The need for an improved access to early diagnosis and linkage to care has never been greater, with the 350 million people worldwide living with chronic hepatitis B, killing 1.34 million people annually, more than Malaria, from related liver cancer.

Despite the availability of a vaccine, Hepatitis B Virus (HBV) infection still remains a global public health threat. In Nigeria, the burden of Hepatitis B is estimated as 13.7% — translating to 20 million infected people. This means that at least one out of every 10 Nigerians lives with viral hepatitis B.

The high-cost implication and other limitations associated with the existing HBV diagnostics— involving immunoassays and nucleic-acid tests, have made them inaccessible to vulnerable populations in resource-limited countries. These have created barriers to early detection as 9 in 10 people living with this silent killer are unaware of their infection status, and are at risk of transmitting the virus to others.

With patient health frequently evaluated via the results of routine pathology tests in relation to laboratory reference ranges, the availability of massive clinical data sets and machine-learning methods provide opportunities to facilitate early detection of hepatitis B virus in Nigerian patients, without resort to second-tier testing, such as immunoassays, potentially representing significant savings on time, cost and patient anxiety. 

Hence, to address barriers to HBV testing and facilitate prompt diagnosis, my research aims to identify biomarker patterns in routine clinical (pathology) data, and explore how these patterns can be used to provide early indications of whether a patient has been infected with HBV. The early indication of infection will provide quicker responses and may prevent the development of extrahepatic manifestations including fatal end-stage liver disease and liver cancer through timely intervention.

* PhD thesis proposal review

About Busayo

Busayo is a PhD student at the Research School of Population Health. He completed a Bachelor of Science degree in Microbiology and a Master’s degree in Medical Microbiology. Prior to starting his PhD programme, he has worked at the Department of Microbiology, Kwara State University, Malete, Nigeria.

Earlier research experience was gained in Medical Microbiology. However, there has been a shift from his previous laboratory-based research activities to a new research interest that centres on the application of systems biology strategies— particularly machine learning algorithms, to fundamental infectious disease and population health problems. His PhD research centres on the application of machine learning (pattern recognition) interrogation to routine pathology markers, and associated clinical data, to predict hepatitis B virus (HBV) infection in vulnerable populations as early as possible, and to monitor the persistence of the virus in patients.