Philosophical Transactions of the Royal Society B: Biological Sciences

Background. Given uncertainty about whether later-life health at similar ages is improving over time, we examined trends across multiple health domains. Methods. We analysed data from community-dwelling adults aged 50 and older in the English Longitudinal Study of Ageing in 2004/05, 2012/13, and 2023/24 (main survey: N=8389, 8549, and 6090, respectively). Outcomes included self-rated health, limiting long-standing illness, pain, mobility limitations, cardiometabolic and chronic conditions, obesity, inflammation, mental health, quality of life and memory. Weighted pooled modified Poisson and linear regressions compared outcomes over time, overall, and by age group and education, with additional adjustment for sex and wealth. Results. Adjusted estimates showed divergent trends. Fair/poor self-rated health increased from 27% to 34%, and any pain from 37% to 47%, whereas mobility impairments declined from 58% to 52%. Self-reported high cholesterol increased from 19% to 39%, while biomarker-defined high cholesterol declined from 78% to 54%; diabetes increased on both measures. Psychiatric problems increased from 6% to 10%, quality of life declined, and memory improved. However, trends differed by age and education, particularly for limiting long-standing illness, mobility limitations, cholesterol biomarkers, and mental health, indicating that aggregate trends masked unevenly distributed changes. Conclusion. Later-life health in England has not improved uniformly. Gains in functioning, biomarkers, and cognition coexist with rising pain and poorer mental health. Trends were also socially and age patterned, producing increasingly multidimensional and socially patterned health outcomes. Multidomain health monitoring is essential for interpreting population health trends and planning healthy ageing, prevention, long-term care, and work policies.

We evaluated aggregated, privacy-preserving smartphone-detected nocturnal cough activity from the Sleep Cycle application as a potential syndromic surveillance signal in England. Weekly cough metrics from January 2023 to January 2026 were compared with UK Health Security Agency indicators: NHS 111 acute respiratory infection (ARI) triage calls, influenza and COVID-19 PCR positivity, and hospital admission rates for influenza, COVID-19, and respiratory syncytial virus. We evaluated total cough counts alongside two population-normalised metrics, coughs per user and coughs per hour of sleep, and assessed temporal relationships nationally and regionally using cross-correlation with prewhitening. The strongest and most consistent associations were observed for NHS 111 ARI triage calls, where population-normalised cough metrics showed raw national correlations of approximately 0.95 and retained prewhitened correlations above 0.55 at lag 0. This indicates that nocturnal cough activity closely tracks short-term variation in an established syndromic surveillance indicator, beyond shared seasonality, long-term trends, and autocorrelation. Similar near-contemporaneous patterns were observed across regions. Population-normalised cough metrics also showed epidemiologically plausible leading associations with pathogen-specific indicators: coughs per hour of sleep peaked one week before influenza PCR positivity, while both coughs per user and coughs per hour of sleep peaked one week before COVID-19 PCR positivity. Hospital-based indicators showed weaker and more heterogeneous relationships, but the normalised cough metrics still showed plausible temporal alignment with influenza and COVID-19 admissions, including contemporaneous associations with influenza admissions and short leading associations with COVID-19 admissions. In contrast, unnormalised total cough counts produced less stable and often non-interpretable lag structures, consistent with sensitivity to variation in observation volume. These findings suggest that passive, near-real-time nocturnal cough monitoring can provide a population-level signal of respiratory symptom burden, with greatest utility as a broad syndromic indicator that complements surveillance sources affected by healthcare-seeking behaviour, laboratory turnaround times, backfilling, and reporting delays.

