The idea is to develop an artificial intelligence model capable of simultaneously analyzing data from the Laboratory Information System and from the Hospital Information System. This technology aims to enable the delivery to hospital physicians of a ranked list of antimicrobials that are more suitable to treat infection by multi-resistant microorganism with a focus on newborn and young children.

This project proposes the development of the One Health Brazilian Resistance (OneBR), a curated and integrated genomic database. OneBR will use algorithms based on artificial intelligence to conduct surveillance, diagnosis, management and treatment of antimicrobial resistance (AMR) in the human-animal-environment interface. The goal is for this platform to be used by Brazilian health professionals in diverse settings, particularly within the Unified Healthcare System (SUS).

The project proposes to characterize the resistant determinants of microbial communities from key sources in hospitals, environment and farms to model the dynamics of the flow of antibiotic resistant microorganisms. The goal is to understand how the hospital environment and animal farming affect the ecology of antibiotic resistance movement. The project will rely on a methodology that allows the analysis of genes related to antibiotic resistance in a complex microbial community derived from specific samples instead of culture based methods for AMR identification.

This project will test a sustainable solar oxidation system as a way to remove antibiotic resistant bacteria from wastewater. The hypothesis is that this technology can enable the inactivation of antibiotic resistant bacteria and the elimination of antibiotic resistant genes from effluents in Brazil.

The project proposes to use an aerobic granular sludge (AGS) - a technology based on microbial community - to remove antibiotics and antimicrobial resistant genes from hospital wastewater. AGS is one of the latest innovations and it has not yet been applied for the treatment of hospital wastewater.

Aims to validate the International Fetal and Newborn Growth Consortium for the 21st century (Intergrowth-21st) standards for gestational weight gain (GWG) and create new recommendations of GWG based on those standards for first trimester normal and overweight women to be used in the Brazilian Unified Health System (SUS). GWG recommendations currently used in SUS have not been properly tested or validated, thus the project might improve prenatal nutritional care and reduce post gestational weight retention.

The study aims to develop an Early Childhood Development friendly index (ECD-FI) based on a core set of evidence-based nurturing care indicators to assess the factors contributing to enabling environments and promote ECD at the municipal level by monitoring and identifying opportunities to scale up ECD programs. The index will be created through machine learning and will run analytical models considering demographic information and risk factors at the municipal level. This disaggregated data is not available in Brazil.

The project will develop a platform to provide services for decision-making support for neonatal death preventive actions by using data from CIDACS cohort. The platform will offer three services: cohort data visualization for decision-making support by comparative human visual analysis, prediction of risk of neonatal death based on machine learning models, and simulator of public policies impact influencing on the risk of neonatal death.

By analyzing national children vaccination coverage from spatial perspectives, the study aims to uncover insights into the traditional surveillance. This will help to identify coverage rates, regions of greater vulnerability by providing a differentiated look at the logic of equity in health. Understanding the low childhood vaccination coverage will help to guide public policies for the purpose of interventions.

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 proposal will develop a platform for the analysis and visualization of data that will allow managers, public servants and other stakeholders involved in the Mãe Coruja Program at Pernambuco state (PMCP) to extract strategic information to improve the intervention. The focus will be on the implementation and actual enforcement of public policies, considering the high gestational risk and sexually transmitted infections (STI). Currently, health databases are for consultations only. The innovation of this proposal is to create an intelligent cloud platform for the analysis and distribution of health information to improve health care of women enrolled in PMCP.

Infectious diseases may have only transitory impacts on pregnant mothers, but they can have lasting impacts on children. Can public interventions mitigate these impacts? This project aims to identify how exposure to localized epidemiological risk factors in the fetal period influences developmental outcomes for children through the early years of life. The researchers propose to evaluate in what extent the access to primary health care and social welfare programs mitigate negative impacts in child development.