in English | MEDLINE | ID: covidwho-1128905

ABSTRACT

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is spreading around the globe since December 2019. There is an urgent need to develop sensitive and online methods for on-site diagnosing and monitoring of suspected COVID-19 patients. With the huge development of Internet of Things (IoT), the impact of Internet of Medical Things (IoMT) provides an impressive solution to this problem. In this paper, we proposed a 5G-enabled fluorescence sensor for quantitative detection of spike protein and nucleocapsid protein of SARS-CoV-2 by using mesoporous silica encapsulated up-conversion nanoparticles (UCNPs@mSiO2) labeled lateral flow immunoassay (LFIA). The sensor can detect spike protein (SP) with a detection of limit (LOD) 1.6 ng/mL and nucleocapsid protein (NP) with an LOD of 2.2 ng/mL. The feasibility of the sensor in clinical use was further demonstrated by utilizing virus culture as real clinical samples. Moreover, the proposed fluorescence sensor is IoMT enabled, which is accessible to edge hardware devices (personal computers, 5G smartphones, IPTV, etc.) through Bluetooth. Medical data can be transmitted to the fog layer of the network and 5G cloud server with ultra-low latency and high reliably for edge computing and big data analysis. Furthermore, a COVID-19 monitoring module working with the proposed the system is developed on a smartphone application (App), which endows patients and their families to record their medical data and daily conditions remotely, releasing the burdens of going to central hospitals. We believe that the proposed system will be highly practical in the future treatment and prevention of COVID-19 and other mass infectious diseases.


Subject(s)
Biosensing Techniques , COVID-19/diagnosis , Computer Systems , Immunoassay , Fluorescence , Humans , Prognosis , SARS-CoV-2
16.
Nat Commun ; 12(1): 1058, 2021 02 16.
Article in English | MEDLINE | ID: covidwho-1087441

ABSTRACT

Coronavirus disease 2019 (COVID-19) is a respiratory disease with rapid human-to-human transmission caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Due to the exponential growth of infections, identifying patients with the highest mortality risk early is critical to enable effective intervention and prioritisation of care. Here, we present the COVID-19 early warning system (CovEWS), a risk scoring system for assessing COVID-19 related mortality risk that we developed using data amounting to a total of over 2863 years of observation time from a cohort of 66 430 patients seen at over 69 healthcare institutions. On an external cohort of 5005 patients, CovEWS predicts mortality from 78.8% (95% confidence interval [CI]: 76.0, 84.7%) to 69.4% (95% CI: 57.6, 75.2%) specificity at sensitivities greater than 95% between, respectively, 1 and 192 h prior to mortality events. CovEWS could enable earlier intervention, and may therefore help in preventing or mitigating COVID-19 related mortality.


Subject(s)
COVID-19/mortality , Computer Systems , Electronic Health Records , Early Warning Score , Humans , Organ Dysfunction Scores , SARS-CoV-2/physiology , Survival Analysis , Time Factors
17.
JCO Clin Cancer Inform ; 5: 24-29, 2021 01.
Article in English | MEDLINE | ID: covidwho-1067368

ABSTRACT

Cancer surveillance is a field focused on collection of data to evaluate the burden of cancer and apply public health strategies to prevent and control cancer in the community. A key challenge facing the cancer surveillance community is the number of manual tasks required to collect cancer surveillance data, thereby resulting in possible delays in analysis and use of the information. To modernize and automate cancer data collection and reporting, the Centers for Disease Control and Prevention is planning, developing, and piloting a cancer surveillance cloud-based computing platform (CS-CBCP) with standardized electronic reporting from laboratories and health-care providers. With this system, automation of the cancer case collection process and access to real-time cancer case data can be achieved, which could not be done before. Furthermore, the COVID-19 pandemic has illustrated the importance of continuity of operations plans, and the CS-CBCP has the potential to provide such a platform suitable for remote operations of central cancer registries.


Subject(s)
Cloud Computing , Data Collection/methods , Data Management/methods , Neoplasms/epidemiology , Automation , Centers for Disease Control and Prevention, U.S. , Computer Systems , Epidemiological Monitoring , Health Policy , Humans , Registries , United States
18.
Euro Surveill ; 26(2)2021 01.
Article in English | MEDLINE | ID: covidwho-1067623

ABSTRACT

The European monitoring of excess mortality for public health action (EuroMOMO) network monitors weekly excess all-cause mortality in 27 European countries or subnational areas. During the first wave of the coronavirus disease (COVID-19) pandemic in Europe in spring 2020, several countries experienced extraordinarily high levels of excess mortality. Europe is currently seeing another upsurge in COVID-19 cases, and EuroMOMO is again witnessing a substantial excess all-cause mortality attributable to COVID-19.


