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1.
BMC Public Health ; 22(1): 82, 2022 01 13.
Article in English | MEDLINE | ID: covidwho-1622227

ABSTRACT

BACKGROUND: Antigen tests for SARS-CoV-2 offer advantages over nucleic acid amplification tests (NAATs, such as RT-PCR), including lower cost and rapid return of results, but show reduced sensitivity. Public health organizations recommend different strategies for utilizing NAATs and antigen tests. We sought to create a framework for the quantitative comparison of these recommended strategies based on their expected performance. METHODS: We utilized a decision analysis approach to simulate the expected outcomes of six testing algorithms analogous to strategies recommended by public health organizations. Each algorithm was simulated 50,000 times in a population of 100,000 persons seeking testing. Primary outcomes were number of missed cases, number of false-positive diagnoses, and total test volumes. Outcome medians and 95% uncertainty ranges (URs) were reported. RESULTS: Algorithms that use NAATs to confirm all negative antigen results minimized missed cases but required high NAAT capacity: 92,200 (95% UR: 91,200-93,200) tests (in addition to 100,000 antigen tests) at 10% prevalence. Selective use of NAATs to confirm antigen results when discordant with symptom status (e.g., symptomatic persons with negative antigen results) resulted in the most efficient use of NAATs, with 25 NAATs (95% UR: 13-57) needed to detect one additional case compared to exclusive use of antigen tests. CONCLUSIONS: No single SARS-CoV-2 testing algorithm is likely to be optimal across settings with different levels of prevalence and for all programmatic priorities. This analysis provides a framework for selecting setting-specific strategies to achieve acceptable balances and trade-offs between programmatic priorities and resource constraints.


Subject(s)
COVID-19 , SARS-CoV-2 , Algorithms , COVID-19 Testing , Decision Support Techniques , Humans , Nucleic Acid Amplification Techniques , Sensitivity and Specificity
2.
J Healthc Eng ; 2022: 8091383, 2022.
Article in English | MEDLINE | ID: covidwho-1622120

ABSTRACT

With the development of wireless network, communication technology, cloud platform, and Internet of Things (IOT), new technologies are gradually applied to the smart healthcare industry. The COVID-19 outbreak has brought more attention to the development of the emerging industry of smart healthcare. However, the development of this industry is restricted by factors such as long construction cycle, large investment in the early stage, and lagging return, and the listed companies also face the problem of financing difficulties. In this study, machine learning algorithm is used to predict performance, which can not only deal with a large amount of data and characteristic variables but also analyse different types of variables and predict their classification, increasing the stability and accuracy of the model and helping to solve the problem of poor performance prediction in the past. After analysing the sample data from 53 listed companies in smart healthcare industry, we argued that the conclusion of this study can not only provide reference for listed companies in smart healthcare industry to formulate their own strategies but also provide shareholders with strategies to avoid risks and help the development of this emerging industry.


Subject(s)
COVID-19 , Health Care Sector , Algorithms , Humans , Machine Learning , SARS-CoV-2
3.
Sensors (Basel) ; 22(1)2021 Dec 31.
Article in English | MEDLINE | ID: covidwho-1613946

ABSTRACT

COVID-19 was responsible for devastating social, economic, and political effects all over the world. Although the health authorities imposed restrictions provided relief and assisted with trying to return society to normal life, it is imperative to monitor people's behavior and risk factors to keep virus transmission levels as low as possible. This article focuses on the application of deep learning algorithms to detect the presence of masks on people in public spaces (using RGB cameras), as well as the detection of the caruncle in the human eye area to make an accurate measurement of body temperature (using thermal cameras). For this task, synthetic data generation techniques were used to create hybrid datasets from public ones to train state-of-the-art algorithms, such as YOLOv5 object detector and a keypoint detector based on Resnet-50. For RGB mask detection, YOLOv5 achieved an average precision of 82.4%. For thermal masks, glasses, and caruncle detection, YOLOv5 and keypoint detector achieved an average precision of 96.65% and 78.7%, respectively. Moreover, RGB and thermal datasets were made publicly available.


