Davis Amani

Davis Amani is a Tanzanian medical doctor, epidemiologist and global-health researcher whose work brings together cancer epidemiology, population health, implementation science, health systems, infectious diseases, reproductive health and advanced quantitative methods. He is pursuing doctoral training in Translational Medicine at Queen’s University and remains academically and professionally connected with the Muhimbili University of Health and Allied Sciences in Tanzania.

His research has developed across national surveillance systems, implementation studies, health-system evaluations, public-health programmes and secondary-data analyses. He has contributed to work on HIV and tuberculosis, HIV drug resistance, antenatal HIV and syphilis surveillance, NCDs, maternal and reproductive health, kidney health, respiratory and critical illness, early-childhood development, digital-health strategy and the organization of evidence for policy and practice. These experiences have increasingly converged around a central interest: understanding why health outcomes and access differ across populations and how rigorous evidence can help programmes and health systems respond.

A major strand of his current work uses large population datasets, including Demographic and Health Surveys, to study contraception, intimate-partner violence, women’s empowerment, reproductive decision-making, adolescent girls and young women, and health inequalities across countries and over time. His methodological interests include complex-survey analysis, multilevel and hierarchical modelling, pooled and country-specific analyses, reproducible R workflows, epidemiologic methods and the interpretation of heterogeneity across settings.

Within GHIRL, Davis contributes epidemiology, secondary-data research, scientific writing, research coordination and mentorship. His work links the lab’s population-data and methods platform with cancer, NCD, HIV/TB, reproductive-health and health-system questions, with the broader goal of building research systems that can move efficiently from data to evidence and from evidence to better decisions.