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• Journal article
  Shah A, 2025,

  Patients’ perspectives on antimicrobial resistance in chronic respiratory disease: an AMR-Lung – European Lung Foundation global patient survey

  , ERJ Open Research, ISSN: 2312-0541
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• Journal article
  Ellis J, Anderson R, 2025,

  Pre-school Age Participation in Mass Drug Administration: Analysing the Impact on Community-wide Schistosomiasis Control

  , International Journal of Infectious Diseases, ISSN: 1201-9712
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• Journal article
  Caleiro GS, Claro IM, Hua X, Basile GV, Nuevo KMB, da Costa CA, Tubaki RM, de Menezes RMT, Scandar SAS, Colebrusco LAR, Araújo ELL, de Souza WM, Sabino EC, Faria NR, Cunha MSet al., 2025,

  Molecular Epidemiology of St. Louis Encephalitis Virus, São Paulo State, Brazil, 2016–2018

  , Emerging Infectious Diseases, Vol: 31, Pages: 1052-1053, ISSN: 1080-6040

  We detected St. Louis encephalitis virus (SLEV) in 0.16% (3/3,375) of Aedes and Sabethes spp. mosquitoes captured during 2016–2018 in São Paulo State, Brazil. We also isolated and confirmed that the SLEV strains belong to genotype III. Continued surveillance is required to clarify the burden of SLEV in Brazil.

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• Journal article
  Degtyareva S, Hamada Y, Baggaley RF, Hassan N, Capocci S, van Crevel R, van Geuns D, Miller R, Pareek M, Pozniak A, Rangaka MX, Tiberi S, de Vries G, Lipman M, Brown Jet al., 2025,

  Tuberculosis Preventive Treatment care pathways in people living with HIV: a systematic review and meta-analysis

  , European Respiratory Journal, Pages: 2302174-2302174, ISSN: 0903-1936

  <jats:sec><jats:title>Background</jats:title><jats:p>Tuberculosis Preventive Treatment (TPT) can reduce TB incidence and mortality in people living with HIV. However, low levels of screening and uptake, poor adherence, and loss to follow-up considerably reduce its effectiveness. We aimed, therefore, to assess the losses within all steps of the screening and treatment cascade.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>To enhance data generalizibility we included articles which reported the proportion of people living with HIV completing any step of the TPT cascade in low- and high-TB burden countries published before March 2024. Random effects meta-analysis produced pooled estimates of the proportion proceeding to the next step along the cascade. Results were explored through subgroup analyses and meta-regression. PROSPERO registration: CRD42020162396.</jats:p></jats:sec><jats:sec><jats:title>Findings</jats:title><jats:p>Data from 368 cohorts containing 2.7 million participants were included. High levels of heterogeneity in outcomes were seen. Most participants were from Africa (80.6%). Isoniazid monotherapy was used for TPT in 92.6% of cohorts, usually for six months. Substantial loss to follow-up was found throughout the treatment cascade with more than one in six patients lost at the following steps: initial screening, immunological testing, treatment start and completion. Treatment regimens lasting four months or less were more likely to be completed than longer ones – 88.4% compared to 61.6%.</jats:p></jats:sec><jats:sec><jats:title>Interpretation</jats:title><jats:p>Our analysis highlights substantial loss to follow-up at multiple steps during the care cascade. This may significantly lower the reported effectiveness of TPT in real-world settings. Research and policy should focus on simplified care p

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• Journal article
  Rawson T, Morgenstern C, Knock E, Hicks J, Pham A, Morel G, Cabezas Murillo A, Sanderson M, Forchini G, Fitzjohn R, Hauck K, Ferguson Net al., 2025,

  A mathematical model of H5N1 influenza transmission in US dairy cattle

  , Nature Communications, ISSN: 2041-1723
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• Journal article
  Coupland H, Scheidwasser N, Katsiferis A, Davies M, Flaxman S, Hulvej Rod N, Mishra S, Bhatt S, Unwin HJTet al., 2025,

  Exploring the potential and limitations of deep learning and explainable AI for longitudinal life course analysis

  , BMC Public Health, Vol: 25, ISSN: 1471-2458

  BackgroundUnderstanding the complex interplay between life course exposures, such as adverse childhood experiences and environmental factors, and disease risk is essential for developing effective public health interventions. Traditional epidemiological methods, such as regression models and risk scoring, are limited in their ability to capture the non-linear and temporally dynamic nature of these relationships. Deep learning (DL) and explainable artificial intelligence (XAI) are increasingly applied within healthcare settings to identify influential risk factors and enable personalised interventions. However, significant gaps remain in understanding their utility and limitations, especially for sparse longitudinal life course data and how the influential patterns identified using explainability are linked to underlying causal mechanisms.MethodsWe conducted a controlled simulation study to assess the performance of various state-of-the-art DL architectures including CNNs and (attention-based) RNNs against XGBoost and logistic regression. Input data was simulated to reflect a generic and generalisable scenario with different rules used to generate multiple realistic outcomes based upon epidemiological concepts. Multiple metrics were used to assess model performance in the presence of class imbalance and SHAP values were calculated.ResultsWe find that DL methods can accurately detect dynamic relationships that baseline linear models and tree-based methods cannot. However, there is no one model that consistently outperforms the others across all scenarios. We further identify the superior performance of DL models in handling sparse feature availability over time compared to traditional machine learning approaches. Additionally, we examine the interpretability provided by SHAP values, demonstrating that these explanations often misalign with causal relationships, despite excellent predictive and calibrative performance.ConclusionsThese insights provide a foundation for

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• Journal article
  Stapley J, Walker M, 2025,

  Modelling transmission thresholds and hypoendemic stability for onchocerciasis elimination

  , PLoS Computational Biology, ISSN: 1553-734X
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• Journal article
  Topazian H, 2025,

  Estimating the potential impact of surveillance test-and-treat posts to reduce malaria in border regions in sub-Saharan Africa: a modelling study

  , Malaria Journal
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• Journal article
  Morgenstern C, Rawson T, Hinsley W, Perez Guzman P, Bhatt S, Ferguson Net al., 2025,

  Socioeconomic and temporal heterogeneity in SARS-CoV-2 exposure and disease in England from May 2020 to February 2023

  , Science Advances, ISSN: 2375-2548
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• Journal article
  Jombart T, 2025,

  Contrasting the impact and cost-effectiveness of successive intervention strategies in response to Ebola in the Democratic Republic of the Congo, 2018-2020

  , BMJ Global Health, ISSN: 2059-7908
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