Subject(s)
COVID-19 , Deep Learning , Algorithms , Humans , SARS-CoV-2
4.
BMC Med Inform Decis Mak ; 22(1): 4, 2022 01 06.
Article in English | MEDLINE | ID: covidwho-1611098

ABSTRACT

BACKGROUND: There have been several destructive pandemic diseases in the human history. Since these pandemic diseases spread through human-to-human infection, a number of non-pharmacological policies has been enforced until an effective vaccine has been developed. In addition, even though a vaccine has been developed, due to the challenges in the production and distribution of the vaccine, the authorities have to optimize the vaccination policies based on the priorities. Considering all these facts, a comprehensive but simple parametric model enriched with the pharmacological and non-pharmacological policies has been proposed in this study to analyse and predict the future pandemic casualties. METHOD: This paper develops a priority and age specific vaccination policy and modifies the non-pharmacological policies including the curfews, lockdowns, and restrictions. These policies are incorporated with the susceptible, suspicious, infected, hospitalized, intensive care, intubated, recovered, and death sub-models. The resulting model is parameterizable by the available data where a recursive least squares algorithm with the inequality constraints optimizes the unknown parameters. The inequality constraints ensure that the structural requirements are satisfied and the parameter weights are distributed proportionally. RESULTS: The results exhibit a distinctive third peak in the casualties occurring in 40 days and confirm that the intensive care, intubated, and death casualties converge to zero faster than the susceptible, suspicious, and infected casualties with the priority and age specific vaccination policy. The model also estimates that removing the curfews on the weekends and holidays cause more casualties than lifting the restrictions on the people with the chronic diseases and age over 65. CONCLUSION: Sophisticated parametric models equipped with the pharmacological and non-pharmacological policies can predict the future pandemic casualties for various cases.


Subject(s)
COVID-19 , Pandemics , Algorithms , Humans , Pandemics/prevention & control , SARS-CoV-2 , Vaccination
5.
Biophys J ; 120(14): 2872-2879, 2021 07 20.
Article in English | MEDLINE | ID: covidwho-1605779

ABSTRACT

We study the transition of an epidemic from growth phase to decay of the active infections in a population when lockdown health measures are introduced to reduce the probability of disease transmission. Although in the case of uniform lockdown, a simple compartmental model would indicate instantaneous transition to decay of the epidemic, this is not the case when partially isolated active clusters remain with the potential to create a series of small outbreaks. We model this using the Gillespie stochastic simulation algorithm based on a connected set of stochastic susceptible-infected-removed/recovered networks representing the locked-down majority population (in which the reproduction number is less than 1) weakly coupled to a large set of small clusters in which the infection may propagate. We find that the presence of such active clusters can lead to slower than expected decay of the epidemic and significantly delayed onset of the decay phase. We study the relative contributions of these changes, caused by the active clusters within the population, to the additional total infected population. We also demonstrate that limiting the size of the inevitable active clusters can be efficient in reducing their impact on the overall size of the epidemic outbreak. The deceleration of the decay phase becomes apparent when the active clusters form at least 5% of the population.


Subject(s)
Disease Outbreaks , Epidemics , Algorithms , Computer Simulation , Humans , Probability
6.
BMC Med Inform Decis Mak ; 22(1): 2, 2022 01 04.
Article in English | MEDLINE | ID: covidwho-1606711

