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Bester, P. A.

Publications and source records attributed to Bester, P. A..

2 recordsLinked to original sources

HBV seroepidemiology data for Africa provides insights into transmission and prevention

International goals for elimination of hepatitis B virus (HBV) infection set ambitious targets for 2030. In many African populations, HBV prevalence remains high ([&ge;]8%) despite the roll-out of infant HBV immunisation from the mid-1990s onwards. Enhanced efforts are now urgently required to improve an understanding of population epidemiology, in order to determine which interventions are most likely to be effective in advancing populations towards elimination goals. In populations with a high prevalence of infection, catch-up HBV vaccination of adults has sometimes been deployed as a preventive strategy. An alternative approach of test and treat could be applied as a tool to interrupt transmission. We used a systematic approach to investigate the relationship between prevalence of HBV infection (HBsAg) and exposure (anti-HBc) in Africa, and then applied a mathematical model to investigate the impact of catch-up vaccination and a test and treat strategy in Uganda, representing a high prevalence setting. We demonstrate a strong relationship between the prevalence of HBsAg and anti-HBc (p<0.0001), but with region-specific differences that may reflect different patterns of transmission. In high prevalence settings, catch-up vaccination may have a transient effect but this intervention does not contribute to a sustained decline in prevalence. In contrast, diagnosing and treating infection has a marked impact on reducing prevalence, equivalent to that of infant immunisation. Conclusion: We have developed a high-resolution picture of HBV epidemiology across Africa. Developing insights into regional differences provides an evidence base for the most effective interventions. In combination with robust neonatal immunisation programmes, testing and treating infection is likely to be of most impact in making advances towards elimination targets.

epidemiology↗

PhyloPi: an affordable, purpose built phylogenetic pipeline for the HIV drug resistance testing facility

Phylogenetic analysis plays a crucial role in quality control in the HIV drug resistance testing laboratory. If previous patient sequence data is available sample swaps can be detected and investigated. As Antiretroviral treatment coverage is increasing in many developing countries, so is the need for HIV drug resistance testing. In countries with multiple languages, transcription errors are easily made with patient identifiers. Here a self-contained blastn integrated phylogenetic pipeline can be especially useful. Even though our pipeline can run on any unix based system, a Raspberry Pi 3 is used here as a very affordable and integrated solution.\n\nThe computational capability of this single board computer is demonstrated as well as the utility thereof in the HIV drug resistance laboratory. Benchmarking analysis against a large public database shows excellent time performance with minimal user intervention. This pipeline also contains utilities to find previous sequences as well as phylogenetic analysis and a graphical sequence mapping utility against the pol area of the HIV HXB2 reference genome. Sequence data from the Los Alamos HIV database was analyzed for inter- and intra-patient diversity and logistic regression was conducted on the calculated genetic distances. These findings show that allowable clustering and genetic distance between viral sequences from different patients is very dependent on subtype as well as the area of the viral genome being analyzed.\n\nThe Raspberry Pi image for PhyloPi, source code of the pipeline, sequence data, bash-, python- and R-scripts for the logistic regression, benchmarking as well as helper scripts are available at http://scholar.ufs.ac.za:8080/xmlui/handle/11660/7638 and https://github.com/ArmandBester/phylopi.\n\nThe PhyloPi image and the source code are published under the GPLv3 license. A demo version of the PhyloPi pipeline is available at http://phylopi.hpc.ufs.ac.za/.

bioinformatics↗