Hepatitis B virus seroepidemiology data for Africa:Modelling intervention strategies based on a systematic review and meta-analysis

BACKGROUND: International Sustainable Development Goals (SDGs) for elimination of hepatitis B virus (HBV) infection set ambitious targets for 2030. In African populations, infant immunisation has been fundamental to reducing incident infections in children, but overall population prevalence of chronic hepatitis B (CHB) infection remains high. In high-prevalence populations, adult catch-up vaccination has sometimes been deployed, but an alternative Test and Treat (T&T) approach could be used as an intervention to interrupt transmission. Universal T&T has not been previously evaluated as a population intervention for HBV infection, despite high-profile data supporting its success with human immunodeficiency virus (HIV). METHODS AND FINDINGS: We set out to investigate the relationship between prevalence of HBV infection and exposure in Africa, undertaking a systematic literature review in November 2019. We identified published seroepidemiology data representing the period 1995-2019 from PubMed and Web of Science, including studies of adults that reported prevalence of both hepatitis B surface antigen (HBsAg; prevalence of HBV infection) and antibody to hepatitis B core antigen (anti-HBc; prevalence of HBV exposure). We identified 96 studies representing 39 African countries, with a median cohort size of 370 participants and a median participant age of 34 years. Using weighted linear regression analysis, we found a strong relationship between the prevalence of infection (HBsAg) and exposure (anti-HBc) (R2 = 0.45, p < 0.001). Region-specific differences were present, with estimated CHB prevalence in Northern Africa typically 30% to 40% lower (p = 0.007) than in Southern Africa for statistically similar exposure rates, demonstrating the need for intervention strategies to be tailored to individual settings. We applied a previously published mathematical model to investigate the effect of interventions in a high-prevalence setting. The most marked and sustained impact was projected with a T&T strategy, with a predicted reduction of 33% prevalence by 20 years (95% CI 30%-37%) and 62% at 50 years (95% CI 57%-68%), followed by routine neonatal vaccination and prevention of mother to child transmission (PMTCT; at 100% coverage). In contrast, the impact of catch-up vaccination in adults had a negligible and transient effect on population prevalence. The study is constrained by gaps in the published data, such that we could not model the impact of antiviral therapy based on stratification by specific clinical criteria and our model framework does not include explicit age-specific or risk-group assumptions regarding force of transmission. CONCLUSIONS: The unique data set collected in this study highlights how regional epidemiology data for HBV can provide insights into patterns of transmission, and it provides an evidence base for future quantitative research into the most effective local interventions. In combination with robust neonatal immunisation programmes, ongoing PMTCT efforts, and the vaccination of high-risk groups, diagnosing and treating HBV infection is likely to be of most impact in driving advances towards elimination targets at a population level

Bimodal distribution and set point HBV DNA viral loads in chronic infection:retrospective analysis of cohorts from the UK and South Africa

CITATION: Downs, L. O. 2020. Bimodal distribution and set point HBV DNA viral loads in chronic infection : retrospective analysis of cohorts from the UK and South Africa. Wellcome Open Research, 14(5):113, doi: 10.12688/wellcomeopenres.15941.2.The original publication is available at: https://pubmed.ncbi.nlm.nih.govENGLISH ABSTRACT: Hepatitis B virus (HBV) viral load (VL) is used as a biomarker to assess risk of disease progression, and to determine eligibility for treatment. While there is a well recognised association between VL and the expression of the viral e-antigen protein, the distributions of VL at a population level are not well described. We here present cross-sectional, observational HBV VL data from two large population cohorts in the UK and in South Africa, demonstrating a consistent bimodal distribution. The right skewed distribution and low median viral loads are different from the left-skew and higher viraemia in seen in HIV and hepatitis C virus (HCV) cohorts in the same settings. Using longitudinal data, we present evidence for a stable 'set-point' VL in peripheral blood during chronic HBV infection. These results are important to underpin improved understanding of HBV biology, to inform approaches to viral sequencing, and to plan public health interventions.Publisher's versio

Bimodal distribution and set point HBV DNA viral loads in chronic infection:retrospective analysis of cohorts from the UK and South Africa

CITATION: Downs, L. O. 2020. Bimodal distribution and set point HBV DNA viral loads in chronic infection : retrospective analysis of cohorts from the UK and South Africa. Wellcome Open Research, 14(5):113, doi: 10.12688/wellcomeopenres.15941.2.The original publication is available at: https://pubmed.ncbi.nlm.nih.govENGLISH ABSTRACT: Hepatitis B virus (HBV) viral load (VL) is used as a biomarker to assess risk of disease progression, and to determine eligibility for treatment. While there is a well recognised association between VL and the expression of the viral e-antigen protein, the distributions of VL at a population level are not well described. We here present cross-sectional, observational HBV VL data from two large population cohorts in the UK and in South Africa, demonstrating a consistent bimodal distribution. The right skewed distribution and low median viral loads are different from the left-skew and higher viraemia in seen in HIV and hepatitis C virus (HCV) cohorts in the same settings. Using longitudinal data, we present evidence for a stable 'set-point' VL in peripheral blood during chronic HBV infection. These results are important to underpin improved understanding of HBV biology, to inform approaches to viral sequencing, and to plan public health interventions.Publisher's versio

