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• Journal article
  Scheidwasser N, Nag A, Penn MJ, Jakob A, Andersen FM, Khurana MP, Setiawan L, Duchêne DA, Bhatt Set al., 2025,

  phylo2vec: a library for vector-based phylogenetic tree manipulation

  , Journal of Open Source Software, Vol: 10, Pages: 9040-9040
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• Journal article
  Zhu B, Chen L, He Y, Zhang N, Xue H, Bhatt S, Ren M, Mao Yet al., 2025,

  Beyond distance: integrating economic burden into large-scale primary healthcare accessibility analysis

  , GLOBAL HEALTH RESEARCH AND POLICY, Vol: 10
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• Journal article
  Abdalla L, Mata ASD, Fraser KJ, Jahn S, Krempser E, Pinter A, Pecego Martins Romano A, Medeiros-Sousa AR, Garkauskas Ramos D, Junji Shimozako H, Mucci LF, Costa Gomes LA, Alcantra LCJ, Silva Oliviera R, Pereira Sayago Soares RO, Pereira Feijó V, Augusto D, Chame M, Gaythorpe KAMet al., 2025,

  Mechanistic yellow fever modelling under climate change in Brazil and beyond: Information gaps and future steps

  , Wellcome Open Research, Vol: 10, Pages: 596-596

  <ns3:p>Yellow fever (YF) remains a significant public health threat in tropical regions, particularly in South America and Africa. The combined forces of climate change, land-use, urbanisation, globalisation, and insufficient surveillance and health infrastructure are driving the re-emergence and expansion of YF into new areas. While mathematical models have been used to estimate transmission risk, disease burden, and the impact of vaccination, there remains a crucial gap in mechanistic models that explicitly capture how climate and environmental changes directly influence YF transmission. To address this gap, we convened a workshop in Brazil as part of the Vaccine Impact Modelling Consortium’s Climate Change programme, bringing together national and international experts. The workshop aimed to present current modelling approaches, identify key knowledge gaps, and develop strategies to improve data collection and model applicability. Discussions highlighted major uncertainties regarding vectors, non-human primates, surveillance sensitivity, vaccination, and climatic and environmental drivers. This paper synthesises the outcomes of the workshop, including priority areas for future research and recommendations for advancing mechanistic YF modelling in the context of climate change, with a focus on both Brazil and broader tropical regions.</ns3:p>

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• Journal article
  Boni MF, Soulama I, Opigo J, Watson OJ, Ogutu Bet al., 2025,

  Slowing artemisinin resistance in Africa

  , SCIENCE ADVANCES, Vol: 11
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• Journal article
  Penn MJ, Donnelly CA, Bhatt S, 2025,

  Continuous football player tracking from discrete broadcast data

  , ROYAL SOCIETY OPEN SCIENCE, Vol: 12, ISSN: 2054-5703
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• Journal article
  Turner H, Rivillas-Garcia JC, Prinja S, Hung TM, Dabak SV, Asare BA, Jit M, Teerawattananon Yet al., 2025,

  An introduction to costing and the types of costs used within health economic studies

  , PharmacoEconomics - Open, ISSN: 2509-4254

  The number of published health economic analyses, especially economic evaluations, has rapidly expanded globally since the 1990s, and costs are an essential component of such studies. Cost is a general term that refers to the value of the resources/inputs used to produce a good or service. However, within health economics, there are several different types of costs (such as financial, economic, unit, average, etc). The terminology and application of these cost types often differ, leading to inconsistencies in the health economics literature. These inconsistencies create challenges in comparing studies and hinder the use of health economic analyses to effectively inform policy decisions. This paper aims to provide an up-to-date overview of the cost types, key cost terms, and definitions of different cost measures used within health economics, while highlighting key inconsistencies in the literature. We also discuss common adjustments made to cost data, such as accounting for inflation, discounting, and currency conversions, as well as the influence of economies of scale and scope on cost estimates. We highlight the different definitions/categories for the different types of costs are not mutually exclusive and that the type of cost that should be used will depend on the purpose of the study, highlighting recommendations of what to do in practice where relevant. The content was tailored to be relevant across both high-income and low and middle-income (LMIC) country contexts.

