Publications by authors named "Douglas A Augusto"
Article Synopsis
- * Researchers utilized phylogenetic and epidemiological models to map YFV transmission patterns over different epidemic seasons and identified areas of high infection risk linked to low vaccination rates in major urban centers.
- * By analyzing the genomic data, the study revealed three distinct YFV lineages and demonstrated the connectivity between the endemic North and the extra-Amazonian region, suggesting that genomics combined with eco-epidemiology can enhance understanding and strategies for controlling the virus.
The lack of georeferencing in geospatial datasets hinders the accomplishment of scientific studies that rely on accurate data. This is particularly concerning in the field of health sciences, where georeferenced data could lead to scientific results of great relevance to society. The Brazilian health systems, especially those for Notifiable Diseases, in practice do not register georeferenced data; instead, the records indicate merely the municipality in which the event occurred.
View Article and Find Full Text PDFThe well-being of human and wildlife health involves many challenges, such as monitoring the movement of pathogens; expanding health surveillance; collecting data and extracting information to identify and predict risks; integrating specialists from different areas to handle data, species and distinct social and environmental contexts; and the commitment to bringing relevant information to society. In Brazil, there is still the difficulty of building a system that is not impaired by its large territorial extension and its poorly integrated sectoral policies. The Brazilian Wildlife Health Information System, SISS-Geo (SISS-Geo is the abbreviation of "" (which translates to "Georeferenced Wildlife Health Information System") and can be accessed at http://www.
View Article and Find Full Text PDFObjective: This paper describes NICeSim, an open-source simulator that uses machine learning (ML) techniques to aid health professionals to better understand the treatment and prognosis of premature newborns.
Methods: The application was developed and tested using data collected in a Brazilian hospital. The available data were used to feed an ML pipeline that was designed to create a simulator capable of predicting the outcome (death probability) for newborns admitted to neonatal intensive care units.