相关论文: Estimation of underreporting in Brazilian tubercul…

Missing data are ubiquitous in public health research. When estimating causal effects, there are well-established methods to address bias to due missing outcomes. Commonly, causal estimands are defined under hypothetical interventions to…

This paper proposes a data-driven approximate Bayesian computation framework for parameter estimation and uncertainty quantification of epidemic models, which incorporates two novelties: (i) the identification of the initial conditions by…

Molecular structures of RNA molecules reconstructed from X-ray crystallography frequently contain errors. Motivated by this problem we examine clustering on a torus since RNA shapes can be described by dihedral angles. A previously…

The largest collection of medical evidence in the world is PubMed. However, the significant barrier in accessing and extracting information is information organization. A factor that contributes towards this barrier is managing medical…

Textual health records of cancer patients are usually protracted and highly unstructured, making it very time-consuming for health professionals to get a complete overview of the patient's therapeutic course. As such limitations can lead to…

Deep learning (DL) has drawn tremendous attention in object localization and recognition for both natural and medical images. U-Net segmentation models have demonstrated superior performance compared to conventional handcrafted…

Crohn's disease and intestinal tuberculosis share many overlapping features such as clinical, radiological, endoscopic, and histological features - particularly granulomas, making it challenging to clinically differentiate them. Our…

At the end of 2019, the latest novel coronavirus Sars-CoV-2 emerged as a significant acute respiratory disease that has become a global pandemic. Countries like Brazil have had difficulty in dealing with the virus due to the high…

In this paper, we use a Bayesian method to estimate the effective reproduction number (R(t)), in the context of monitoring the time evolution of the COVID-19 pandemic in Brazil at different geographic levels. The focus of this study is to…

Tuberculosis (TB) is a chronic lung disease that occurs due to bacterial infection and is one of the top 10 leading causes of death. Accurate and early detection of TB is very important, otherwise, it could be life-threatening. In this…

his paper describes and analyzes the spatial spread of tuberculosis (TB) on complex metapopulation, that is, networks of populations connected by migratory flows whose configurations are described in terms of connectivity distribution of…

Clustering is a crucial task in various domains of knowledge, including medicine, epidemiology, genomics, environmental science, economics, and visual sciences, among others. Methodologies for inferring the number of clusters have often…

The estimate of the cumulative incidence of SARS-CoV-2 of 76% in Manaus by October 2020 by Buss et al. relies on the assumption of an exponentially-declining probability of sero-reversion over time. We present an alternative,…

The fragmentation of public data in Brazil, coupled with inconsistent standards and limited interoperability, hinders effective research, evidence-based policymaking and access to data-driven insights. To address these issues, we introduce…

Automatic classification of active tuberculosis from chest X-ray images has the potential to save lives, especially in low- and mid-income countries where skilled human experts can be scarce. Given the lack of available labeled data to…

This paper has a twofold contribution. The first is a data driven approach for predicting the Covid 19 pandemic dynamics, based on data from more advanced countries. The second is to report and discuss the results obtained with this…

Tuberculosis (TB), a bacterial disease mainly affecting the lungs, is one of the leading infectious causes of mortality worldwide. To prevent TB from spreading within the body, which causes life-threatening complications, timely and…

Predicting an infectious disease can help reduce its impact by advising public health interventions and personal preventive measures. Novel data streams, such as Internet and social media data, have recently been reported to benefit…

We apply optimal control theory to a tuberculosis model given by a system of ordinary differential equations. Optimal control strategies are proposed to minimize the cost of interventions, considering reinfection and post-exposure…

In conducting preliminary analysis during an epidemic, data on reported disease cases offer key information in guiding the direction to the in-depth analysis. Models for growth and transmission dynamics are heavily dependent on preliminary…