The project will study the genetic material from environmental samples from humans (healthy and ill), cattle and their meat to estimate the proportion of E. coli and K. pneumoniae in the microbiome. The main objective is to better understand the distribution of bacteria and its resistance genes, Escherichia coli and Klebsiella pneumoniae bacteria and extended spectrum beta-lactamase (EsβL) and carbapenemases encoding genes in distinct ecological sources.

Identifying the preventable causes and performing early risk stratification of pregnant women are instrumental to develop strategies to prevent and reduce preterm birth (PTB). The ability to identify at-risk pregnancies and to enroll women in prevention strategies has been difficult due to complexity of associated risk factors. The study aims to combine different national level data sources to understand the main predictors of PTB and develop a machine-learning-based predictive model to conduct automated risk stratification at the point of care level, integrated with advanced data visualization for clinical decision support.

Studies show that seasonal influenza in Ceará, in the Northeast region of Brazil, occurs 2 to 3 months earlier than in the South and Southeast, which guides the national calendar of vaccination. By using data science approaches, the study will test if Brazil's current national policy targeting vaccination only during the months of April and May inadequately protects against the harmful maternal-fetal effects of influenza in the Semi-Arid and northern regions of Brazil. If the hypothesis confirms, the study has the potential to change policy and modify the vaccination calendar.

The study is aimed at evaluating the effectiveness of Mãe Coruja intervention in reducing low birthweight and preterm birth. By using appropriate statistical methods, the study will use the Cidacs dataset combined with the data from Mãe Coruja program to carry out the quasi-experimental study. With the support of machine learning techniques, the project will also Identify social, economic, geographic and environmental conditions that are associated with the outcomes. The researchers will also build an index of perinatal health risk to inform improvements in targeting populations and the deployment of similar strategies and programs elsewhere in Brazil.