ArticleMicroscopy research and technique2021
Machine learning techniques to detect and forecast the daily total COVID-19 infected and deaths cases under different lockdown types.
Article in Microscopy research and technique, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
19 citing papers in PubMed.
- Spatio-temporal analysis of COVID-19 lockdown effect to survive in the US counties using ANN.Scientific reports · 2024Article
- A novel bidirectional LSTM deep learning approach for COVID-19 forecasting.Scientific reports · 2023Article
- Comparison of Six Scoring Systems for Predicting In-hospital Mortality among Patients with SARS-COV2 Presenting to the Emergency Department.Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine · 2023Article
- Data analytics and knowledge management approach for COVID-19 prediction and control.International journal of information technology : an official journal of Bharati Vidyapeeth's Institute of Computer Applications and Management · 2023Article
- Impact of Post-Covid-19 on driver behaviour: A perspective towards pandemic-sustained transportation.Journal of transport & health · 2023Review
- Twitter conversations predict the daily confirmed COVID-19 cases.Applied soft computing · 2022Article
- Deep learning for Covid-19 forecasting: State-of-the-art review.Neurocomputing · 2022Article
- COVID-DAI: A novel framework for COVID-19 detection and infection growth estimation using computed tomography images.Microscopy research and technique · 2022Article
- Prediction of Patients with COVID-19 Requiring Intensive Care: A Cross-sectional Study Based on Machine-learning Approach from Iran.Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine · 2022Article
- Predicting the impact of the third wave of COVID-19 in India using hybrid statistical machine learning models: A time series forecasting and sentiment analysis approach.Computers in biology and medicine · 2022Article
- Panel Associations Between Newly Dead, Healed, Recovered, and Confirmed Cases During COVID-19 Pandemic.Journal of epidemiology and global health · 2022Article
- Individual Factors Associated With COVID-19 Infection: A Machine Learning Study.Frontiers in public health · 2022Article
- Microscopic segmentation and classification of COVID-19 infection with ensemble convolutional neural network.Microscopy research and technique · 2022Article
- An intelligence design for detection and classification of COVID19 using fusion of classical and convolutional neural network and improved microscopic features selection approach.Microscopy research and technique · 2021Article
- Associations between mobility patterns and COVID-19 deaths during the pandemic: A network structure and rank propagation modelling approach.Array (New York, N.Y.) · 2021Article
- Applications of Artificial Intelligence, Machine Learning, Big Data and the Internet of Things to the COVID-19 Pandemic: A Scientometric Review Using Text Mining.International journal of environmental research and public health · 2021Review
- Machine learning techniques to detect and forecast the daily total COVID-19 infected and deaths cases under different lockdown types.Microscopy research and technique · 2021Article
- Viral reverse engineering using Artificial Intelligence and big data COVID-19 infection with Long Short-term Memory (LSTM).Environmental technology & innovation · 2021Article
- Machine Learning-Based Prediction of Growth in Confirmed COVID-19 Infection Cases in 114 Countries Using Metrics of Nonpharmaceutical Interventions and Cultural Dimensions: Model Development and Validation.Journal of medical Internet research · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
COVID-19 has impacted the world in many ways, including loss of lives, economic downturn and social isolation. COVID-19 was emerged due to the SARS-CoV-2 that is highly infectious pandemic. Every country tried to control the COVID-19 spread by imposing different types of lockdowns. Therefore, there is an urgent need to forecast the daily confirmed infected cases and deaths in different types of lockdown to select the most appropriate lockdown strategies to control the intensity of this pandemic and reduce the burden in hospitals. Currently are imposed three types of lockdown (partial, herd, complete) in different countries. In this study, three countries from every type of lockdown were studied by applying time-series and machine learning models, named as random forests, K-nearest neighbors, SVM, decision trees (DTs), polynomial regression, Holt winter, ARIMA, and SARIMA to forecast daily confirm infected cases and deaths due to COVID-19. The models' accuracy and effectiveness were evaluated by error based on three performance criteria. Actually, a single forecasting model could not capture all data sets' trends due to the varying nature of data sets and lockdown types. Three top-ranked models were used to predict the confirmed infected cases and deaths, the outperformed models were also adopted for the out-of-sample prediction and obtained very close results to the actual values of cumulative infected cases and deaths due to COVID-19. This study has proposed the auspicious models for forecasting and the best lockdown strategy to mitigate the causalities of COVID-19.
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