Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods

Results in Physics, Journal Year: 2021, Volume and Issue: 27, P. 104495 - 104495

Published: June 26, 2021

The first known case of Coronavirus disease 2019 (COVID-19) was identified in December 2019. It has spread worldwide, leading to an ongoing pandemic, imposed restrictions and costs many countries. Predicting the number new cases deaths during this period can be a useful step predicting facilities required future. purpose study is predict rate one, three seven-day ahead next 100 days. motivation for every n days (instead just day) investigation possibility computational cost reduction still achieving reasonable performance. Such scenario may encountered real-time forecasting time series. Six different deep learning methods are examined on data adopted from WHO website. Three LSTM, Convolutional GRU. bidirectional extension then considered each method forecast Australia Iran This novel as it carries out comprehensive evaluation aforementioned their extensions perform prediction COVID-19 death To best our knowledge, that Bi-GRU Bi-Conv-LSTM models used presented form graphs Friedman statistical test. results show have lower errors than other models. A several error metrics compare all models, finally, superiority determined. research could organisations working against determining long-term plans.

Language: Английский

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Hybrid Genetic Algorithm and Machine Learning Method for COVID-19 Cases Prediction

Lecture notes in networks and systems, Journal Year: 2021, Volume and Issue: unknown, P. 169 - 184

Published: Jan. 1, 2021

Language: Английский

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Intelligent Internet of Things and Advanced Machine Learning Techniques for COVID-19

EAI Endorsed Transactions on Pervasive Health and Technology, Journal Year: 2021, Volume and Issue: 7(26), P. e1 - e1

Published: Jan. 28, 2021

INTRODUCTION: Coronavirus disease (COVID-19) has recently emerged around the world. The beginning of was in Chinese city Wuhan and then it been spread became a global epidemic. An early diagnosis COVID-19 is absolutely necessary to control epidemic.OBJECTIVES: aim this paper present review contribution machine learning (ML) IoT confront epidemic.METHODS: Diagnosis using real-time reverse transcriptase-polymerase chain reaction (RT-PCR) definite diagnosis, but method takes time, while computed tomography (CT) scan faster approach diagnosis. However, large number patients need CT scan, which puts lot pressure on radiologist so visual fatigue may lead diagnostic errors there an urgent for additional solutions. Artificial intelligence (AI) efficient tool combat disease. Computer scientists have developing many systems handle epidemic.RESULTS: It found that ML powerful AI technology can be used trustworthy detecting from X-ray images potential radiology department. In addition, segmentation, prediction purposes COVID-19. Furthermore, effectively support drug discovery procedure reduce clinical failures.CONCLUSION: significant role monitoring individual's health This also highlights challenges employing intelligent fighting

Language: Английский

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An overview of healthcare data analytics with applications to the COVID-19 pandemic

IEEE Transactions on Big Data, Journal Year: 2021, Volume and Issue: unknown, P. 1 - 1

Published: Jan. 1, 2021

In the era of big data, standard analysis tools may be inadequate for making inference and there is a growing need more efficient innovative ways to collect, process, analyze interpret massive complex data. We provide an overview challenges in data problems describe how analytical methods, machine learning metaheuristics can tackle general healthcare with focus on current pandemic. particular, we give applications modern digital technology, statistical methods,data platforms integration systems improve diagnosis treatment diseases clinical research novel epidemiologic infection source problems, such as finding Patient Zero spread epidemics. make case that analyzing interpreting very challenging task requires multi-disciplinary effort continuously create effective methodologies powerful transfer information into knowledge enables informed decision making.

Language: Английский

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Winter is Coming: A Southern Hemisphere Perspective of the Environmental Drivers of SARS-CoV-2 and the Potential Seasonality of COVID-19

Published: July 20, 2020

SARS-CoV-2 virus infections in humans were first reported December 2019, the boreal winter. The resulting COVID-19 pandemic was declared by WHO March 2020. By July 2020 is present 213 countries and territories, with over 12 million confirmed cases half a attributed deaths. Knowledge of other viral respiratory diseases suggests that transmission could be modulated seasonally-varying environmental factors such as temperature humidity. Many studies on sensitivity are appearing online, some have been published peer-reviewed journals. Initially, these raised hypothesis climatic conditions would subdue rate places entering summer southern hemisphere experience enhanced disease. For latter, peak coincide influenza season, increasing misdiagnosis placing an additional burden health systems. In this review, we assess evidence drivers significant factor trajectory pandemic, globally regionally. We critically assessed 42 80 preprint publications met qualifying criteria. Since disease has prevalent for only year northern, quarter hemisphere, datasets capturing full seasonal cycle one locality not yet available. Analyses based space-for-time substitutions, i.e. using data from climatically distinct locations surrogate progression, inconclusive. strong northern bias. Socio-economic peculiar to ‘Global South’ omitted confounding variables, thereby weakening signals. explore why research date failed show convincing modulation COVID-19, discuss directions future research. conclude thus far weak effect, currently overwhelmed scale spread COVID-19. Seasonally-modulated transmission, if it exists, will more evident 2021 subsequent years.