ABSTRACT

BACKGROUND: The coronavirus disease (COVID-19) hospitalized patients are always at risk of death. Machine learning (ML) algorithms can be used as a potential solution for predicting mortality in COVID-19 hospitalized patients. So, our study aimed to compare several ML algorithms to predict the COVID-19 mortality using the patient's data at the first time of admission and choose the best performing algorithm as a predictive tool for decision-making. METHODS: In this study, after feature selection, based on the confirmed predictors, information about 1500 eligible patients (1386 survivors and 144 deaths) obtained from the registry of Ayatollah Taleghani Hospital, Abadan city, Iran, was extracted. Afterwards, several ML algorithms were trained to predict COVID-19 mortality. Finally, to assess the models' performance, the metrics derived from the confusion matrix were calculated. RESULTS: The study participants were 1500 patients; the number of men was found to be higher than that of women (836 vs. 664) and the median age was 57.25 years old (interquartile 18-100). After performing the feature selection, out of 38 features, dyspnea, ICU admission, and oxygen therapy were found as the top three predictors. Smoking, alanine aminotransferase, and platelet count were found to be the three lowest predictors of COVID-19 mortality. Experimental results demonstrated that random forest (RF) had better performance than other ML algorithms with accuracy, sensitivity, precision, specificity, and receiver operating characteristic (ROC) of 95.03%, 90.70%, 94.23%, 95.10%, and 99.02%, respectively. CONCLUSION: It was found that ML enables a reasonable level of accuracy in predicting the COVID-19 mortality. Therefore, ML-based predictive models, particularly the RF algorithm, potentially facilitate identifying the patients who are at high risk of mortality and inform proper interventions by the clinicians.


Subject(s)
COVID-19 , Algorithms , Female , Humans , Machine Learning , Male , Middle Aged , ROC Curve , Retrospective Studies , SARS-CoV-2
7.
BMC Public Health ; 22(1): 10, 2022 01 05.
Article in English | MEDLINE | ID: covidwho-1604673

ABSTRACT

BACKGROUND: Narrowing a large set of features to a smaller one can improve our understanding of the main risk factors for in-hospital mortality in patients with COVID-19. This study aimed to derive a parsimonious model for predicting overall survival (OS) among re-infected COVID-19 patients using machine-learning algorithms. METHODS: The retrospective data of 283 re-infected COVID-19 patients admitted to twenty-six medical centers (affiliated with Shiraz University of Medical Sciences) from 10 June to 26 December 2020 were reviewed and analyzed. An elastic-net regularized Cox proportional hazards (PH) regression and model approximation via backward elimination were utilized to optimize a predictive model of time to in-hospital death. The model was further reduced to its core features to maximize simplicity and generalizability. RESULTS: The empirical in-hospital mortality rate among the re-infected COVID-19 patients was 9.5%. In addition, the mortality rate among the intubated patients was 83.5%. Using the Kaplan-Meier approach, the OS (95% CI) rates for days 7, 14, and 21 were 87.5% (81.6-91.6%), 78.3% (65.0-87.0%), and 52.2% (20.3-76.7%), respectively. The elastic-net Cox PH regression retained 8 out of 35 candidate features of death. Transfer by Emergency Medical Services (EMS) (HR=3.90, 95% CI: 1.63-9.48), SpO2≤85% (HR=8.10, 95% CI: 2.97-22.00), increased serum creatinine (HR=1.85, 95% CI: 1.48-2.30), and increased white blood cells (WBC) count (HR=1.10, 95% CI: 1.03-1.15) were associated with higher in-hospital mortality rates in the re-infected COVID-19 patients. CONCLUSION: The results of the machine-learning analysis demonstrated that transfer by EMS, profound hypoxemia (SpO2≤85%), increased serum creatinine (more than 1.6 mg/dL), and increased WBC count (more than 8.5 (×109 cells/L)) reduced the OS of the re-infected COVID-19 patients. We recommend that future machine-learning studies should further investigate these relationships and the associated factors in these patients for a better prediction of OS.


Subject(s)
COVID-19 , Algorithms , Hospital Mortality , Humans , Machine Learning , Proportional Hazards Models , Retrospective Studies , Risk Factors , SARS-CoV-2
8.
Sci Rep ; 11(1): 24470, 2021 12 28.
Article in English | MEDLINE | ID: covidwho-1594859

