Impact of lockdowns on the spread of COVID-19 in Saudi Arabia

Informatics in Medicine Unlocked, Год журнала: 2020, Номер 20, С. 100420 - 100420

Опубликована: Янв. 1, 2020

Epidemiological models have been used extensively to predict disease spread in large populations. Among these models, Susceptible Infectious Exposed Recovered (SEIR) is considered be a suitable model for COVID-19 predictions. However, SEIR its classical form unable quantify the impact of lockdowns. In this work, we introduce variable system equations study various degrees social distancing on disease. As case study, apply our modified initial data available (till April 9, 2020) Kingdom Saudi Arabia (KSA). Our analysis shows that with no lockdown around 2.1 million people might get infected during peak 2 months from date was first enforced KSA (March 25th). On other hand, Kingdom's current strategy partial lockdowns, predicted number infections can lowered 0.4 by September 2020. We further demonstrate stricter level curve effectively flattened KSA.

Язык: Английский

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Random-Forest-Bagging Broad Learning System With Applications for COVID-19 Pandemic

IEEE Internet of Things Journal, Год журнала: 2021, Номер 8(21), С. 15906 - 15918

Опубликована: Март 17, 2021

The rapid geographic spread of COVID-19, to which various factors may have contributed, has caused a global health crisis. Recently, the analysis and forecast COVID-19 pandemic attracted worldwide attention. In this work, large data set consisting pandemic, testing capacity, economic level, demographic information, location in 184 countries 1241 areas from December 18, 2019, September 30, 2020, were developed public reports released by national authorities bureau statistics. We proposed machine learning model for prediction based on broad system (BLS). Here, we leveraged random forest (RF) screen out key features. Then, combine bagging strategy BLS develop random-forest-bagging (RF-Bagging-BLS) approach trend pandemic. addition, compared forecasting results with linear regression (LR) model, [Formula: see text]-nearest neighbors (KNN), decision tree (DT), adaptive boosting (Ada), RF, gradient DT (GBDT), support vector (SVR), extra trees (ETs) regressor, CatBoost (CAT), LightGBM (LGB), XGBoost (XGB), BLS.The RF-Bagging showed better performance terms relative mean-square error (RMSE), coefficient determination ([Formula: text]), adjusted median absolute (MAD), mean percentage (MAPE) than other models. Hence, demonstrates superior predictive power over benchmark

Язык: Английский

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Application of machine learning in the prediction of COVID-19 daily new cases: A scoping review

Heliyon, Год журнала: 2021, Номер 7(10), С. e08143 - e08143

Опубликована: Окт. 1, 2021

COVID-19 has produced a global pandemic affecting all over of the world. Prediction rate spread and modeling its course have critical impact on both health system policy makers. Indeed, making depends judgments formed by prediction models to propose new strategies measure efficiency imposed policies. Based nonlinear complex nature this disorder difficulties in estimation virus transmission features using traditional epidemic models, artificial intelligence methods been applied for spread. importance machine deep learning approaches spreading trend, present study, we review studies which used these predict number cases COVID-19. Adaptive neuro-fuzzy inference system, long short-term memory, recurrent neural network multilayer perceptron are among mostly regard. We compared performance several Root means squared error (RMSE), mean absolute (MAE), R

Язык: Английский

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A Review of the Machine Learning Algorithms for Covid-19 Case Analysis

IEEE Transactions on Artificial Intelligence, Год журнала: 2022, Номер 4(1), С. 44 - 59

Опубликована: Янв. 11, 2022

The purpose of this article is to see how machine learning (ML) algorithms and applications are used in the COVID-19 inquiry for other purposes. available traditional methods international epidemic prediction, researchers authorities have given more attention simple statistical epidemiological methodologies. inadequacy absence medical testing diagnosing identifying a solution one key challenges preventing spread COVID-19. A few statistical-based improvements being strengthened answer challenge, resulting partial resolution up certain level. ML advocated wide range intelligence-based approaches, frameworks, equipment cope with issues industry. application inventive structure, such as handling relevant outbreak difficulties, has been investigated article. major goal 1) Examining impact data type nature, well obstacles processing 2) Better grasp importance intelligent approaches like pandemic. 3) development improved types prognosis. 4) effectiveness influence various strategies 5) To target on potential diagnosis order motivate academics innovate expand their knowledge research into additional COVID-19-affected industries.

Язык: Английский

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Exploratory data analysis, classification, comparative analysis, case severity detection, and internet of things in COVID-19 telemonitoring for smart hospitals

Journal of Experimental & Theoretical Artificial Intelligence, Год журнала: 2022, Номер 35(4), С. 507 - 534

Опубликована: Фев. 9, 2022

The proportion of COVID-19 patients is significantly expanding around the world. Treatment with serious consideration has become a significant problem. Identifying clinical indicators succession towards severe conditions desperately required to empower hazard stratification and optimise resource allocation in pandemic COVID-19. Consequently, classification severity level for patient's triaging. It categorise as mild, moderate, severe, critical based on patients' symptoms. Various symptomatic parameters may encourage evaluation infection seriousness. Likewise, rapid spread transmissibility patients, it crucial utilise telemonitoring schemes patients. Telemonitoring mediation encourages remote data information exchange among medicinal services, suppliers, furthermore, risk mitigation provision appropriate medical facilities. This paper provides explorative analysis symptoms, comorbidities, other parameters, comparing different machine learning algorithms case detection. also system (based degree truthfulness) detection that might be utilised stratify levels anticipated moderate Finally, we provide model ensure continuous monitoring progression strategies.

