Join us for a three-day intensive training organized by the Statistical Leaning Laboratory (SaLLy; www.SaLLy.ufba.br) and the State University of Campinas (UNICAMP), sponsored by the Applied Malaria Modelling Network (AMMnet; https://ammnet.org/), aiming to equip participants with the necessary skills and knowledge to employ artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL), in modeling malaria (and infectious diseases in general) spread and intervention strategies in Brazil and Latin America. This training aligns with AMMnet’s mission, leveraging advanced ML and DL methodologies to enhance disease surveillance, prediction, and control efforts, ultimately contributing to reducing malaria transmission and burden in the region.
Dates: August 28-30, 2024
Time: From 09:00 to 16:45 every day
Venue: Hebe de Azevedo Biagioni Auditorium, Institute of Mathematics, Statistics and Scientific Computing (IMECC), Rua Sérgio Buarque de Holanda, 651, 13083-859, Campinas, SP, Brazil. https://www.ime.unicamp.br/administracao/informacoes-para-visitantes/como-chegar
Expected Number of Participants: 30-50 Participants
- Keynote Presentations: Gain valuable insights from renowned experts in AI and malaria research.
- Workshops: Participate in hands-on workshops to enhance your skills in AI-driven malaria (and infectious diseases in general) modeling techniques.
- Networking Opportunities: Connect with peers, potential collaborators, and industry professionals during dedicated networking sessions.
This event is ideal for researchers, scientists, healthcare professionals, modelers, data scientists, graduate students, policymakers, NGOs, and anyone interested in the intersection of AI and malaria modeling, as well as researchers engaged in vector-borne diseases, particularly malaria, in Brazil and other Latin American countries.
- Explore cutting-edge AI technologies transforming malaria and infectious disease research.
- Network with industry leaders and experts in the field.
- Gain actionable insights to advance your research and initiatives.
- Contribute to discussions shaping the future of malaria modeling in Brazil and Latin America.
- Be part of the AMMnet Local Chapter in Brazil.
- Abstract submission for the poster session: July 15, 2024
- Decision on the abstract acceptance: July 25, 2024
- Registration and payment of the registration fee with discount (R$ 100.00/R$50.00): July 27, 2024
- AIMM 2024: August 28-30, 2024
- Registration: https://forms.gle/4GqyTk7XYnUNJJ9Q6
- Abstract submission: Send by email to olawaleawe@gmail.com. It should be a Word file with a title, up to 250 words abstract, list of authors and affiliations.
- Poster format: The posters must be printed in A0 and with a rope to hang.
- Registration fee for non-students:
R$100.00 until July 27, 2024.
R$150.00 after July 28, 2024.
- Registration fee for students:
R$50.00 until July 27, 2024.
R$75.00 after July 28, 2024.
Payment should be made to PIX 19-999999189 and the proof of payment sent to essaypublicationsml@gmail.com.
- Meeting Schedule (subject to changes):
Day 1: Introduction to Machine Learning and Malaria Modelling
9h00 – 9h30: Registration
9h30 – 9h40: Welcome Address
9h40 – 10h30: Keynote Speech
10h30 – 11h00: Coffee Break
11h00 – 12h30: Overview of Machine Learning (ML) concepts,
tools, and applications in healthcare. Introduction to malaria epidemiology in Brazil and Latin America
12h30 – 14h00: Lunch Break
14h00 – 15h15: Data handling and preprocessing techniques for health data.
Case studies on ML applications in infectious diseases (Practical Sessions and Group Discussions).
15h15 – 15h45: Networking Break
15h45 – 16h45: Compartmental Models: Mapping the Pathways of Infections
Day 2: Machine Learning for Malaria Prediction and Intervention
9h00 – 10h30: Detailed exploration of ML models (supervised and unsupervised learning) focusing on malaria prediction
10h30 – 11h00: Coffee Break
11h00 – 12h30: Mathematical Mysteries of Malaria: SEIR Model
12h30 – 14h00: Lunch Break
14h00 – 15h15: Workshop Continues. Group work on model development and optimization.
15h15 – 15h45: Networking Break
15h45 – 16h45: Poster Session
Day 3: Advanced Topics and Project Presentations
9h00 – 10h30: Understanding Disease Spread through Mathematical Lenses
10h30 – 11h00: Coffee Break
11h00 – 12h30: Practical session on developing ML and Deep Learning models using malaria datasets
12h30 – 14h00: Lunch Break
14h00 – 15h30: Group project presentations. Teams present proposed ML models for malaria prediction and intervention/or related topics
15h30 – 16h00: Networking Break
16h00 – 16h15: AMMnet Brazil Chapter Inauguration
16h15 – 16h45: Feedback session, awards presentation, and closing remarks.
Students and early career scientists (up to five years after their last academic degree) attending the AIMM 2024 will have the opportunity to compete for the “Best Poster Award in Malaria and Infectious Disease Modelling.” To be considered for these awards, the student or young researcher must attend the event and be the presenter of the poster. If you are interested in participating in this competition, please send an extended abstract between 4 and 6 pages, your Curriculum Vitae, and a cover letter to olawaleawe@gmail.com by August 10, 2024. Opportunity will be given to the award winners and honourable mentions to present their works orally on the third day. Quality papers will have the chance to be published at a later date as conference proceedings by Springer.
Predominantly English.
For inquiries, please contact Olawale Awe (olawaleawe@gmail.com; +55 19 98965-9889).
Statistical Learning Laboratory - UFBA
IMECC, Universidade Estadual de Campinas (UNICAMP), Campinas, São Paulo
- Veronica Gonzalez-Lopez (Co-Chair)
- O. Olawale Awe (Co-Chair)
- Paulo Canas Rodrigues
- João Vitor Rocha Silva
- Deborah Awe
- O. Olawale Awe, Research Coordinator of the SaLLy; President/National Coordinator of AMMnet in Brazil, Vice-President of the International Association for Statistical Education (IASE)
- Paulo Canas Rodrigues, Director of the SaLLy, President of the International Society for Business and Industrial Statistics (ISBIS)