Swiss Medical Weekly

We describe a fast, noninvasive, low-cost survey method designed to understand the mode of transmission of an emerging pathogen. It is inspired from the standard household prevalence survey consisting in sampling households and counting the total number of people infected in each household, but refines it with the aim of improving diagnosis and estimating more parameters of the model of intra-household transmission. The survey was carried out in May-June 2020, during part of the first national French lockdown and received responses from more than 6,000 households involving a total of 20,000 people. We explain how we conceived the questionnaire, how we disseminated it, to the public through an open website hosted by CNRS, marketed through media and social media, and to a socially representative panel hosted by two survey institutes (BVA, Bilendi). We used the data obtained from the representative panel to correct for sampling biases in the CNRS survey using a classical raking procedure. Our results indicate that raking correctly canceled statistical biases between the two populations. We obtain the empirical distribution in households of the number and nature of symptoms. The main factors affecting the presence of symptoms are age, gender, body mass index (BMI), household size, but not necessarily in the expected direction. Our study shows that combining self-reporting and representative surveys allows investigators to obtain information on prevalence and household transmission mechanisms on emerging diseases at low cost.

PurposeThe COVID-19 pandemic has had profound and multifaceted impacts on children and adolescents, exposing and deepening pre-existing inequalities while creating unique health, social, and educational challenges. In response to the limited evidence-based knowledge available, the SEROCoV-KIDS study was launched in 2021 as a prospective cohort and biobank to assess the pandemics effects on youth health and well-being. It focuses on the general population of Geneva, Switzerland, as well as subgroups with vulnerabilities, including prior SARS-CoV-2 infection, chronic health conditions, and socioeconomic disadvantage. ParticipantsA total of 2199 children and adolescents, aged 6 months to 17 years, from 1340 households were enrolled in the SEROCoV-KIDS cohort, 2048 from the general population and 151 from clinics (i.e., children with chronic health conditions). At baseline, between December 2021 and June 2022, participants provided blood samples for serological testing and for long-term storage in the biobank, while comprehensive questionnaires were completed by a referent adult and adolescents aged 14 and older. Five follow-up online assessments were conducted until October 2025, addressing physical and mental health, development, quality of life, health behaviors, family dynamics, and education-related topics. The quantitative findings of the cohort study were enriched by a cross-sectional qualitative study conducted between October 2023 and March 2024, focusing specifically on socioeconomically disadvantaged populations. Findings to dateIn the population-based sample, 66.3% of participants tested seropositive for anti-SARS-CoV-2 nucleocapsid antibodies at baseline, and 4.1% reported symptoms consistent with post-COVID condition. Most children were minimally or not at all affected by the pandemic, showing good mental health over time. However, 8% of participants reported a positive pandemic impact, whereas 8-13% experienced negative impacts, mainly due to disrupted routines and reduced social support. Health behaviors like physical activity and sleep remained largely stable over the study period. Higher screen time at baseline was associated with poorer well-being. Children with chronic health conditions or experiencing socioeconomic and family disadvantage were disproportionately affected in terms of the health and psychosocial consequences of the pandemic. Future plansSEROCoV-KIDS demonstrates the value of child-focused cohorts for understanding the consequences of major societal events and for guiding evidence-based policy. Our next priority is to secure funding to prolong follow-up of this cohort and to maintain systematic surveillance of children and adolescents, so that emerging findings can directly inform public health and education policy over the coming years. Strengths and limitations of this studyO_LIThis large population-based pediatric cohort provides insights into the health and well-being of children and adolescents aged 6 months to 17 years at baseline, during and after the COVID-19 pandemic (from December 2021 to October 2025). C_LIO_LIThis study integrates multifaceted findings on the general pediatric population and on subgroups of children with clinical and/or social vulnerabilities, combining quantitative and qualitative data to provide a deeper understanding of how the pandemic has affected their lives. C_LIO_LIThis unique pediatric cohort and its associated biobank offer a rare opportunity to advance future pediatric research in Switzerland and abroad. C_LIO_LIGeneralizability may be limited, as participating families tended to be more highly educated and somewhat more socioeconomically advantaged than the general Geneva population, despite nearly one-fifth reporting financial difficulties. C_LIO_LIThe study was designed in response to the pandemic, and individual-level pre-pandemic data are lacking, which limits direct comparisons over time, relying instead on parent-reported perception of changes and impacts. C_LI

