A review of graph neural networks in epidemic modeling
Since the onset of the COVID-19 pandemic, there has been a growing interest in studying
epidemiological models. Traditional mechanistic models mathematically describe the …
epidemiological models. Traditional mechanistic models mathematically describe the …
Spatio-temporal graph neural networks for predictive learning in urban computing: A survey
With recent advances in sensing technologies, a myriad of spatio-temporal data has been
generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal …
generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal …
Dynamic hypergraph structure learning for traffic flow forecasting
This paper studies the problem of traffic flow forecasting, which aims to predict future traffic
conditions on the basis of road networks and traffic conditions in the past. The problem is …
conditions on the basis of road networks and traffic conditions in the past. The problem is …
Dynamic adaptive spatio–temporal graph network for COVID‐19 forecasting
Appropriately characterising the mixed space–time relations of the contagion process
caused by hybrid space and time factors remains the primary challenge in COVID‐19 …
caused by hybrid space and time factors remains the primary challenge in COVID‐19 …
MSGNN: Multi-scale Spatio-temporal Graph Neural Network for epidemic forecasting
M Qiu, Z Tan, B Bao - Data Mining and Knowledge Discovery, 2024 - Springer
Infectious disease forecasting has been a key focus and proved to be crucial in controlling
epidemic. A recent trend is to develop forecasting models based on graph neural networks …
epidemic. A recent trend is to develop forecasting models based on graph neural networks …
[HTML][HTML] The variations of SIkJalpha model for COVID-19 forecasting and scenario projections
A Srivastava - Epidemics, 2023 - Elsevier
We proposed the SIkJalpha model at the beginning of the COVID-19 pandemic (early 2020).
Since then, as the pandemic evolved, more complexities were added to capture crucial …
Since then, as the pandemic evolved, more complexities were added to capture crucial …
Challenges of COVID-19 Case Forecasting in the US, 2020–2021
During the COVID-19 pandemic, forecasting COVID-19 trends to support planning and
response was a priority for scientists and decision makers alike. In the United States, COVID …
response was a priority for scientists and decision makers alike. In the United States, COVID …
Epidemiology-aware deep learning for infectious disease dynamics prediction
Infectious disease risk prediction plays a vital role in disease control and prevention. Recent
studies in machine learning have attempted to incorporate epidemiological knowledge into …
studies in machine learning have attempted to incorporate epidemiological knowledge into …
EpiLearn: A Python Library for Machine Learning in Epidemic Modeling
EpiLearn is a Python toolkit developed for modeling, simulating, and analyzing epidemic
data. Although there exist several packages that also deal with epidemic modeling, they are …
data. Although there exist several packages that also deal with epidemic modeling, they are …
Machine Learning for Infectious Disease Risk Prediction: A Survey
Infectious diseases, either emerging or long-lasting, place numerous people at risk and
bring heavy public health burdens worldwide. In the process against infectious diseases …
bring heavy public health burdens worldwide. In the process against infectious diseases …