To estimate the life expectancy gains that could be realised from increases in Queenslanders physical activity (PA) levels. Design Lifetable analysis Setting, Participants We modelled the 2025 Queensland population aged [≥]40 years. Modelled scenarios We applied two approaches. In the first, we estimated life expectancy differences between device-measured PA quartiles, with quartile1 representing the least active and quartile 4 the most active. In the second, we compared observed device-measured PA levels in Queensland with scenarios in which all individuals moved to either [≥]12,000 steps/day or [≤]2,000 steps/day. We converted the steps per day by age group and PA quartile into equivalent daily minutes of moderate-intensity walking at 4.8 km/h. Additional scenarios were explored in sensitivity analyses. Main outcomes Changes in life expectancy, and total life-years gained over the lifetime of the modelled population. Benefits were also translated into minutes of life gained per additional hour walked. Results If all Queenslanders aged [≥]40 years were as active as the most active quartile, life expectancy at birth could be 88.3 years, an increase of 4.8 years above the life expectancy at observed activity levels. The life expectancy differences between individuals in the least active quartile and the most active quartile was 9.7 years. Achieving the activity level of the most active quartile would require individuals in the lowest activity quartile to undertake an additional 85.9 minutes/day of moderate-intensity walking, with each extra hour of PA associated with an average gain of approximately 3 hours (177 minutes) of life. In step-based modelling, life expectancy in the most active scenario (all achieving [≥]12,000 steps/day) was higher by {approx}7.1 years compared with the least active scenario (all at [≤]2,000 steps/day). Conclusions Increasing PA could yield meaningful gains in life expectancy for Queenslanders, with the largest gains seen in least active individuals. Our findings strengthen the case for prioritising investment in PA -promoting programs and environments.

Background. The 2018-2020 Ebola virus disease outbreak in the Democratic Republic of the Congo (DRC) was the country's largest, and the second largest globally, amid armed conflict and community mistrust. Transmission heterogeneity (superspreading) is recognised in Ebola epidemics, but empirical estimates of its extent and determinants remain scarce for DRC outbreaks. We quantified transmission heterogeneity and its determinants during this outbreak. Methods. In this retrospective observational study, we reconstructed transmission chains for confirmed and probable cases (Aug 1, 2018, to June 25, 2020) using routinely collected Ministry of Health and Medecins Sans Frontieres surveillance data. We modelled the offspring distribution with a Bayesian negative binomial framework, correcting for incomplete contact tracing, to estimate the effective reproduction number (Reff), dispersion parameter (k), and proportion of cases responsible for 80% of transmission (prop80), overall, by subgroup, and over time. Individual-level determinants were assessed with a regression extension, adjusting for covariates. Findings. Among 3481 cases, 2008 transmission events linked 2402 (69%) individuals into 415 chains (median size 3, range 2-102). Overall Reff was 1.00 (95% CI 0.92-1.08) with k 0.29 (0.26-0.32); 17.8% of cases generated 80% of transmission. Overdispersion stayed stable despite fluctuating Reff. Non-isolation (IRR 1.79), death outside a treatment centre (IRR 4.34), and unfollowed contact status (IRR up to 4.48) predicted more secondary cases; vaccination cut transmission by about 60%. Interpretation. Epidemiological investigations linked 67.7% (2356/3481) of cases into 415 transmission chains (median size 3, range 2-102); linkage to a known infector fell to 10% during the November 2018-February 2019 period of peak insecurity. Transmission was heterogeneous overall, with dispersion parameter k of 0.29 (0.26-0.32), such that 17.8% (16.8-18.9) of cases generated 80% of onward transmission confirming superspreading as a stable, structural feature of Ebola dynamics. Critically, k remained stable throughout the outbreak, including during periods of elevated Reff, indicating that transmission surges reflected intensification of the same underlying process rather than new superspreading contexts, and that Reff alone is an insufficient summary of epidemic potential. Regression analyses identified predominantly modifiable determinants: cases not isolated in an Ebola treatment centre (IRR 1.79 [1.54-2.06]) or who died outside one (IRR 4.34 [3.47-5.31]) generated substantially more secondary cases, as did those registered as contacts but not followed up (IRR 3.01 [2.39-3.70]) or unregistered altogether (IRR 4.48 [3.67-5.38]) relative to actively followed-up contacts. Vaccination reduced onward transmission by 60-64% (IRR 0.36-0.40). These findings indicate that transmission was shaped less by gaps in epidemiological knowledge than by the operational reach of contact tracing, isolation, and vaccination delivery, particularly during periods of insecurity.

