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Awarded Grants

Grand Challenges is a family of initiatives fostering innovation to solve key global health and development problems. Each initiative is an experiment in the use of challenges to focus innovation on making an impact. Individual challenges address some of the same problems, but from differing perspectives.

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Initiatives: Grand Challenges Brazil

An Artificial Intelligence System to Strengthen Antimicrobial Prescription in a Children's Hospital: SMART-EP

Marcelo Pillonetto, Pontifícia Universidade Católica do Paraná (Curitiba, Paraná, Brazil)
Nov 1, 2018
Grand Challenges Brazil> Drug Resistance Burden

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.

Application of Low-Cost and Sustainable Solar Oxidation Treatment to Prevent Microbial Resistance in Effluents in Brazil

Camila Amaral, Universidade Federal de Minas Gerais (Belo Horizonte, Minas Gerais, Brazil)
Nov 1, 2018
Grand Challenges Brazil> Drug Resistance Burden

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.

Applying the Metagenomic Approach for the Detection of EsβL- and Carbapenemase-Producing Enteric Pathogens Recovered from Different Hosts

Ana Gales, Universidade Federal de São Paulo (São Paulo, São Paulo, Brazil)
Nov 1, 2018
Grand Challenges Brazil> Drug Resistance Burden

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.

Assessing the Impact of Hospital-Based Breastfeeding Interventions on Infant Health

Cristiano Boccolini, Fiocruz (Rio de Janeiro, Rio de Janeiro, Brazil)
Nov 1, 2018
Grand Challenges Brazil> Data Science Approaches

Aims to access all 68.3 million living births certificates from Brazil, from 1994 to 2016, and compare them with breastfeeding policies in all Brazilian hospitals to assess the impact of the initiatives on infant health. The study also plans to estimate the number of avoidable deaths during this time period, if those initiatives were adopted in Brazil.

Data Science on Drug-Resistant Tuberculosis in Brazil

Rejane Pinheiro, Universidade Federal do Rio de Janeiro (Rio de Janeiro, Rio de Janeiro, Brazil)
Nov 1, 2018
Grand Challenges Brazil> Drug Resistance Burden

The researcher will use machine learning techniques and a linked database to analyze mortality from drug-resistant tuberculosis. The goal is to better understand how the flow of patients through the health services network have influenced, or not, the occurrence of resistance.

Data Science to Inform the Design and Evaluation of Interventions to Improve Perinatal Outcomes: Lessons from the Mãe Coruja Program

Jailson Correia, Municipal Health Secretariat (Recife, Pernambuco, Brazil)
Nov 1, 2018
Grand Challenges Brazil> Data Science Approaches

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.

Data-Driven Risk Stratification for Preterm Birth in Brazil: Development of a Machine Learning-Based Innovation for Health Care

Erika Thomaz, Universidade Federal do Maranhão (São Luís, Maranhão, Brazil)
Nov 1, 2018
Grand Challenges Brazil> Data Science Approaches

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.

Decision-Making Support Platform Based on Visual Analytics and Machine Learning to Subsidize Public Politics Focused on Gestational Health

Tiago Carvalho, Instituto Federal de São Paulo (Campinas, São Paulo, Brazil)
Nov 1, 2018
Grand Challenges Brazil> Data Science Approaches

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.

Early Childhood Development Friendly Index: Assessing the Enabling Environment for Nurturing Care in Brazilian Municipalities

Muriel Gubert, Universidade de Brasília (Brasília, Distrito Federal, Brazil)
Nov 1, 2018
Grand Challenges Brazil> Data Science Approaches

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.

Engineers, Pharmacists and Chemists Collaborating on the Development of an Aerobic Granular Sludge (AGS) to Remove Antibiotics from Hospital Wastewater

Leonardo Moura, Universidade Federal do Rio de Janeiro (Rio de Janeiro, Rio de Janeiro, Brazil)
Nov 1, 2018
Grand Challenges Brazil> Drug Resistance Burden

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.

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View the Grand Challenges partnership network

The Bill & Melinda Gates Foundation is part of the Grand Challenges partnership network. Visit grandchallenges.org to view the map of awarded grants across this network and grant opportunities from partners.