The COVID-19 pandemic exposed major vulnerabilities of hospital capacity and management worldwide, particularly in intensive care units (ICUs) and emergency rooms (ER), imposing prompt adaptation and resource reallocation. Although SARS-CoV-2 is no longer endangering healthcare systems, winter seasons continue to bring recurrent overload of critical care services, primarily due to respiratory infections. In France e.g., this pattern led to the reactivation of the national emergency response plan during the 2024-2025 seasonal influenza peak, highlighting the continuous need for improved predictive tools. However, forecasting hospitalization surges at a local scale remains a methodological challenge because the (very) low incidence numbers are subject to strong stochasticity and therefore require additional input of information and dedicated approaches. This study investigates the potential for early forecasting of respiratory infection peaks by analyzing ER visit trends. By clustering all-cause ER visits during the 2023-2025 winter seasons from the Nimes University Hospital (France), we identified a strong temporal correlation between early pediatric hospitalizations ([≤]5 years old) and the following weeks adult hospitalization incidence for respiratory infections. The results suggest that tracking hospital admissions of pediatric ER visits, even without hospital care needs, can serve as a valuable early warning signal for upcoming peaks in respiratory-related hospitalizations. This predictive approach could improve hospital preparedness and resource management during seasonal influenza outbreaks. Author summaryThe epidemics of respiratory viruses present a significant challenge to hospitals in the temperate zone on an annual basis. Frequently, the hospital overload is mitigated by the late reactive allocation of human and material resources that are, hence, suboptimal. This study proposes a statistical framework to assist hospitals in anticipating bed requirements during seasonal influenza waves, despite high noise at the local level, by enhancing hospitalization forecasting with emergency room (ER) visit data. The prediction of the adult epidemic peak is possible through the analysis of the respiratory pediatric ER visits, which facilitates hospital management.

The COVID-19 pandemic has presented severe challenges in understanding and predicting the spread of infectious diseases, necessitating innovative approaches beyond traditional epidemiological models. This study introduces an advanced method for automated model discovery using the Sparse Identification of Nonlinear Dynamics (SINDy) algorithm, leveraging a dataset from the COVID-19 outbreak in Thuringia, Germany, encompassing over 400,000 patient records and vaccination data. By analysing this dataset, we develop a flexible, data-driven model that captures many aspects of the complex dynamics of the pandemics spread. Our approach incorporates external factors and interventions into the mathematical framework, leading to more accurate modelling of the pandemics behaviour. The fixed coefficient values of the differential equation as globally determined by the SINDy were not found to be accurate for locally modelling the measured data. We therefore refined our technique based on the differential equations as found by SINDy, by investigating three modifications that account for recent local data. In a first approach, we re-optimized the coefficient values using seven days of past data, without changing the globally determined differential equation. In a second approach, we allowed a temporal dependence of the coefficient values fitted using all previous data in combination with regularization. As a last method, we kept the coefficients fixed to the original values but augmented the differential equation with a small neural network, locally optimized to the data of the past week. Our findings reveal the critical role of vaccination and public health measures in the pandemics trajectory. The proposed model offers a robust tool for policymakers and health professionals to mitigate future outbreaks, providing insights into the efficacy of intervention strategies and vaccination campaigns. This study advances the understanding of COVID-19 dynamics and lays the groundwork for future research in epidemic modelling, emphasising the importance of adaptive, data-informed approaches in public health planning.

