Testimonials

5

I am writing to express my deep appreciation for the exceptional educational experience I received through the COHORT 2023 Mathematical Modelling Short Course. This course, conducted by The Institute for Disease Modeling (IDM) and generously funded by the Gates Foundation, has had a profound impact on my academic and professional journey.

The knowledge and skills acquired from the course have not only enriched my understanding of mathematical modeling but have also allowed me to actively contribute to addressing real-world challenges. I would like to share a specific example of how these skills were put into practice.

Recently, I had the privilege of participating in the Total Energies Revolution Hackathon Competition under OUTBOX HUB as a key member of Team Shalom. I am proud to share that our team emerged 3rd in the hackathon. Our mission was to predict optimal locations for electric vehicle (EV) charging points in the city of Kampala. To achieve this, we needed to develop a robust model that considered various factors influencing charging station placement.

Drawing upon the mathematical modeling skills I acquired during the IDM course, I played a central role in the development of our model. Specifically, I utilized Decision Tree algorithms and implemented a weighted factor analysis to model the complex decision-making process involved in selecting charging station locations. These skills enabled us to make informed and data-driven decisions, leading to a highly effective model.

I am deeply grateful to IDEMU, IDM, and the Gates Foundation for making the COHORT 2023 Mathematical Modelling Short Course a reality. This course has not only equipped me with valuable skills but has also empowered me to contribute meaningfully to projects that have a positive impact on society.

Thank you for your dedication to advancing the field of epidemiology and mathematical modeling. I look forward to continued learning and collaboration with IDEMU in the future.

Noah Kiwanuka

Kiwanuka Noah Ssekamatte

Pharmacist

5

Am writing to share my feedback on the Mathematical Modeling training, a transformative experience. As a Master of Biostatistics student, it enhanced my data analysis skills, especially in mathematical modeling and Python. I delved into infectious disease models, understanding their vital role in public health.

The course emphasized epidemic modeling’s necessity, broadening my perspective. I grasped compartmental models like IR, SIR, and SIER and their relevant assumptions. Proficiency in Excel and Python, as well as Jupyter Notebooks, data visualization, and disease model interpretation, was immensely beneficial. Beyond modeling, I learned to use ChatGPT and GitHub.

This training, with its hands-on group work, has prepared me to model diseases like dengue fever and malaria, making it a comprehensive and enriching program.

I appreciate the guidance and support from instructors, and I believe the acquired knowledge and skills have practical applications in addressing real-world challenges, particularly in infectious disease modeling. Thank you for this invaluable learning opportunity.

Nakalanzi Victoria

Nakalanzi Victoria

Master of Biostatistics student

5

My name is Nakasujja Proscovia, and I am currently a graduate student pursuing a Master’s degree in Biostatistics at the School of Public Health. I am in the final stages of completing my research thesis, which focuses on developing an optimal control strategy for Tuberculosis (TB) in the Karamoja region of Uganda through the application of mathematical modeling.

Throughout my academic journey, I’ve gained a solid foundation in mathematical modeling as part of my coursework. However, it was a transformative experience when I had the opportunity to participate in specialized training that provided a practical understanding of applying these concepts, particularly through the utilization of Python software. This training has been invaluable in enabling me to effectively incorporate various modeling techniques into my research thesis.

During this training, we delved into modeling the impact of temperature and rainfall on the prevalence of Dengue fever, which significantly expanded our expertise in mathematical modeling, particularly within the Python software environment. Furthermore, under the guidance of our skilled facilitators, we had the privilege of modeling the cost-effectiveness of interventions such as insecticide-treated nets and vaccines in the control of malaria among children aged less than 5 years. This experience not only honed my skills in cost-effective analysis but also introduced me to the use of software tools like Tree Age for this purpose.

I am eager to continue expanding my knowledge and skills in mathematical modeling through further training opportunities. These experiences have not only enriched my academic journey but have also ignited my passion for using mathematical modeling to address critical public health challenges.

I look forward to the prospect of more training opportunities and to further contribute to the field of mathematical modeling in the context of public health.

Nakasujja Proscovia

Nakasujja Proscovia

Master of Biostatistics student

5

My Testimony from Mathematical Modeling July 2023.
I was faced with a challenge in handling repeated predictors and a single outcome binary variable. Within 1-1 session, I advised to apply machine learning. This was a new concept, however, I was given a hint about starting out in Python.
Within the teaching sessions, I was introduced to ChatGPT. This was my game-changer. At the moment, I have more knowledge in machine learning and applied it as a solution to the previous problem. ChatGPT has eased machine learning in R.
The training was quite beneficial to my side. Thanks for organizing team, Abel and our facilitators, MakSPH and Bill & Melinda Gates Foundation

Rogers Nsubuga

Rogers Nsubuga