Automated Infectious Disease Forecasting: Use-cases and Practical Considerations for Pipeline Implementation

VP Nagraj, Chris Hulme-Lowe, Shakeel Jessa, Stephen D Turner

Keywords: automation, cloud computing, infectious disease forecasting, real-time forecasting, forecasting challenge

TL;DR: Here we describe the technical implementation, benefits, and limitations of an automated cloud computing approach to real-time infectious disease forecasting.

Abstract: 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.