Analysis of the COVID-19, Outbreak in Brazil Using Topological Weighted Centroid: An Intelligent Geographic Information System Approach

This study used Topological Weighted Centroid (TWC) to analyze the Coronavirus outbreak in Brazil. This analysis only uses latitude and longitude in formation of the capitals with the confirmed cases on May 24, 2020 to illustrate the usefulness of TWC though any date could have been used. There are three types of TWC analyses, each type having five associated algorithms that produce fifteen maps, TWC-Original, TWC-Frequency and TWC-Windowing. We focus on TWC-Original to illustrate our approach. The TWC method without using the transportation information predicts the network for COVID-19 outbreak that matches very well with the main radial transportation routes network in Brazil.

Possible contribution of artificial neural networks and linear discriminant analysis in recognition of patients with suspected atrophic body gastritis

AIM: To investigate whether ANNs and LDA could recognize patients with ABG in a database, containing only clinical and biochemical variables, of a pool of patients with and without ABG, by selecting the most predictive variables and by reducing input data to the minimum.METHODS: Data was collected from 350 consecutive outpatients (263 with ABG, 87 with non-atrophic gastritis and/or celiac disease [controls]). Structured questionnaires with 22 items (anagraphic, anamnestic, clinical, and biochemical data) were filled out for each patient. All patients underwent gastroscopy with biopsies. ANNs and LDA were applied to recognize patients with ABG.Experiment 1: random selection on 37 variables, experiment 2: optimization process on 30 variables, experiment 3:input data reduction on 8 variables, experiment 4: use of only clinical input data on 5 variables, and experiment 5:use of only serological variables.RESULTS: In experiment 1, overall accuracies of ANNs and LDA were 96.6% and 94.6%, respectively, for predicting patients with ABG. In experiment 2, ANNs and LDA reached an overall accuracy of 98.8% and 96.8%,respectively. In experiment 3, overall accuracy of ANNs was 98.4%. In experiment 4, overall accuracies of ANNs and LDA were, respectively, 91.3% and 88.6%. In experiment 5, overall accuracies of ANNs and LDA were,respectively, 97.7% and 94.5%.CONCLUSION: This preliminary study suggests that advanced statistical methods, not only ANNs, but also LDA,may contribute to better address bioptic sampling during gastroscopy in a subset of patients in whom ABG may be suspected on the basis of aspecific gastrointestinal symptoms or non-digestive disorders.

Artifi cial neural networks in the recognition of the presence of thyroid disease in patients with atrophic body gastritis

AIM: To investigate the role of artifi cial neural networks in predicting the presence of thyroid disease in atrophic body gastritis patients. METHODS: A dataset of 29 input variables of 253 atrophic body gastritis patients was applied to artifi cial neural networks (ANNs) using a data optimisation procedure (standard ANNs,T&T-IS protocol,TWIST protocol). The target variable was the presence of thyroid disease. RESULTS: Standard ANNs obtained a mean accuracy of 64.4% with a sensitivity of 69% and a specifi city of 59.8% in recognizing atrophic body gastritis patients with thyroid disease. The optimization procedures (T&T-IS and TWIST protocol) improved the performance of the recognition task yielding a mean accuracy,sensitivity and specifi city of 74.7% and 75.8%,78.8% and 81.8%,and 70.5% and 69.9%,respectively. The increase of sensitivity of the TWIST protocol was statistically signifi cant compared to T&T-IS. CONCLUSION: This study suggests that artificial neural networks may be taken into consideration as a potential clinical decision-support tool for identifying ABG patients at risk for harbouring an unknown thyroid disease and thus requiring diagnostic work-up of their thyroid status.