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07 April 2021 | Story Rulanzen Martin | Photo istock
Social media discussions have provided a lens on how people are dealing with and talking about COVID-19. This has given risk communication new insights into online audiences.

The lingering effects of the COVID-19 pandemic on society presented the experts at the University of the Free State (UFS) with an opportunity – to conduct a scientific study by analysing our social media data in order to assist government health communicators to reflect on their communication strategies and, in turn, gain new perspectives from the general social media user (public). 

The study – led by Herkulaas Combrink, a data and medical scientist in the UFS initiative for Digital Futures, and Prof. Katinka de Wet, medical sociologist in both the UFS initiative for Digital Futures and the Department of Sociology at the UFS – uses “real-time snapshots of online interactions as a means to augment more traditional methods of conducting research on a given topic; in this case, responses to COVID-19”, said Combrink. 

The findings and ongoing work of the research project were presented to the Parliamentary Portfolio Committee on Communications. “During this meeting, critical engagement took place around risk communication and areas where we can strengthen this research,” said Combrink. Several international influential risk communicators on the African continent were present. 

Digital science at the forefront 

The opportunity to pursue this study was the result of Herkulaas Combrink’s secondment to the Free State Department of Health (FSDOH), where he identified the need to develop additional analytics for the already existing processes in risk communication in order to assist various communication strategies linked to developments regarding COVID-19 infections.  

Combrink also said “because the analysis of social media data does not normally form part of the traditional toolbox of investigation for this type of work, this novel application serves as an addition to the already existing communication analytics”. This research project will strengthen the level of cooperation between the UFS, other institutions, and the FSDOH to “synergistically strengthen communication strategies in relation to COVID-19”. 

By looking at how new knowledge around COVID-19 is developing the method (of analysing social media data), is to stay abreast of trending and burning issues on open-source social media platforms. “It is important to conduct this work using well-defined scientific methodology to extract, explore, analyse, and report on the data,” Combrink says. 

Given the rapidity with which new knowledge around COVID-19 is developing all over the globe, this method lends itself to staying abreast of emergent and burning issues that are trending on open-source social media sites. 

Variety of stakeholders needed

The magnitude of the research study required the involvement of stakeholders from different institutions. “A variety of stakeholders from different institutions are needed not only to contextualise the data, but also to provide social and technical input to solve the problem,” Combrink said.  

Experts included in the project are Dr Vukosi Marivate from the Department of Computer Science at the University of Pretoria, Dr Ming-Han Mothloung from the Department of Community Health at the UFS and the FSDOH, and Dr Samuel Mokoena, Priscilla Monyobo, Mondli Mvambi, and Elke de Witt from the FSDOH. “Without this core team, the work would not have been contextually relevant,” Combrink said. 

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.

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