A review of social media data utilization for the prediction of disease outbreaks and understanding public perception
Infectious diseases take a large toll on the global population, not only through risks of illness
but also through economic burdens and lifestyle changes. With both emerging and re …
but also through economic burdens and lifestyle changes. With both emerging and re …
Machine learning for data-centric epidemic forecasting
The COVID-19 pandemic emphasized the importance of epidemic forecasting for decision
makers in multiple domains, ranging from public health to the economy. Forecasting …
makers in multiple domains, ranging from public health to the economy. Forecasting …
Predicting Kyasanur forest disease in resource-limited settings using event-based surveillance and transfer learning
R Keshavamurthy, LE Charles - Scientific Reports, 2023 - nature.com
In recent years, the reports of Kyasanur forest disease (KFD) breaking endemic barriers by
spreading to new regions and crossing state boundaries is alarming. Effective disease …
spreading to new regions and crossing state boundaries is alarming. Effective disease …
Einns: epidemiologically-informed neural networks
We introduce EINNs, a framework crafted for epidemic forecasting that builds upon the
theoretical grounds provided by mechanistic models as well as the data-driven expressibility …
theoretical grounds provided by mechanistic models as well as the data-driven expressibility …
Differentiable agent-based epidemiology
Mechanistic simulators are an indispensable tool for epidemiology to explore the behavior of
complex, dynamic infections under varying conditions and navigate uncertain environments …
complex, dynamic infections under varying conditions and navigate uncertain environments …
Healthcare sustainability: hospitalization rate forecasting with transfer learning and location-aware news analysis
Monitoring and forecasting hospitalization rates are of essential significance to public health
systems in understanding and managing overall healthcare deliveries and strategizing long …
systems in understanding and managing overall healthcare deliveries and strategizing long …
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 …
CAMul: Calibrated and Accurate Multi-view Time-Series Forecasting
Probabilistic time-series forecasting enables reliable decision making across many
domains. Most forecasting problems have diverse sources of data containing multiple …
domains. Most forecasting problems have diverse sources of data containing multiple …
Joint covid-19 and influenza-like illness forecasts in the United States using internet search information
Background As the prolonged COVID-19 pandemic continues, severe seasonal Influenza
(flu) may happen alongside COVID-19. This could cause a “twindemic”, in which there are …
(flu) may happen alongside COVID-19. This could cause a “twindemic”, in which there are …
Fast mining and forecasting of co-evolving epidemiological data streams
T Kimura, Y Matsubara, K Kawabata… - Proceedings of the 28th …, 2022 - dl.acm.org
Given a large, semi-infinite collection of co-evolving epidemiological data containing the
daily counts of cases/deaths/recovered in multiple locations, how can we incrementally …
daily counts of cases/deaths/recovered in multiple locations, how can we incrementally …