ABSTRACT

A novel severe acute respiratory syndrome coronavirus 2 emerged in December 2019, and it took only a few months for WHO to declare COVID-19 as a pandemic in March 2020. It is very challenging to discover complex spatial-temporal transmission mechanisms. However, it is crucial to capture essential features of regional-temporal patterns of COVID-19 to implement prompt and effective prevention or mitigation interventions. In this work, we develop a novel framework of compatible window-wise dynamic mode decomposition (CwDMD) for nonlinear infectious disease dynamics. The compatible window is a selected representative subdomain of time series data, in which compatibility between spatial and temporal resolutions is established so that DMD can provide meaningful data analysis. A total of four compatible windows have been selected from COVID-19 time-series data from January 20, 2020, to May 10, 2021, in South Korea. The spatiotemporal patterns of these four windows are then analyzed. Several hot and cold spots were identified, their spatial-temporal relationships, and some hidden regional patterns were discovered. Our analysis reveals that the first wave was contained in the Daegu and Gyeongbuk areas, but it spread rapidly to the whole of South Korea after the second wave. Later on, the spatial distribution is seen to become more homogeneous after the third wave. Our analysis also identifies that some patterns are not related to regional relevance. These findings have then been analyzed and associated with the inter-regional and local characteristics of South Korea. Thus, the present study is expected to provide public health officials helpful insights for future regional-temporal specific mitigation plans.


Subject(s)
COVID-19/epidemiology , Algorithms , COVID-19/mortality , COVID-19/virology , Humans , Republic of Korea/epidemiology , SARS-CoV-2/isolation & purification , Spatio-Temporal Analysis , Time Factors
9.
Sensors (Basel) ; 21(24)2021 Dec 14.
Article in English | MEDLINE | ID: covidwho-1598728

ABSTRACT

Camera-based remote photoplethysmography (rPPG) is a low-cost and casual non-contact heart rate measurement method suitable for telemedicine. Several factors affect the accuracy of measuring the heart rate and heart rate variability (HRV) using rPPG despite HRV being an important indicator for healthcare monitoring. This study aimed to investigate the appropriate setup for precise HRV measurements using rPPG while considering the effects of possible factors including illumination, direction of the light, frame rate of the camera, and body motion. In the lighting conditions experiment, the smallest mean absolute R-R interval (RRI) error was obtained when light greater than 500 lux was cast from the front (among the following conditions-illuminance: 100, 300, 500, and 700 lux; directions: front, top, and front and top). In addition, the RRI and HRV were measured with sufficient accuracy at frame rates above 30 fps. The accuracy of the HRV measurement was greatly reduced when the body motion was not constrained; thus, it is necessary to limit the body motion, especially the head motion, in an actual telemedicine situation. The results of this study can act as guidelines for setting up the shooting environment and camera settings for rPPG use in telemedicine.


Subject(s)
Photoplethysmography , Telemedicine , Algorithms , Heart Rate , Motion
10.
PLoS One ; 16(12): e0261307, 2021.
Article in English | MEDLINE | ID: covidwho-1598199

ABSTRACT

Medical images commonly exhibit multiple abnormalities. Predicting them requires multi-class classifiers whose training and desired reliable performance can be affected by a combination of factors, such as, dataset size, data source, distribution, and the loss function used to train deep neural networks. Currently, the cross-entropy loss remains the de-facto loss function for training deep learning classifiers. This loss function, however, asserts equal learning from all classes, leading to a bias toward the majority class. Although the choice of the loss function impacts model performance, to the best of our knowledge, we observed that no literature exists that performs a comprehensive analysis and selection of an appropriate loss function toward the classification task under study. In this work, we benchmark various state-of-the-art loss functions, critically analyze model performance, and propose improved loss functions for a multi-class classification task. We select a pediatric chest X-ray (CXR) dataset that includes images with no abnormality (normal), and those exhibiting manifestations consistent with bacterial and viral pneumonia. We construct prediction-level and model-level ensembles to improve classification performance. Our results show that compared to the individual models and the state-of-the-art literature, the weighted averaging of the predictions for top-3 and top-5 model-level ensembles delivered significantly superior classification performance (p < 0.05) in terms of MCC (0.9068, 95% confidence interval (0.8839, 0.9297)) metric. Finally, we performed localization studies to interpret model behavior and confirm that the individual models and ensembles learned task-specific features and highlighted disease-specific regions of interest. The code is available at https://github.com/sivaramakrishnan-rajaraman/multiloss_ensemble_models.


