Causalgnn: Causal-based graph neural networks for spatio-temporal epidemic forecasting
Infectious disease forecasting has been a key focus in the recent past owing to the COVID-
19 pandemic and has proved to be an important tool in controlling the pandemic. With the …
19 pandemic and has proved to be an important tool in controlling the pandemic. With the …
Score-driven time series models
AC Harvey - Annual Review of Statistics and Its Application, 2022 - annualreviews.org
The construction of score-driven filters for nonlinear time series models is described, and
they are shown to apply over a wide range of disciplines. Their theoretical and practical …
they are shown to apply over a wide range of disciplines. Their theoretical and practical …
Monitoring COVID‐19 contagion growth
We present a statistical model that can be employed to monitor the time evolution of the
COVID‐19 contagion curve and the associated reproduction rate. The model is a Poisson …
COVID‐19 contagion curve and the associated reproduction rate. The model is a Poisson …
Modelling the COVID-19 infection trajectory: A piecewise linear quantile trend model
We propose a piecewise linear quantile trend model to analyse the trajectory of the COVID-
19 daily new cases (ie the infection curve) simultaneously across multiple quantiles. The …
19 daily new cases (ie the infection curve) simultaneously across multiple quantiles. The …
The forecast of COVID-19 spread risk at the county level
MD Hssayeni, A Chala, R Dev, L Xu, J Shaw, B Furht… - Journal of big …, 2021 - Springer
The early detection of the coronavirus disease 2019 (COVID-19) outbreak is important to
save people's lives and restart the economy quickly and safely. People's social behavior …
save people's lives and restart the economy quickly and safely. People's social behavior …
GrowthPredict: A toolbox and tutorial-based primer for fitting and forecasting growth trajectories using phenomenological growth models
Simple dynamic modeling tools can help generate real-time short-term forecasts with
quantified uncertainty of the trajectory of diverse growth processes unfolding in nature and …
quantified uncertainty of the trajectory of diverse growth processes unfolding in nature and …
Forecasting COVID‐19 cases using dynamic time warping and incremental machine learning methods
L Miralles‐Pechuán, A Kumar… - Expert …, 2023 - Wiley Online Library
The investment of time and resources for developing better strategies is key to dealing with
future pandemics. In this work, we recreated the situation of COVID‐19 across the year …
future pandemics. In this work, we recreated the situation of COVID‐19 across the year …
The value of robust statistical forecasts in the Covid-19 pandemic
The Covid-19 pandemic has put forecasting under the spotlight, pitting epidemiological
models against extrapolative time-series devices. We have been producing real-time short …
models against extrapolative time-series devices. We have been producing real-time short …
A survey on spatio-temporal series prediction with deep learning: taxonomy, applications, and future directions
F Sun, W Hao, A Zou, Q Shen - Neural Computing and Applications, 2024 - Springer
With the rapid development of data acquisition and storage technology, spatio-temporal (ST)
data in various fields are growing explosively, so many ST prediction methods have …
data in various fields are growing explosively, so many ST prediction methods have …
Examining deep learning models with multiple data sources for COVID-19 forecasting
The COVID-19 pandemic represents the most significant public health disaster since the
1918 influenza pandemic. During pandemics such as COVID-19, timely and reliable spatio …
1918 influenza pandemic. During pandemics such as COVID-19, timely and reliable spatio …