Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Influenza Forecasting Framework based on Gaussian Processes
Authors: Christoph Zimmer, Reza Yaesoubi
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compare our framework to several state of the art benchmarks and show competitive performance. We, therefore, believe that our GP based framework for seasonal epidemics forecasting will play a key role for future influenza forecasting and, lead to further research in the area. |
| Researcher Affiliation | Collaboration | 1Bosch Center for Artificial Intelligence, Renningen, Germany 2Health Policy and Management, Yale School of Public Health, New Haven, USA. |
| Pseudocode | Yes | Algorithm 1 Seasonal Epidemics Forecasting |
| Open Source Code | No | The paper mentions using the GPML package but does not state that the code for their proposed framework is open-source or provide a link to it. |
| Open Datasets | Yes | We use the official CDC data on influenza-like illness (ILI) as data source (Centers for Disease Control and Prevention, a). |
| Dataset Splits | Yes | We use the seasons 2003/04 – 2007/08 as training data and the seasons 2010/11 and 2011/12 as validation data to determine appropriate feature sets I of previous weeks for the training data. |
| Hardware Specification | No | The paper does not specify any hardware used for running the experiments. |
| Software Dependencies | Yes | We use Matlab 2016a and the GPML package (Rasmussen & Nickisch, 2019) for training Gaussian processes and predictions. |
| Experiment Setup | Yes | We use the seasons 2003/04 – 2007/08 as training data and the seasons 2010/11 and 2011/12 as validation data to determine appropriate feature sets I of previous weeks for the training data. We use various sets I (with up to five past weeks as features, I {i − 4, i − 3, . . . , i }) and choose the best three performing sets to become part of the ensemble as stated in Algorithm 1. We perform 1-4 week forecasts and therefore, end up with a total of 7 seasons, 29 weeks per season and 4 targets is in the order of minutes with our framework. |