Facilitators & hosts

Dr. James Azam

Centre for Mathematical Modelling of Infectious Diseases (CMMID)

London School of Hygiene and Tropical Medicine

London, United Kingdom

I am a Research Fellow at the London School of Hygiene and Tropical Medicine, based at the Centre for Mathematical Modelling of Infectious Diseases (CMMID), where I work on modelling the spatial spread and control of infectious diseases, including polio and measles. Previously, I was a Research Software Engineer with the Epiverse-TRACE Initiative, developing and maintaining R packages such as epichains and EpiNow2 for reproducible analytics during epidemics.  I develop health data science methods, models, and open-source software that help public health teams understand disease transmission and respond to outbreaks. I hold a PhD in outbreak response modelling and decision-making, with a focus on quantifying the impact of pharmaceutical and non-pharmaceutical interventions against vaccine-preventable diseases. I am the lead facilitator on MPPR, where I coordinate the scientific content and teach on topics such as introduction to infectious disease models, model fitting and calibration, and co-supervise the capstone projects. See my personal website for more information: https://jamesmbaazam.github.io

Vincent Donkoh

National Institute for Mathematical Sciences (NIMS)

Kwame Nkrumah University of Science and Technology (KNUST)

Kumasi, Ghana

I am a PhD Candidate in Scientific Computing and Industrial Modelling at the National Institute for Mathematical Sciences (NIMS), Kwame Nkrumah University of Science and Technology (KNUST), and am affiliated with the German-West African Centre for Global Health and Pandemic Prevention (G-WAC). I hold an MPhil in Applied Mathematics, and my work spans mathematical modelling, scientific computing, optimization, data-driven systems, and software engineering, with a focus on developing computational solutions to real-world problems. My research and interdisciplinary work include infectious disease modelling and pandemic preparedness, automated timetabling and scheduling systems, optimization, and computational approaches to complex systems. I am also a software engineer with experience designing, developing, and deploying scalable digital platforms and information systems, particularly for education, research, and institutional applications. I have contributed to large-scale digital learning initiatives supporting learners and institutions across multiple countries, combining software engineering with learning technologies, data systems, and computational methods. My broader interests lie at the intersection of applied mathematics, computational science, software engineering, optimization, and data-driven technologies, particularly where these disciplines can be used to build practical systems for education, public health, and other complex real-world applications.

Andrzej Jarynowski

Department of Veterinary Medicine, Freie Universität Berlin

Wroclaw, Poland

Course Facilitator Polish State Sanitary Inspection/Wrocław Medical University G-WAC One Health / MPPR faculty Dr. Andrzej Jarynowski is a specialist in infectious disease modelling and computational epidemiology/epizootiology, an adjunct in the Division of Healthcare Innovation at Wroclaw Medical University, and an analyst at the Provincial Sanitary and Epidemiological Station in Wrocław. His work focuses on epidemiological data analysis, the study of physical social contacts, One Health, infodemiology, and the application of artificial intelligence tools in public health.  He has served as a correspondent, among others, for The Washington Post and Bloomberg on epizootiology in Central and Eastern Europe. In his work, he combines population-based, registry-based, and modelling perspectives with public health practice, risk analysis, and risk communication.

Opanin Adu Agyei

Kumasi, Ghana

I am a data-driven scientist with more than 5 years of hands-on experience in scientific computing. I have a strong technical background in Python and R, which I have extensively used to gain insights from high-dimensional data using a variety of statistical techniques and cutting-edge machine learning algorithms