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.

The economic and social impacts due to diseases transmitted by mosquitoes in the latest years have been significant. Currently, no specific treatment or commercial vaccine exists for the control and prevention of arboviruses, thereby making entomological characterization fundamental in combating diseases such as dengue, chikungunya, and Zika. The morphological identification of mosquitos includes a visual exam of the samples. It is time consuming and requires adequately trained professionals. Accordingly, the development of a new automated method for realizing mosquito-perception and-classification is becoming increasingly essential. Therefore, in this study, a computational model based on a convolutional neural network (CNN) was developed to extract features from the images of mosquitoes and then classify the species Aedes aegypti, Aedes albopictus, and Culex quinquefasciatus. In addition, the model was trained to detect the mosquitoes of the genus Aedes. To train CNNs to perform the automatic morphological classification of mosquitoes, a dataset, which included 7,561 images of the target mosquitoes and 1,187 images of other insects, was acquired. Various neural networks, such as Xception and DenseNet, were used for developing the automatic-classification model based on images. A structured optimization process of random search and grid search was developed to select the hyperparameters set and increase the accuracy of the model. In addition, strategies to eliminate overfitting were implemented to increase the generalization of the model. The optimized model, during the test phase, obtained the balanced accuracy (BA) of 93.5% in classifying the target mosquitoes and other insects and the BA of 97.3% in detecting the mosquitoes of the genus Aedes in comparison to Culex. The results provide fundamental information for performing the automatic morphological classification of mosquito species. Using a CNN-embedded entomological tool is a valuable and accessible resource for health workers and non-taxonomists for identifying insects that can transmit infectious diseases.

La politique du ministère fronçais de l'Éducation nationale pour la santé scolaire prévoit une contribution des disciplines scolaires à l'éducation à la santé. L'article vise à signaler l'importance des travaux disponibles sur l'expérience des Etats-Unis et du Royaume-Uni dans ce domaine, et à en dégager les principaux thèmes. L'intégration de l'éducation à la santé aux disciplines scolaires est examinée sous différents angles : les enjeux, les dispositifs, les acteurs et les contenus. Un consensus semble acquis pour la référence à une acception élargie de la santé (aux dimensions psychologique et sociale). Par contre, la variété des intervenants et de leurs formations est très grande, ainsi que celle des champs et disciplines d'enseignement impliqués et de leurs modalités d'implication. L'indétermination qui en résulte nuit aux mises en œuvre. Il est souhaité que des recherches en didactiques accompagnent les nouveaux enseignements et les formations.

The SARS-CoV-2 pandemic in Brazil has grown rapidly since the first case was reported on 26 February 2020. As the pandemic has spread, the low availability of medical equipment has increased, especially mechanical ventilators. The Brazilian Unified Health System (SUS) claimed to have only 40,508 mechanical ventilators, which would be insufficient to support the Brazilian population at the pandemic peak. This lack of ventilators, especially in public hospitals, required quick, assertive, and effective actions to minimize the health crisis. This work provides an overview of the rapid deployment of a network for maintaining disused mechanical ventilators in public and private healthcare units in some regions of Brazil during the SARS-CoV-2 pandemic. Data referring to the processes of maintaining equipment, acquiring parts, and conducting national and international training were collected and analyzed. In total, 4047 ventilators were received by the maintenance sites, and 2516 ventilators were successfully repaired and returned to the healthcare units, which represents a success rate of 62.17%. The results show that the maintenance initiative directly impacted the availability and reliability of the equipment, allowing access to ventilators in the public and private health system and increasing the capacity of beds during the pandemic.

Artificial intelligence has many fields of application with an increasing computational processing power, and the algorithms are reaching human performance on complex tasks. Entomological characterization of insects represents an essential activity to drive actions to control the vector-borne diseases. Identification of the species and sex of insects is essential to map and organize the control measurements by the public health system in most areas where transmission is actively occurring. In many places in the world, the methodology done for identification of the mosquitos is by visual examination from human trained researchers or technicians. This activity is time-consuming and requires several years of experience to have skills to do the job. This chapter addresses the application of artificial intelligence for identification of mosquitos associated with vector-borne diseases. Benefits, limitations, and challenges of the use of artificial intelligence on the control of vector-borne diseases are discussed in this review.