Estimating time-varying epidemiological parameters and underreporting of Covid-19 cases in Brazil using a mathematical model with fuzzy transitions between epidemic periods.

Our study conducts a comprehensive analysis of the Covid-19 pandemic in Brazil, spanning five waves over three years. We employed a novel Susceptible-Infected-Recovered-Dead-Susceptible (SIRDS) model with a fuzzy transition between epidemic periods to estimate time-varying parameters and evaluate case underreporting. The initial basic reproduction number (R0) is identified at 2.44 (95% Confidence Interval (CI): 2.42-2.46), decreasing to 1.00 (95% CI: 0.99-1.01) during the first wave. The model estimates an underreporting factor of 12.9 (95% CI: 12.5-13.2) more infections than officially reported by Brazilian health authorities, with an increasing factor of 5.8 (95% CI: 5.2-6.4), 12.9 (95% CI: 12.5-13.3), and 16.8 (95% CI: 15.8-17.5) in 2020, 2021, and 2022 respectively. Additionally, the Infection Fatality Rate (IFR) is initially 0.88% (95% CI: 0.81%-0.94%) during the initial phase but consistently reduces across subsequent outbreaks, reaching its lowest value of 0.018% (95% CI: 0.011-0.033) in the last outbreak. Regarding the immunity period, the observed uncertainty and low sensitivity indicate that inferring this parameter is particularly challenging. Brazil successfully reduced R0 during the first wave, coinciding with decreased human mobility. Ineffective public health measures during the second wave resulted in the highest mortality rates within the studied period. We attribute lower mortality rates in 2022 to increased vaccination coverage and the lower lethality of the Omicron variant. We demonstrate the model generalization by its application to other countries. Comparative analyses with serological research further validate the accuracy of the model. In forecasting analysis, our model provides reasonable outbreak predictions. In conclusion, our study provides a nuanced understanding of the Covid-19 pandemic in Brazil, employing a novel epidemiological model. The findings contribute to the broader discourse on pandemic dynamics, underreporting, and the effectiveness of health interventions.

A systematic review and repeatability study on the use of deep learning for classifying and detecting tuberculosis bacilli in microscopic images.

Tuberculosis (TB) is among the leading causes of death worldwide from a single infectious agent. This disease usually affects the lungs (pulmonary TB) and can be cured in most cases with a quick diagnosis and proper treatment. Microscopic sputum smear is widely used to diagnose and manage pulmonary TB. Despite being relatively fast and low cost, it can be exhausting because it depends on manually counting TB bacilli (Mycobacterium tuberculosis) in microscope images. In this context, different Deep Learning (DL) techniques are proposed in the literature to assist in performing smear microscopy. This article presents a systematic review based on the PRISMA procedure, which investigates which DL techniques can contribute to classifying TB bacilli in microscopic images of sputum smears using the Ziehl-Nielsen method. After an extensive search and a careful inclusion/exclusion procedure, 28 papers were selected from a total of 400 papers retrieved from nine databases. Based on these articles, the DL techniques are presented as possible solutions to improve smear microscopy. The main concepts necessary to understand how such techniques are proposed and used are also presented. In addition, replication work is also carried out, verifying reproducibility and comparing different works in the literature. In this review, we look at how DL techniques can be a partner to make sputum smear microscopy faster and more efficient. We also identify some gaps in the literature that can guide which issues can be addressed in other works to contribute to the practical use of these methods in laboratories.

CML-Cardio: a cascade machine learning model to predict cardiovascular disease risk as a primary prevention strategy.

Cardiovascular diseases are among the leading causes of mortality worldwide, with more than 23 million related deaths per year by 2030, according to the World Heart Federation. Although most of these diseases may be prevented, population awareness strategies are still ineffective. In this context, we propose the CML-Cardio tool, a machine learning application to automate the risk classification process of developing CVDs. For this, researchers in our group collected data on diabetes, blood pressure, and other risk factors in a private company. Our final model consists of a cascade system to handle highly imbalanced data. In the first stage, a binary model is responsible for predicting whether a patient has a low risk of developing CVDs or if has a risk that needs attention. In this step, we use six algorithms: logistic regression, SVM, random forest, XGBoost, CatBoost, and multilayer perceptron. The better results presented an average accuracy of 0.86 ± 0.03 and f-score of 0.85 ± 0.04. We interpret each feature's impact on the models' output and validate the subsystem for the next step. In the second stage, we use an anomaly detection model to learn the intermediate risk patterns present in the instances that need attention. The cascade model presented an average accuracy of 0.80 ± 0.07 and f-score of 0.70 ± 0.07. Finally, we develop the CML-Cardio prototype of an actual application as a primary prevention strategy. Graphical abstract In this work, we propose the CML-Cardio tool, a cascade machine learning method to classify cardiovascular disease risk.

Explaining machine learning based diagnosis of COVID-19 from routine blood tests with decision trees and criteria graphs.

The sudden outbreak of coronavirus disease 2019 (COVID-19) revealed the need for fast and reliable automatic tools to help health teams. This paper aims to present understandable solutions based on Machine Learning (ML) techniques to deal with COVID-19 screening in routine blood tests. We tested different ML classifiers in a public dataset from the Hospital Albert Einstein, São Paulo, Brazil. After cleaning and pre-processing the data has 608 patients, of which 84 are positive for COVID-19 confirmed by RT-PCR. To understand the model decisions, we introduce (i) a local Decision Tree Explainer (DTX) for local explanation and (ii) a Criteria Graph to aggregate these explanations and portrait a global picture of the results. Random Forest (RF) classifier achieved the best results (accuracy 0.88, F1-score 0.76, sensitivity 0.66, specificity 0.91, and AUROC 0.86). By using DTX and Criteria Graph for cases confirmed by the RF, it was possible to find some patterns among the individuals able to aid the clinicians to understand the interconnection among the blood parameters either globally or on a case-by-case basis. The results are in accordance with the literature and the proposed methodology may be embedded in an electronic health record system.