Subject(s)
COVID-19/mortality , Mortality/trends , Adolescent , Adult , Aged , Aged, 80 and over , COVID-19/epidemiology , Cause of Death , Child , Child, Preschool , Computer Systems , Epidemiological Monitoring , Europe/epidemiology , Humans , Infant , Infant, Newborn , Middle Aged , SARS-CoV-2 , Young Adult
19.
JMIR Mhealth Uhealth ; 8(10): e23148, 2020 10 29.
Article in English | MEDLINE | ID: covidwho-976119

ABSTRACT

BACKGROUND: Effective contact tracing is labor intensive and time sensitive during the COVID-19 pandemic, but also essential in the absence of effective treatment and vaccines. Singapore launched the first Bluetooth-based contact tracing app-TraceTogether-in March 2020 to augment Singapore's contact tracing capabilities. OBJECTIVE: This study aims to compare the performance of the contact tracing app-TraceTogether-with that of a wearable tag-based real-time locating system (RTLS) and to validate them against the electronic medical records at the National Centre for Infectious Diseases (NCID), the national referral center for COVID-19 screening. METHODS: All patients and physicians in the NCID screening center were issued RTLS tags (CADI Scientific) for contact tracing. In total, 18 physicians were deployed to the NCID screening center from May 10 to May 20, 2020. The physicians activated the TraceTogether app (version 1.6; GovTech) on their smartphones during shifts and urged their patients to use the app. We compared patient contacts identified by TraceTogether and those identified by RTLS tags within the NCID vicinity during physicians' 10-day posting. We also validated both digital contact tracing tools by verifying the physician-patient contacts with the electronic medical records of 156 patients who attended the NCID screening center over a 24-hour time frame within the study period. RESULTS: RTLS tags had a high sensitivity of 95.3% for detecting patient contacts identified either by the system or TraceTogether while TraceTogether had an overall sensitivity of 6.5% and performed significantly better on Android phones than iPhones (Android: 9.7%, iPhone: 2.7%; P<.001). When validated against the electronic medical records, RTLS tags had a sensitivity of 96.9% and specificity of 83.1%, while TraceTogether only detected 2 patient contacts with physicians who did not attend to them. CONCLUSIONS: TraceTogether had a much lower sensitivity than RTLS tags for identifying patient contacts in a clinical setting. Although the tag-based RTLS performed well for contact tracing in a clinical setting, its implementation in the community would be more challenging than TraceTogether. Given the uncertainty of the adoption and capabilities of contact tracing apps, policy makers should be cautioned against overreliance on such apps for contact tracing. Nonetheless, leveraging technology to augment conventional manual contact tracing is a necessary move for returning some normalcy to life during the long haul of the COVID-19 pandemic.


Subject(s)
Computer Systems , Contact Tracing/instrumentation , Coronavirus Infections/prevention & control , Mobile Applications , Pandemics/prevention & control , Pneumonia, Viral/prevention & control , Wearable Electronic Devices , COVID-19 , COVID-19 Testing , Clinical Laboratory Techniques , Coronavirus Infections/diagnosis , Coronavirus Infections/epidemiology , Cross-Sectional Studies , Electronic Health Records , Humans , Physician-Patient Relations , Pneumonia, Viral/epidemiology , Reproducibility of Results , Singapore/epidemiology
20.
Med Hypotheses ; 146: 110443, 2021 Jan.
Article in English | MEDLINE | ID: covidwho-957309

ABSTRACT

Managing respiratory status of patients with COVID-19 is a high resource, high risk healthcare challenge. Interventions that decrease need for invasive respiratory support and utilization of bedside staff would benefit patients and healthcare personnel alike. Proning has been established as optimal positioning that may reduce the need for escalation of respiratory support. We propose a new application of a wearable device to decrease supine positioning and ameliorate these risks.


Subject(s)
COVID-19/physiopathology , COVID-19/therapy , Lung/physiopathology , Models, Biological , Prone Position/physiology , SARS-CoV-2 , Wearable Electronic Devices , Accelerometry/instrumentation , COVID-19/complications , Computer Systems , Humans , Patient Positioning/instrumentation , Patient Positioning/methods , Remote Sensing Technology/instrumentation , Remote Sensing Technology/methods , Respiratory Distress Syndrome/etiology , Respiratory Distress Syndrome/physiopathology , Respiratory Distress Syndrome/therapy
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