Purpose In late 2019, the SARS-CoV-2 virus spread worldwide. The virus has high rates of proliferation and causes severe respiratory symptoms, such as pneumonia. The standard diagnostic method for pneumonia is chest X-ray image. There are many advantages to using COVID-19 diagnostic X-rays: low cost, fast, and widely available. Methods We propose an intelligent system to support diagnosis by X-ray images. We tested Haralick and Zernike moments for feature extraction. Experiments with classic classifiers were done. Results Support vector machines stood out, reaching an average accuracy of 89.78%, average sensitivity of 0.8979, and average precision and specificity of 0.8985 and 0.9963, respectively. Conclusion Using features based on textures and shapes combined with classical classifiers, the developed system was able to differentiate COVID-19 from viral and bacterial pneumonia with low computational cost.

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A new kind of coronavirus, the SARS-Cov2, started the biggest pandemic of the century. It has already killed more than 250,000 people. Because of this, it is necessary quick and precise diagnosis test. The current gold standard is the RT-PCR with DNA sequencing and identification, but its results takes too long to be available. Tests base on IgM/IgG antibodies have been used, but their sensitivity and specificity may be very low. Many studies have been demonstrating the Covid-19 impact in hematological parameters. This work proposes an intelligent system to support Covid-19 diagnosis based on blood testing. We tested several machine learning methods, and we achieved high classification performance: 95.159% ± 0.693 of overall accuracy, kappa index of 0.903 ± 0.014, sensitivity of 0.968 ± 0.007, precision of 0.938 ± 0.010 and specificity of 0.936 ± 0.011. These results were achieved using classical and low computational cost classifiers, with Bayes Network being the best of them. In addition, only 24 blood tests were needed. This points to the possibility of a new rapid test with low cost. The desktop version of the system is fully functional and available for free use. *

In late 2019, the SARS-Cov-2 spread worldwide. The virus has high rates of proliferation and causes severe respiratory symptoms, such as pneumonia. There is still no specific treatment and diagnosis for the disease. The standard diagnostic method for pneumonia is chest X-ray image. There are many advantages to using Covid-19 diagnostic X-rays: low cost, fast and widely available. We propose an intelligent system to support diagnosis by X-ray images.We tested Haralick and Zernike moments for feature extraction. Experiments with classic classifiers were done. Support vector machines stood out, reaching an average accuracy of 89.78%, average recall and sensitivity of 0.8979, and average precision and specificity of 0.8985 and 0.9963 respectively. The system is able to differentiate Covid-19 from viral and bacterial pneumonia, with low computational cost.

Background: Periodically, humanity is often faced with new and emerging viruses that can be a significant global threat. It has already been over a century post—the Spanish Flu pandemic, and we are witnessing a new type of coronavirus, the SARS-CoV-2, which is responsible for Covid-19. It emerged from the city of Wuhan (China) in December 2019, and within a few months, the virus propagated itself globally now resulting more than 50 million cases with over 1 million deaths. The high infection rates coupled with dynamic population movement demands for tools, especially within a Brazilian context, that will support health managers to develop policies for controlling and combating the new virus.Methods: In this work, we propose a tool for real-time spatio-temporal analysis using a machine learning approach. The COVID-SGIS system brings together routinely collected health data on Covid-19 distributed across public health systems in Brazil, as well as taking to under consideration the geographic and time-dependent features of Covid-19 so as to make spatio-temporal predictions. The data are sub-divided by federative unit and municipality. In our case study, we made spatio-temporal predictions of the distribution of cases and deaths in Brazil and in each federative unit. Four regression methods were investigated: linear regression, support vector machines (polynomial kernels and RBF), multilayer perceptrons, and random forests. We use the percentage RMSE and the correlation coefficient as quality metrics.Results: For qualitative evaluation, we made spatio-temporal predictions for the period from 25 to 27 May 2020. Considering qualitatively and quantitatively the case of the State of Pernambuco and Brazil as a whole, linear regression presented the best prediction results (thematic maps with good data distribution, correlation coefficient >0.99 and RMSE (%) <4% for Pernambuco and around 5% for Brazil) with low training time: [0.00; 0.04 ms], CI 95%.Conclusion: Spatio-temporal analysis provided a broader assessment of those in the regions where the accumulated confirmed cases of Covid-19 were concentrated. It was possible to differentiate in the thematic maps the regions with the highest concentration of cases from the regions with low concentration and regions in the transition range. This approach is fundamental to support health managers and epidemiologists to elaborate policies and plans to control the Covid-19 pandemics.