Objectives: Although HIV incidence has declined in some settings, the overall global burden of sexually transmitted infections remains a major public health concern. In the context of the World Health Organization's call for people-centred STI prevention and care, identifying the shared geographic pattern of multiple STIs using data-driven analysis may help detect vulnerable areas and inform integrated prevention strategies. Methods: We analysed country-level incidence counts from the Global Burden of Disease 2023 study for 204 countries and territories over 1990-2023. A Bayesian shared-component spatiotemporal model was fitted, decomposing each disease's log-rate into a shared spatial component (scaled intrinsic conditional autoregressive prior), disease-specific spatial deviations, disease-specific first-order random walk temporal effects, and five socioeconomic covariates, with a negative binomial likelihood to accommodate overdispersion. The shared spatial score - the posterior mean of the shared spatial component - was used as a continuous index of STI co-occurrence burden. Posterior exceedance probabilities quantified directional stability. External validity was assessed via Spearman correlation with the Socio-demographic Index and generalised estimating equation regression of HIV/AIDS mortality on the shared score. Results: The shared spatial score exhibited marked geographic heterogeneity. The five highest-scoring countries were Eswatini (2.25), Lesotho (2.13), Malawi (1.90), Mozambique (1.89), and South Africa (1.85), all in southern Africa. Fifty-seven countries had high directional stability (posterior exceedance probability >0.95), concentrated in sub-Saharan Africa and the Caribbean. The score correlated negatively with SDI (Spearman rho = -0.619, p = 6.4 x 10^-23) and positively with HIV/AIDS mortality (incidence rate ratio = 14.64 per standard deviation, 95% CI: 11.90-18.01). Prior sensitivity analysis confirmed near-perfect ranking stability (rho >= 0.9999). Conclusions: STI co-occurrence is geographically concentrated, with the highest shared burden in sub-Saharan Africa and persistently elevated shared spatial signals also observed in parts of mainland Southeast Asia and the Caribbean. The shared spatial score provides a data-driven tool for prioritising integrated STI screening and prevention resources across countries.

The basic reproduction number R0 confounds pathogen biology with adaptive human contact behavior. Earlier epidemiological--economic theory predicted a forward-looking behavioral contact response but could not test it in the absence of appropriate behavioral data. Using directly measured mobility as an observable proxy for contact, we (i) estimate the behavioral response function directly from data; (ii) show that the biology/behavior decomposition and hence the behavioral correction to R0 is not identified from an epidemic trajectory, the apparent constant-contact R0 being one endpoint of an observational-equivalence class that fits the factual curve identically yet diverges under counterfactual; and (iii) characterize that divergence ("what R0 deletes") as state-dependent, unimodal in counterfactual severity and vanishing when behavior saturates. We then show that, across US jurisdictions, the correction is empirically bounded because risk-responsiveness and behavioral non-saturation are confounded (r=-0.57, n=51): where behavior could compensate, it was already maximal, and where it was not maximal it did not respond. What R0 deletes is thus real and structurally characterizable yet empirically modest here, for reasons the framework itself supplies.

We present FootNet, a 453-image multi-view smartphone foot dataset for binary foot segmentation, with expertannotated masks across six anatomical views (dorsal, medial, and plantar, both left and right). We benchmark four segmentation models under a controlled protocol: U-Net with a MobileNetV2 encoder achieves the best performance (IoU 0.9268, Dice 0.9608, 95 % CI [0.9209, 0.9320]); DeepLabV3 with MobileNetV3-Large scores IoU 0.8984 (Dice 0.9449); UNet++ with MobileNetV2 scores IoU 0.8913 (Dice 0.9391); and SAM ViT-B with oracle boundingbox prompt scores IoU 0.9219 on the matched 191-image subset. Bonferroni-corrected Wilcoxon signed-rank tests (k = 6 comparisons) show U-Net significantly outperforms DeepLab (p < 0.001, r = 0.638) and SAM ViT-B with oracle boundingbox (p = 0.005, r = 0.202); UNet++ does not significantly differ from DeepLab (p = 0.062). Connected-component postprocessing yields negligible benefit (mean {triangleup}IoU = +0.0003, 12 of 453 images improved). The extended dataset is available upon request

