Prediction of malaria using deep learning models: A case study on city clusters in the state of Amazonas, Brazil, from 2003 to 2018

• Autor(a) principal: Barboza,Matheus Félix Xavier
• Data de Publicação: 2022
• Outros Autores: Monteiro,Kayo Henrique de Carvalho, Rodrigues,Iago Richard, Santos,Guto Leoni, Monteiro,Wuelton Marcelo, Figueira,Elder Augusto Guimaraes, Sampaio,Vanderson de Souza, Lynn,Theo, Endo,Patricia Takako
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Revista da Sociedade Brasileira de Medicina Tropical
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0037-86822022000100324
• topic: Malaria; Machine learning; Deep learning; Prediction; LSTM; GRU

ABSTRACT Background: Malaria is curable. Nonetheless, over 229 million cases of malaria were recorded in 2019, along with 409,000 deaths. Although over 42 million Brazilians are at risk of contracting malaria, 99% percent of all malaria cases in Brazil are located in or around the Amazon rainforest. Despite declining cases and deaths, malaria remains a major public health issue in Brazil. Accurate spatiotemporal prediction of malaria propagation may enable improved resource allocation to support efforts to eradicate the disease. Methods: In response to calls for novel research on malaria elimination strategies that suit local conditions, in this study, we propose machine learning (ML) and deep learning (DL) models to predict the probability of malaria cases in the state of Amazonas. Using a dataset of approximately 6 million records (January 2003 to December 2018), we applied k-means clustering to group cities based on their similarity of malaria incidence. We evaluated random forest, long-short term memory (LSTM) and dated recurrent unit (GRU) models and compared their performance. Results: The LSTM architecture achieved better performance in clusters with less variability in the number of cases, whereas the GRU presents better results in clusters with high variability. Although Diebold-Mariano testing suggested that both the LSTM and GRU performed comparably, GRU can be trained significantly faster, which could prove advantageous in practice. Conclusions: All models showed satisfactory accuracy and strong performance in predicting new cases of malaria, and each could serve as a supplemental tool to support regional policies and strategies.