The past 2 years, during which waves of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants swept the globe, have starkly highlighted health disparities across nations. Tegally et al. show how the coordinated efforts of talented African scientists have in a short time made great contributions to pandemic surveillance and data gathering. Their efforts and initiatives have provided early warning that has likely benefited wealthier countries more than their own. Genomic surveillance identified the emergence of the highly transmissible Beta and Omicron variants and now the appearance of Omicron sublineages in Africa. However, it is imperative that technology transfer for diagnostics and vaccines, as well the logistic wherewithal to produce and deploy them, match the data-gathering effort.

Five years before the 2022 multi-country mpox outbreak, Nigeria and Cameroon reported their first cases in over three decades.1,2 While Nigeria’s outbreak is recognized as an ongoing human epidemic, the drivers of Cameroon’s resurgence remain unclear.3,4 The rate of zoonoses remains uncertain in both countries, and gaps in genomic data obscure the timing, zoonotic and geographic origin of mpox virus (MPXV) emergence in humans. To address these uncertainties, we generated 118 MPXV genomes from Nigeria and Cameroon from 2018-2023. Our findings show that, in contrast to Nigeria, cases in Cameroon are the result of repeated zoonoses, with two distinct zoonotic lineages circulating across the Nigeria-Cameroon border. Our findings suggest that shared animal populations in the cross-border forest ecosystems drive virus emergence and spread. Accordingly, we identify the closest zoonotic outgroup to the Nigerian human epidemic lineage (hMPXV-1) in a southern Nigerian border state. We estimate that the shared ancestor of the zoonotic outgroup and hMPXV-1 circulated in animals in southern Nigeria in late 2013. We estimate that hMPXV-1 emerged in humans in August 2014 in the southern Rivers State and circulated undetected for three years. Rivers State acted as the main source of viral spread across the human epidemic. Our study sheds light on MPXV’s recent establishment in the human population and highlights the risk of persistent zoonotic emergence of MPXV in the complex border regions of Cameroon and Nigeria.

High-income countries have a wealth of genomics expertise that can be rapidly activated to deal with disease threats. African countries should invest in a federated data-management system for genomics epidemiology to deal with such threats better.

Rapid expansion of pathogen sequencing capacity in Africa has led to a paradigm shift from relying on others to locally generating genomic data and sharing it with the global community. However, several barriers remain to be unlocked for timely processing, analysis, dissemination, and effective use of pathogen sequence data for pandemic prevention, preparedness, and response.

The haemoglobinopathies transfusion-dependent β-thalassemia (TDT) and sickle-cell disease (SCD) are two globally prevalent and severe monogenic blood disorders. These conditions impose substantial morbidity and mortality, and their burden falls disproportionately on populations in sub-Saharan Africa, the Middle East and parts of Asia, underscoring the global health significance of any curative approach. In 2021, a study by Frangoul et al. marked a defining moment for genomic medicine and global health. Published in The New England Journal of Medicine, the authors reported the clinical application of CRISPR–Cas9 genome editing to treat two patients with TDT or SCD.

Antimicrobial resistance (AMR) remains a significant global health threat particularly impacting low- and middle-income countries (LMICs). These regions often grapple with limited healthcare resources and access to advanced diagnostic tools. Consequently, there is a pressing need for innovative approaches that can enhance AMR surveillance and management. Machine learning (ML) though underutilized in these settings, presents a promising avenue. This study leverages ML models trained on whole-genome sequencing data from England, where such data is more readily available, to predict AMR in E. coli, targeting key antibiotics such as ciprofloxacin, ampicillin, and cefotaxime. A crucial part of our work involved the validation of these models using an independent dataset from Africa, specifically from Uganda, Nigeria, and Tanzania, to ascertain their applicability and effectiveness in LMICs.

Polygenic risk scores (PRS) show promise but have accuracy disparities across ancestries due to underrepresentation in the existing genomic databases. Here, we outline the initiative of All of Us Research Program in refining PRS and advancing genomic medicine for all.

We propose a novel, non-discriminatory classification of monkeypox virus diversity. Together with the World Health Organization, we named three clades (I, IIa and IIb) in order of detection. Within IIb, the cause of the current global outbreak, we identified multiple lineages (A.1, A.2, A.1.1 and B.1) to support real-time genomic surveillance.

