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Laboratory of Ubiquitous, Mobile and Applied Computing (LUMAC), Federal University of Santa Maria, Roraima Av. 1000, Santa Maria, Brazil

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2 Papers

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

Topics: Software Engineering; Tools, Techniques and Methodologies for System Development

Authors: Denilson Ebling ¹ , Felipe Machado ¹ , Glenio Descovi ¹ , Nicolas Cardenas ² , Gustavo Machado ² , Vinicius Maran ¹ and Alencar Machado ¹

Affiliations: ¹ Laboratory of Ubiquitous, Mobile and Applied Computing (LUMAC), Federal University of Santa Maria, Roraima Av. 1000, Santa Maria, Brazil , ² Department of Population Health and Pathobiology, College of Veterinary Medicine, North Carolina State University, Raleigh, U.S.A.

Digital Object Identifier: 10.5220/0012742700003690

Proceedings of ICEIS 2024, ISBN: 978-989-758-692-7

Quick 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.

Speeding Up the Simulation Animals Diseases Spread: A Study Case on R and Python Performance in PDSA-RS Platform

Topic: Digital Transformation

Authors: Rodrigo Schneider , Felipe Machado , Celio Trois , Glênio Descovi , Vinícius Maran and Alencar Machado

Affiliation: Laboratory of Ubiquitous, Mobile and Applied Computing, Polytechnic School, Federal University of Santa Maria (UFSM), Roraima Av. 1000, Santa Maria, Brazil

Digital Object Identifier: 10.5220/0012556200003690

Proceedings of ICEIS 2024, ISBN: 978-989-758-692-7

Quick Abstract

The control and prevention of livestock diseases play a crucial role in safeguarding business continuity, simulating disease prevention and control measures are vital to mitigate future epidemics. In this sense, modelling systems can be an effective tool that allows the simulation of different ways of spreading diseases by configuring parameters allowing testing of different prevention measures. This work investigates enhancing a system that simulates disease spread processes in animals. The stochastic model system was developed in R; however, given a large amount of data and intense processing of stochastic functions that simulate spreading and control actions, it required optimization. We focused on translating and modifying it to Python using packages focused on data analysis, aiming to speed up the system execution time. We conducted experiments comparing high computational cost functions executed in the actual model R with the new proposal implemented in Python. The results showed that rewriting the code in Python has advantages such as performance in time execution, which in Python is more than four times faster than R, memory usage consumption in R uses 460 MB and 315 MB in Python.