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• Journal article
  Da Silva Candido D, Dellicour S, Cooper LV, Prete Jr CA, Jorgensen D, Uzzell CB, Voorman A, Lyons H, Klapsa D, Majumdar M, Arowolo K, Peak CM, Bandyopadhyay AS, Martin J, Grassly NC, Blake IMet al., 2025,

  Historical and current spatiotemporalpatterns of wild and vaccine-derivedpoliovirus spread

  , Nature Microbiology, ISSN: 2058-5276
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• Journal article
  Leber W, Farooq HZ, Panovska-Griffiths J, Larvin H, Baggaley RF, Divall P, Haigh D, Davies EA, Choudhry S, Hicks SR, Goodwin L, Foster GR, Orkin CM, Zenner D, Vickerman P, Hickman M, Reid L, Worrall S, Pareek M, Lais S, Anderson J, Robson J, Griffiths CJet al., 2025,

  Risk prediction models for targeted testing of HIV, hepatitis B and hepatitis C: a systematic review and meta-analysis.

  , BMC Infect Dis, Vol: 25

  BACKGROUND: Diagnosing human immunodeficiency virus (HIV), hepatitis B virus (HBV), and hepatitis C virus (HCV) infections in general population settings is challenging. We conducted a systematic review and meta-analysis of prediction tools designed to help identify individuals at risk of these blood-borne viruses. METHODS: We included studies on individuals of any age at risk of blood-borne viruses from healthcare, community settings, and national databases. We searched the Web of Science, MEDLINE, EMBASE, and CENTRAL databases (from database inception to 2023) and used the Prediction model Risk Of Bias ASsessment Tool (PROBAST) to evaluate the quality and systematic risk of bias of these studies. We extracted model accuracy using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. A mixed-effects model (for AUC) and bivariate random-effects model (for sensitivity/specificity) were used to generate pooled values for these studies. RESULTS: Of the 41,585 records, 71 were included, covering over 31 million participants and more than 65,000 cases of blood-borne viruses. We examined 67 models: 47 for HIV, 13 for HCV, 5 for HBV, and 2 from studies that assessed multiple viruses separately. The studies were conducted in 41 low- and middle-income and 30 high-income countries. They covered 11 different populations (including men who have sex with men, the general population, and women), 8 types of settings (including sexual health, secondary care, and primary care) and 7 types of risk factors (behavioural, clinical, and demographic). The methods comprised traditional regression (n = 50), machine-learning models (n = 17), and others (n = 4). The risk of bias was high in 64 studies and low in seven. Among 33 studies reporting mean and 95% CI, pooled AUC values were 0.73 (95% CI:0.67–0.80, [Formula: see text] = 74%) across HIV studies (including 8 machine-learning models)

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• Journal article
  Leng T, Whittles LK, Nikitin D, White PJet al., 2025,

  Publisher Correction: Modeling gonorrhea vaccination to find optimal targeting strategies that balance impact with cost-effectiveness.

  , NPJ Vaccines, Vol: 10
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• Journal article
  Allen T, Dadzie S, Dheerasinghe A, Fall G, Faria N, Javelle E, Grillet ME, Guzman MG, Luz KG, Kafy HT, Mostafavi E, Ng LC, Reusken C, Scott T, Tuli NR, Venter Met al., 2025,

  Strengthening global preparedness and response to arboviral disease threats: a call to action

  , The Lancet Infectious Diseases, ISSN: 1473-3099
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• Journal article
  Bosetti P, Peckeu-Abboud L, Andrianasolo RM, Baguelin M, Fonteneau L, Deghamne A-E, Taha M-K, Barret A-S, Grimprel E, Opatowski L, Levy-Bruhl D, Cauchemez Set al., 2025,

  Modelling the impact of a quadrivalent ACWY meningococcal vaccination and vaccination targeting serogroup B in France

  , VACCINE, Vol: 67, ISSN: 0264-410X
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• Journal article
  Silhol R, Booton R, Mitchell K, Stannah J, Stevens O, Dimitrov D, Bershteyn A, Johnson L, Kelly S, Kim H-Y, Maheu-Giroux M, Martin-Hughes R, Mishra S, Stone J, Stuart R, Stover J, Vickerman P, Wilson D, Baral S, Donnell D, Imai-Eaton J, Boily M-Cet al., 2025,

  Identifying priority populations for HIV interventions using acquisition and transmission indicators: a combined analysis of 15 mathematical models from 10 African countries