Rationale: Airborne transmission of Mycobacterium tuberculosis results from incompletely characterized host, bacterial, and environmental factors. Sputum smear microscopy is associated with considerable variability in transmission. Objectives: To evaluate the use of cough-generated aerosols of M. tuberculosis to predict recent transmission. Methods: Patients with pulmonary tuberculosis (TB) underwent a standard evaluation and collection of cough aerosol cultures of M. tuberculosis. We assessed household contacts for new M. tuberculosis infection. We used multivariable logistic regression analysis with cluster adjustment to analyze predictors of new infection. Measurements and main results: From May 2009 to January 2011, we enrolled 96 sputum culture-positive index TB cases and their 442 contacts. Only 43 (45%) patients with TB yielded M. tuberculosis in aerosols. Contacts of patients with TB who produced high aerosols (≥10 CFU) were more likely to have a new infection compared with contacts from low-aerosol (1-9 CFU) and aerosol-negative cases (69%, 25%, and 30%, respectively; P = 0.009). A high-aerosol patient with TB was the only predictor of new M. tuberculosis infection in unadjusted (odds ratio, 5.18; 95% confidence interval, 1.52-17.61) and adjusted analyses (odds ratio, 4.81; 95% confidence interval, 1.20-19.23). Contacts of patients with TB with no aerosols versus low and high aerosols had differential tuberculin skin test and interferon-γ release assay responses. Conclusions: Cough aerosols of M. tuberculosis are produced by a minority of patients with TB but predict transmission better than sputum smear microscopy or culture. Cough aerosols may help identify the most infectious patients with TB and thus improve the cost-effectiveness of TB control programs.

The recent re-emergence of multidrug-resistant pathogens has exacerbated their threat to worldwide public health. The evolution of the genomics era has led to the generation of huge volumes of sequencing data at an unprecedented rate due to the ever-reducing costs of whole-genome sequencing (WGS). We have developed the Rapid Microbial Analysis Pipeline (rMAP), a user-friendly pipeline capable of profiling the resistomes of ESKAPE pathogens ( Enterococcus faecium , Staphylococcus aureus , Klebsiella pneumoniae , Acinetobacter baumannii , Pseudomonas aeruginosa and Enterobacter species) using WGS data generated from Illumina’s sequencing platforms. rMAP is designed for individuals with little bioinformatics expertise, and automates the steps required for WGS analysis directly from the raw genomic sequence data, including adapter and low-quality sequence read trimming, de novo genome assembly, genome annotation, single-nucleotide polymorphism (SNP) variant calling, phylogenetic inference by maximum likelihood, antimicrobial resistance (AMR) profiling, plasmid profiling, virulence factor determination, multi-locus sequence typing (MLST), pangenome analysis and insertion sequence characterization (IS). Once the analysis is finished, rMAP generates an interactive web-like html report. rMAP installation is very simple, it can be run using very simple commands. It represents a rapid and easy way to perform comprehensive bacterial WGS analysis using a personal laptop in low-income settings where high-performance computing infrastructure is limited.

Your Editorial on efforts to agree a global pandemic treaty (Nature 629, 727; 2024) emphasizes that genomic surveillance and pathogen and data sharing are key to effective infection control and development of therapies. With investment spurred by COVID-19, more than 70% of African Union (AU) member states have developed local capacity to sequence pathogen genomes (M. Makoni Lancet Microbe 1, e318; 2020), and more than 170,000 genomes of the virus SARS-CoV-2 have been sequenced and shared. In collaboration with AU member states, the Africa Centres for Disease Control and Prevention (CDC) is creating a biobanking network across the continent to address pathogen sharing. Led by the AU and the Africa CDC, a Common African Position on Pandemic Prevention, Preparedness, and Response was adopted in February 2024. Agreement on a pathogen-access and benefit-sharing system — which will allow rapid exchange of sequence data and equitable and timely access to countermeasures for pandemic preparedness and response — will be the most crucial outcome of the pandemic treaty. Sharing of pathogen data, reciprocated with multilateral benefits, can provide a guarantee of safety and equity for people all over the world, whether in higher- or lower-income countries.