BackgroundUnderstanding the drivers of protective behavior during infectious disease outbreaks is critical for public health policy. App-based experimental epidemic games (epigames) offer a novel method to study these behaviors empirically, but their external validity--how well in-game choices reflect real-life beliefs--still needs to be rigorously tested. MethodsWe conducted a two-week randomized controlled trial (N=567) using the Epigames smartphone app at the American University of Iraq - Baghdad (AUIB) campus in the Middle East. This app used Bluetooth communication to sample the contact network between participants in sub-minute resolution and simulated the spread of a hypothetical respiratory virus through this network. Participants were randomized into two groups with differing opportunity costs (in-game points) for adopting voluntary quarantine within the game that would protect them from the simulated infection: Group 1 (Low Barrier) faced a small point difference between quarantine and non-quarantine choices, while Group 2 (High Barrier) faced a much larger difference. The optimal point difference between groups was determined by a Willing to Accept (WTA) pilot survey prior to the epigame. We measured real-life health beliefs and in-game beliefs through surveys at the beginning of the epigame, including questions on susceptibility, severity, self-efficacy, and benefits, to calculate the correlation between the two and to construct a Health Belief Model (HBM) parameterized by game data. We also measured self-assessment of game realism and behavior via an exit survey. ResultsReal-life and in-game beliefs showed moderate positive (Spearmans coefficient {rho} from 0.13 to 0.36) and statistically significant (p-value < 0.05) correlation across all survey measures, suggesting indicator (behavior) parallelism between real-life and the epigame. The exit survey also yielded positive self-assessment of context and behavior realism during the epigame. While simple aggregate analysis showed no significant difference in quarantine rates between Groups 1 and 2, a Poisson regression model revealed a significant crossover interaction. High economic barriers significantly reduced quarantine adoption among participants with low in-game motivation (interaction coef. = -2.22, p < 0.01). Perceived benefits appeared to moderate this effect at a near significance level (coef. = +0.33, p < 0.08). Demographic factors such as gender appeared to be significantly correlated with quarantine choice (p < 0.01). Analysis of the contact network measured with the app through Bluetooth showed assortative properties of several belief variables. ConclusionThis is the first systematic pre-registered study on the external validity of app-based epigames and the impact of economic cost and individual beliefs on protective behaviors. We found that economic barriers act as a "gatekeeper" for quarantine during the game, suppressing action among the skeptical while allowing highly motivated individuals to act. The significant correlation between real-life, in-game beliefs, and network structure suggests that epigames are a valid experimental tool for network-aware behavioral epidemiology. This warrants further studies for replication, examining variance across settings, and addressing limitations (e.g., crosstalk between groups) and technical issues (e.g., Bluetooth connectivity) in the study design. Study pre-registration: https://osf.io/qev6n/

This study shows that the quality of the morbidity data is sufficient to allow for meaningful analyses of spatiotemporal dynamics and provides a significant contribution to understanding the 1918-1920 influenza pandemic in Switzerland by complementing existing mortality- focused research with a morbidity perspective. Previous studies have examined the spatial patterns of mortality during the 1918-1920 influenza pandemic and associated explanatory factors. However, while mortality reflects the severity of a pandemic, a full understanding requires analysis of both morbidity and mortality. For the first time, this study systematically analysed district-level morbidity data for all of Switzerland and their associations with several ecological determinants. The spatial pattern of morbidity with the spatial pattern of mortality were also compared to investigate potential differences. Spatial clustering was assessed using the Getis-Ord Gi* statistic, and geographically weighted regression was employed to evaluate local relationships between incidence and ecological variables. Across all waves, higher incidence rates were positively associated with population density, GDP per capita, the share of industry, and the number of private physicians per km{superscript 2}. Conversely, GDP per capita and industrial activity were associated with lower mortality, while a higher proportion of men in a district correlated with lower incidence and higher mortality. The share of individuals aged 20- 39 years was associated with both higher incidence and higher mortality. These findings highlight that those factors shaping morbidity patterns can differ from those influencing mortality, emphasizing the importance of examining both dimensions for a comprehensive understanding of pandemic dynamics.

Neural tube defects (NTDs) represent frequent and severe congenital anomalies of the central nervous system, including anencephaly, spina bifida, and encephalocele. This retrospective study evaluates the occurrence, prenatal diagnosis, and sex distribution of NTDs in the Czech Republic during the period 1961-2020. Data were obtained from the National Registry of Congenital Anomalies within the National Registry of Reproductive Health. Both prenatally and postnatally diagnosed cases of anencephaly, spina bifida, and encephalocele were analyzed. A total of 2,521 cases of anencephaly, 3,391 cases of spina bifida, and 704 cases of encephalocele were recorded. Prenatal diagnosis with subsequent termination of pregnancy accounted for a substantial proportion of cases, particularly in anencephaly. The mean total incidence per 10,000 live births was 2.91 for anencephaly, 4.38 for spina bifida, and 1.24 for encephalocele. Sex distribution analysis across six consecutive decades demonstrated a persistent predominance of affected females in spina bifida, with statistically significant differences in selected periods. In contrast, anencephaly and encephalocele showed a female predominance only in earlier decades, whereas a higher proportion of affected males has been observed in recent years. Although advances in prenatal diagnostics have markedly reduced the incidence of NTDs among live-born children, the overall population incidence of these defects has remained stable.

The growth of generative AI and easily available Open Access health datasets has transformed researcher productivity, leading to an explosion in publications that has in part been attributed to paper mills (organisations that provide manuscripts for payment) and other unethical actors. These entities are not, however, homogenous, and have a range of products and target markets. While the demand from China has received much attention, here we provide a case study of CDC WONDER, a dataset that has been exploited by a network of researchers reporting affiliations in Pakistan, the United States and the UK, potentially linked to medical residency driven demand from junior clinicians or trainees. The number of publications using CDC WONDER grew from 88 in 2021 to 1223 in 2025. Over the same time period, the proportion of papers reporting at least one author from Pakistan grew from 0.5% in 2021 to 27.2% in 2025, with unusually extensive collaboration networks. In some cases these works featured over 15 co-authors, often including representation from Western institutions, but in spite of this high level of resourcing only resulted in straightforward analyses of well-described conditions using publicly available data. The majority of these outputs additionally show evidence of being produced from a template, with formulaic titles and identical methods, for example using the same statistical model and platform (Joinpoint regression). Identifying papers produced by fast-churn workflows is essential to protect the integrity of the scientific literature from being flooded with low-quality research. This can be achieved through more proactive desk rejection of misleading and formulaic mass-produced submissions, and through better understanding of which use cases are appropriate for different Open Science resources. With the growing capabilities of AI to mass produce research, education will be essential to assist critical appraisal and preserve the benefits of Open Science.

1Parameter estimation is often necessary to inform transmission models of infectious diseases. This estimation requires choosing an observation model that links the model outputs to the observed data. Although potentially consequential, this choice has received little attention in the literature. Here, we aimed to compare eight observation models, including common distributions such as the Poisson, binomial, negative binomial, and normal (equivalent to least-squares estimation). Using Bayesian inference methods, we fit an SIR-like model to daily case reports during the first wave of COVID-19 in Belgium, Finland, Germany, and the UK. We found considerable differences in the log-likelihoods of the observation models, spanning three orders of magnitude between the best and the worst. Compared with the best models, the binomial, Poisson, and normal models received no support due to their rigid variance structures. Additionally, the binomial and Poisson models produced overly narrow prediction and confidence intervals, especially for key parameters such as the basic reproduction number. The other five models--each with a free dispersion parameter scaling the variance to the mean--performed significantly better, with the negative binomial model ranking first in three countries. We conclude that flexible observation models are essential for transmission models to accurately capture all sources of uncertainty.

We present results from the second season of Influcast, a multi-model collaborative forecasting hub focused on influenza in Italy. During the 2024/25 winter season, Influcast collected one-to four-week-ahead probabilistic forecasts of influenza-like illness (ILI) incidence alongside influenza A and B ILI+ incidence signals. New ILI+ targets were constructed integrating syndromic surveillance data with virological detections collected weekly by the Italian National Institute of Health. Forecasts were submitted by six independent models (including compartmental, metapopulation, and statistical approaches) and combined into an ensemble. Ensemble forecasts for ILI+ consistently outperformed both the baseline (a naive persistence model) and most individual models in terms of Weighted Interval Score (WIS), Absolute Error (AE), and prediction coverage. Importantly, ensemble ILI+ forecasts achieved significantly lower WIS and AE ratios (i.e., ratio between the ensemble and the baseline models) and improved calibration compared to ILI forecasts. Our findings support the integration of virological surveillance data in forecasting target definition to improve the reliability of epidemic forecasts and strengthen their utility for situational awareness, communication, and targeted intervention.

PurposeTo develop and validate a prediction model for sleep apnea syndrome (SAS) treated with continuous positive airway pressure (CPAP) in the general population. MethodsUsing claims and health checkup data held by JMDC Inc., linked to personal health records (Pep Up), we developed and internally validated a prediction model for SAS treated with CPAP, defined as a diagnosis of SAS and reimbursement records of CPAP. Every three months from January 1, 2022 to July 1, 2024 (i.e., 11 timepoints), we identified eligible individuals with available data both 1 year before and 1 year after that timepoint to define the presence/absence of SAS treated with CPAP, as well as 279 predictor variables. We developed a LightGBM model for the training and tuning datasets and evaluated its performance on the validation dataset. ResultsAmong 18,692,873 observations (mean age 44.8{+/-}11.3 years, women 37.5%) obtained from 1,858,566 people, 300,868 (1.6%) had SAS treated with CPAP. The area under the receiver operating characteristic curve was 0.898 (95% confidence interval 0.895-0.901). The positive predictive values among people with the top 1% and 10% prediction scores were 28.3% and 10.3%, respectively. According to the SHapley Additive exPlanations plot, male sex was the most important predictor, followed by age, body mass index, and waist circumference. We also demonstrated that personal health records significantly improved the predictive performance. ConclusionWe developed a prediction model to identify people at high risk of SAS and encourage them to undergo polysomnography or related tests.

