Audêncio Victor
BSc, PGDip, MPH, MBA, PhD
Researcher • Epidemiologist • RWE • HEOR • HTA
Evidence Synthesis • Data Science & Big Data • Artificial Intelligence • Predictive Models • Machine Learning • Data Analysis (R, Python, Stata)
BSc, PGDip, MPH, MBA, PhD
Evidence Synthesis • Data Science & Big Data • Artificial Intelligence • Predictive Models • Machine Learning • Data Analysis (R, Python, Stata)
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 am currently a Researcher in Health Data Science at the London School of Hygiene & Tropical Medicine (LSHTM), University of London, where I contribute to a Wellcome Trust–funded project on predictive modelling for stillbirths and neonatal deaths across Sub-Saharan Africa, teach the postgraduate course Statistical Methods in Epidemiology, and supervise MSc dissertations. I am also a Visiting Researcher at PROADI-SUS, Hospital Israelita Albert Einstein (São Paulo), conducting methodological research on non-communicable disease surveillance, multimodal health-survey strategies, and predictive modelling using national surveillance data.
I hold a PhD in Public Health – Epidemiology from the University of São Paulo (USP), with a visiting fellowship at LSHTM. I also 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 Postgraduate Diploma in Public Health (Monitoring, Evaluation & Strategic Information, UFBA). I have completed MBAs in Data Science & Analytics (USP-Esalq), Artificial Intelligence & Big Data (ICMC-USP), and Project Management (USP-Esalq), and a specialization in Health Economics & HTA (UNICAMP, ongoing).
Professionally, I worked as a Technical Consultant (Epidemiologist and Data Scientist) at the World Health Organization (PAHO/WHO), supporting Brazil's Ministry of Health with COVID-19 data analysis and policy guidance. At the São Paulo State Health Department I conducted spatial analyses, disease forecasting (meningitis, influenza, COVID-19), and predictive modelling. As Scientific Curator and Data Analyst at Pacto Contra a Fome, I translated complex evidence into actionable insights for national food-security policy. Earlier, I worked as a nutritionist for the Ministry of Health in Mozambique, leading district-level nutrition programmes in Maganja da Costa and contributing to national malnutrition studies with UNICEF and the World Food Programme (WFP).
I am an active member of research groups including LABDAPS (USP), the MARCH Centre (LSHTM), Rede CoVida, and the Epidemiology, Statistics and Applied Mathematics group at UFMS, and I serve as a reviewer for many national and international journals. My research focuses on quantitative methods applied to public health — descriptive, inferential and Bayesian statistics, mathematical modelling, and predictive machine-learning approaches. In daily work I use SPSS, Stata, R and Python, along with Git/GitHub, R Markdown, LaTeX, Quarto, MLflow, REDCap and KoBoToolbox to support reproducible research.
I am always open to discussing research collaborations, consulting opportunities, and data science projects in public health. Feel free to reach out. References available upon request.