  , The Lancet HIV, ISSN: 2352-3018

  Background. Characterising disparities in HIV infection across populations by gender, age, and HIV risk is key information to guide intervention priorities. We aimed to assess how indicators measuring HIV acquisitions, transmissions, or potential long-term infections influence estimates of the contribution of different populations to new infections, including key populations (KPs, including female sex workers (FSW), their clients, men who have sex with men).Methods. Using 9 models representing 15 different settings across Africa, we evaluated four indicators: I1) acquisition indicator measuring the annual fraction of all new infections acquired by a specific population, I2) direct transmission indicator measuring the annual fraction of all new infections directly transmitted by a specific population, I3) 1-year and I4) 10-year transmission population-attributable fractions (tPAFs). tPAFs measure the fraction of new infections averted if transmission involving a specific population was blocked over a specific time period. We compared estimates of the four indicators across 7 populations and 15 settings and assessed if the contribution of specific populations is ranked differently across indicators for 10 settings.Findings. Indicators identified distinct priority populations as the largest contributors: The acquisition indicator (I1) identified women aged 25+ years outside KPs as contributing the most to acquired infections in 8/10 settings in 2020, but to direct transmissions (I2) in only two settings. In 6/10 settings, the 10-year tPAFs (I4) identified non-KP men aged 25+ years and clients of FSW as the largest contributors to HIV transmission. Notably, non-KP women aged 15-24 years acquired (I1) more infections in 2020 (median of 1·7-fold across models) than they directly transmitted (I2), while non-KP men aged 25+ years and clients of FSWs transmitted more infections than they acquired in all but one model (median: 1·4 and 1·6-fold, respective

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

  A statistical method for evaluating vaccine-induced immune correlates of protection against infection and disease progression: application to the ChAdOx1-S nCoV-19 phase 3 trial

  , Vaccine, Vol: 67, ISSN: 0264-410X

  Background: Correlates of protection (CoPs), defined as immune markers statistically correlated with vaccine efficacy (VE), can be used to accelerate vaccine development. Different components of the immune response may be important for protection against infection and against progression from asymptomatic infection to symptomatic or severe disease. However, CoPs are typically evaluated for these outcomes separately, which can lead to some CoPs not being identified. We propose a novel statistical framework for the integrated evaluation of CoPs for infections with multiple potential outcomes.Methods: We developed a model of the natural history of an infection that can identify CoPs at each stage of infection and disease progression and implemented this model in a Bayesian estimation framework. We validated the model on simulated data then applied it to individual-level clinical and serum neutralising and binding antibody data from COV002 (NCT04400838), a phase II/III trial of the ChAdOx1 nCoV-19 (AZD1222) vaccine. We explored logistic and non-parametric (cubic spline) relationships between VE and the candidate CoPs.Results: Both parametric and non-parametric forms of the model accurately estimated the relationships between the immune CoP and VE against infection (VEin) and against progression to symptoms given infection (VEpr) in 1000 simulated trial datasets. In the COV002 correlates subset (2227 participants, 5315 samples), SARS-CoV-2 spike-specific IgG was positively associated with both VEin and VEpr (average proportion of VE mediated by spike specific IgG, 27 % (95 % CI 2–88 %) for VEin and 41 % (95 % CI 0–96 %) for VEpr). Pseudoneutralisation antibody titres and receptor binding domain (RBD) specific serum IgG showed similar correlations.Conclusion: Integrated analysis of multiple disease outcomes and candidate CoPs enables the identification of CoPs that operate at different stages of disease progression, which are missed when evaluating outcomes se

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• Journal article
  Steyn N, Chadeau M, Whitaker M, Atchison C, Ashby D, Cooke G, Ward H, Elliott P, Donnelly Cet al., 2025,

  Pandemic-risk-related behaviour change in England from June 2020 to March 2022: the cross-sectional REACT-1 study among over 2 million people

  , BMJ Public Health, ISSN: 2753-4294
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• Journal article
  Parag K, Lambert B, Donnelly CA, Beregi Set al., 2025,

  Asymmetric limits on timely interventions from noisy epidemic data

  , Communications Physics, ISSN: 2399-3650

  Deciding on when to initiate or relax an intervention in response to an emerging infectious disease is both difficult and important. Uncertainties from noise in epidemiological surveillance data must be hedged against the potentially unknown and variable costs of false alarms anddelayed actions. Here we clarify and quantify how case under-reporting and latencies in case ascertainment, which are predominant surveillance noise sources, can restrict the timeliness of decision-making. Decisions are modelled as binary choices between responding or not that are informed by reported case curves or transmissibility estimates from those curves. Optimal responses are triggered by thresholds on case numbers or estimate confidence levels, with thresholds set by the costs of the various choices. We show that, for growing epidemics, both noise sources induce additive delays on hitting any case-based thresholds and multiplicative reductions in our confidence in estimated reproduction numbers or growth rates. However, for declining epidemics, these noise sources have counteracting effects on case data and limited cumulative impact on transmissibility estimates. We find this asymmetry persists even if more sophisticated feedback control algorithms that consider the longer-term effects of interventions are employed. Standard surveillance data therefore provide substantially weaker support for deciding when to initiate a control action or intervention than for determining when to relax it.This information bottleneck during epidemic growth may justify proactive intervention choices.

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• Journal article
  Eilerts-Spinelli H, Romero-Prieto JE, Herbst K, Gareta D, Jasseh M, Khagayi S, Obor D, Imai-Eaton JW, Reniers Get al., 2025,

  Pregnancy reporting and biases in under-five mortality in three African HDSSs

  , POPULATION STUDIES-A JOURNAL OF DEMOGRAPHY, ISSN: 0032-4728
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