Objective The new kind of coronavirus SARS-Cov2 spread to countries in all continents in the World. The coronavirus disease 2019 (Covid-19) causes fever, cough, sore throat, and in severe cases shortness of breath and death. To evaluate strategies, it is necessary to forecast the number of cases and deaths, in order to aid the stakeholders in the process of making decisions against the disease. We propose a system for real-time forecast of the cumulative cases of Covid-19 in Brazil. Study Design Monitoring of all Brazilian cities using oficial information from the National Notification System, from March to May 2020, concentrated on Brazil.io databases. Training and evaluation of ARIMA and other machine learning algorithms for temporal forecasting using correlation indexes (Pearson's, Spearman's, and Kendall's) and RMSE(%). Validation from the relative errors of the following six days. Methods Our developed software, COVID-SGIS, captures information from the 26 states and the Distrito Federal at the Brazil.io database. From these data, ARIMA models are created for the accumulation of confirmed cases and death cases by Covid-19. Finally, six-day forecasts graphs are available for Brazil and for each of its federative units, separately, with a 95% CI. In addition to these predictions, the worst and best scenarios are also presented. Results ARIMA models were generated for Brazil and its 27 federative units. The states of Bahia, Maranhao, Piaui, Rio Grande do Norte, Amapa, Rondonia every day of the predictions were in the projection interval. The same happened to the states of Espirito Santo, Minas Gerais, Parana and Santa Catarina. In Brazil, the percentage error between the predicted values and the actual values varied between 2.56% and 6.50%. For the days when the forecasts outside the prediction interval, the percentage errors in relation to the worst case scenario were below 5%. The states of Bahia, Maranhao, Piaui, Rio Grande do Norte, Amapa, and Rondonia every day of the predictions were in the projection interval. The same happened to the states of Espirito Santo, Minas Gerais, Parana and Santa Catarina. Conclusion The proposed method for dynamic forecasting may be used to guide social policies and plan direct interventions in a robust, flexible and fast way. Since it is based on information from multiple databases, it can be adapted to the different realities, becoming an important tool to guide the course of politics and action against Covid-19 pandemic worldwide.

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Purpose In December 2019, the Covid-19 pandemic began in the world. To reduce mortality, in addiction to mass vaccination, it is necessary to massify and accelerate clinical diagnosis, as well as creating new ways of monitoring patients that can help in the construction of specific treatments for the disease. Objective In this work, we propose rapid protocols for clinical diagnosis of Covid-19 through the automatic analysis of hematological parameters using Evolutionary Computing and Machine Learning. These hematological parameters are obtained from blood tests common in clinical practice. Method We investigated the best classifier architectures. Then, we applied the particle swarm optimization algorithm (PSO) to select the most relevant attributes: serum glucose, troponin, partial thromboplastin time, ferritin, D-dimer, lactic dehydrogenase, and indirect bilirubin. Finally, we used decision trees to build four rapid protocols for Covid-19 clinical diagnosis. Results We developed a web system for Covid-19 diagnosis support. Using a 100-tree Random Forest, we obtained results for accuracy, sensitivity and specificity superior to 99%. After feature selection, results were similar. The four empirical clinical protocols returned accuracies, sensitivities and specificities superior to 98%. Conclusion By using a reduced set of hematological parameters common in clinical practice, it was possible to achieve results of accuracy, sensitivity and specificity comparable to those obtained with RT-PCR. It was also possible to automatically generate clinical decision protocols, allowing relatively accurate clinical diagnosis even without the aid of the web decision support system.