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.
@article{arxiv.2208.05019,
title = {Automated Infectious Disease Forecasting: Use-cases and Practical Considerations for Pipeline Implementation},
author = {VP Nagraj and Chris Hulme-Lowe and Shakeel Jessa and Stephen D. Turner},
journal= {arXiv preprint arXiv:2208.05019},
year = {2022}
}
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Submitted to epiDAMIK 5.0: The 5th International workshop on Epidemiology meets Data Mining and Knowledge discovery