TOPIC:
“Training on Malaria Modeling using R Software to Develop Practical Skills in Combating Malaria within the Kenyan Context”
Join us for two days training workshop organized by the Faculty of Science and Technology, Chuka University Kenya and Sponsored by Applied Malaria Modeling Network (AMMnet).
- Aim of the Event
This training aim to introduce practical applications of malaria modeling using R Software. The training will Leverage learning and knowledge-sharing with the goal of improving the quality of analytical support offered to malaria programs and advancing general malaria research.
- Important information
Date: 29th to 30th August 2024
Time: 8:00 a.m – 4:30 p.m daily
Venue: Chuka University, Kenya
Expected No. of Participant: 30-50 Participants
- Registration Details
Register Form at: https://forms.gle/VGsS7eiK5qEsrWVA8
Registration Deadline: Before or on 14th August 2024 at 5:00 pm EAT
Registration Fee: Ksh. 2000/=
(Payable to Chuka University M-Pesa pay bill No: 247 247; A/C No: 0703142459
- Event Highlights
- Key note presentations from the experts
- Hands-on-workshop to enhance your skills in model development/simulations and data analysis
- Net-working opportunities
- PARTICIPANTS
- Faculty members
- Research scientists
- Data scientists
- Mathematician/statistician
- Health workers/Medical practitioners
- Post graduate students(PhD/MSc)
- Detailed schedule (Subject to change)
| DAY ONE | |
| 9.00 – 9.30am | Registration |
| 9.30 – 10.00am | Self-Introduction – Networking |
| 10.00 – 10.10am | Welcome Remarks:AMMnet mission and vision, aim of the event and welcoming new members to AMMnet Kenya Chapter |
| 10.10 – 10.30am | Opening Remarks – Official Welcome |
| 10.30 – 11.00am | Health Break, Networking and Photography session |
| 11.00 – 11.30pm | Overview of different types of Models (Dynamical, Geospatial and Genomics Modelling) |
| 11.30 – 1.00pm | Introduction to R and Data Visualization |
| 1.00 – 2.00pm | Lunch Break, and Networking |
| 2.00– 3.30pm | Computational Malaria Modelling Techniques in Machine Learning Algorithms using R |
| 3.30– 5.00pm | |
| DAY TWO | |
| 8.30– 9.00am | Registration |
| 9.00 – 11.00am | Malaria Modelling Techniques in Machine Learning Algorithms in R & Rstudio using NMCP data |
| 11.00 – 11.30am | Health Break, Networking and Photography session |
| 11.30– 1.00pm | Development/Formulation of Compartmental Models |
| 1.00 – 2.00pm | Lunch Break, and Networking |
| 2.00 – 4.00pm | Simulation and Validation of Compartmental Models in R Using NMCP data |
| 4.00 – 5.00pm | Integrating a Compartmental Model into a Shiny App with R software |
| 5.00 – 5.30pm | Evaluation of the training – Online survey tool |
| 5.30 – 6.00pm | Closing Remarks |
| 6.00pm | Departure |
- ORGANIZERS
Faculty of Science and Technology,
Chuka University, Kenya
- Host Organizing committee
- Prof. Paul Kamweru
- Prof. Dennis Muriithi
- Dr. Mark Okongo
- Dr. Alice Lunani
- Facilitators & scientific organizer
Mark Okongo, PhD
Mark Onyango is currently the head of Mathematics and Statistics section and senior lecturer in Applied mathematics at Chuka University, Kenya. He holds a PhD in Applied Mathematics from Chuka University-Kenya, M.Sc. in Applied Mathematics from Maseno University, Kenya and a B.Ed in Mathematics and Economics from Kenyatta University, Kenya. He specializes in Mathematical Modeling (Mathematical Epidemiology and Phytopathology). Currently, his research focuses on Mathematical Modeling of Malaria Transmission. He actively collaborates with both academic and industry partners to address real-world challenges using statistical tools.
Dennis Muriithi, PhD
Dennis K. Muriithi is an Associate Professor of Applied statistics in the Faculty of Science and Technology at the Chuka University, Kenya. He holds a PhD in Biostatistics from Moi University, Kenya and MSc in Applied statistics form JKUAT, Kenya. He is a dedicated educator, a researcher, and a recognized expert in the field of Applied Statistics and data analytics. Prof. Muriithi is dedicated to advancing scientific knowledge, mentoring future scientists, and promoting interdisciplinary research. His work focuses on Applied Statistics and data analytics: Machine Learning and Predictive Inference with application to Agriculture, Education, Health science and economic studies. He actively collaborates with both academic and industry partners to address real-world challenges using statistical tools.
- Contact details
Email: dataanalytics@chuka.ac.ke
Website: https://fast.chuka.ac.ke/TMMS2024