Causal effect estimates can often be biased in clinical and epidemiological studies as patient cohorts frequently exhibit substantial covariate imbalances between treated and control groups, often amplified in multicentre studies due to heterogeneous recruitment, clinical practice, and case mix. Covariate balancing methods are therefore essential for valid causal inference. However, their application becomes challenging when data are distributed across cohorts and cannot be pooled because of privacy, legal, or institutional constraints, leaving a gap in practical methods for causal effect estimation in federated and imbalanced clinical data settings. We develop a privacy-preserving framework for covariate balancing and causal effect estimation across distributed data providers, combining federated aggregation with differential privacy to enable propensity score subclassification and matching without sharing individual-level records. Matching relies on non-disclosive quantities and differentially private distance evaluation, and the resulting matched subsets remain local to each server. Balance can be assessed through federated diagnostics and privacy-preserving visualisations, and we provide secure estimators for average treatment effects with associated uncertainty quantification. We implement this framework in the DataSHIELD federated analysis platform via 2 R packages. In simulations, we demonstrate agreement between federated and centralised analyses in the absence of privacy noise and quantify the bias--variance trade-offs induced by differential privacy. We illustrate applicability in two multinational settings-a Long COVID cohort and very preterm birth cohorts-showing that the approach enables practical causal analyses under real-world data protection constraints. The DataSHIELD packages are available on Github. Additional methodological details are provided in the Supplementary Material.

An epidemic's expected course is determined by the magnitude and timing of a typical person's infectiousness --- captured, in turn, by the basic reproduction number and the generation-time distribution. These fundamental, population-average quantities can mask individual-level variation that shapes how an epidemic actually unfolds: for example, individual variation in the magnitude of infectiousness (overdispersion) creates superspreading, a key feature of the SARS-CoV-1 and SARS-CoV-2 epidemics. However, the impact of individual variation in infectiousness timing is less well understood. Here, we demonstrate that individual infectiousness timing varies substantially and to different degrees across pathogens. For some common pathogens, including influenza, measles, and SARS-CoV-2, infectiousness is "bursty", or highly concentrated and variably-timed across individuals: for example, the window of appreciable infectiousness for SARS-CoV-2 may last for roughly a day, vs. the 9--12 days usually quoted. We show that bursty infectiousness creates superspreading without inherent superspreaders, makes epidemic timing more variable, amplifies the time-sensitivity of common interventions, and complicates inference of key epidemiological parameters. Together with the reproduction number, the generation-time distribution, and overdispersion, burstiness completes a family of basic parameters that govern how epidemics unfold.

Background. In 2026, the Democratic Republic of the Congo (DRC) experienced the largest recorded outbreak of Ebola disease caused by Bundibugyo virus, with epidemiologically linked importations and secondary transmission in Uganda. This study analysed the publicly reported trajectory and assessed the risk of introduction and onward transmission in North Africa and Europe. Methods. Public surveillance reports from the World Health Organization (WHO), European Centre for Disease Prevention and Control (ECDC), Africa CDC, national ministries of health, and peer-reviewed sources were synthesised through 15-17 July 2026. Headline counts and crude case-fatality ratios were restricted to laboratory-confirmed cases. Average notification rates were calculated from cumulative DRC counts. Exact Poisson intervals used the Garwood method, and the June-July rate ratio was estimated on the log scale. Risk was assessed across introduction likelihood, conditional onward-transmission likelihood, impact, and confidence. Results. By 15 July, the DRC had reported 2,124 confirmed cases and 828 deaths (crude confirmed-case fatality ratio, 39.0%) across 46 health zones in five provinces. Uganda had reported 20 confirmed cases and two confirmed deaths: 15 imported infections and five secondary cases, with no documented community transmission. DRC notifications averaged 47.4 per day during 1-15 July versus 35.9 per day during 2-29 June (rate ratio 1.32; counting-model 95% interval 1.20-1.46). WHO reported that more than 80% of new cases were detected outside known contact lists, while 119 confirmed healthcare-worker infections and 36 deaths had occurred. Introduction likelihood was assessed as very low to low for North Africa and very low for the general European population; delayed recognition in routine healthcare was the principal scenario for limited secondary transmission. Interpretation. Available indicators were inconsistent with effective control in eastern DRC at the data cut-off. Public reporting-date series cannot separate transmission from changing ascertainment, but they showed no sustained decline. Preparedness in North Africa and Europe should prioritise complete exposure histories, rapid isolation, validated diagnostics, protected clinical care, and contact management rather than reliance on border screening. Keywords: Bundibugyo virus; Ebola disease; outbreak surveillance; rapid risk assessment; importation; North Africa; Europe; Algeria; International Health Regulations.

