Latest News Archive

Please select Category, Year, and then Month to display items
Previous Archive
17 June 2025 | Story Tshepo Tsotetsi | Photo Supplied
Dr Herkulaas Combrink
Dr Herkulaas Combrink is representing UFS in a new international research project that aims to improve how evidence is used in public health policymaking.

Dr Herkulaas Combrink, a senior lecturer in the Faculty of Economic and Management Sciences (EMS) at the University of the Free State (UFS), is representing the university in a new international research project that aims to improve how evidence is used in public health policymaking.

Dr Combrink, who is also a co-director of the Interdisciplinary Centre for Digital Futures (ICDF), has been selected as one of the principal investigators in a newly funded project supported by the UK’s International Science Partnerships Fund under the Evidence-Informed Policymaking Programme. Running from April 2025 to March 2026, the project – titled Integrating Evidence for Contextualised Public Health Policy: Lessons from South Africa – explores how different types of evidence can be used more effectively in shaping public health policy. The international collaboration includes researchers from the Centre for Philosophy of Epidemiology, Medicine and Public Health, which is a collaboration between Durham University and the University of Johannesburg; as well as Durham’s Centre for Humanities Engaging Science and Society.

 

From the Free State to global impact

For Dr Combrink, being part of this collaboration highlights the important work being done in the faculty and ICDF that is reaching beyond borders. 

“It’s important to showcase the impact we are making from the Free State that leads to global outcomes,” he said.

The project aims to evaluate an evidence mapping framework to determine how model-based projections and social listening reports can be more effectively integrated and contextualised for policymaking.

“These are two very different data types,” he explained. “The value lies in demonstrating how to apply the framework to different contexts for evidence-based mapping.”

Dr Combrink brings extensive expertise to the team, having worked on both disease modelling and risk communication during South Africa’s COVID-19 response. He was involved in national and provincial social listening initiatives, and used high-frequency social media data to track the spread of misinformation, often referred to as the ‘infodemic.’ 

“We’ve built up enough data within ICDF and EMS to support this study,” he noted.

The goal is not just theoretical. A key outcome of the project is engaging directly with policymakers to refine modelling and risk communication strategies for future pandemics. 

“This will help us to engage with the various departments of health to assist with improving modelling and risk communication work for better social behavioural change,” he explained.

According to Prof Brownhilder Neneh, Vice-Dean for Research and Internationalisation in the EMS faculty, the project reflects the faculty’s growing global presence. 

“Dr Combrink’s participation is a testament to the calibre of scholarship within the faculty,” she said. “It positions EMS as a key contributor to shaping policy and practice with societal impact.”

She added that the collaboration aligns well with the faculty’s vision for global partnerships that are rooted in local relevance.

“By focusing on contextualised evidence for policymaking, this project reflects our commitment to relevance, engagement and global partnership,” she said.

 

What comes next

Over the project’s 12-month timeline, the team will deliver:

• a case study analysis of modelling and social listening during South Africa’s COVID-19 response;
• an extended evidence mapping framework tailored to diverse evidence types;
• policy briefs and practical tools for public health practitioners; and
• a hybrid international workshop in late 2025 bringing together researchers, policymakers and health professionals to test and refine these outputs.

News Archive

Mathematical methods used to detect and classify breast cancer masses
2016-08-10

Description: Breast lesions Tags: Breast lesions

Examples of Acho’s breast mass
segmentation identification

Breast cancer is the leading cause of female mortality in developing countries. According to the World Health Organization (WHO), the low survival rates in developing countries are mainly due to the lack of early detection and adequate diagnosis programs.

Seeing the picture more clearly

Susan Acho from the University of the Free State’s Department of Medical Physics, breast cancer research focuses on using mathematical methods to delineate and classify breast masses. Advancements in medical research have led to remarkable progress in breast cancer detection, however, according to Acho, the methods of diagnosis currently available commercially, lack a detailed finesse in accurately identifying the boundaries of breast mass lesions.

Inspiration drawn from pioneer

Drawing inspiration from the Mammography Computer Aided Diagnosis Development and Implementation (CAADI) project, which was the brainchild Prof William Rae, Head of the department of Medical Physics, Acho’s MMedSc thesis titled ‘Segmentation and Quantitative Characterisation of Breast Masses Imaged using Digital Mammography’ investigates classical segmentation algorithms, texture features and classification of breast masses in mammography. It is a rare research topic in South Africa.

 Characterisation of breast masses, involves delineating and analysing the breast mass region on a mammogram in order to determine its shape, margin and texture composition. Computer-aided diagnosis (CAD) program detects the outline of the mass lesion, and uses this information together with its texture features to determine the clinical traits of the mass. CAD programs mark suspicious areas for second look or areas on a mammogram that the radiologist might have overlooked. It can act as an independent double reader of a mammogram in institutions where there is a shortage of trained mammogram readers. 

Light at the end of the tunnel

Breast cancer is one of the most common malignancies among females in South Africa. “The challenge is being able to apply these mathematical methods in the medical field to help find solutions to specific medical problems, and that’s what I hope my research will do,” she says.

By using mathematics, physics and digital imaging to understand breast masses on mammograms, her research bridges the gap between these fields to provide algorithms which are applicable in medical image interpretation.

We use cookies to make interactions with our websites and services easy and meaningful. To better understand how they are used, read more about the UFS cookie policy. By continuing to use this site you are giving us your consent to do this.

Accept