Recognized in Global Fund Strategy: Data-Driven Approaches to Optimize TB Response

At a time when global health systems are facing increasing funding constraints, the question is no longer whether we need to innovate—but how fast we can scale what works. A recent publication led by Mohammed A. Yassin from the Global Fund highlights exactly this challenge: how to optimise the TB response using evidence-based, cost-effective strategies in an increasingly constrained funding environment.

From Innovation to Necessity

The paper makes a compelling case: despite progress in TB control, funding gaps and inefficiencies are putting global targets at risk. With only a fraction of the required funding currently available, improving efficiency is no longer optional, it is essential.

Among the strategies identified are:

• Smarter screening through AI-enabled tools

• Integration of TB services within broader health systems

• Community-based approaches to improve reach and adherence

• Digital tools to strengthen monitoring, planning, and decision-making

These are not theoretical ideas—they are grounded in real-world evidence and field experience across multiple countries.

A Subtle but Meaningful Recognition

What is particularly meaningful for us is that EPCON’s work has been referenced in this publication as part of the evidence base supporting more efficient TB interventions.

While mentioned in a footnote, this recognition reflects something much larger:

AI-driven, data-informed microplanning and hotspot targeting are becoming part of the global conversation on how to optimise TB programmes. It reinforces a shift we have been seeing across countries and partners:from broad, resource-intensive approachesto targeted, data-driven strategies that maximise impact per dollar spent.

Why This Matters

The publication highlights a key reality:we cannot solve tomorrow’s health challenges with yesterday’s approaches.

Traditional models often lack the precision needed in today’s funding landscape. In contrast, data-driven approaches allow programmes to:

• Identify where the highest-risk populations are

• Prioritize interventions more effectively

• Increase yield of screening activities

• Reduce operational inefficiencies

In fact, the paper highlights how AI-supported approaches, such as predictive mapping combined with mobile screening, can significantly improve case detection while optimising resources.

From Evidence to Action

At EPCON, this is exactly the space we operate in. Our Epi-control platform combines multiple data sources, ranging from epidemiological data to socio-demographic and environmental factors, to generate high-resolution risk maps and actionable microplanning recommendations.

The goal is simple: "help programs do more with the resources they already have".

Seeing this approach reflected in global strategic thinking is encouraging—not as an endpoint, but as validation that the sector is moving in the right direction.

Looking Ahead

The road to ending TB by 2030 remains challenging. But this publication sends a clear message:

• Efficiency matters

• Integration matters

• Data-driven decision-making matters

And most importantly:scalable, evidence-based innovations must be at the centre of the global TB response. We are proud to contribute to this shift—and to work alongside partners, governments, and global organizations to turn these strategies into real-world impact.