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Autori principali: Narwal, Raunak, Abbas, Syed
Natura: Preprint
Pubblicazione: 2025
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Accesso online:https://arxiv.org/abs/2508.07239
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author Narwal, Raunak
Abbas, Syed
author_facet Narwal, Raunak
Abbas, Syed
contents Modelling disease outbreak models remains challenging due to incomplete surveillance data, noise, and limited access to standardized datasets. We have created BIGBOY1.2, an open synthetic dataset generator that creates configurable epidemic time series and population-level trajectories suitable for benchmarking modelling, forecasting, and visualisation. The framework supports SEIR and SIR-like compartmental logic, custom seasonality, and noise injection to mimic real reporting artifacts. BIGBOY1.2 can produce datasets with diverse characteristics, making it suitable for comparing traditional epidemiological models (e.g., SIR, SEIR) with modern machine learning approaches (e.g., SVM, neural networks).
format Preprint
id arxiv_https___arxiv_org_abs_2508_07239
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle BIGBOY1.2: Generating Realistic Synthetic Data for Disease Outbreak Modelling and Analytics
Narwal, Raunak
Abbas, Syed
Populations and Evolution
Machine Learning
92D30, 97M10
I.2.6; J.3
Modelling disease outbreak models remains challenging due to incomplete surveillance data, noise, and limited access to standardized datasets. We have created BIGBOY1.2, an open synthetic dataset generator that creates configurable epidemic time series and population-level trajectories suitable for benchmarking modelling, forecasting, and visualisation. The framework supports SEIR and SIR-like compartmental logic, custom seasonality, and noise injection to mimic real reporting artifacts. BIGBOY1.2 can produce datasets with diverse characteristics, making it suitable for comparing traditional epidemiological models (e.g., SIR, SEIR) with modern machine learning approaches (e.g., SVM, neural networks).
title BIGBOY1.2: Generating Realistic Synthetic Data for Disease Outbreak Modelling and Analytics
topic Populations and Evolution
Machine Learning
92D30, 97M10
I.2.6; J.3
url https://arxiv.org/abs/2508.07239