Language: Английский

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Data science in economics: comprehensive review of advanced machine learning and deep learning methods

Published: Oct. 16, 2020

This paper provides a state-of-the-art investigation of advances in data science emerging economic applications. The analysis was performed on novel methods four individual classes deep learning models, hybrid machine learning, and ensemble models. Application domains include wide diverse range economics research from the stock market, marketing, e-commerce to corporate banking cryptocurrency. Prisma method, systematic literature review methodology, used ensure quality survey. findings reveal that trends follow advancement which, based accuracy metric, outperform other algorithms. It is further expected will converge toward advancements sophisticated

Language: Английский

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Projecting the criticality of COVID-19 transmission in India using GIS and machine learning methods

Journal of Safety Science and Resilience, Journal Year: 2021, Volume and Issue: 2(2), P. 50 - 62

Published: May 30, 2021

There is a new public health catastrophe forbidding the world. With advent and spread of 2019 novel coronavirus (2019-nCoV). Learning from experiences various countries World Health Organization (WHO) guidelines, social distancing, use sanitizers, thermal screening, quarantining, provision lockdown in cities being effective measure that can contain pandemic. Though complete helps containing spread, it generates complexity by breaking economic activity chain. Besides, laborers, farmers, workers may lose their daily earnings. Owing to these detrimental effects, government has open strategically. Prediction COVID-19 analyzing when cases would stop increasing developing strategy. An attempt made this paper predict time after which number stops rising, considering strong implementation conditions using three different techniques such as Decision Tree, Support Vector Machine, Gaussian Process Regression algorithm are used project cases. Thus, projections identifying inflection points, help planning easing few areas The criticality region evaluated index (CI), proposed authors one past research works. This work available dashboard enable decision-makers combat

Language: Английский

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Hybrid Machine Learning Model Coupled with School Closure For Forecasting COVID-19 Cases in the Most Affected Countries

Hittite Journal of Science & Engineering, Journal Year: 2021, Volume and Issue: 8(2), P. 123 - 131

Published: June 30, 2021

Coronavirus disease (Covid-19) caused millions of confirmed cases and thousands deaths worldwide since first appeared in China. Forecasting methods are essential to take precautions early control the spread this rapidly expanding pandemic. Therefore, research, a new customized hybrid model consisting Back Propagation-Based Artificial Neural Network (BP-ANN), Correlated Additive Model (CAM) Auto-Regressive Integrated Moving Average (ARIMA) models were developed forecast Covid-19 prevalence Brazil, US, Russia India. dataset is obtained from World Health Organization website 22 January, 2020 6 2021. Various parameters tested select best ARIMA for these countries based on lowest MAPE values (5.21, 11.42, 1.45, 2.72) India, respectively. On other hand, proposed BP-ANN itself provided less satisfactory values. Finally, was achieved obtain results (4.69, 6.4, 0.63, 2.25) forecasting Those emphasize validity our model. Besides, prediction can assist terms taking important world.

Language: Английский

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COVID-19 Pandemic: A Comparative Prediction Using Machine Learning

International Journal of Automation Artificial Intelligence and Machine Learning, Journal Year: 2020, Volume and Issue: unknown, P. 01 - 19

Published: Oct. 30, 2020

Coronavirus Disease 2019 or COVID-19 is an infectious disease which declared as a pandemic by the World Health Organization (WHO) have noxious effect on entire human civilization. Each and every day number of infected people going higher so death toll. Many country Italy, UK, USA was affected badly, yet since identification first case, after certain days, scenario infection rate has been reduced significantly. However, like Bangladesh couldn't keep down. A algorithms proposed to forecast in terms infection, recovery Here, this work, we present comprehensive comparison based Machine Learning predict outbreak Bangladesh. Among Several algorithms, here used Polynomial Regression (PR) Multilayer Perception (MLP) Long Short Term Memory (LSTM) algorithm epidemiological model Susceptible, Infected Recovered (SIR), projected comparative outcomes.

Language: Английский

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Knowledge graph analysis and visualization of AI technology applied in COVID-19

Environmental Science and Pollution Research, Journal Year: 2021, Volume and Issue: 29(18), P. 26396 - 26408

Published: Dec. 2, 2021

With the global outbreak of coronavirus disease (COVID-19) all over world, artificial intelligence (AI) technology is widely used in COVID-19 and has become a hot topic. In recent 2 years, application AI developed rapidly, more than 100 relevant papers are published every month. this paper, we combined with bibliometric visual knowledge map analysis, WOS database as sample data source, applied VOSviewer CiteSpace analysis tools to carry out multi-dimensional statistical about 1903 pieces literature years (by end July year). The analyzed by several terms main annual article citation count, major publication sources, institutions countries, their contribution collaboration, etc. Since last year, research on sharply increased; especially corresponding fields expanding, such medicine, management, economics, informatics. China USA most prolific countries COVID-19, which have made significant high-level international collaboration increasing impactful. Moreover, studied issues: detection, surveillance, risk prediction, therapeutic research, virus modeling, COVID-19. Finally, put forward perspective challenges limits for researchers practitioners facilitate future

Language: Английский

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