Abstract Background: Remote clinical reviews have become an integral component of contemporary nursing practice across community and acute care settings. Nurses increasingly make autonomous clinical decisions using telephone, video, and online/digital systems, often with limited sensory information and under conditions of uncertainty. However, empirical understanding of how nurses make clinical decisions via remote reviews remains limited. Aim: To explore and understand how registered nurses (RNs) make clinical decisions about patient care via remote reviews. Methods: A convergent mixed-methods design was employed. Quantitative data (analytic quantitative sample N=53) were collected using validated questionnaires that measured decision-making processes, physician-nurse collaboration, decision-making stress, and perceived decision-making ability. Qualitative data (N=23) were generated through semi-structured interviews. Data collection took place between October 2024 and April 2025. Quantitative data were analysed using descriptive statistics, correlation, and multiple regression. Qualitative data were analysed using framework analysis. Integration was achieved through pillar-building and theory-driven synthesis and illustrated by joint display tables. Results: Most nurses demonstrated a flexible decision-making style, integrating analytical and intuitive reasoning. Both analytical and intuitive processes were positively associated with perceived decision-making ability. Physician-nurse collaboration emerged as a strong predictor of decision-making confidence, while decision-related stress was not a significant predictor. Qualitative findings identified three themes: characteristics of remote review; making adaptive decisions shaped by both internal and external constraints and enablers; and external influencing factors. The integrated findings informed a theory-informed ICE framework to illustrate how nurses make clinical decisions via remote reviews. Conclusion: Remote clinical decision-making is a dynamic cognitive-environmental process rather than a purely individual cognitive act. The ICE framework conceptualises this interaction, extending existing decision-making theories to digitally mediated care. Impact: Understanding remote decision-making supports training design, clinical governance, and the development of Artificial Intelligence-enhanced decision-support tools grounded in ecological bounded rationality. Patient or Public Contribution: Patient and public representatives contributed to stakeholder discussions that informed the development of the interview topic guide and the theoretical model. Patients or members of the public were not involved in recruitment, data collection, analysis, interpretation of findings, or preparation of the manuscript. Keywords: clinical decision-making, remote reviews, telehealth, nursing, mixed methods, ecological bounded rationality

Introduction Adolescent idiopathic scoliosis (AIS) requires frequent x-rays for management, exposing young patients to cumulative radiation risks. While radiation-sparing imaging modalities exist, access across the National Health Service (NHS) remains uneven and information given to patients is variable. This qualitative study investigated the systemic, geographic, and interpersonal dynamics of AIS imaging in England. Design This qualitative study employed in-depth semi-structured interviews with healthcare professionals (HCPs) from NHS paediatric spinal centres, patients aged 13 to 25 years old with AIS and parents/carers of young people with AIS. Setting England. Participants A total of 22 HCPs from 13/24 NHS paediatric spinal centres in England, 19 10-25 years with AIS and 11 parents/carers. Results Conventional x-ray remains the main imaging modality. Significant geographic inequality exists. The most commonly available radiation-sparing imaging modality available is the EOS system, which uses slot-scanning technology, is available at 7 centres in England, primarily in London imaging networks. Acquisition of EOS systems is currently driven by local charitable funding rather than a centralised strategy, with high capital and installation costs cited as primary barriers. Inconsistent knowledge of imaging within primary care and a lack of specialist expertise in local secondary care services led to diagnostic redundancy, gatekeeping, and low value inconsistent imaging. These systemic delays frequently closed the window for conservative treatments like bracing. A professional balancing act exists between the duty to inform and the desire to minimise patient anxiety. HCPs often use selective communication regarding radiation risks. Conversely, families demonstrate high relational trust with HCPs and low baseline knowledge of cumulative exposure, often viewing frequent imaging as a reassuring marker of clinical progress. In centres with EOS systems, clinicians felt empowered to lead proactive, transparent risk discussions. In standard X-ray settings, dialogue remains reactive and infrequent, leading to a reliance on implied rather than truly informed consent. Conclusions AIS imaging in England is variable. Geographic location dictates access to low-dose radiation technology and the quality of informed consent. Systemic inefficiencies and fragmented referral pathways contribute to diagnostic redundancy and delayed specialist care. National standardisation of clinical pathways, information provision and a centralised strategy for low-dose technology procurement are essential to eliminate structural inequalities and ensure equitable, transparent care for all patients.