Reunion island just experienced a massive chikungunya virus outbreak in 2024-2025, with more than 54,000 confirmed cases. This is the second major chikungunya outbreak on the island, following the first one that peaked 20 years ago. It has been assessed that this new outbreak finds its origin in a single introduction event into the island, offering a unique opportunity to exploit viral genomic data to understand the epidemiological and dispersal dynamics of the introduced transmission chain. We sequenced >3,000 near-full viral genomes collected during the course of the epidemic. Harnessing this genomic dataset, we used a set of phylodynamic and phylogeographic approaches to unravel the paths taken by the transmission chain and the external factors having impacted its dynamics on the island. Our analyses highlight a dispersal pattern in line with a gravity-model dynamic with viral transition events being more frequent from and toward more populated areas. While we find that dispersal events were on average more likely between geographically close locations, our analyses also reveal that the transmission chain was overall spatially intermixed, with frequent exchanges among distant residential areas. In addition, we show that the decrease in transmission rate leading to the end of the epidemic can, at least to a large extent, be attributed to the population immunity resulting from both the current and the 2005-2006 epidemic. While a short-term resurgence of viral transmission cannot be excluded, the impact of herd immunity constitutes an encouraging outcome that should at least contribute to limiting the spread of the virus in the upcoming seasons.

BackgroundDuring the early phase of the Covid-19 pandemic in England, people with pre-existing conditions that put them at severe clinical risk if infected were advised to drastically reduce face-to-face contacts in a policy known as "shielding". The impact of this policy in preventing COVID-19 hospitalisations and deaths has not been evaluated at the national level using transmission-dynamic modelling. MethodsWith the approval of NHS England, we present a retrospective cohort evaluation of the shielding policy, drawing data from electronic health records (EHRs) for 24 million patients in England accessed through the OpenSAFELY platform. The study is from 1 January to 1 December 2020, prior to vaccination and new SARS-CoV-2 variants. We used a dynamic transmission model of SARS-CoV-2 transmission, infection, and hospitalisation, stratified by age and shielding status for the general population (excluding care homes). We estimated transmission rates in the shielding and non-shielding groups using data from the CoMix social contact survey, and fitted the model to hospitalisations and deaths in and outside hospital. FindingsWe found that the risk of hospitalisation was higher for shielding people in all age groups and increased with age. The hospital fatality ratio was similar between shielding and non-shielding people from January to June 2020 and greater in shielding people from July 2020 onward. By comparing the observed epidemic to a counterfactual scenario without shielding, we projected that between 7800 and 10,600 hospitalisations and 2300 to 3500 deaths due to COVID-19 were directly averted by the policy, corresponding to reductions of 25% (24, 28%) and 23% (21, 25%), respectively, in the shielding population in England up to 1 December 2020. Including also the indirect effect in the non-shielding population, we projected between 14,700, and 21,800 hospitalisations and 3700 and 5500 deaths due to COVID-19 were averted by the policy in the total population, each corresponding to reductions of 13% (11, 16%). InterpretationBased on the data and assumptions in this study, the shielding policy reduced pressure on the NHS and severe illness and mortality in clinically-extremely vulnerable shielding patients in England up to 1 December 2020, and, through indirectly-reduced exposure, also in the non-shielding population. Similar policies for other infections could have a comparable public health impact in reducing both mortality and pressure on public health services. FundingMedical Research Foundation, Medical Research Council, National Institute for Health and Care Research, NHS England, The Wellcome Trust.