Subject(s)
Algorithms , Diagnostic Imaging , Image Processing, Computer-Assisted/classification , Area Under Curve , Entropy , Humans , Lung/diagnostic imaging , ROC Curve , Thorax/diagnostic imaging , X-Rays
11.
Gigascience ; 10(12)2021 12 29.
Article in English | MEDLINE | ID: covidwho-1595199

ABSTRACT

BACKGROUND: Network propagation has been widely used for nearly 20 years to predict gene functions and phenotypes. Despite the popularity of this approach, little attention has been paid to the question of provenance tracing in this context, e.g., determining how much any experimental observation in the input contributes to the score of every prediction. RESULTS: We design a network propagation framework with 2 novel components and apply it to predict human proteins that directly or indirectly interact with SARS-CoV-2 proteins. First, we trace the provenance of each prediction to its experimentally validated sources, which in our case are human proteins experimentally determined to interact with viral proteins. Second, we design a technique that helps to reduce the manual adjustment of parameters by users. We find that for every top-ranking prediction, the highest contribution to its score arises from a direct neighbor in a human protein-protein interaction network. We further analyze these results to develop functional insights on SARS-CoV-2 that expand on known biology such as the connection between endoplasmic reticulum stress, HSPA5, and anti-clotting agents. CONCLUSIONS: We examine how our provenance-tracing method can be generalized to a broad class of network-based algorithms. We provide a useful resource for the SARS-CoV-2 community that implicates many previously undocumented proteins with putative functional relationships to viral infection. This resource includes potential drugs that can be opportunistically repositioned to target these proteins. We also discuss how our overall framework can be extended to other, newly emerging viruses.


Subject(s)
COVID-19 , SARS-CoV-2 , Algorithms , Humans , Protein Interaction Maps , Proteins/metabolism
12.
Sci Rep ; 11(1): 24171, 2021 12 17.
Article in English | MEDLINE | ID: covidwho-1593554

ABSTRACT

The transmission of COVID-19 is dependent on social mixing, the basic rate of which varies with sociodemographic, cultural, and geographic factors. Alterations in social mixing and subsequent changes in transmission dynamics eventually affect hospital admissions. We employ these observations to model and predict regional hospital admissions in Sweden during the COVID-19 pandemic. We use an SEIR-model for each region in Sweden in which the social mixing is assumed to depend on mobility data from public transport utilisation and locations for mobile phone usage. The results show that the model could capture the timing of the first and beginning of the second wave of the pandemic 3 weeks in advance without any additional assumptions about seasonality. Further, we show that for two major regions of Sweden, models with public transport data outperform models using mobile phone usage. We conclude that a model based on routinely collected mobility data makes it possible to predict future hospital admissions for COVID-19 3 weeks in advance.


Subject(s)
Algorithms , COVID-19/transmission , Cell Phone/statistics & numerical data , Hospitalization/statistics & numerical data , Models, Theoretical , Patient Admission/statistics & numerical data , COVID-19/epidemiology , COVID-19/virology , Disease Transmission, Infectious/statistics & numerical data , Forecasting/methods , Geography , Hospitalization/trends , Humans , Pandemics/prevention & control , Patient Admission/trends , Retrospective Studies , SARS-CoV-2/physiology , Sweden/epidemiology , Travel/statistics & numerical data
13.
Front Public Health ; 9: 797808, 2021.
Article in English | MEDLINE | ID: covidwho-1581100

ABSTRACT

The presented deep learning and sensor-fusion based assistive technology (Smart Facemask and Thermal scanning kiosk) will protect the individual using auto face-mask detection and auto thermal scanning to detect the current body temperature. Furthermore, the presented system also facilitates a variety of notifications, such as an alarm, if an individual is not wearing a mask and detects thermal temperature beyond the standard body temperature threshold, such as 98.6°F (37°C). Design/methodology/approach-The presented deep Learning and sensor-fusion-based approach can also detect an individual in with or without mask situations and provide appropriate notification to the security personnel by raising the alarm. Moreover, the smart tunnel is also equipped with a thermal sensing unit embedded with a camera, which can detect the real-time body temperature of an individual concerning the prescribed body temperature limits as prescribed by WHO reports. Findings-The investigation results validate the performance evaluation of the presented smart face-mask and thermal scanning mechanism. The presented system can also detect an outsider entering the building with or without mask condition and be aware of the security control room by raising appropriate alarms. Furthermore, the presented smart epidemic tunnel is embedded with an intelligent algorithm that can perform real-time thermal scanning of an individual and store essential information in a cloud platform, such as Google firebase. Thus, the proposed system favors society by saving time and helps in lowering the spread of coronavirus.


