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• Journal article
  Spadar A, Mahindroo J, Troman C, Owusu M, Adu-Sarkodie Y, Owusu-Dabo E, Abraham D, Benny B, Govindan K, Mohan VR, Dyson ZA, Grassly N, Holt KEet al., 2025,

  AmpliconTyper – a tool for analysing ONT multiplex PCR data from environmental and other complex samples

  , Microbial genomics, Vol: 11, ISSN: 2057-5858

  Amplicon sequencing is a popular method for understanding the diversity of bacterial communities in samples containing multiple organisms as exemplified by 16S rRNA sequencing. Another application of amplicon sequencing includes multiplexing both primer sets and samples, allowing sequencing of multiple targets in multiple samples in the same sequencing run. Multiple tools exist to process the amplicon sequencing data produced via the short-read Illumina platform, but there are fewer options for long-read Oxford Nanopore Technologies (ONT) sequencing, or for processing data from environmental surveillance or other sources with many different organisms. We have developed AmpliconTyper (v0.1.28, DOI: 10.5281/zenodo.15045111) for analysing multiplex amplicon sequencing data from environmental (e.g. wastewater) or similarly complex samples, generated using ONT devices. The tool uses machine learning to classify sequencing reads into target and non-target organisms with very high specificity and sensitivity. The user can train models using public and/or user-generated data, which can subsequently be applied to analyse new data. The tool can also generate amplicon consensus sequences, as well as identify SNPs and report their genotype implications, such as association with lineages or antimicrobial resistance (AMR). The tool is freely available via Bioconda and GitHub (https://github.com/AntonS-bio/AmpliconTyper). AmpliconTyper allows robust identification of target organism reads in ONT-sequenced environmental samples and can identify user-specified lineage or AMR markers.

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• Journal article
  Cliff M, Jahn S, Bita Fouda A, Latt A, Lingani C, Trotter Cet al., 2025,

  Updating the meningitis belt: associations between environmental factors and epidemic meningitis risk across Africa

  , Wellcome Open Research, Vol: 10, Pages: 497-497

  <ns3:p>Background Previous analytical work, defining the distribution of meningitis epidemics in Africa is over 20 years old, with climate change representing an ongoing issue. We aim to update this analysis and determine if the meningitis belt geography and associated environmental risk factors have changed in the last two decades. Methods Epidemic bacterial meningitis data from 2003–2022 were provided by WHO-AFRO. Districts across Africa were coded 1 if they experienced a meningitis outbreak and 0 if not. Monthly means of windspeed, rainfall, dust, and humidity were processed into climatic profiles using k-means clustering. We undertook logistic regression with meningitis epidemic history as the dependent variable and k-means clusters of rainfall, dust, humidity, and windspeed, alongside land-cover type as independent variables. A sensitivity analysis was conducted, excluding the Democratic Republic of Congo (DRC), due to limited laboratory confirmation of cases. Results Rainfall, dust, and humidity demonstrated the strongest statistical association with outbreaks and were included in our final model. With a probability cut-off &gt;0.4, our model had specificity and sensitivity of 81.0% and 84.3%, respectively, in identifying districts having experienced a meningitis epidemic. The Sahelian region had the highest risk of meningitis outbreaks (probability &gt;0.8), consistent with previous findings. The inclusion/exclusion of the DRC had a significant impact on our model. In the full model the Republic of the Congo, Gabon, Liberia, and Angola had a moderate risk of meningitis (probability &gt;0.4), suggesting a possible south-westerly expansion of the belt. However, when the DRC was excluded, no countries surrounding the meningitis belt were at risk for outbreaks, highlight the importance of laboratory testing and case confirmation. Conclusions The apparent extension of risk beyond the belt possibly reflects surveillance limitations rather th

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• Journal article
  Mohan V, Strepis N, Mitsakakis K, Becker K, Chindelevitch L, Shivaperumal N, Swe-Han KS, Hays JPet al., 2025,

  Erratum to Antimicrobial resistance in Campylobacter spp. focussing on C. jejuni and C. coli – A Narrative Review’ Journal of Global Antimicrobial Resistance Volume 43 (2025) Pages 372-389

  , Journal of Global Antimicrobial Resistance, Vol: 44, Pages: 453-454, ISSN: 2213-7165
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• Journal article
  Fenske L, Jauneikaite E, Getino M, Wan Y, Goesmann A, Eisenberg Tet al., 2025,

  Evidence of a novel sublineage of Streptococcus agalactiae in elephants from zoo populations in Germany

  , Microbial Genomics, Vol: 11, ISSN: 2057-5858

  Streptococcus agalactiae research primarily centres on investigating human and bovine infections, although this pathogen also can be carried and cause infections in a wider range of animal species. Moreover, infections with S. agalactiae are posing significant health implications, and recent studies furthermore are highlighting a potential zoonotic risk. Despite the relatively frequent isolation of S. agalactiae from elephants, only a few reports document infections in wild and zoo populations. We performed a comparative genomic analysis of 24 elephant isolates from three different zoos in Germany to achieve a comprehensive characterization. Elephant isolates showed pronounced phylogenetic divergence from isolates of other host species, while also forming clusters based on zoo of origin and their genotypes (MLST profiles). Capsular serotypes could not be predicted for the majority of the isolates (n=20/24). Several genes, exclusively associated with the elephant host, may underlie the pathogen's capacity to improve its survival and virulence across varied ecological niches. This study not only deepens our understanding of S. agalactiae across diverse species and environments but also represents the first whole-genome sequencing characterization of S. agalactiae isolates from elephants, helping to expand our knowledge about infections in animals.

