Estimation of the SIR model parameters using neural networks

dc.contributor.advisorCoelho, Flávio Codeço
dc.contributor.authorMoreno Junior, Valter de Assis
dc.contributor.memberCarvalho, Luiz Max Fagundes de
dc.contributor.memberCunha Junior, Américo
dc.contributor.memberGomes, Marcelo
dc.contributor.unidadefgvEscolas::EMAppor
dc.date.accessioned2021-12-13T18:22:18Z
dc.date.available2021-12-13T18:22:18Z
dc.date.issued2021-05-21
dc.degree.date2021-05-21
dc.description.abstractIn the last decades, dengue fever has become the most prevalent epidemic disease caused by an arborvirus in the world. Its socio-economic impact has been especially overloading to developing countries, which struggle with the lack of appropriate resources and policies to contain the disease. Good planning has been essential to this end and dramatically benefits from outbreak forecasts. Over time, several deterministic and stochastic mathematical models of dengue epidemics have been proposed. However, the methods used to estimate their parameters usually require complex calculations and strong distributional assumptions that may not be realistic. The goal of this study was to develop a data-driven method to estimate the parameters of epidemiological models using Machine Learning and Artificial Neural Networks (ANNs) that could circumvent such demands. To accomplish this, we created a data set of infectives time series generated with SIR models using parameters derived from previous dengue epidemics and additional random noise. We used the data to train and validate several neural network configurations using the Mean Squared Error (MSE) and the Mean Absolute Percentage Error (MAPE) as the loss function. The test of the best models showed that the MAPE network tended to estimate SIR models that fitted the data better than the MSE network. We then applied the MAPE model to the time series of dengue epidemics that occurred in Brazilian state capitals between 2007 and 2020. The overall results indicate that ANN data-driven estimation methods can be used to fit a deterministic epidemiological model to noisy data, at least in cases where the dynamic processes that underlie the generation of observations are similar to those specified in the model.eng
dc.identifier.urihttps://hdl.handle.net/10438/31394
dc.language.isoeng
dc.subjectDenguepor
dc.subjectEpidemicseng
dc.subjectArtificial neural networkseng
dc.subjectEpidemiological modelseng
dc.subjectParameter estimationeng
dc.subject.areaMatemáticapor
dc.subject.bibliodataDenguepor
dc.subject.bibliodataEpidemias - Modelos matemáticospor
dc.subject.bibliodataRedes neurais (Computação)por
dc.subject.bibliodataAnálise de séries temporaispor
dc.subject.bibliodataAprendizado do computadorpor
dc.titleEstimation of the SIR model parameters using neural networkseng
dc.typeDissertationeng
Arquivos
Pacote Original
Agora exibindo 1 - 1 de 1
Carregando...
Imagem de Miniatura
Nome:
Thesis_EMAp__complete.pdf
Tamanho:
15.64 MB
Formato:
Adobe Portable Document Format
Descrição:
PDF
Licença do Pacote
Agora exibindo 1 - 1 de 1
Nenhuma Miniatura disponível
Nome:
license.txt
Tamanho:
4.6 KB
Formato:
Item-specific license agreed upon to submission
Descrição: