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Applied Mathematics for Well-Being: Studies Highlight the Impact of Digital Tools on Public Health

Researchers from FGV EMAp welcomed scientists from Bio-Manguinhos/Fiocruz to explore how mathematics can drive progress in Brazil’s public health system.

On the morning of January 21, researchers from the School of Applied Mathematics at Fundação Getulio Vargas (FGV EMAp) hosted a visit from researchers at Bio-Manguinhos/Fiocruz to explore potential synergies and areas for collaboration. During the meeting, FGV researchers presented projects based on machine learning and artificial intelligence designed to promote public health in Brazil.

Claudio Struchiner, Vice Director of FGV EMAp, emphasized that mathematical systems can play a key role in supporting decision-making around immunization and other public health topics. “The School was founded in 2011 with the goal of developing contemporary mathematics tailored to the challenges of the information and knowledge era,” he stated.

Mathematical Epidemiology

During the meeting with Bio-Manguinhos/Fiocruz, FGV researchers presented several projects with potential impact on the health sector.

FGV EMAp researcher Guilherme Goedert highlighted the importance of mathematical epidemiology, a field that quantitatively analyzes data on infectious diseases. This knowledge can support everything from patient care to pandemic preparedness.

“At FGV EMAp, we have a highly interdisciplinary team focused on strengthening the health system, evaluating public health interventions, and preparing society for future epidemics,” he explained.

Among the projects led by Goedert are studies aimed at preventing epidemics, improving cancer treatment, and supporting health policy decision-making.

In the field of epidemics, he highlighted the “COmVIDa” project, developed in partnership with Fiocruz and researcher Lara Coelho. The project conducted a serological study in one of the world’s largest favela complexes, Complexo da Maré, and analyzed the links between social inequality, epidemiological vulnerability, and the pandemic’s impact on food security.

“Using Big Data and Machine Learning, we can assess the pandemic’s impact on society and the health system across multiple dimensions,” he said.

FGV EMAp’s Mathematical Epidemiology group is already designing a protocol for the Rio de Janeiro State Health Department (SES) to implement near real-time syndromic surveillance—a strategy that detects clusters of clinical symptoms common to multiple diseases.

“Our machine learning model will help organize medical data and apply AI to accelerate the analysis of case clusters and atypical cases. This clustering enables faster mobilization of vector control teams, improves immunization efforts, and provides better data flows for mathematical and statistical models to assess epidemic scenarios and design interventions,” said the researcher.

These and other projects are part of the AutoAI-Pandemics working group, a partnership with the Institute of Mathematics and Computer Science at the University of São Paulo (ICMC-USP). It was the top-rated initiative selected to form the international network Artificial Intelligence for Pandemic and Epidemic Preparedness (AI4PEP).

This network, funded by Canada’s International Development Research Centre (IDRC), aims to develop solutions for challenges in the Global South to better prepare for future epidemics. In addition to developing AI-based tools for pandemic preparedness, the project seeks to reduce inequalities in access to these technologies.

Using mathematical models, researchers can also estimate how infections spread across an entire city based on specific scenarios—for example, understanding the effect of keeping schools closed to reduce virus transmission.

Goedert noted that technological tools also help extract data from unstructured medical records. “By training AI models, we can develop systems capable of automated analysis and intervention,” he explained.

Support for Cancer Treatment and Clinical Trials

According to Goedert, these systems have numerous applications. One is automating the monitoring of cancer patients—a major global public health issue, according to Brazil’s National Cancer Institute (INCA).

“Even with new treatments, mortality remains high in developing countries due to limited access, basic infrastructure challenges, and difficulties with data and indicators for managing and optimizing services,” he said.

The system developed by the researchers can extract and analyze hospital data from multiple sources to automatically monitor individual patients and alert medical teams to necessary interventions.

Researcher Luiz Max, also from FGV EMAp, added that mathematical models can help create more efficient and cost-effective tests for drug efficacy and side effects.

“We combine historical data with grounded methods to generate predictions about the side effects of certain substances,” he said.

Partnerships with Health Institutions

Given the wide range of technological solutions that can impact the health sector, Goedert emphasized the potential for collaboration with health research institutions to apply mathematical models in practice.

“We could implement new AI-based protocols to optimize patient care, enhance syndromic and epidemiological surveillance, and develop and validate new testing protocols,” he suggested.

Cintia Nunes, Advisor to the Vice Directorate of Innovation at Bio-Manguinhos/Fiocruz, believes that public health gains agility when it incorporates other fields of knowledge, such as applied mathematics.

“From a market and technology forecasting perspective, applied mathematics allows us to act more strategically toward our goals. Mathematical and AI models contribute to the entire planning process for the future of public health,” she said.

To learn more about applied mathematics projects with potential impact on the health sector, explore the links below: