The yellow fever virus (YFV) caused a severe outbreak in Brazil in 2016–2018 that rapidly spread across the Atlantic Forest in its most populated region without viral circulation for almost 80 years. A comprehensive entomological survey combining analysis of distribution, abundance and YFV natural infection in mosquitoes captured before and during the outbreak was conducted in 44 municipalities of five Brazilian states. In total, 17,662 mosquitoes of 89 species were collected. Before evidence of virus circulation, mosquitoes were tested negative but traditional vectors were alarmingly detected in 82% of municipalities, revealing high receptivity to sylvatic transmission. During the outbreak, five species were found positive in 42% of municipalities. Haemagogus janthinomys and Hg. leucocelaenus are considered the primary vectors due to their large distribution combined with high abundance and natural infection rates, concurring together for the rapid spread and severity of this outbreak. Aedes taeniorhynchus was found infected for the first time, but like Sabethes chloropterus and Aedes scapularis , it appears to have a potential local or secondary role because of their low abundance, distribution and infection rates. There was no evidence of YFV transmission by Aedes albopictus and Aedes aegypti, although the former was the most widespread species across affected municipalities, presenting an important overlap between the niches of the sylvatic vectors and the anthropic ones. The definition of receptive areas, expansion of vaccination in the most affected age group and exposed populations and the adoption of universal vaccination to the entire Brazilian population need to be urgently implemented.

Dengue, chikungunya and Zika are arboviruses transmitted by mosquitos of the genus Aedes and have caused several outbreaks in world over the past ten years. Morphological identification of mosquitos is currently restricted due to the small number of adequately trained professionals. We implemented a computational model based on a convolutional neural network (CNN) to extract features from mosquito images to identify adult mosquitoes from the species Aedes aegypti, Aedes albopictus and Culex quinquefasciatus. To train the CNN to perform automatic morphological classification of mosquitoes, we used a dataset that included 4,056 mosquito images. Three neural networks, including LeNet, AlexNet and GoogleNet, were used. During the validation phase, the accuracy of the mosquito classification was 57.5% using LeNet, 74.7% using AlexNet and 83.9% using GoogleNet. During the testing phase, the best result (76.2%) was obtained using GoogleNet; results of 52.4% and 51.2% were obtained using LeNet and AlexNet, respectively. Significantly, accuracies of 100% and 90% were achieved for the classification of Aedes and Culex, respectively. A classification accuracy of 82% was achieved for Aedes females. Our results provide information that is fundamental for the automatic morphological classification of adult mosquito species in field. The use of CNN's is an important method for autonomous identification and is a valuable and accessible resource for health workers and taxonomists for the identification of some insects that can transmit infectious agents to humans.

Para determinar a prevalência de geo-helmintíases e identificar fatores associados a sua ocorrência, foram realizados inquéritos coprológicos em amostra de crianças entre 5 e 14 anos de idade, residentes em dez municípios brasileiros com baixo Índice de Desenvolvimento Humano. Aplicou-se questionário aos responsáveis, obtendo-se informações sócio-econômicas e ambientais e foi feita coleta de fezes. Estimaram-se prevalências de geohelmintos segundo variáveis de interesse e se avaliaram os fatores de risco mediante regressão logística multinível. Das 2.523 crianças estudadas, 36,5% eram portadoras de um ou mais geohelmintos (Ascaris lumbricoides 25,1%; ancilostomídeos 15,3%, Trichuris trichiura 12,2%). A proporção de geo-helmintíases para o conjunto na zona rural foi 45,7%; na urbana, 32,2%. Baixa renda familiar (OR = 1,75; 1,38-2,23), baixa escolaridade materna (OR = 1,69; 1,39-2,06), presença de lixo próximo ao domicílio (OR = 1,50; 1,22-1,84) e maior número de pessoas no domicílio (OR = 1,41; 1,17-1,71) mostraram-se associadas a tais infecções. Conclui-se que a ocorrência destas parasitoses está relacionada às condições sócio-econômicas e evidencia a importância de intervenções públicas direcionadas à melhoria das condições de vida para sua prevenção.

BackgroundThe identification of Trypanosoma cruzi and blood-meal sources in synanthropic triatomines is important to assess the potential risk of Chagas disease transmission. We identified T. cruzi infection and blood-meal sources of triatomines caught in and around houses in the state of Bahia, northeastern Brazil, and mapped the occurrence of infected triatomines that fed on humans and domestic animals.MethodsTriatominae bugs were manually captured by trained agents from the Epidemiologic Surveillance team of Bahia State Health Service between 2013 and 2014. We applied conventional PCR to detect T. cruzi and blood-meal sources (dog, cat, human and bird) in a randomized sample of triatomines. We mapped triatomine distribution and analyzed vector hotspots with kernel density spatial analysis.ResultsIn total, 5906 triatomines comprising 15 species were collected from 127 out of 417 municipalities in Bahia. The molecular analyses of 695 triatomines revealed a ~10% T. cruzi infection rate, which was highest in the T. brasiliensis species complex. Most bugs were found to have fed on birds (74.2%), and other blood-meal sources included dogs (6%), cats (0.6%) and humans (1%). Trypanosoma cruzi-infected triatomines that fed on humans were detected inside houses. Spatial analysis showed a wide distribution of T. cruzi-infected triatomines throughout Bahia; triatomines that fed on dogs, humans, and cats were observed mainly in the northeast region.ConclusionsSynanthropic triatomines have a wide distribution and maintain the potential risk of T. cruzi transmission to humans and domestic animals in Bahia. Ten species were recorded inside houses, mainly Triatoma sordida, T. pseudomaculata, and the T. brasiliensis species complex. Molecular and spatial analysis are useful to reveal T. cruzi infection and blood-meal sources in synanthropic triatomines, identifying areas with ongoing threat for parasite transmission and improving entomological surveillance strategies.

Introduction Leptospirosis has emerged as an important health problem in developing countries due to the growth of slum settlements worldwide, where poor sanitation favours rat-borne transmission. Large urban epidemics occur during seasonal periods of heavy rainfall. However, a detailed analysis has not been performed to determine how rainfall, as well as other climatic factors, specifically influences the risk of leptospirosis in these endemic settings. Methods We analysed data from 2083 leptospirosis cases which were identified during active population-based surveillance performed in the city of Salvador, Brazil between 1996 and 2010. Information on daily rainfall, humidity and temperature were obtained for the same period. A generalised additive model was fitted, using a negative binomial distribution for weekly aggregated data (729 weeks). We incorporated a non-parametric term to estimate the time trend and a sin-cosine term to control for seasonal confounding. Results Rainfall and humidity were positively associated with the number of cases two weeks later, linearly and without a threshold. Temperature protected, even though the range between maximum and minimum temperatures is small: from 22 to 338C. A decreasing trend was highly significant, possibly due to intervention of sewerage and garbage collection systems. Conclusion Leptospirosis is expected to become an increasingly important slum health problem as predicted global climate change and growth of the world's slum population evolves, and models adequate to estimate the impact of both environment and climate variables on incidence of all environmental related diseases should be incorporated in the epidemiologists toolbox.