Background: Bacteraemia is associated with poor outcomes but the diagnostic gold standard, peripheral blood culture, takes up to 24 hours to become clinically actionable, hampering early management decisions in suspected infection. Single predictors and existing sepsis risk scores discriminate poorly, and few multivariable bacteraemia models have been adequately validated in UK populations. Methods: We developed a logistic regression model, using backwards AIC based selection of predefined candidate predictors routinely available within hours of hospital attendance, in a retrospective cohort of 33,874 hospital encounters at University College London Hospitals (UCLH) between 2019 and 2024. Continuous predictors were modelled using restricted cubic splines and missing data handled using multiple imputation. Model performance was assessed via internal external cross validation and prediction instability analysis, before temporal validation in held-out 2024 UCLH data and external validation in 53,669 hospital encounters from the Infections in Oxfordshire Research Database (IORD). Results: Bacteraemia occurred in 5.2% of UCLH and 8.9% of IORD encounters, respectively. Twenty predictors were retained, spanning demographics, comorbidities, vital signs and blood tests. Discrimination was stable across development time periods (pooled c-statistic 0.82, 95%CI 0.81 to 0.84) and was maintained in temporal (0.83, 0.79 to 0.87) and external validation (0.83, 0.82 to 0.83), with excellent calibration in external validation (calibration slope 1.08 (1.05 to 1.11); calibration-in-the-large 0.01 (-0.02 to 0.04)). The model outperformed single predictors, established risk scores, and a reconstructed comparator model, and showed superior net benefit in decision curve analysis. Performance was consistent across age, sex, ethnicity and socioeconomic subgroups but degraded when blood cultures were sampled more than six hours after attendance and varied by likely infection site. Conclusions: This model accurately predicts bacteraemia using routinely collected data available within hours of hospital attendance, with performance maintained in a large, independent external validation cohort. It offers a generalisable, clinically interpretable tool to support early decision-making in suspected infection, pending further work to establish optimal implementation thresholds.

Background Nipah virus (NiV) is a priority zoonotic pathogen causing high-fatality outbreaks. Early NiV outbreaks in Malaysia and Singapore had limited transmission beyond spillover events. However, since 2001, NiV outbreaks with person-to-person transmission have occurred in Bangladesh and India, driven by the NiV-Bangladesh genotype and NiV-India genotype. Our study aims to estimate the reproduction number, offspring dispersion, and serial interval governing NiV transmission in Bangladesh and India during 2001-2026. Methods We conducted a systematic review of NiV outbreak investigations in Bangladesh and India, searching PubMed, Embase, Web of Science, and grey literature through 28 February 2026. Case-level offspring counts from 27 eligible sources (323 cases across 67 outbreaks) were used as input to a hierarchical Bayesian negative binomial offspring distribution model. The serial interval was estimated by parametric distribution fitting to 137 transmission pairs. Country-stratified and sensitivity analyses were performed to evaluate the robustness of estimates. Results Pooling across 67 outbreaks, we estimated a median reproduction number of 0.46 (95% CrI: 0.28-0.73), an offspring dispersion parameter of 0.07 (0.05-0.10), and a serial interval of 13.3 days (95% CI: 12.8-13.8). Country-stratified median reproduction numbers were 0.48 (0.23-0.97) for India and 0.35 (0.19-0.59) for Bangladesh, and dispersion parameters were 0.04 (0.02-0.07) and 0.11 (0.06-0.18), respectively, indicating marked overdispersion in both settings. Conclusion NiV transmission is self-limiting on average and highly overdispersed, suggesting that a disproportionate share of onward transmission arises from a small number of cases. This epidemiological profile supports targeted containment measures, including contact tracing and quarantine, for effective NiV outbreak control.

Stochastic epidemic models are a cornerstone of infectious disease epidemiology and are often used to study intervention scenarios. However, large run-to-run variability can make intervention effects difficult to estimate precisely. We revisit the epidemic Sellke construction, which assigns each individual an infection threshold for the cumulative infection hazard such that, conditional on the thresholds, the epidemic trajectory becomes deterministic. This enables coupling of simulations with and without an intervention, yielding low-variance effect estimates even when outcomes such as final size or peak incidence vary widely between runs. We develop an exact, event-driven implementation that maintains infection and recovery events in priority queues. Cumulative infection-hazard updates require O(log N) time per event, yielding overall complexity O(Elog N) for E events in a population of size N. The implementation achieves computational performance comparable to the classical Gillespie algorithm while naturally accommodating non-Markovian infectious periods and complex infectiousness profiles. We illustrate the approach using distance-dependent spread of avian influenza between poultry farms in the Netherlands and a multilayer population with households, schools, and workplaces. In both examples, coupling enables efficient within-run comparisons of intervention scenarios across stochastic realisations.

