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.

An entomological survey was conducted from July-December 2009 and September-December 2010, as part of the epidemiological monitoring of American cutaneous leishmaniasis (ACL) in the municipality of Lábrea, state of Amazonas (AM), Brazil. Sandflies were collected using CDC light traps installed in intra and peridomiciliary locations, as well as the border of forested areas around houses where autochthonous cases of ACL were recorded. A total of 510 sandflies belonging to 26 species were collected. The most abundant species was Nyssomyia antunesi (44.5%) followed by Evandromyia walkeri (10.6%) and Micropygomyia rorotaensis (9.8%). Here we also describe Evandromyia (Aldamyia) apurinan sp. nov. and report new records for Trichophoromyia flochi and Evandromyia sipani in AM and Brazil, respectively. Our results describe the composition of the sandfly fauna in the south of AM and suggest Ny. antunesi as the putative vector in the transmission of Leishmania in this area of the Amazon Region.