Язык: Английский

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A hybrid intelligent genetic algorithm for truss optimization based on deep neutral network

Swarm and Evolutionary Computation, Год журнала: 2022, Номер 73, С. 101120 - 101120

Опубликована: Июнь 17, 2022

Язык: Английский

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The economics of deep and machine learning-based algorithms for COVID-19 prediction, detection, and diagnosis shaping the organizational management of hospitals

Oeconomia Copernicana, Год журнала: 2024, Номер 15(1), С. 27 - 58

Опубликована: Март 30, 2024

Research background: Deep and machine learning-based algorithms can assist in COVID-19 image-based medical diagnosis symptom tracing, optimize intensive care unit admission, use clinical data to determine patient prioritization mortality risk, being pivotal qualitative provision, reducing errors, increasing survival rates, thus diminishing the massive healthcare system burden relation severe inpatient stay duration, while operational costs throughout organizational management of hospitals. Data-driven financial scenario-based contingency planning, predictive modelling tools, risk pooling mechanisms should be deployed for additional equipment unforeseen demand expenses. Purpose article: We show that deep decision making systems likelihood treatment outcomes with regard susceptible, infected, recovered individuals, performing accurate analyses by modeling based on vital signs, surveillance data, infection-related biomarkers, furthering hospital facility optimization terms bed allocation. Methods: The review software employed article screening quality evaluation were: AMSTAR, AXIS, DistillerSR, Eppi-Reviewer, MMAT, PICO Portal, Rayyan, ROBIS, SRDR. Findings & value added: support tools forecast spread, confirmed cases, infection rates data-driven appropriate resource allocations effective therapeutic protocol development, determining suitable measures regulations using symptoms comorbidities, laboratory records across units, impacting financing infrastructure. As a result heightened personal protective equipment, pharmacy medication, outpatient treatment, supplies, revenue loss vulnerability occur, also due expenses related hiring staff critical expenditures. Hospital care, screening, capacity expansion, lead further losses affecting frontline workers patients.

Язык: Английский

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A predictive analytics model for COVID-19 pandemic using artificial neural networks

Decision Analytics Journal, Год журнала: 2021, Номер 1, С. 100007 - 100007

Опубликована: Окт. 30, 2021

The COVID-19 pandemic spread rapidly around the world and is currently one of most leading causes death heath disaster in world. Turkey, like countries, has been negatively affected by COVID-19. aim this study to design a predictive model based on artificial neural network (ANN) predict future number daily cases deaths caused generalized way fit different countries' spreads. In study, we used dataset between 11 March 2020 23 January 2021 for countries. This provides an ANN assist government take preventive action hospitals medical facilities. results show that there 86% overall accuracy predicting mortality rate 87% cases.

Язык: Английский

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Deep GRU-CNN Model for COVID-19 Detection From Chest X-Rays Data

IEEE Access, Год журнала: 2021, Номер 10, С. 35094 - 35105

Опубликована: Май 5, 2021

In the current era, data is growing exponentially due to advancements in smart devices. Data scientists apply a variety of learning-based techniques identify underlying patterns medical address various health-related issues. this context, automated disease detection has now become central concern science. Such approaches can reduce mortality rate through accurate and timely diagnosis. COVID-19 modern virus that spread all over world affecting millions people. Many countries are facing shortage testing kits, vaccines, other resources significant rapid growth cases. order accelerate process, around have sought create novel methods for virus. paper, we propose hybrid deep learning model based on convolutional neural network (CNN) gated recurrent unit (GRU) detect viral from chest X-rays (CXRs). proposed model, CNN used extract features, GRU as classifier. The been trained 424 CXR images with 3 classes (COVID-19, Pneumonia, Normal). achieves encouraging results 0.96, 0.95 terms precision, recall, f1-score, respectively. These findings indicate how significantly contribute early patients analysis X-ray scans. indications pave way mitigate impact disease. We believe be an effective tool practitioners

Язык: Английский

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COVID-19 Pandemic Forecasting Using CNN-LSTM: A Hybrid Approach

Journal of Control Science and Engineering, Год журнала: 2021, Номер 2021, С. 1 - 23

Опубликована: Июль 30, 2021

COVID-19 has sparked a worldwide pandemic, with the number of infected cases and deaths rising on regular basis. Along recent advances in soft computing technology, researchers are now actively developing enhancing different mathematical machine-learning algorithms to forecast future trend this pandemic. Thus, if we can accurately globally, spread pandemic be controlled. In study, hybrid CNN-LSTM model was developed time-series dataset confirmed COVID-19. The proposed evaluated compared 17 baseline models test data. primary finding research is that outperformed them all, lowest average MAPE, RMSE, RRMSE values both Conclusively, our experimental results show that, while standalone CNN LSTM provide acceptable efficient forecasting performance for time series, combining encoder-decoder structure provides significant boost performance. Furthermore, demonstrated suggested produced satisfactory predicting even small amount

Язык: Английский

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