Automated Infectious Disease Forecasting: Use-cases and Practical Considerations for Pipeline Implementation
Real-time forecasting of disease outbreaks requires standardized outputs generated in a timely manner. Development of pipelines to automate infectious disease forecasts can ensure that parameterization and software dependencies are common to any execution of the forecasting code. Here we present our implementation of an automated cloud computing pipeline to forecast infectious disease outcomes, with examples of usage to forecast COVID-19 and influenza targets. We also offer our perspective on the limits of automation and importance of human-in-the-loop automated infectious disease forecasting.
Code (1)
Tasks
Cloud ComputingSimilar Papers 제목 키워드 기반
Physics-informed deep learning for infectious disease forecasting
Accurate forecasting of contagious diseases is critical for public health policymaking and pandemic preparedness. We propose a new infectious disease forecasting model based on physics-informed neural networks (PINNs), a…
Deep LearningSpatio-temporal stochastic graph-based learning for infectious disease forecasting
Spatio-temporal graph-based models have typically been used to forecast new cases of infectious diseases such as COVID-19 and chickenpox outbreaks. However, the use of stochastic modelling into their learning process has…
Cross-Country Learning for National Infectious Disease Forecasting Using European Data
Accurate forecasting of infectious disease incidence is critical for public health planning and timely intervention. While most data-driven forecasting approaches rely primarily on historical data from a single country, …
Data AugmentationTransfer Learning using 66 Diseases for Disease Forecasting Applications
Disease forecasting models typically rely on a single data stream, making models brittle when histories are short or noisy. Recent top-performing models have shown that synthesizing multiple reporting systems for the sam…
Transfer LearningIDOBE: Infectious Disease Outbreak forecasting Benchmark Ecosystem
Epidemic forecasting has become an integral part of real-time infectious disease outbreak response. While collaborative ensembles composed of statistical and machine learning models have become the norm for real-time for…