Subject(s)
COVID-19 , Deep Learning , Algorithms , Disease Outbreaks/prevention & control , Humans , Masks
14.
Sensors (Basel) ; 22(1)2021 Dec 23.
Article in English | MEDLINE | ID: covidwho-1580507

ABSTRACT

The COVID-19 pandemic has spread to almost all countries of the World and affected people both mentally and economically. The primary motivation of this research is to construct a model that takes reviews or evaluations from several people who are affected with COVID-19. As the number of cases has accelerated day by day, people are becoming panicked and concerned about their health. A good model may be helpful to provide accurate statistics in interpreting the actual records about the pandemic. In the proposed work, for sentimental analysis, a unique classifier named the Sentimental DataBase Miner algorithm (SADBM) is used to categorize the opinions and parallel processing, and is applied on the data collected from various online social media websites like Twitter, Facebook, and Linkedin. The accuracy of the proposed model is validated with trained data and compared with basic classifiers, such as logistic regression and decision tree. The proposed algorithm is executed on CPU as well as GPU and calculated the acceleration ratio of the model. The results show that the proposed model provides the best accuracy compared with the other two models, i.e., 96% (GPU).


Subject(s)
COVID-19 , Algorithms , Attention , Humans , Pandemics , SARS-CoV-2
15.
Sci Rep ; 11(1): 23378, 2021 12 16.
Article in English | MEDLINE | ID: covidwho-1585808

ABSTRACT

Emissions of black carbon (BC) particles from anthropogenic and natural sources contribute to climate change and human health impacts. Therefore, they need to be accurately quantified to develop an effective mitigation strategy. Although the spread of the emission flux estimates for China have recently narrowed under the constraints of atmospheric observations, consensus has not been reached regarding the dominant emission sector. Here, we quantified the contribution of the residential sector, as 64% (44-82%) in 2019, using the response of the observed atmospheric concentration in the outflowing air during Feb-Mar 2020, with the prevalence of the COVID-19 pandemic and restricted human activities over China. In detail, the BC emission fluxes, estimated after removing effects from meteorological variability, dropped only slightly (- 18%) during Feb-Mar 2020 from the levels in the previous year for selected air masses of Chinese origin, suggesting the contributions from the transport and industry sectors (36%) were smaller than the rest from the residential sector (64%). Carbon monoxide (CO) behaved differently, with larger emission reductions (- 35%) in the period Feb-Mar 2020, suggesting dominance of non-residential (i.e., transport and industry) sectors, which contributed 70% (48-100%) emission during 2019. The estimated BC/CO emission ratio for these sectors will help to further constrain bottom-up emission inventories. We comprehensively provide a clear scientific evidence supporting mitigation policies targeting reduction in residential BC emissions from China by demonstrating the economic feasibility using marginal abatement cost curves.


Subject(s)
Air Pollutants/analysis , Air Pollution/analysis , COVID-19/prevention & control , Particulate Matter/analysis , SARS-CoV-2/isolation & purification , Soot/analysis , Algorithms , Atmosphere/analysis , COVID-19/epidemiology , COVID-19/virology , China , Climate Change , Environmental Monitoring/methods , Environmental Monitoring/statistics & numerical data , Geography , Human Activities , Humans , Models, Theoretical , Pandemics , Residence Characteristics , SARS-CoV-2/physiology , Seasons , Wind
16.
Sci Rep ; 11(1): 24065, 2021 12 15.
Article in English | MEDLINE | ID: covidwho-1585806