The time-varying reproduction number (Rt) is a critical quantity in monitoring an infectious disease outbreak. We propose a new method for estimating Rt from an infectivity profile, expressed as a generation time distribution, and a time series of probabilistic estimates of disease incidence, modelled as log-normally distributed random variables. This is a common output of disease incidence models that are based on Poisson or negative binomial regression of case counts with a logarithmic link function. The method is deterministic, computationally inexpensive and propagates inherent uncertainty in incidence estimates. We validate the method when applied to the output of two simple statistical incidence models, and using simulated data with a defined Rt and infectivity profile. This combination produces comparable outputs to the de-facto standard EpiEstim. The method can be applied to estimates of disease incidence from a wide variety of incidence models, including those derived from weekly case counts, or that account for right censoring in observed data. Author summaryIn our experience estimating the reproduction number during the COVID-19 pandemic, we found that estimating the incidence rate was a useful first step to correct for artefacts and biases in the raw count data, and to estimate the exponential growth rate. With modelled incidence estimates available, and correcting data issues, we wanted to use them to derive the time-varying reproduction number to help monitor the state of the pandemic. We present a mathematical method and supporting software to estimate Rt from modelled incidence estimates, rather than raw count data, and which is readily applicable to many incidence models.

Widespread screening for Adolescent Idiopathic Scoliosis (AIS) is critical for timely intervention but is currently constrained by the radiation risks of X-rays and the subjectivity of physical examinations. Here, we present PointScol, a radiation-free triage system leveraging 3D back surface point clouds. To reconcile the conflicting clinical demands for "zero-miss" screening and "fine-grained" severity assessment, we developed a two-stage deep learning framework. First, an automated segmentation module extracts the dorsal region of interest (ROI) to standardize input geometry. Second, the system employs a dual-branch diagnostic strategy: a binary classification network designed for maximal sensitivity to rule out health, and a 5-class grading network designed to stratify severity (0-10{degrees}, 11-20{degrees}, 21-30{degrees}, 31-40{degrees}, >40{degrees}). Validation on a multi-center dataset (n=128) confirmed the distinct utility of this hierarchical approach. For the scoliosis screening task using a 10{degrees} Cobb angle threshold, the binary classification model achieved a sensitivity of 100.00% in the external cohort, ensuring that no cases requiring further clinical attention were missed. While the 5-class grading task inherently faces greater complexity, it successfully achieved an overall accuracy of 84.48% and, crucially, demonstrated a high specificity of 98.42% for severe surgical cases (>40{degrees}). This performance profile establishes PointScol as a safe clinical filter: the binary module reliably excludes healthy individuals, while the 5-class module flags high-risk patients for prioritized intervention, collectively offering a non-invasive, resource-efficient paradigm for scoliosis management.

BackgroundDystonia is a debilitating movement disorder that is difficult to assess when co-existing with spasticity, as is typical in cerebral palsy (CP). Querying caregivers about their childrens movements is known to increase clinical dystonia identification. However, beyond identification, determining whether dystonia is the predominant vs. accompanying movement feature in a child with CP can guide clinical decision making, particularly regarding surgical candidacy. ObjectiveTo determine whether caregivers movement descriptions differed between children with predominant dystonia, predominant spasticity with accompanying dystonia, and predominant spasticity without dystonia. MethodsIn this cross-sectional study, we used conventional content analysis to codify caregivers descriptions of triggered involuntary movements in children with CP seen in a tertiary care CP center between 4/2023 and 12/2024. Movement feature frequencies were compared across tone types using Chi-square tests with Bonferroni corrections for multiple comparisons. ResultsOf 180 children with CP (mean age 9.2, 47.8% male), caregivers of children with predominant dystonia (50/180, 27.8%) more frequently described movements triggered by negative emotions (p<0.002) and affecting their back, trunk, and whole body (p<0.04). Caregivers of children with predominant spasticity with dystonia (99/180, 55.0%) more frequently described movements affecting a single limb (p<0.04). Caregivers of children without dystonia (31/180, 17.2%) described movements as being slight or small (p<0.008). These differences persisted even for caregivers unaware their child had dystonia (77/149, 51.6%). ConclusionsCaregivers movement descriptions differ between children with different combinations of dystonia and spasticity, which may help inform clinical management and guide communication with families about dystonia.

