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.
Keyword(s):
Intelligent Systems, Decision-Support Systems, Diseases Control, Outreabk Control.
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 backgr
ound 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.
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