WAMCAD Session November 2025
As the fight against malaria enters a new era of data-driven decision-making, the need for locally grounded modelling expertise has […]
As the fight against malaria enters a new era of data-driven decision-making, the need for locally grounded modelling expertise has […]
The LASU–ICAMMDA Symposium on Infectious Disease Modelling & Analytics has been rescheduled to September 9–10, 2025 at Lagos State University. Join leading experts and emerging professionals for two days of insights, collaboration, and hands-on learning.
ICAMMDA’s August 2025 Webinar, scheduled for 21 August 2025 at 12:00 Noon (WAT), will feature Surabhi Pandey, PhD, Founder of the BL Foundation for Social Initiatives, speaking on “Data Driven Disease Modelling: Lessons from India.” Dr. Pandey brings global expertise in health policy research and mathematical modelling, with leadership roles in TB-MAC and the Gates Foundation’s TB Vaccine Discovery collaboration.
Join ICAMMDA for its July 2025 Monthly Webinar featuring Prof. Matthew Ferrari of Penn State University as he explores the use of modelling to inform measles and rubella vaccination policies. The session will offer insights into data-driven strategies that shape global health decisions.
A 3-day hands-on training on spatial data analysis and Bayesian modelling held from December 18–20, 2023, facilitated by Dr. Abass Adigun at ICAMMDA.
A 3-day workshop focused on applying mathematical modelling and optimization to real-world challenges, held at ICAMMDA from November 27–29, 2023.
ICAMMDA participated in the inaugural WAMCAD Symposium held in Accra, Ghana, showcasing innovative research and strengthening regional collaborations in health modelling. The event brought together leading partners from West Africa and the global community, advancing the mission to build modelling capacity for public health impact.
This one-week training at ICAMMDA, led by Dr. Adigun Abass Bolaji, focused on equipping participants with practical skills in spatial data analysis and Bayesian modelling for public health research.