What's new?

At EPCON, we believe that the future of public health lies in transforming data and AI into actionable intelligence. In Kenya, we are putting this belief into practice, working closely with the National TB Program (NTP) and the Centre for Health Solutions, Kenya (CHS) to strengthen tuberculosis (TB) programs through advanced AI-driven modeling.

Growth in a company often shows up in numbers: more projects, more partners, more countries. But sometimes, growth shows up in a much more tangible way — like realizing your team has simply outgrown the office.

Public health programs often face a fundamental challenge: where should limited resources be deployed to have the greatest impact?While national statistics provide an overview, they rarely capture the local inequalities that determine who can actually access care. Communities only a few kilometers apart may experience vastly different realities in terms of healthcare access, poverty, infrastructure, or social vulnerability.

We are proud to share that our latest research on AI-driven tuberculosis hotspot mapping in Uganda has been published as a preprint:“AI-Driven Tuberculosis Hotspot Mapping to Optimize Active Case-Finding: Implementing the Epi-Control Platform in Uganda.” 

EPCON developed the MATCH-AI platform (also referred to as the Epi-control platform), based on the MATCH framework developed by KIT Royal Tropical Institute and implemented in close collaboration with Mercy Corps. The prospective study in Pakistan was conducted by Mercy Corps, with independent evaluation support from the Center for Global Public Health (CGPH).

We’re excited to welcome Jason Joannou to EPCON as our newest Backend Engineer. Jason joined earlier this year and, just a few weeks in, has already immersed himself in the systems that power our work – with a focus on building technology that serves a purpose beyond the code.

Despite progress, there’s still a gap between people likely to develop TB and those ultimately recorded by NTLP. Risk isn’t uniform: factors like HIV prevalence can differ four-fold across regions, so national averages hide district-level hotspots. The solution is finer-grained estimates, district by district, and targeted action, which is exactly what EPCON’s AI and data-driven modelling enables.