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Article
Engineering, Electrical & Electronic
Erdinc Koc et al.
Summary: The outbreak of COVID-19 has led to an increased demand for healthcare equipment, and forecasting these demands using AI-based models can effectively predict the amount needed. This study presents a deep model approach based on a multilayer long short-term memory network for forecasting medical equipment demand and outbreak spreading during the COVID-19 pandemic.
SIGNAL IMAGE AND VIDEO PROCESSING
(2022)
Article
Computer Science, Artificial Intelligence
Madini O. Alassafi et al.
Summary: The study developed a prediction model for the spread of COVID-19 in Malaysia, Morocco, and Saudi Arabia using public datasets from the European Centre for Disease Prevention and Control. Deep learning models were utilized with a focus on LSTM networks. The study also compared the number of cases and deaths in the three countries.
Article
Economics
Mohammad Masum et al.
Summary: This study utilized a mathematical epidemic model, statistical model, and recurrent neural network variants to forecast cumulative confirmed cases of COVID-19. The experimental results demonstrated the superiority of recurrent neural network variants in forecasting accuracy, while the mathematical epidemic model presented comprehensive insights into the virus spread and potential control strategies.
SOCIO-ECONOMIC PLANNING SCIENCES
(2022)
Article
Mathematical & Computational Biology
Sweeti Sah et al.
Summary: Due to the spread of COVID-19, the world is in a dire condition and human lives are at risk. This research focuses on building a COVID-19 predictive analytic technique using models such as Prophet, ARIMA, and stacked LSTM-GRU. The stacked LSTM-GRU model was found to have better prediction results, aiding in determining future transmission paths of the virus.
COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE
(2022)
Article
Chemistry, Analytical
Mario Munoz-Organero et al.
Summary: This paper proposes a new machine learning model that combines time pattern extraction and spatial analysis to optimize predictions of COVID-19 transmission. The model, which utilizes deep learning algorithms for data processing, achieves better results compared to previous models.
Article
Biology
Essam A. Rashed et al.
Summary: Due to the variability of COVID-19 and various factors, it is challenging to predict its incidence using traditional mathematical models. In this study, machine learning was used to forecast the epidemic, taking into account the effectiveness of vaccination, infectivity of viral variants, and changes in public behavior. The results suggest that the effectiveness of vaccines and the infectivity of viral variants are important factors in predicting the incidence of COVID-19.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Review
Medicine, General & Internal
Dan H. Barouch
Summary: This article reviews the protective effects of vaccination and prior infection on severe Covid-19, and proposes future research directions.
NEW ENGLAND JOURNAL OF MEDICINE
(2022)
Article
Computer Science, Interdisciplinary Applications
Aoyong Li et al.
Summary: The study analyzed the changes in micro-mobility trip patterns during the pandemic, finding that the number of trips significantly decreased during the lockdown period, with changes in trip times. The origin-destination pairs remained consistent, but the number of trips between each pair decreased due to the pandemic. Additionally, there was an increase in proportions of Home, Park, and Grocery activities, while Leisure and Shopping activities decreased during the lockdown period.
COMPUTERS ENVIRONMENT AND URBAN SYSTEMS
(2021)
Article
Computer Science, Interdisciplinary Applications
Abdelkader Dairi et al.
Summary: This study compared the performances of machine learning methods for COVID-19 transmission forecasting, revealing that hybrid deep learning models can efficiently predict COVID-19 cases. The research confirmed the superiority of deep learning models over baseline machine learning models, with LSTM-CNN showing the best performance with an averaged mean absolute percentage error of 3.718%.
JOURNAL OF BIOMEDICAL INFORMATICS
(2021)
Article
Multidisciplinary Sciences
Xiao Hou et al.
Summary: The COVID-19 pandemic poses global threats with varied impacts on different communities due to population heterogeneity in terms of factors such as race and age. Studies have shown significant differences in transmission patterns based on geographic and demographic factors, highlighting the importance of considering these heterogeneities in policy making for managing and reopening during the pandemic.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2021)
Article
Environmental Sciences
Essam A. Rashed et al.
Summary: A deep learning model combining meteorological data and public mobility data was proposed to predict the spread of COVID-19 and tested on data collected in Japan, showing significant improvement compared to Google Cloud forecasts. The model could provide public awareness regarding the morbidity risk of the COVID-19 pandemic in a feasible manner.
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
(2021)
Review
Social Sciences, Interdisciplinary
Fabian Lorig et al.
Summary: Simulations play a crucial role in limiting the spread of Covid-19, but there are discrepancies in the models that need to be addressed to meet the needs of decision-makers. The current ABSS models still have shortcomings in terms of depth, transparency, and validation.
JASSS-THE JOURNAL OF ARTIFICIAL SOCIETIES AND SOCIAL SIMULATION
(2021)
Review
Mathematical & Computational Biology
Yue Xiang et al.
Summary: Research on COVID-19 epidemic models shows that most models use gamma distribution to describe key infection periods, while assumptions vary regarding the latent period.
INFECTIOUS DISEASE MODELLING
(2021)
Article
Public Administration
Ritanjali Majhi et al.
Summary: This paper develops machine learning models that can predict the number of positive COVID-19 cases more accurately, which can work in real-time prediction effectively.
JOURNAL OF PUBLIC AFFAIRS
(2021)
Article
Mathematical & Computational Biology
Yifan Zhu et al.
Summary: Since December 2019, a novel strain of coronavirus (COVID-19) has caused a disease outbreak in China, infecting a large number of people and spreading globally. A statistical disease transmission model was used to estimate the transmissibility of the early-phase outbreak, with sensitivity analyses and evaluation of lockdown intervention efficacy.
STATISTICS IN BIOSCIENCES
(2021)
Article
Infectious Diseases
Hocheol Lee et al.
INTERNATIONAL JOURNAL OF INFECTIOUS DISEASES
(2020)
Article
Engineering, Civil
Scott Parr et al.
NATURAL HAZARDS REVIEW
(2020)
Article
Mathematics, Interdisciplinary Applications
Farah Shahid et al.
CHAOS SOLITONS & FRACTALS
(2020)
Article
Computer Science, Artificial Intelligence
Sina F. Ardabili et al.
