Dr. Miracle Amadi
Awards
About me
I am a Computational Scientist and Applied Mathematician with expertise in mathematical modelling, Bayesian inference, uncertainty quantification, and inverse problems. I hold a PhD in Computational Science from LUT University, Finland, and conduct interdisciplinary research at the interface of epidemiology, ecology, and data science. My work focuses on integrating mechanistic and data-driven approaches to understand complex biological and environmental systems. I have led and contributed to international research projects across Europe and Africa, and my doctoral research on hybrid modelling methods was recognised with the Finnish Inverse Problems Society Award in 2022. I am also actively involved in teaching, supervision, mentoring, and scientific service.
Dr. Miracle Chinalu Amadi is a Computational Scientist and Applied Mathematician whose research lies at the intersection of mathematical modelling, statistical inference, and computational methods for biological and ecological systems. She holds a Doctor of Science (Technology) in Computational Science from LUT University, Finland, and has built an interdisciplinary research career spanning applied mathematics, epidemiology, ecology, public health, and uncertainty quantification.
Her research focuses on the development of computational and statistical methods for understanding complex real-world systems, with particular interests in inverse problems, Bayesian inference, uncertainty quantification, mechanistic modelling, and the integration of data-driven and process-based approaches. She has applied these methods to a wide range of challenges, including infectious disease transmission, malaria epidemiology, vaccine-preventable diseases, ecological monitoring, and environmental systems. Her doctoral work on hybrid modelling methods for epidemiological studies was recognised with the Finnish Inverse Problems Society Award in 2022.
Dr. Amadi has led and contributed to several internationally funded research projects and has secured competitive research funding as principal investigator from major Finnish foundations. Her work has involved collaborations with researchers and institutions across Europe and Africa, reflecting her commitment to interdisciplinary and international research. Beyond her methodological contributions, she is particularly interested in developing computational tools that support evidence-based decision-making in public health and environmental management.
In addition to her research activities, Dr. Amadi has extensive experience in higher education teaching, supervision, and mentoring. She has taught courses in scientific computing, numerical simulation, data analysis, machine learning, and mathematical modelling, and has supervised students and early-career researchers at Bachelor’s, Master’s, and PhD levels. She is actively involved in capacity building in mathematical sciences and infectious disease modelling through collaborations with universities and research institutes in Africa and Europe.
Dr. Amadi serves as an Academic Editor for PLOS ONE and is a member of the Institutional Review Board of the AIMS Research and Innovation Centre, Rwanda. She also contributes to the scientific community through peer review, conference organization, editorial activities, and mentoring programmes. Her research has received international visibility through features by public health and scientific communication platforms, including the Finnish Institute for Health and Welfare (THL) and EurekAlert.
Through her research, teaching, and scientific service, Dr. Amadi aims to advance the use of mathematical and computational methods to address challenges in global health, environmental sustainability, and data-informed policy development.
Degrees:
1. Amadi MC, Vänskä S, Davidkin I, Haario H, Leino T, Melin M, Auranen K, et al. (2026). Sustaining herd immunity against measles: Insights from a serological cohort study in an outbreak-free population. Infection.
2. Amadi MC, Erandi KKWH. (2024). Assessing the relationship between malaria incidence levels and meteorological factors using cluster-integrated regression. BMC Infectious Diseases, 24, 664.
3. Amadi MC, Shcherbacheva A, Haario H. (2021). Agent-based modelling of complex factors impacting malaria prevalence. Malaria Journal, 20, 185.
4. Ganegoda NC, Wijaya KP, Amadi M, Erandi KKWH, Aldila D. (2021). Interrelationship between daily COVID-19 cases and average temperature as well as relative humidity in Germany. Scientific Reports, 11, 11302.
5. García-Merino JC, Amadi M, Haario H, Calvo-Jurado C, García-Macías E. (2026). Combining Adaptive MCMC and Nested Sampling for Robust Bayesian Model Selection with Reduced Prior Sensitivity. Statistics and Computing.
6. Renald E, Amadi M, Haario H, Buza J, Tchuenche JM, Masanja VG. (2025). A comparative approach of analyzing data uncertainty in parameter estimation for a Lumpy Skin Disease model. Computer Methods and Programs in Biomedicine Update, 7, 100178.
7. Mchwampaka JP, Amadi M, Mbare NS. (2025). Modeling the impact of antiretroviral therapy on HIV and related kidney diseases in Tanzania. Scientific Reports, 15, 9094.
8. Landguth EL, Williams A, Roseman M, Amadi M, et al. (2026). Simulating the landscape eco-evolution of host-pathogen systems with CDMetaPOP. Ecological Informatics.
A complete list of publications is available via my Google Scholar profile and LUT Research Portal:
Google Scholar: https://scholar.google.com/citations?user=bYZyA7IAAAAJ&hl=fi
LUT Research Portal: https://www.lut.fi/en/profiles/miracle-amadi