Deep learning via LSTM models for COVID-19 infection forecasting in India

PLoS ONE, Год журнала: 2022, Номер 17(1), С. e0262708 - e0262708

Опубликована: Янв. 28, 2022

The COVID-19 pandemic continues to have major impact health and medical infrastructure, economy, agriculture. Prominent computational mathematical models been unreliable due the complexity of spread infections. Moreover, lack data collection reporting makes modelling attempts difficult unreliable. Hence, we need re-look at situation with reliable sources innovative forecasting models. Deep learning such as recurrent neural networks are well suited for spatiotemporal sequences. In this paper, apply long short term memory (LSTM), bidirectional LSTM, encoder-decoder LSTM multi-step (short-term) infection forecasting. We select Indian states hotpots capture first (2020) second (2021) wave infections provide two months ahead forecast. Our model predicts that likelihood another in October November 2021 is low; however, authorities be vigilant given emerging variants virus. accuracy predictions motivate application method other countries regions. Nevertheless, challenges remain reliability difficulties capturing factors population density, logistics, social aspects culture lifestyle.

Язык: Английский

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Review on COVID‐19 diagnosis models based on machine learning and deep learning approaches

Expert Systems, Год журнала: 2021, Номер 39(3)

Опубликована: Июль 28, 2021

COVID-19 is the disease evoked by a new breed of coronavirus called severe acute respiratory syndrome 2 (SARS-CoV-2). Recently, has become pandemic infecting more than 152 million people in over 216 countries and territories. The exponential increase number infections rendered traditional diagnosis techniques inefficient. Therefore, many researchers have developed several intelligent techniques, such as deep learning (DL) machine (ML), which can assist healthcare sector providing quick precise diagnosis. this paper provides comprehensive review most recent DL ML for studies are published from December 2019 until April 2021. In general, includes 200 that been carefully selected publishers, IEEE, Springer Elsevier. We classify research tracks into two categories: present public datasets established extracted different countries. measures used to evaluate methods comparatively analysed proper discussion provided. conclusion, diagnosing outbreak prediction, SVM widely mechanism, CNN mechanism. Accuracy, sensitivity, specificity measurements previous studies. Finally, will guide community on upcoming development inspire their works future development. This

Язык: Английский

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

Results in Physics, Год журнала: 2021, Номер 27, С. 104495 - 104495

Опубликована: Июнь 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.

Язык: Английский

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Machine Learning (ML) in Medicine: Review, Applications, and Challenges

Mathematics, Год журнала: 2021, Номер 9(22), С. 2970 - 2970

Опубликована: Ноя. 21, 2021

Today, artificial intelligence (AI) and machine learning (ML) have dramatically advanced in various industries, especially medicine. AI describes computational programs that mimic simulate human intelligence, for example, a person’s behavior solving problems or his ability learning. Furthermore, ML is subset of intelligence. It extracts patterns from raw data automatically. The purpose this paper to help researchers gain proper understanding its applications healthcare. In paper, we first present classification learning-based schemes According our proposed taxonomy, healthcare are categorized based on pre-processing methods (data cleaning methods, reduction methods), (unsupervised learning, supervised semi-supervised reinforcement learning), evaluation (simulation-based practical implementation-based real environment) (diagnosis, treatment). classification, review some studies presented We believe helps familiarize themselves with the newest research medicine, recognize their challenges limitations area, identify future directions.

Язык: Английский

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Modelling and forecasting of COVID-19 spread using wavelet-coupled random vector functional link networks

Applied Soft Computing, Год журнала: 2020, Номер 96, С. 106626 - 106626

Опубликована: Авг. 13, 2020

Язык: 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

International Journal of Environmental Research and Public Health, Год журнала: 2020, Номер 17(16), С. 5634 - 5634

Опубликована: Авг. 5, 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, 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 spread. 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 has prevalent for only year northern, one-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 transmission, if it exists, will more evident 2021 subsequent years.

