Guilherme Lopes de Oliveira

Mathematics

Statistician Guilherme Lopes de Oliveira addresses the critical issue of underreporting in epidemiological and public health studies, a problem particularly prevalent in socially disadvantaged regions. His project focuses on this issue to test new data processing models where underreporting occurs. 

Born in Paraopeba and a passionate fan of Atlético Mineiro, he acquired his expertise in statistics at the Federal University of Minas Gerais, where he received his bachelor’s, master’s, and doctoral degrees. His doctoral thesis received significant recognition, including an honorable mention in the CAPES Award and an award from the Brazilian Statistics Association.

A trailblazer in his family as the first to earn a PhD, Guilherme showed a passion for teaching at an early age, often making his younger sister his student. Now a professor at the Federal Technological Training Center of Minas Gerais, he continues to inspire new generations of statisticians and demonstrate the power of statistics to improve public health outcomes.

Open Calls

Science Call 7

Projects

Discrete spatiotemporal models with zero inflation: how to address underreporting of infectious disease cases in small areas?
Science / Mathematics

The United Nations Sustainable Development Goals (UN SDGs) for 2030 emphasize the importance of improving health and well-being around the world, especially in less developed regions. This requires reliable health data, but accurately tracking infectious diseases in these areas can be challenging due to underreporting and limited resources. This is particularly problematic when dealing with small populations, where a lack of reported cases can distort our understanding of the spread of disease.

This project aims to develop new statistical methods to address this challenge. We will build models that account for “zero inflation,” a common problem in these contexts where many regions may report zero cases of disease. These models will also consider how disease patterns vary over space and time. By providing more accurate estimates of disease incidence, these new methods will help us better understand the progress of health indicators, tailor public health interventions to specific regions, and guide resource allocation to effectively combat infectious diseases and achieve the UN SDGs.

Amount invested

Grant Serrapilheira: R$ 545.500,00 (R$395.500,000 + R$ 150.000,00 optional bonuses aimed at the integration and training of individuals from underrepresented groups in science)
  • Topics
  • Computational models
  • epidemiology
  • saúde pública