A Distributed Processing Architecture for Disease Spread Analysis in the PDSA-RS Platform

Denilson Ebling, Felipe Machado, Glenio Descovi, Nicolas Cardenas, Gustavo Machado, Vinicius Maran, Alencar Machado

2024

Abstract

In today’s world, machine learning systems have permeated various domains, from object detection to disease spread prediction, playing pivotal roles in decision-making processes. Amid the COVID-19 pandemic, the utilization of machine learning methods like artificial neural networks and LSTM networks has significantly enhanced forecasting accuracy for disease outbreaks. This paper delves into the development of an intelligent system proposed by Cardenas et al. (2022a), focusing on simulating disease spread in animals and facilitating control measures through a stochastic model. Leveraging Docker containers for deployment, this system offers valuable insights for public health interventions, enabling swift responses to disease outbreaks. The primary objective of this work is to provide veterinarians with a user-friendly tool that integrates a stochastic model through an intuitive interface, aiding in critical decision-making processes in a scalable manner. The paper outlines the background of the stochastic model, introduces the proposed system for integrating and addressing the identified problem, presents an evaluation scenario to validate the system’s efficacy, and concludes with insights drawn from this research endeavor.

Paper Citation

in Harvard Style

Ebling D., Machado F., Descovi G., Cardenas N., Machado G., Maran V. and Machado A. (2024). A Distributed Processing Architecture for Disease Spread Analysis in the PDSA-RS Platform. In Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 2: ICEIS; ISBN 978-989-758-692-7, SciTePress, pages 313-320. DOI: 10.5220/0012742700003690

in Bibtex Style

@conference{iceis24,
author={Denilson Ebling and Felipe Machado and Glenio Descovi and Nicolas Cardenas and Gustavo Machado and Vinicius Maran and Alencar Machado},
title={A Distributed Processing Architecture for Disease Spread Analysis in the PDSA-RS Platform},
booktitle={Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 2: ICEIS},
year={2024},
pages={313-320},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012742700003690},
isbn={978-989-758-692-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 2: ICEIS
TI - A Distributed Processing Architecture for Disease Spread Analysis in the PDSA-RS Platform
SN - 978-989-758-692-7
AU - Ebling D.
AU - Machado F.
AU - Descovi G.
AU - Cardenas N.
AU - Machado G.
AU - Maran V.
AU - Machado A.
PY - 2024
SP - 313
EP - 320
DO - 10.5220/0012742700003690
PB - SciTePress