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• Journal article
  Walters MK, Bulterys MA, Barry M, Hicks S, Richey A, Sabin M, Louden D, Mahy M, Stover J, Glaubius R, Kyu HH, Boily M-C, Mofenson L, Powis K, Imai-Eaton JWet al., 2025,

  Probability of vertical HIV transmission: a systematic review and meta-regression

  , The Lancet HIV, Vol: 12, Pages: e638-e648, ISSN: 2352-3018

  BackgroundEliminating HIV vertical transmission is a global priority and monitored by estimating paediatric HIV infections with the UNAIDS-supported Spectrum AIDS Impact Module (Spectrum-AIM). Recent innovations in antiretroviral therapy (ART) service-delivery models and first-line regimens aimed to reduce vertical transmission probabilities. We did a systematic review and meta-analysis to estimate vertical transmission probabilities by maternal immunological and treatment status.MethodsIn this systematic review and meta-regression, we combined an updated systematic review with previous data in meta-regression models to estimate vertical transmission probabilities and determinants. We searched PubMed, Embase, the Global Health Database, WHO Global Index Medicus, CINAHL Complete, and Cochrane CENTRAL for peer-reviewed English-language studies from all regions published between Jan 1, 2018 and Feb 8, 2024, with search term domains mentioning “HIV”, “transmission”, “perinatal”, and “breastfeeding periods”, and “infants born to women living with HIV” or related terms from randomised trials, cohort studies, or observational studies. Four meta-regression models estimated vertical transmission probabilities. We assessed model sensitivity and compared estimates to Spectrum-AIM's previous results. Finally, we fit a meta-regression model to assess the association of ART class and initiation timing on viral load suppression (VLS) at delivery.FindingsOf 12 588 potential studies, we identified 24 new studies, which along with the 86 from previous reviews yielded 110 total studies included in meta-regression analysis. For women not receiving ART, higher CD4 count was associated with lower odds of perinatal vertical transmission (odds ratio [OR] 0·80, 95% CI 0·75–0·84, per 100 cells per μL increase). For pregnant women on ART, each additional week on ART before delivery reduced odds of vertic

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• Journal article
  Connelly S, Muller JG, Ali M, Ngasala BE, Hassan W, Mohamed B, Thwai KL, Sadler JM, Marglous J, Fola AA, Zacharia A, Shija SJ, Mohammed S, Msolo DC, Said H, Peter EE, Odas M, Rutha IJ, Nwange M, Naire K, Ruybal-Pesantez S, Verity R, Crudale R, Goel V, Choloi BB, Bjorkman A, Bailey JA, Lin JT, Juliano JJet al., 2025,

  Artemisinin Partial Resistance Mutations in Zanzibar and Tanzania Suggest Regional Spread and African Origins, 2023

  , JOURNAL OF INFECTIOUS DISEASES, ISSN: 0022-1899
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• Journal article
  Waldock W, Thould H, Chindelevitch L, Croucher NJ, de la Fuente C, Collins JJ, Ashrafian H, Darzi Aet al., 2025,

  Mitigating antimicrobial resistance by innovative solutions in AI (MARISA): a modified James Lind Alliance Analysis

  , npj Antimicrobials and Resistance, Vol: 3, Pages: 1-9, ISSN: 2731-8745

  Antimicrobial resistance (AMR) is a critical global health threat and artificial intelligence (AI) presents new opportunities for our response. However, research priorities at the AI-AMR intersection remain undefined. This study aimed to identify and prioritise key areas for future investigation. Using a modified James Lind Alliance approach, we conducted semi-structured interviews with eight experts in AI and AMR between February and June 2024. Analysis of 338 coded responses revealed 44 distinct themes. Major barriers included fragmented data access, integration challenges and economic disincentives. The top ten priorities identified were: Combination Therapy, Novel Therapeutics, Data Acquisition, AMR Public Health Policy, Prioritisation, Economic Resource Allocation, Diagnostics, Modelling Microbial Evolution, AMR Prediction and Surveillance. A notable limitation was the underrepresentation of data from high-burden regions, limiting the generalisability of findings. To address these gaps, we propose the novel BARDI framework: Brokered Data-sharing, AI-driven Modelling, Rapid Diagnostics, Drug Discovery and Integrated Economic Prevention.

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• Journal article
  Munsey A, Digre P, Hicks J, Wagman J, Robertson M, Alao M, Hounto AO, Gansane A, Debe S, Candrinho B, Uhomoibhi P, Okoko OO, Lemwayi R, Aron S, Kangale CC, Kabamba BM, Miller J, Walker P, Gutman Jet al., 2025,

  Antenatal care surveillance for monitoring malaria prevalence and intervention coverage: a multicountry analysis

  , BMJ GLOBAL HEALTH, Vol: 10, ISSN: 2059-7908
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• Journal article
  Hendier L, Soule H, Abbas M, Pittet D, Pignel R, Boet Set al., 2025,

  Evaluation of bacterial survival on inert surfaces in a hyperbaric environment

  , DIVING AND HYPERBARIC MEDICINE, Vol: 55, Pages: 197-201, ISSN: 1833-3516
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• Journal article
  Sherrard-Smith E, Ngufor C, 2025,

  Can behaviour change communication improve malaria control?

  , Lancet Glob Health, Vol: 13, Pages: e1500-e1501
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