Epidemiological inquiries study and evaluate the health status of the population. For dental caries, the World Health Organization (WHO) recommends the DMFT and DMFS indexes, which represent the sum of the decayed, missing and filled teeth, divided by the population studied. Traditionally these surveys are conducted using cellulose paper sheet. This study describes the development and presents the field performance of NutriOdonto, a software created for an Oral Health Survey carried out in 2018 and 2019 involving 2578 students from the municipal schools of Palmas/TO, located in the Brazilian Amazon region. This is a descriptive, applied research on the development of a software for the collecting, analysis, management and reproducibility of oral health epidemiological research. A software applied to the collecting, analysis and formation of the database was developed through the information obtained from the questionnaires applied to the participants of the study and the completion of the electronic oral examination form. Recent Information and Communication Technologies (ICT) are intelligently configured to create models and mobile applications (Apps) that can be useful to manage health issues, thus broadening the perspective of service provision in this sector. Some of these mobile devices, tablets and smartphones are being developed to generate information, for collection, recording, storage and analysis of oral health epidemiological research data. NutriOdonto contributed to the rapid collection, recording and storage of information, in the construction of the database and its analysis. Replacing paper forms with electronic forms minimized possible typos, reduced the use of cellulose paper and the financial costs, among other things. This software can contribute to decision making by managers and professionals and to improving the planning and implementation of actions in health promotion and oral disease prevention.

This work aimed to apply the ARIMA model to predict the under-reporting of new Hansen’s disease cases during the COVID-19 pandemic in Palmas, Tocantins, Brazil. This is an ecological time series study of Hansen’s disease indicators in the city of Palmas between 2001 and 2020 using the autoregressive integrated moving averages method. Data from the Notifiable Injuries Information System and population estimates from the Brazilian Institute of Geography and Statistics were collected. A total of 7035 new reported cases of Hansen’s disease were analyzed. The ARIMA model (4,0,3) presented the lowest values for the two tested information criteria and was the one that best fit the data, as AIC = 431.30 and BIC = 462.28, using a statistical significance level of 0.05 and showing the differences between the predicted values and those recorded in the notifications, indicating a large number of under-reporting of Hansen’s disease new cases during the period from April to December 2020. The ARIMA model reported that 177% of new cases of Hansen’s disease were not reported in Palmas during the period of the COVID-19 pandemic in 2020. This study shows the need for the municipal control program to undertake immediate actions in terms of actively searching for cases and reducing their hidden prevalence.

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O crescimento populacional mundial e a respectiva concentração de grande parte destas pessoas nas áreas urbanas gera desafios para os gestores. Neste contexto, surgem as cidades inteligentes, que são definidas como um novo método para o desenvolvimento sustentável das cidades. Este artigo analisa as concepções conceituais quanto às cidades inteligentes comparado ao crescimento populacional urbano das cidades e discutindo se o processo de expansão territorial e a verticalização das cidades. A problemática desenvolvida procura discutir se de fato as cidades inteligentes são o caminho para o preparo do crescimento populacional. Os resultados mostram que há significativa hipótese para essa condição.

Despite the large number of techniques and applications in the field of image segmentation, it is still an open research field. A recent trend in image segmentation is the usage of graph theory. This work proposes an approach which combines community detection in multiplex networks, in which a layer represents a certain image feature, with super pixels. There are approaches for the segmentation of images of good quality that use a single feature or the combination of several features of the image forming a single graph for the detection of communities and the segmentation. However, with the use of multiplex networks it is possible to use more than one image feature without the need for mathematical operations that can lead to the loss of information of the image features during the generation of the graphs. Through the related experiments, presented in this work, it is possible to identify that such method can offer quality and robust segmentations.