The importance of investing in data, models, experiments, team science, and public trust to help policymakers prepare for the next pandemic

R Grieve, Y Yang, S Abbott, GR Babu… - PLOS Global Public …, 2023 - journals.plos.org
The COVID-19 pandemic has brought about valuable insights regarding models, data, and
experiments. In this narrative review, we summarised the existing literature on these three …

PETLFC: Parallel ensemble transfer learning based framework for COVID-19 differentiation and prediction using deep convolutional neural network models

P Misra, N Panigrahi, S Gopal Krishna Patro… - Multimedia Tools and …, 2024 - Springer
Despite a worldwide research involvement in the global COVID-19 pandemic, the research
community is still struggling to develop reliable and faster prediction mechanisms for this …

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 …

FlightKoopman: Deep Koopman for Multi-Dimensional Flight Trajectory Prediction

J Lu, J Jiang, Y Bai, W Dai, W Zhang - International Journal of …, 2025 - World Scientific
Multi-dimensional Flight Trajectory Prediction (MFTP) in Flight Operations Quality
Assessment (FOQA) refers to the estimation of flight status at the future time, accurate …

IoT-Cloud-Centric Smart Healthcare Monitoring System for Heart Disease Prediction Using a Gated-Controlled Deep Unfolding Network with Crayfish Optimization

H Kumar, A Taluja, RG Prasad… - International Journal of …, 2025 - World Scientific
The rising incidence of heart disease requires effective and robust prediction algorithms,
especially in Internet of Things (IoT)-cloud-based smart healthcare frameworks. This study …

Interpretable deep learning and transfer learning-based spatial-temporal modeling for vaccines demand prediction

L Altarawneh - 2023 - search.proquest.com
This study proposes prediction models to identify vaccination rates and anticipate vaccine
demand in develo** countries or regions, with a focus on the COVID-19 vaccination. The …

Hybrid and Ensemble Artificial Intelligence-Based Time Series Techniques with Applications in Power and Health Sectors

SO Akinola - 2024 - search.proquest.com
In the current fourth industrial revolution (4IR), governments and businesses increasingly
leverage large datasets and artificial intelligence (AI) to glean insights for competitive …