Predicting Spatio-Temporal Propagation of Seasonal Influenza Using Variational Gaussian Process Regression
Authors: Ransalu Senanayake, Simon O'Callaghan, Fabio Ramos
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on two datasets of state-wide US weekly flu-counts consisting of 19,698 and 89,474 data points, ranging over several years, illustrate the robustness of the model as a tool for further epidemiological investigations. Experiments To demonstrate the predictive power of the method for the propagation of influenza, two datasets were used: |
| Researcher Affiliation | Collaboration | Ransalu Senanayake,1,2 Simon O Callaghan,2 and Fabio Ramos1,2 1School of Information Technologies, The University of Sydney, Australia 2National ICT Australia (NICTA) |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | 1Supplementary materials https://goo.gl/VCu VW3 |
| Open Datasets | Yes | 1) Google flu trend (GFT): Recent studies show an increasing interest in using web query data to predict flu (Chakraborty et al. 2014). The US state-wide GFT (Ginsberg et al. 2009) data (flu count/population) of 402 weeks from 02/Dec/2007 to 09/Aug/2015 were used in the experiments. ... 2) 1972-2006 dataset (CDC): In order to analyze long-term trends, exact ILI counts (CDC ) of 1826 weeks from 1972 to 2006 (Viboud et al. 2006) were used. |
| Dataset Splits | No | The paper mentions 'training data' and 'test' data, but does not explicitly provide details about a separate 'validation' dataset split or its size/percentage for reproducibility. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. It only vaguely refers to a 'standard PC'. |
| Software Dependencies | No | The paper does not provide specific software dependency details, such as library names with version numbers, used for the implementation or experiments. |
| Experiment Setup | Yes | in our experiments empirical rates of 10 2 and 10 5 were used as the learning rates of NGD and SGD respectively. Choosing only M = N/4 of data as inducing points average of MSE was found to be 0.0014 for GFT. M = N/10. |