ABSTRACT

COVID-19 is a respiratory disease that causes infection in both lungs and the upper respiratory tract. The World Health Organization (WHO) has declared it a global pandemic because of its rapid spread across the globe. The most common way for COVID-19 diagnosis is real-time reverse transcription-polymerase chain reaction (RT-PCR) which takes a significant amount of time to get the result. Computer based medical image analysis is more beneficial for the diagnosis of such disease as it can give better results in less time. Computed Tomography (CT) scans are used to monitor lung diseases including COVID-19. In this work, a hybrid model for COVID-19 detection has developed which has two key stages. In the first stage, we have fine-tuned the parameters of the pre-trained convolutional neural networks (CNNs) to extract some features from the COVID-19 affected lungs. As pre-trained CNNs, we have used two standard CNNs namely, GoogleNet and ResNet18. Then, we have proposed a hybrid meta-heuristic feature selection (FS) algorithm, named as Manta Ray Foraging based Golden Ratio Optimizer (MRFGRO) to select the most significant feature subset. The proposed model is implemented over three publicly available datasets, namely, COVID-CT dataset, SARS-COV-2 dataset, and MOSMED dataset, and attains state-of-the-art classification accuracies of 99.15%, 99.42% and 95.57% respectively. Obtained results confirm that the proposed approach is quite efficient when compared to the local texture descriptors used for COVID-19 detection from chest CT-scan images.


Subject(s)
COVID-19/diagnostic imaging , Radiographic Image Interpretation, Computer-Assisted/methods , Algorithms , COVID-19 Testing/methods , Deep Learning , Heuristics , Humans , Neural Networks, Computer , Tomography, X-Ray Computed
17.
Sci Rep ; 11(1): 24124, 2021 12 16.
Article in English | MEDLINE | ID: covidwho-1585805

ABSTRACT

The quantification of spreading heterogeneity in the COVID-19 epidemic is crucial as it affects the choice of efficient mitigating strategies irrespective of whether its origin is biological or social. We present a method to deduce temporal and individual variations in the basic reproduction number directly from epidemic trajectories at a community level. Using epidemic data from the 98 districts in Denmark we estimate an overdispersion factor k for COVID-19 to be about 0.11 (95% confidence interval 0.08-0.18), implying that 10 % of the infected cause between 70 % and 87 % of all infections.


Subject(s)
Algorithms , Basic Reproduction Number/statistics & numerical data , COVID-19/transmission , Models, Theoretical , SARS-CoV-2/isolation & purification , COVID-19/epidemiology , COVID-19/virology , Denmark/epidemiology , Epidemics/prevention & control , Geography , Humans , SARS-CoV-2/physiology
18.
Sci Rep ; 11(1): 24416, 2021 12 24.
Article in English | MEDLINE | ID: covidwho-1585784

ABSTRACT

Since the beginning of the COVID -19 pandemic, many contact sport teams are facing major challenges to safely continue training and competition. We present the design and implementation of a structured monitoring concept for the Austrian national football league. 146 professional players from five clubs of the professional Austrian football league were monitored for a period of 12 weeks. Subjective health parameters, PCR- test results and data obtained from a geo-tracking app were collected. Simulations modelling the consequences of a COVID-19 case with increasing reproduction number were computed. No COVID -19 infection occurred during the observation period in the players. Infections in the nearer surroundings lead to increased perceived risk of infection. Geo tracking was particularly hindered due to technical problems and reluctance of users. Simulation models suggested a hypothetical shut-down of all training and competition activities. A structured monitoring concept can help to continue contact sports safely in times of a pandemic. Cooperation of all involved is essential. Trial registration: ID: DRKS00022166 15/6/2020 https://www.who.int/ictrp/search/en/ .


