Audêncio Victor

I am an epidemiologist, data scientist, economist, and public health nutritionist working with maternal and neonatal health, and machine learning applied to large health datasets. I have a PhD in Epidemiology at the University of São Paulo (USP), and the London School of Hygiene & Tropical Medicine (LSHTM) in the UK. I hold a Master's degree in Epidemiology from the Federal University of Bahia (UFBA), a Bachelor's degree in Nutrition from Lúrio University, and a specialization in Public Health with a focus on Monitoring, Evaluation, and Strategic Information (UFBA). I have completed an MBA in Data Science and Analytics at USP, an MBA in Artificial Intelligence and Big Data at the Institute of Mathematical and Computer Sciences (ICMC), and an MBA in Project Management (USP).

Professionally, I have worked as a Researcher at the London School of Hygiene & Tropical Medicine (LSHTM) in the UK, Technical Consultant in Health (Epidemiologist and Data Scientist) at the World Health Organization (WHO), where I provided technical consultancy to the Ministry of Health in Brazil, focusing on the analysis of COVID-19 indicators and translating data into actionable insights. At the São Paulo State Department of Health, I performed spatial analyses to identify high-risk areas, conducted nowcasting and forecasting for diseases such as meningitis, influenza, and COVID-19.

Additionally, as a Scientific Curator and Data Analyst at the NGO Pacto Contra a Fome — Brazil, I simplified complex scientific data to guide public policies on food security. Earlier in my career, I worked as a nutritionist for the Ministry of Health in Mozambique, managing district-level nutrition programs in Maganja da Costa and contributing to national studies on malnutrition prevalence in collaboration with UNICEF and the World Food Programme (WFP).

I am an active member of research groups such as LABDAPS (USP) and Rede-CoVida, and I serve as a reviewer for national and international scientific journals. I have a strong interest in quantitative methods applied to health, including descriptive, inferential, and Bayesian statistics, mathematical modeling, and predictive methods using machine learning algorithms. My goal is to contribute to global health by leveraging these skills. In my daily work, I utilize tools such as SPSS, STATA, R, Python, and QGIS to provide data-driven solutions to public health challenges.