Enhancing Sustainability of Complex Epidemiological Models through a Generic Multilevel Agent-based Approach
Authors: Sébastien Picault, Yu-Lin Huang, Vianney Sicard, Pauline Ezanno
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This framework has been extensively tested on several variations of the theoretical SIR-like models, to check that all usage patterns above, including those corresponding to classical epidemiological paradigms, produce equivalent results. To assess the interest of our approach on real cases, we chose two IBM studies, carried out within the same team on the same disease in dairy cattle (Q fever, a zoonosis affecting mainly ruminants) at different scales, thus we can compare our outputs to each model: one for the within-herd spread [Courcoul et al., 2011], and the other for the between-herd spread [Pandit et al., 2016]. The EMu LSion implementation reproduces the outcomes of the reference model [Courcoul et al., 2011]. |
| Researcher Affiliation | Academia | a Bioepar, INRA, Oniris, La Chantrerie, 44307 Nantes, France b Univ. Lille, CNRS, Centrale Lille, UMR 9189 CRISt AL, F-59000 Lille, France |
| Pseudocode | No | The paper describes the architecture and framework in detail but does not include any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper mentions that the design principles 'have been implemented and experimented through a Python framework' but does not provide any statement about making this code open-source or provide a link to a repository. |
| Open Datasets | No | The paper mentions using two IBM studies on 'Q fever' in dairy cattle for validation, referencing '[Courcoul et al., 2011]' and '[Pandit et al., 2016]'. While these are specific studies, the paper does not provide concrete access information (e.g., a direct link, DOI, or explicit statement of public availability) for the datasets used in its own experiments derived from or compared to these studies. |
| Dataset Splits | No | The paper includes a section titled 'Experimentation and Validation' but does not specify any dataset splits (e.g., percentages or counts for training, validation, or test sets). It mentions comparing outputs to reference models but not the specific data partitioning strategy. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, memory, or cloud computing specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions 'a Python framework' and the use of 'a symbolic mathematics library (Sympy)' but does not provide specific version numbers for Python, Sympy, or any other software dependencies required to reproduce the experiments. |
| Experiment Setup | No | The paper describes the conceptual framework and how it applies to different modeling paradigms, and it mentions the 'implementation of the within-herd model involves three main concerns', but it does not provide specific experimental setup details such as hyperparameter values, training configurations, or system-level settings used in the validation. |