Subject(s)
COVID-19/diagnosis , Computer Simulation , Algorithms , Austria , COVID-19/virology , Humans , SARS-CoV-2/genetics , SARS-CoV-2/isolation & purification , Soccer
19.
Comput Intell Neurosci ; 2021: 6089677, 2021.
Article in English | MEDLINE | ID: covidwho-1582880

ABSTRACT

The rapid emergence of the novel SARS-CoV-2 poses a challenge and has attracted worldwide attention. Artificial intelligence (AI) can be used to combat this pandemic and control the spread of the virus. In particular, deep learning-based time-series techniques are used to predict worldwide COVID-19 cases for short-term and medium-term dependencies using adaptive learning. This study aimed to predict daily COVID-19 cases and investigate the critical factors that increase the transmission rate of this outbreak by examining different influential factors. Furthermore, the study analyzed the effectiveness of COVID-19 prevention measures. A fully connected deep neural network, long short-term memory (LSTM), and transformer model were used as the AI models for the prediction of new COVID-19 cases. Initially, data preprocessing and feature extraction were performed using COVID-19 datasets from Saudi Arabia. The performance metrics for all models were computed, and the results were subjected to comparative analysis to detect the most reliable model. Additionally, statistical hypothesis analysis and correlation analysis were performed on the COVID-19 datasets by including features such as daily mobility, total cases, people fully vaccinated per hundred, weekly hospital admissions per million, intensive care unit patients, and new deaths per million. The results show that the LSTM algorithm had the highest accuracy of all the algorithms and an error of less than 2%. The findings of this study contribute to our understanding of COVID-19 containment. This study also provides insights into the prevention of future outbreaks.


Subject(s)
COVID-19 , Algorithms , Artificial Intelligence , Humans , SARS-CoV-2 , Saudi Arabia/epidemiology
20.
J Med Internet Res ; 23(2): e23693, 2021 02 10.
Article in English | MEDLINE | ID: covidwho-1575481

ABSTRACT

BACKGROUND: COVID-19 has spread very rapidly, and it is important to build a system that can detect it in order to help an overwhelmed health care system. Many research studies on chest diseases rely on the strengths of deep learning techniques. Although some of these studies used state-of-the-art techniques and were able to deliver promising results, these techniques are not very useful if they can detect only one type of disease without detecting the others. OBJECTIVE: The main objective of this study was to achieve a fast and more accurate diagnosis of COVID-19. This study proposes a diagnostic technique that classifies COVID-19 x-ray images from normal x-ray images and those specific to 14 other chest diseases. METHODS: In this paper, we propose a novel, multilevel pipeline, based on deep learning models, to detect COVID-19 along with other chest diseases based on x-ray images. This pipeline reduces the burden of a single network to classify a large number of classes. The deep learning models used in this study were pretrained on the ImageNet dataset, and transfer learning was used for fast training. The lungs and heart were segmented from the whole x-ray images and passed onto the first classifier that checks whether the x-ray is normal, COVID-19 affected, or characteristic of another chest disease. If it is neither a COVID-19 x-ray image nor a normal one, then the second classifier comes into action and classifies the image as one of the other 14 diseases. RESULTS: We show how our model uses state-of-the-art deep neural networks to achieve classification accuracy for COVID-19 along with 14 other chest diseases and normal cases based on x-ray images, which is competitive with currently used state-of-the-art models. Due to the lack of data in some classes such as COVID-19, we applied 10-fold cross-validation through the ResNet50 model. Our classification technique thus achieved an average training accuracy of 96.04% and test accuracy of 92.52% for the first level of classification (ie, 3 classes). For the second level of classification (ie, 14 classes), our technique achieved a maximum training accuracy of 88.52% and test accuracy of 66.634% by using ResNet50. We also found that when all the 16 classes were classified at once, the overall accuracy for COVID-19 detection decreased, which in the case of ResNet50 was 88.92% for training data and 71.905% for test data. CONCLUSIONS: Our proposed pipeline can detect COVID-19 with a higher accuracy along with detecting 14 other chest diseases based on x-ray images. This is achieved by dividing the classification task into multiple steps rather than classifying them collectively.


Subject(s)
Algorithms , COVID-19/diagnostic imaging , Deep Learning , Thoracic Diseases/diagnostic imaging , Humans , Neural Networks, Computer , Radiography, Thoracic , SARS-CoV-2 , Thorax
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