Язык: Английский

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An approach to forecast impact of Covid‐19 using supervised machine learning model

Software Practice and Experience, Год журнала: 2021, Номер 52(4), С. 824 - 840

Опубликована: Апрель 1, 2021

Abstract The Covid‐19 pandemic has emerged as one of the most disquieting worldwide public health emergencies 21st century and thrown into sharp relief, among other factors, dire need for robust forecasting techniques disease detection, alleviation well prevention. Forecasting been powerful statistical methods employed world over in various disciplines detecting analyzing trends predicting future outcomes based on which timely mitigating actions can be undertaken. To that end, several machine learning have harnessed depending upon analysis desired availability data. Historically speaking, predictions thus arrived at short term country‐specific nature. In this work, multimodel technique is called EAMA related parameters long‐term both within India a global scale proposed. This proposed hybrid model well‐suited to past present For study, two datasets from Ministry Health & Family Welfare Worldometers, respectively, exploited. Using these datasets, data outlined, observed predicted being very similar real‐time values. experiment also conducted statewise countrywise across it included Appendix.

Язык: Английский

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Potential neutralizing antibodies discovered for novel corona virus using machine learning

Scientific Reports, Год журнала: 2021, Номер 11(1)

Опубликована: Март 4, 2021

The fast and untraceable virus mutations take lives of thousands people before the immune system can produce inhibitory antibody. recent outbreak COVID-19 infected killed in world. Rapid methods finding peptides or antibody sequences that inhibit viral epitopes SARS-CoV-2 will save life thousands. To predict neutralizing antibodies for a high-throughput manner, this paper, we use different machine learning (ML) model to possible synthetic SARS-CoV-2. We collected 1933 virus-antibody their clinical patient neutralization response trained an ML response. Using graph featurization with variety methods, like XGBoost, Random Forest, Multilayered Perceptron, Support Vector Machine Logistic Regression, screened hypothetical found nine stable potentially combined bioinformatics, structural biology, Molecular Dynamics (MD) simulations verify stability candidate

Язык: Английский

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A Novel Heuristic Algorithm for the Modeling and Risk Assessment of the COVID-19 Pandemic Phenomenon

Computer Modeling in Engineering & Sciences, Год журнала: 2020, Номер 125(2), С. 815 - 828

Опубликована: Янв. 1, 2020

The modeling and risk assessment of a pandemic phenomenon such as COVID-19 is an important complicated issue in epidemiology, attempt great interest for public health decision-making. To this end, the present study, based on recent heuristic algorithm proposed by authors, time evolution investigated six different countries/states, namely New York, California, USA, Iran, Sweden UK. number COVID-19-related deaths used to develop model it believed that predicted daily each country/state includes information about quality system area, age distribution population, geographical environmental factors well other conditions. Based derived epidemic curves, new 3D-epidemic surface assess at any its evolution. This research highlights potential tool which can assist COVID-19. Mapping development through revealing dynamic nature differences similarities among districts.

Язык: Английский

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Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19

Pathogens, Год журнала: 2021, Номер 10(8), С. 1048 - 1048

Опубликована: Авг. 18, 2021

As of August 6th, 2021, the World Health Organization has notified 200.8 million laboratory-confirmed infections and 4.26 deaths from COVID-19, making it worst pandemic since 1918 flu. The main challenges in mitigating COVID-19 are effective vaccination, treatment, agile containment strategies. In this review, we focus on potential Artificial Intelligence (AI) surveillance, diagnosis, outcome prediction, drug discovery vaccine development. With help big data, AI tries to mimic cognitive capabilities a human brain, such as problem-solving learning abilities. Machine Learning (ML), subset AI, holds special promise for solving problems based experiences gained curated data. Advances methods have created an unprecedented opportunity building surveillance systems using deluge real-time data generated within short span time. During pandemic, many reports discussed utility approaches prioritization, delivery, supply chain drugs, vaccines, non-pharmaceutical interventions. This review will discuss clinical AI-based models also limitations faced by systems, model generalizability, explainability, trust pillars real-life deployment healthcare.

Язык: Английский

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