Background: Rheumatic heart disease (RHD) remains a major cause of premature death in low- and middle-income countries and First Nations communities. Early detection and management can prevent progression, but requires echocardiography, which is limited in high-burden settings. Task-sharing echocardiographic screening is an accessible, evidence-based approach but implementation remains unclear. Methods: We conducted a prospective implementation evaluation of a co-designed task-sharing screening programme across five remote First Nations Australian communities between May 2023 and November 2025. Predominantly community health workers (CHWs), alongside nurses and doctors, were trained to scan using handheld devices with off-site cardiologist interpretation. We assessed implementation outcomes and used a realist evaluation to explore how context shaped CHWs ability to complete training and embed screening into routine work. Data included scanning activity, surveys, costing, interviews, focus groups, and field notes. Findings: We trained 32 staff (21 CHWs, 8 nurses, 3 doctors) to scan across five sites. Scanning frequency was lower and more variable than anticipated: 360 scans (including training and post-certification) of 5 - 20 year olds over 14 months, with site-level coverage of 3 - 85%. Fidelity was limited by device unavailability, charging problems, and delays in uploads and reviews. Set-up and training cost A$51,903 per site, plus A$9,858/year in implementation support. Screening was easier for CHWs to embed when the legitimacy of their role as a scanner was communicated, but harder when invisible work outweighed opportunities to scan. Interpretation: Future implementation will require efforts to legitimise CHWs scanning and support invisible work. Event-based screening offers a promising complementary strategy. Scale-up requires policy support. Funding: This research was funded by the Australian Medical Research Futures Fund Cardiovascular Health Mission (GNT2015869), in addition to philanthropic donations from Medtronic Australasia, Edwards Life Sciences and the Rotary Club of Kiama. Hand-held devices (Philips Lumify, USA) were donated by Humpty Dumpty Foundation and East Timor Hearts Fund.

Background: Cholera outbreaks can spread rapidly, which means that the optimal decision-making window for a large, coordinated response is very narrow. Alerts for triggering interventions need to balance tradeoffs between wasting resources on false positives and delaying decisions until they lose effectiveness. A systematic evaluation of such tradeoffs across settings is needed to understand which alerts may have the greatest public health utility and where. Methods: Using weekly suspected cholera surveillance across 4,081 subnational administrative units across 34 countries in Africa from 2010-2023, we evaluated 24 alert definitions (4 alert types with different numeric thresholds) over a 1-year post-alert period on five utility dimensions - potential health impact, potential intervention efficiency, positive predictive value (PPV) for large outbreaks, proportion of missed outbreaks, and timeliness of alert trigger. The dimensions were combined into a utility score, which was used to identify the best alert across the continent and by country. For top-performing alerts, we estimated the reduction in potential health impact for additional delays in response using Bayesian hierarchical models. Results: Fifty suspected cases for three consecutive weeks was the definition with the highest utility score across most contexts. In administrative units with 50,000 to 500,000 people, the year following such an alert experienced a mean of 376 suspected cases (standard deviation: 618.3) and 2 cases per 1000 population (SD: 4.1). Forty percent of such alerts (N alerts: 265) were followed by a 1-year period with over 300 cases, yet the definition missed 40% of outbreaks with over 300 cases (N outbreaks: 278) and was triggered 5.3 weeks (SD: 5) after outbreak start. Each week of delay was estimated to result in an additional 20% reduction (95% CrI: -23 to -17) of potential health impact in the outbreak response. One hundred cases over a three-week period was another definition that had high utility, particularly in administrative units with smaller populations and country-specific evaluations. Conclusion: We present a decision analytic framework that can be deployed in a short decision-making window using case-based surveillance to trigger large-scale cholera response activities with moderately high utility across most African transmission contexts. Future work should consider adaptations based on local data availability and priorities and examine the generalizability of early case-based signals outside Africa.