Background: Klebsiella michiganensis, a member of the Klebsiella oxytoca species complex, is increasingly recognized in healthcare-associated infection and can persist in hospital environmental reservoirs. However, genome-resolved data on environmental K. michiganensis from Africa remain limited. Methods: We performed short-read whole-genome sequencing (WGS) of an isolate recovered from a wheelchair handle in the orthopedic ward of a tertiary hospital in Mwanza, Tanzania. The isolate was initially recorded during routine surveillance as an unidentified Gram-negative ESBL bacterium and was provisionally identified phenotypically as K. oxytoca using routine laboratory methods. Draft assemblies were analyzed using the rMAP-2.0 bioinformatics pipeline. Genome-based taxonomic assignment using GTDB-Tk and ANI reclassified the study isolate as Klebsiella michiganensis. We then contextualized this genome with publicly available African K. michiganensis genomes and generated a core-genome phylogeny visualized in iTOL. Results: GTDB-Tk reclassified the study isolate A55848, and the comparator public genomes included in the final revised phylogeny as Klebsiella michiganensis, with the closest GTDB reference genome being GCF_002925905.1 and an ANI of 98.97% for A55848. The Tanzanian environmental isolate carried IncFIB(K) and IncFII(K) plasmid replicons, together with Col440I/Col440II. ResFinder-based screening identified a multidrug resistance gene repertoire including blaOXY family alleles (e.g., blaOXY-1-2), blaTEM-1B, oqxA/oqxB, aph(3′)-Ia, aac(6′)-Ib, and aadA2, consistent with an MDR genotype. Phenotypic testing demonstrated an ESBL profile with reduced susceptibility to oximino-cephalosporins and piperacillin–tazobactam, while susceptibility to meropenem, amikacin, and ciprofloxacin was retained. MLST did not assign a recognized sequence type. Comparative analysis showed that African genomes frequently harbored IncFIB(K)/IncFII(K) replicons and clinically important β-lactamases, including blaCTX-M-15 and blaOXA-1, with one isolate carrying blaNDM-1. Core-genome phylogeny further indicated that related K. michiganensis lineages occur across multiple African countries and that MDR is not confined to a single lineage, supporting resistance acquisition within shared genomic backgrounds. Conclusion: Recovery of an MDR Klebsiella michiganensis from an orthopedic ward wheelchair handle, carrying IncF-family plasmid replicon signatures together with multiple antimicrobial resistance determinants, underscores the potential for hospital environmental surfaces to serve as reservoirs of resistance. These findings highlight the value of strengthening targeted environmental hygiene and integrating routine genome-informed surveillance to detect, monitor, and mitigate transmission risks in African hospital settings.

The scale-up of antiretroviral therapy (ART) has transformed the HIV epidemic in sub-Saharan Africa, yet longitudinal public health insights from national programmatic cohorts remain scarce. As global targets move toward epidemic control and elimination of virological failure, understanding how profiles and outcomes of people living with HIV (PLWH) evolve in real-world settings is essential. The AIDS Support Organisation (TASO), Uganda’s largest HIV care provider, maintains one of the longest-running national registries of PLWH. We aimed to generate insights to inform targeted interventions, optimize programmatic responses and improve long-term care for PLWH in resource-constrained settings. We conducted a secondary analysis of routinely collected data within an open cohort of 54,348 PLWH enrolled at 11 TASO clinics between 2014–2024. Descriptive statistics summarized demographic, clinical, immunological, behavioral and socioeconomic characteristics at baseline and follow-up. At most recent recorded follow-up, 60.4% of PLWH were female with median age of 39 years (IQR 31–47); 55% aged 25–44 years. At ART initiation, non-nucleoside reverse transcriptase inhibitor (NNRTI)-based regimens dominated (>90% through 2017), by 2024, > 99% of ART initiations were dolutegravir (DTG)- based, aligning with national policy. Immune recovery improved over study period; however, 12% still presented with CD4 < 200 cells/µL. Most PLWH initiated care in WHO stage I–II, and prevalence of advanced disease declined further at last record. Self-reported adherence exceeded 95%. Tuberculosis was the most frequent infectious comorbidity (5% prior history), whereas hypertension (3.9%) and diabetes (2.5%) were the leading non-communicable conditions. Socioeconomic vulnerability reported 57% with irregular income and 21% unemployed. Psychosocial stressors, including poverty and stigma, were common. Findings show progress alongside persistent gaps and structural barriers. They mandate earlier diagnosis, integrated tuberculosis and non-communicable disease management, strengthened socioeconomic support, and risk-stratified care using predictive modeling to reduce virological failure and drive Uganda toward 95–95–95 targets in resource-constrained settings.

Motivation: Antimicrobial resistance surveillance in ESKAPEE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, Enterobacter spp., and Escherichia coli) requires reproducible, scalable, and portable workflows that can operate under data-sovereignty constraints across laptops, institutional servers, and cloud environments without dependency conflicts. rMAP 2.0 addresses these needs using a containerized Workflow Description Language pipeline executed with Cromwell. Results: rMAP 2.0 standardizes end-to-end bacterial whole-genome analysis, including read quality control, trimming, assembly, annotation, resistance/virulence/mobile-element profiling, sequence typing, pangenome inference, and phylogenetic reconstruction, using containerized execution. It generates a single interactive HTML report for rapid review. The workflow supports fully offline execution, including BLAST searches, for data-sovereign deployments and runs on local workstations, institutional servers, and cloud backends with Docker support.In a benchmark of 20 Enterobacterales isolates, rMAP 2.0 completed analysis in 4.5 hours on an 8-core/16-GB laptop and identified a misannotated record (SRR9703249), reclassified from K. pneumoniae to Enterobacter cloacae sequence type 182, while confirming lineage assignments such as E. coli ST131.

