Data & Technical Resources Committee
Share, discuss, and develop sources and materials related to modeling malaria, and to promote and support open and reproducible research practices.

Francophone Committee
Aims to make activities (meetings, workshops, courses) 100% in French in order to allow researchers who do not understand English to be at the same level as their English speaking counterparts.

Membership Committee
Aims to ensure that the objectives of the network are well represented by the members. This includes trying to ensure active membership from across different professional backgrounds, including academia, ministries of health, global malaria stakeholders and malaria NGOs.

Seminars Committee
Responsible for designing and organizing an interesting repertoire of content for monthly meetings; with input from across AMMnet’s membership, we invite presenters from diverse topical areas to share their knowledge in ways that improve our impact as malaria modelers.

Social Media & Website Committee
Share news and information relevant to AMMnet and the wider malaria modeling community with researchers and partners globally. The main channels to stay up to date with AMMnet are Twitter, LinkedIn, Facebook and the website.

Training & Exchanges Committee
Compile available online training resources, list past and future courses, identify training needs, provide a platform for trainers to exchange training material, connect experts and learners, provide opportunities for student exchange and mentoring programs.

Values & Guidance Committee
Aims to ensure a respectful, transparent, and problem-targeted engagement between modelers, country partners, and national and global stakeholders; the committee is responsible for assembling existing reference sources of guiding principles for all parties involved and extending these with best practices as new lessons are learned.

Workshops/Special Events Committee
Organizes virtual and in-person conferences and events for members to meet each other, exchange knowledge, and strengthen local capacity for modeling.