Snakebite envenoming is a major neglected tropical disease disproportionately affecting rural populations in sub-Saharan Africa. In Ghana, evidence on the spatial and temporal distribution of risk remains limited, constraining targeted prevention and resource allocation. This study quantified district-level snakebite risk across Ghana, identified persistent hotspots and environmental drivers, and evaluated the relationship between snakebite burden and geographic access to treatment. Monthly district-level snakebite cases from Ghana's District Health Information Management System (2020 to 2025) were analyzed across all 261 districts using a Bayesian spatio-temporal model incorporating spatial effects, a temporal random effect, and a space-time interaction, fitted via Integrated Nested Laplace Approximation. Environmental covariates including rainfall, temperature, humidity, and NDVI quantified associations with risk. Relative risks, exceedance probabilities, Local Indicators of Spatial Association, and geographic accessibility identified priority districts. Snakebite risk showed strong spatial clustering and temporal variation. Persistent high risk districts were concentrated in Upper West (Daffiama Bussie Issa, Wa East, Wa West, Sissala East), Savannah (Bole, Gonja), North East (Mamprugu Moagduri), Western North (Bia East), Bono (Banda), Oti (Krachi Nchumuru), Western (Wassa East), and Eastern Region (Nsawam Adoagyiri, Fanteakwa North), though patterns evolved. Fanteakwa North emerged as the highest risk district nationally in 2025. Humidity and temperature were associated with increased risk, while rainfall and NDVI showed no significant effect. High risk districts often had poor treatment access, revealing inequities. This first nationwide Bayesian spatio temporal assessment provides an evidence base for surveillance, antivenom distribution, and interventions supporting WHO's 2030 snakebite reduction goals.

Background Bloodstream infections are a major cause of mortality, yet the primary testing method, blood cultures, have low positivity (<10%) and turnaround times of 24 - 48 hours. Many are taken from patients at low risk of infection, while some bloodstream infections are diagnosed late or missed entirely. We aimed to develop and externally validate machine learning models to improve targeting of blood culture testing. Methods In this retrospective cohort study, we used routinely collected clinical and laboratory data available around culture collection from a large multi-site NHS trust (Oxford University Hospitals; Infections in Oxfordshire Research Database), between 1 January 2016 and 17 March 2025. All blood cultures taken from adults and children were included. XGBoost models were trained to predict pathogenic blood culture positivity using a temporal split (training before 1 January 2024; held-out test thereafter). External validation used emergency department data (between 1st May 2019 and 30th April 2024) from University College London Hospitals. An additional analysis examined blood culture reallocation towards the highest-risk untested admissions. Findings 294,064 cultures were included (positivity 5.6%). In the temporal hold-out test set (n=46,339), AUROC (Area Under the Receiver Operating Characteristic) was 0.853 (95% CI 0.846 - 0.860), rising to 0.876 in emergency department patients, and the model was well calibrated (slope 1.046). In external validation (n=37,326), AUROC was 0.847 (95% CI 0.839 - 0.856) with preserved calibration. In a simulated resource-neutral reallocation, replacing the 10,000 lowest-risk sent cultures with the highest-risk untested emergency admissions yielded 627 additional positive cultures (28.3% relative increase in yield). Performance was reduced when restricted to data available at the point of culture collection (AUROC 0.769, 95% CI 0.760 - 0.779). Interpretation An externally validated, well calibrated machine learning model built from broadly available, routinely collected data could improve blood culture yield without increasing testing volume, supporting resource-neutral diagnostic stewardship across NHS sites.

We develop a mathematical model of Rift Valley Fever integrating mosquito vectors, ruminants, and humans, based on an SEIR-type structure with vertical transmission in vectors. Local data from the Sudanian and especially the Sahelian zones are used to capture the impact of climatic variations on mosquito population dynamics. The mathematical analysis establishes the models positivity, determines the basic reproduction number R0, and demonstrates the local and global stability of the disease-free equilibrium. Sensitivity analysis (PRCC) highlights the most influential parameters, while the stochastic approach using a continuous-time Markov chain confirms the major role of seasonal rainfall. Numerical simulations reveal a peak in animal and human infections around the 9th month, correlating with periods of heavy rainfall. This model provides a relevant tool for surveillance and prevention within a "One Health" approach in Chad.