{"id":2364,"date":"2025-10-01T09:07:15","date_gmt":"2025-10-01T09:07:15","guid":{"rendered":"https:\/\/dhis2.udsm.ac.tz\/?p=2364"},"modified":"2025-10-01T09:09:00","modified_gmt":"2025-10-01T09:09:00","slug":"artificial-intelligence-for-disease-modeling","status":"publish","type":"post","link":"https:\/\/dhis2.udsm.ac.tz\/pt\/artificial-intelligence-for-disease-modeling\/","title":{"rendered":"Artificial Intelligence for Disease Modeling"},"content":{"rendered":"<p>Africa is advancing AI-driven disease modelling by aligning innovators, public-health leaders, and funders around decision-ready use cases.<\/p>\n<p>At WHO Hub in Dakar (6-8 August 2025), the focus was turning promising AI methods into trustworthy tools for burden-of-disease estimation and infectious-disease surveillance, while lowering the barrier to modelling across low and middle income settings.<\/p>\n<p>Led by WHO\/AFRO and the Gates Foundation together with Africa CDC, national public-health institutes, leading universities, and technology partners, the convening prioritized practical, high-impact applications.<\/p>\n<p>The University of Dar es Salaam (UDSM) DHIS2 Kab, represented by Simon Machera and Vincent Minde, contributed to use-case prioritization, co-design sprints, and integration discussions, alongside demonstrations such as Google&#8217;s cholera-modelling work and a funder&#8217;s panel aligning investment to needs.<\/p>\n<p>The approach emphasized governance and ethics (including data sovereignity), standards-based and interoperable multi-source integration (genomics, epidemiological\/clinical, and environmental data), and equitable partnerships to scale.<\/p>\n<p>Agreed next steps include co-design with Google Research, advancing HIE interoperability evaluation with Africa CDC, establishing research links with Uganda collaborators, and a forthcoming closed Gates Foundation RFP for institutions engaged at Dakar.<\/p>","protected":false},"excerpt":{"rendered":"<p>Africa is advancing AI-driven disease modelling by aligning innovators, public-health leaders, and funders around decision-ready use cases. At WHO Hub in Dakar (6-8 August 2025), the focus was turning promising AI methods into trustworthy tools for burden-of-disease estimation and infectious-disease surveillance, while lowering the barrier to modelling across low and middle income settings. Led by [&hellip;]<\/p>","protected":false},"author":2,"featured_media":2365,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[1,22],"tags":[],"acf":{"photo_gallery":{"gallery":[[]]}},"_links":{"self":[{"href":"https:\/\/dhis2.udsm.ac.tz\/pt\/wp-json\/wp\/v2\/posts\/2364"}],"collection":[{"href":"https:\/\/dhis2.udsm.ac.tz\/pt\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/dhis2.udsm.ac.tz\/pt\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/dhis2.udsm.ac.tz\/pt\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/dhis2.udsm.ac.tz\/pt\/wp-json\/wp\/v2\/comments?post=2364"}],"version-history":[{"count":1,"href":"https:\/\/dhis2.udsm.ac.tz\/pt\/wp-json\/wp\/v2\/posts\/2364\/revisions"}],"predecessor-version":[{"id":2366,"href":"https:\/\/dhis2.udsm.ac.tz\/pt\/wp-json\/wp\/v2\/posts\/2364\/revisions\/2366"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/dhis2.udsm.ac.tz\/pt\/wp-json\/wp\/v2\/media\/2365"}],"wp:attachment":[{"href":"https:\/\/dhis2.udsm.ac.tz\/pt\/wp-json\/wp\/v2\/media?parent=2364"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/dhis2.udsm.ac.tz\/pt\/wp-json\/wp\/v2\/categories?post=2364"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/dhis2.udsm.ac.tz\/pt\/wp-json\/wp\/v2\/tags?post=2364"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}