Simulação de espalhamento de doenças em animais de produção: tradução do modelo na linguagem R para Python na plataforma PDSA-RS

• Ano de defesa: 2024
• Autor(a) principal: Schneider, Rodrigo
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Santa Maria; Brasil; Ciência da Computação; UFSM; Programa de Pós-Graduação em Ciência da Computação; Centro de Tecnologia
• Palavras-chave em Português: Python; Simulação de espalhamento de doença; Sistemas inteligentes; PDSA-RS; Disease spreading simulation; Intelligent system; CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: http://repositorio.ufsm.br/handle/1/33356

The control and prevention of livestock diseases play a crucial role in safeguarding business continuity, in this context simulations for disease prevention and control are vital to mitigate future epidemics. In this sense, simulation systems can be an effective tool in simulating different forms of disease spread through the configuration of parameters that allow testing different prevention measures. This work aimed to assist in the model translation proposed by Cárdenas (2022) that simulates the spread of the disease AFTOSA (Foot and Mouth Disease) FMD in production animals and embark this model on the Animal Health Defense Platform of the State of Rio Grande do Sul (PDSA -RS). The stochastic model was developed in R; however, given the large amount of data and the intense processing of stochastic functions that simulate spreading and control actions, optimization was necessary. The model was translated into Python using packages focused on data analysis, aiming to speed up the system's execution time. Experiments were carried out comparing high computational cost functions executed in the R model in comparison with the new proposal implemented in Python. The results showed that rewriting the code in Python brings advantages such as runtime performance and file reading.