In regions where both monkeypox virus (MPXV) and varicella zoster virus (VZV) are co-circulating, overlapping clinical manifestations can complicate clinical diagnosis. During the MPXV outbreak declared in Uganda in July, 2024, symptomatic suspected cases tested PCR negative for Mpox. To determine the cause of symptoms, we employed metagenomic sequencing with a targeted Viral Surveillance panel in 284 MPXV negative samples. VZV was identified as the predominant pathogen in 86% of MPXV-negative cases, suggesting a concurrent chickenpox surge. Using the VaricellaGen pipeline for variant calling, clade typing, and phylogeny, 118 (49%) samples that achieved ≥ 70% genome coverage were of clade 5 based on the single-nucleotide polymorphism (SNP) dataset. This data confirms co-circulation of VZV during the MPXV outbreak in Uganda. Our results underscore the need for laboratory confirmation of MPXV and the inclusion of VZV in the testing algorithm during the Mpox outbreak.

Global biomedical and health research is increasingly relying on genomic and computational approaches, largely driven by the increasing volumes of nucleic acid sequencing. Concurrently, epidemiological studies and clinical records are generating enormous amounts of data amenable to disease modeling, machine learning, and artificial intelligence techniques. Bioinformatics and data science expertise is therefore essential for improved population health. Accordingly, in 2012, the US National Institutes of Health (NIH) in partnership with the Wellcome Trust, and with support from the African Society for Human Genetics, initiated the H3Africa (Human Heredity and Health in Africa) consortium. One of its key goals was to build capacity among African scientists to lead research on genetic and environmental contributors to health and disease across the continent. In 2017, the NIH provided funding to support the establishment of four graduate bioinformatics training programs across five African universities. Over seven years, these programs enrolled multiple trainees (n > 270), with >110 earning Master’s degrees and >20 completing PhDs in Bioinformatics. It is thus timely to evaluate the outcomes and impact of these programs, particularly regarding graduation rates, career trajectories, and the institutions and research domains their alumni are serving. We also assess employment outcomes and the nature of the research they are enabling (n > 110 peer-reviewed articles). We additionally include the progress and outputs of the programs’ instructors, which were partially enabled by program resources, networks, and trainees. Overall, this review paints valuable insights into the pioneering role of NIH extramural support in shaping Africa’s biomedical research landscape.

Background Low-density lipoprotein cholesterol (LDL-C) is central to cardiovascular risk assessment, yet its accurate determination remains challenging. The Friedewald formula performs poorly in hypertriglyceridemia, while newer formulas such as Martin/Hopkins and Sampson show improved accuracy. In Uganda and sub-Saharan Africa, contemporary comparisons of these equations against direct assays are lacking, increasing the risk of misclassification. Methods We conducted a retrospective method comparison study at Sebbi Hospital, Uganda, analyzing 1289 lipid profiles (2022–2025). Directly measured LDL-C on the Mindray BS-230 analyzer was compared with Friedewald, Martin/Hopkins, and Sampson formula-calculated LDL-C. Agreement, correlation, root mean square error (RMSE), mean absolute difference (MAD), and misclassification at European Society of Cardiology/European Atherosclerosis Society (ESC/EAS) treatment thresholds were assessed across triglyceride strata (<150, 150–399, ≥400 mg/dL). Results For TG <150 mg/dL, all methods showed comparable agreement with the Sampson equation (84.13 – 85.03%, k = 0.696 – 0.706), with misclassification rates of 14.97 – 15.87% and MAD ranging from 13.87 to 15.54 mg/dL. For TG 150 - 399 mg/dL, agreement declined markedly for Friedewald (67.75%), but remained substantially higher for directly measured LDL-C(86.07%) and Martin/Hopkins (82.12%). Misclassification was highest for Friedewald (32.43%) compared with Martin/Hopkins (17.88%) and directly measured LDL-C (13.93%). For TG≥400 mg/dL, agreement was nominally high for directly measured LDL-C (78.79) but weighted kappa was poor and non-significant for all three methods. MAD was 57.56mg/dL for directly measured LDL-C, 103.45 mg/dL for Friedewald and 64.96 mg/dL for Martin/Hopkins, with over 93% of directly measured LDL-C values differing from Sampson-calculated values by more than 20 mg/dL. Conclusion Agreement and misclassification varied by triglycerides level, with all methods showing comparable concordance with Sampson equation in the low-TG stratum. Friedewald equation showed the poorest agreement and highest misclassification at moderately elevated triglycerides level. In severe hypertriglyceridemia, large differences were observed across all methods, though conclusions were limited by small sample size. Findings highlight the importance of transparent reporting of LDL-C measurement method and careful interpretation near ESC/EAS treatment thresholds.