An evaluation of prospective COVID-19 modelling studies in the USA: from data to science translation

K Nixon, S **dal, F Parker, NG Reich… - The Lancet Digital …, 2022 - thelancet.com
Infectious disease modelling can serve as a powerful tool for situational awareness and
decision support for policy makers. However, COVID-19 modelling efforts faced many …

[HTML][HTML] Pandemic strategies with computational and structural biology against COVID-19: a retrospective

CH Liu, CH Lu, LT Lin - Computational and Structural Biotechnology …, 2022 - Elsevier
The emergence of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2),
which is the etiologic agent of the coronavirus disease 2019 (COVID-19) pandemic, has …

Forecasting the spread of COVID-19 using deep learning and Big Data Analytics methods

C Kiganda, MA Akcayol - SN computer science, 2023 - Springer
To contain the spread of the COVID-19 pandemic, there is a need for cutting-edge
approaches that make use of existing technology capabilities. Forecasting its spread in a …

Conventional dendritic cell 2 links the genetic causal association from allergic asthma to COVID-19: a Mendelian randomization and transcriptomic study

H Liu, S Huang, L Yang, H Zhou, B Chen, L Wu… - Journal of Big Data, 2024 - Springer
Recent evidence suggests that allergic asthma (AA) decreases the risk of Coronavirus
Disease 2019 (COVID-19). However, the reasons remain unclear. Here, we systematically …

Suburban road networks to explore COVID-19 vulnerability and severity

S Uddin, A Khan, H Lu, F Zhou, S Karim - International Journal of …, 2022 - mdpi.com
The Delta variant of COVID-19 has been found to be extremely difficult to contain worldwide.
The complex dynamics of human mobility and the variable intensity of local outbreaks make …

Covid-19 classification using deep learning two-stage approach

M Alsaidi, AS Altaher, MT Jan, A Altaher… - arxiv preprint arxiv …, 2022 - arxiv.org
In this paper, deep-learning-based approaches namely fine-tuning of pretrained
convolutional neural networks (VGG16 and VGG19), and end-to-end training of a developed …

[HTML][HTML] Deep neural network for monitoring the growth of COVID-19 epidemic using meteorological covariates

AR Khan, AH Chowdhury, R Imon - Intelligent Systems with Applications, 2023 - Elsevier
Growth of an epidemic is influenced by the natural variation in climatic conditions and
enforcement variation in government stringency policies. Though these variations do not …

Novel cost-effective method for forecasting COVID-19 and hospital occupancy using deep learning

NI Ajali-Hernández, CM Travieso-González - Scientific Reports, 2024 - nature.com
The emergence of the COVID-19 pandemic in 2019 and its rapid global spread put
healthcare systems around the world to the test. This crisis created an unprecedented level …

A Boosted Evolutionary Neural Architecture Search for Time Series Forecasting with Application to South African COVID-19 Cases.

SO Akinola, QG Wang, P Olukanmi… - … Journal of Online & …, 2023 - search.ebscohost.com
In recent years, there has been an increase in studies on time-series forecasting for the
future occurrence of disease incidents. Improvements in deep learning approaches offer …

GA-Stacking: A New Stacking-Based Ensemble Learning Method to Forecast the COVID-19 Outbreak.

WN Ismail, HA Alsalamah… - Computers, Materials & …, 2023 - search.ebscohost.com
As a result of the increased number of COVID-19 cases, Ensemble Machine Learning (EML)
would be an effective tool for combatting this pandemic outbreak. An ensemble of classifiers …