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Seroepidemiologic Survey of Crimean-Congo Hemorrhagic Fever Virus in Selected Risk Groups, South Africa

Crimean Congo hemorrhagic fever virus (CCHFV) is endemic in South Africa, but whether mild undiagnosed cases occur is unclear. In a seroepidemiologic survey, only 2 of 387 adults considered at risk because of occupational or recreational activities had evidence of previous infection. Seroprevalence in South Africa remains low within the groups investigated

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

INTRODUCTION: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. PERFORMANCE BENCHMARKS:The 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. AVAILABILITY:The 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. The 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/

Emergence of SARS-CoV-2 Omicron lineages BA.4 and BA.5 in South Africa

DATA AVAILABILITY : All of the SARS-CoV-2 genomes generated and presented in this article are publicly accessible through the GISAID platform (https://www.gisaid.org/). The GISAID accession identifiers of the sequences analyzed in this study are provided as part of Supplementary Table 1. Other raw data for this study are provided as a supplementary dataset at https://github.com/krisp-kwazulu-natal/SARSCoV2_South_Africa_Omicron_BA4_BA5. The reference SARS-CoV-2 genome (MN908947.3) was downloaded from the National Center for Biotechnology Information database (https://www.ncbi.nlm.nih.gov/).CODE AVAILABILITY : All custom scripts to reproduce the analyses and figures presented in this article are available at https://github.com/krisp-kwazulu-natal/ SARSCoV2_South_Africa_Omicron_BA4_BA5.Three lineages (BA.1, BA.2 and BA.3) of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant of concern predominantly drove South Africa’s fourth Coronavirus Disease 2019 (COVID-19) wave. We have now identified two new lineages, BA.4 and BA.5, responsible for a fifth wave of infections. The spike proteins of BA.4 and BA.5 are identical, and similar to BA.2 except for the addition of 69–70 deletion (present in the Alpha variant and the BA.1 lineage), L452R (present in the Delta variant), F486V and the wild-type amino acid at Q493. The two lineages differ only outside of the spike region. The 69–70 deletion in spike allows these lineages to be identified by the proxy marker of S-gene target failure, on the background of variants not possessing this feature. BA.4 and BA.5 have rapidly replaced BA.2, reaching more than 50% of sequenced cases in South Africa by the first week of April 2022. Using a multinomial logistic regression model, we estimated growth advantages for BA.4 and BA.5 of 0.08 (95% confidence interval (CI): 0.08–0.09) and 0.10 (95% CI: 0.09–0.11) per day, respectively, over BA.2 in South Africa. The continued discovery of genetically diverse Omicron lineages points to the hypothesis that a discrete reservoir, such as human chronic infections and/or animal hosts, is potentially contributing to further evolution and dispersal of the virus.The South African Medical Research Council (SAMRC) with funds received from the National Department of Health. Sequencing activities for the National Institute for Communicable Diseases (NICD) are supported by a conditional grant from the South African National Department of Health as part of the emergency COVID-19 response; a cooperative agreement between the NICD of the NHLS and the US Centers for Disease Control and Prevention (CDC) (U01IP001048 and 1 NU51IP000930); the African Society of Laboratory Medicine (ASLM) and Africa Centers for Disease Control and Prevention through a sub-award from the Bill and Melinda Gates Foundation (grant number INV-018978); the UK Foreign, Commonwealth and Development Office and Wellcome (221003/Z/20/Z); and the UK Department of Health and Social Care, managed by the Fleming Fund and performed under the auspices of the SEQAFRICA project. This research was also supported by the Coronavirus Aid, Relief, and Economic Security Act (CARES ACT) through the CDC and COVID International Task Force (ITF) funds through the CDC under the terms of a subcontract with the African Field Epidemiology Network (AFENET) (AF-NICD-001/2021). Sequencing activities at KRISP and the Centre for Epidemic Response and Innovation are supported, in part, by grants from the World Health Organization, the Rockefeller Foundation (HTH 017), the Abbott Pandemic Defense Coalition (APDC), the US National Institutes of Health (U01 AI151698) for the United World Antivirus Research Network (UWARN) and the INFORM Africa project through IHVN (U54 TW012041) and the South African Department of Science and Innovation (SA DSI) and the SAMRC under the BRICS JAF (2020/049). Sequencing at the Botswana Harvard AIDS Institute Partnership was supported by funding from the Bill and Melinda Gates Foundation, the Foundation for Innovation in Diagnostics, the National Institutes of Health Fogarty International Centre (3D43TW009610-09S1) and the HHS/NIH/ National Institute of Allergy and Infectious Diseases (NIAID) (5K24AI131928-04 and 5K24AI131924-04).http://www.nature.com/naturemedicineam2023Medical VirologySDG-03:Good heatlh and well-bein