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• Journal article
  Williams LR, Voysey M, Pollard AJ, Grassly NCet al., 2025,

  A novel approach for estimating vaccine efficacy for infections with multiple outcomes: application to a COVID-19 vaccine trial

  , AJE Advances: Research in Epidemiology, Vol: 1

  <jats:title>Abstract</jats:title> <jats:p>Vaccines can provide protection against infection or limit disease severity. Vaccine efficacy (VE) is typically evaluated independently for different outcomes, but this does not provide insight into the mechanism of the protective effect and can cause biased estimates of VE. We propose a new conceptual framework and statistical implementation for VE estimation for infections with multiple possible outcomes of infection: joint analysis of multiple outcomes in vaccine efficacy trials (JAMOVET). JAMOVET is a Bayesian hierarchical regression model that controls for biases and can evaluate covariates for VE, the hazard of infection, and the probability of progression. We applied JAMOVET to simulated data, and data from COV002 (NCT04400838), a phase 2/3 trial of ChAdOx1 nCoV-19 (AZD1222) vaccine. Simulations showed that biases are corrected by explicitly modeling disease progression and imperfect test characteristics. JAMOVET estimated ChAdOx1 nCoV-19 VE against infection (${\mathrm{VE}}_{in}$) at 55% (95% credible interval [CrI] 35-70) and progression to symptoms (${\mathrm{VE}}_{pr}$) at 44% (95% CrI 26-59). This implies a VE against symptomatic infection of 75% (95% CrI 62-85), consistent with published trial estimates. JAMOVET is a powerful tool for evaluating diseases with multiple dependent outcomes and can be used to adjust for biases and identify predictors of key outcomes.</jats:p>

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• Journal article
  Agostinho A, Chalot E, Teixeira D, Bosetti D, Buetti N, Catho G, Harbarth S, Abbas Met al., 2025,

  Semi-automated surveillance of surgical site infections using machine learning and rule-based classification models

  , NPJ DIGITAL MEDICINE, Vol: 8, ISSN: 2398-6352
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• Journal article
  Wehrli S, Hartner A-M, Boender TS, Arnrich B, Irrgang Cet al., 2025,

  Information Pathways and Voids in Critical German Online Communities During the COVID-19 Vaccination Discourse: Cross-Platform and Mixed Methods Analysis

  , JOURNAL OF MEDICAL INTERNET RESEARCH, Vol: 27, ISSN: 1439-4456
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• Journal article
  Hancock P, Hui T-Y, Epopa PS, Milogo A, McKemey AR, Yao FA, Diabate A, Burt Aet al., 2025,

  Requirements for designing cluster randomised control trials to detect suppression of malaria vector population densities

  , BMC Biology, Vol: 23, ISSN: 1741-7007

  BackgroundNovel interventions for mosquito-borne disease control which release modified mosquitoes that are sterilised or genetically modified to cause offspring inviability are progressing towards field applications. Cluster randomised control trials (CRCTs) could provide robust assessment of intervention efficacy in suppressing mosquito populations in field environments, but guidance on designing CRCTs to detect mosquito suppression impacts is limited.ResultsWe developed statistical models to simulate CRCTs, informed by a 5-year time series measuring densities of malaria vector species from the Anopheles gambiae complex in four villages in western Burkina Faso. We estimated requirements for parallel and step wedge designs, varying the targeted vector species, the suppression effect and the monitoring regime. For a suppression effect of 50%, 21–22 clusters were required to detect suppression with 90% power when all An. gambiae complex species were targeted, while 24–26 clusters were required when only An. coluzzii was targeted and 60–66 clusters were required when only An. gambiae was targeted. For stronger suppression effects, required trial sizes depended less on target species, with 9–10 clusters being sufficient to detect a 90% suppression effect. We investigated how reducing sampling effort, by sampling fewer houses and restricting sampling to rainy season months, affected statistical power.ConclusionsOur results provide empirically based guidance for designing CRCTs to evaluate interventions aiming to suppress malaria vector populations.

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