Efficient and secure integration of epidemiological data with pathogen genome sequence data is essential for identification of transmission clusters, monitoring of emerging mutations and targeting public health responses. However, this information is often collected across different organisations: epidemiological data is collected by public health units while genome sequence data is collected by diagnostic laboratories. Linking these sources often requires manual or semi-manual approaches, leading to unnecessary delays in identifying emerging outbreaks. To address this, we developed a proof-of-concept privacy-preserving distributed platform, SecureEpiLink, for automatic linkage of pathogen genome sequences and notification data across public health and diagnostic laboratories. SecureEpiLink uses cryptographic hashing to establish linkage, without exposing personal identifying information. This ensures that the resulting linked data can be used to identify the emergence of transmission clusters without identification of individuals comprising each cluster. The original identifiable data can continue to be stored at source labs. We benchmarked SecureEpiLink against manual linkage and another linkage service using HIV and HCV datasets from New South Wales, Australia. SecureEpiLink performed similarly to manual linkage and outperformed previously used linkage algorithms, with all errors attributable to data entry errors in the underlying dataset. Lastly, we demonstrate how SecureEpiLink can be integrated with automated genomic epidemiology pipelines. SecureEpiLink is available from https://github.com/jolenefarrell/SecureEpiLink. Author summaryLinkage of epidemiological data collected in public health units with pathogen genome sequence data generated by diagnostic labs in real-time is essential for targeted public health responses. However, linkage creates risks of stigmatisation and criminalisation for individuals. We have designed, implemented and tested a privacy preserving real-time system for linking pathogen genome sequence data with public health notification data within different jurisdictions.

Sleep is essential for childrens health and development, yet sleep problems are common worldwide. Comfort items such as soft toys or blankets are widely used to promote independent sleep, but their effects have not been evaluated in a randomised controlled trial (RCT). The REST trial emerged from a child-led citizen-science study (The Kids Trial) where children co-created and designed the trial. Therefore, this paper had two aims, to assess whether sleeping with a comfort item affected childrens sleep; and to assess the feasibility of conducting an online, child-led citizen-science RCT. The REST (Randomised Evaluation of Sleeping with a Toy or comfort item) trial was an online two-arm, parallel-group, superiority RCT. Children, aged 7 to 12 years, were randomised (1:1) to either sleep with a self-chosen comfort item ( Try-it-Out group) or refrain from using one ("Wait-and-See" group) for one week. The primary outcome was sleep-related impairment (SRI; PROMIS Pediatric Short Form v1.0 SRI 4a). The secondary outcome was overall sleep quality (Single Item Sleep Quality Scale, SQS). Analyses followed an intention-to-treat principle using mixed-effects models adjusted for baseline measures. A total of 139 children from 11 countries were randomised (mean age: 9.8 years; 45% female); 101 children (73%) completed post-test measures at one week. The adjusted mean difference (Intervention minus Control) in SRI T-scores was -0.53 (95% CI: -3.40 to 2.34; p = 0.714), equivalent to approximately -0.05 SD on a scale where 10 points = 1 SD. This indicated a trivial effect, well below the minimal important difference (MID) of 3 points. The adjusted mean difference in SQS was 0.28 (95% CI: 0.01 to 0.55; p = 0.040), suggesting a small and uncertain difference in favour of the intervention group. However, this result was not supported in subsequent sensitivity or exploratory subgroup analyses. No adverse events were reported. Sleeping with a comfort item for one week did not influence sleep-related impairment. A small statistically significant difference in perceived sleep quality was observed in the primary analysis, but was not sustained in the per-protocol analysis. Together, these findings suggest that any benefit of comfort items for sleep is small and uncertain. The trial demonstrated that children can meaningfully engage in online, citizen-science research, supporting the feasibility of child-led RCTs. Trial registrationISRCTN13756306 (registered 10 January 2025)

Using the example of an unknown emerging disease with simple SIR (susceptible-infectious-recovered) dynamics, we show that an efficacy randomized clinical trial (RCT) for a vaccine can be misleading when it comes to the cost-effectiveness of that vaccine. An RCT is more likely to demonstrate efficacy with a high confidence level if it is carried out during the peak of the outbreak. However, in this scenario, the vaccine also has a higher chance of being approved too late to be cost-effective. A vaccine is more likely to be cost-effective if vaccination is implemented in the early stages of an epidemic, but an RCT is more likely to fail to demonstrate efficacy if it is implemented too early, that is when disease transmission is too low.

ObjectiveTo describe the Swedish 2020 switch to primary cervical screening using Human Papillomavirus (HPV) self-sampling at home and compare with cervical cancer incidence trends. MethodsStatistics on HPV self-sampling and cervical cancer incidence were obtained from comprehensive nationwide registries. ResultsHPV self-sampling was recommended in 2017 for non-attending women and was in 2020 recommended as a primary screening for all women aged 23-70 years of age. During 2016-2020 there were <20,000 annual self-samples taken, but since 2021 >200,000 self-samples are taken annually (about half of all screening tests). During 2015 to 2020 the cervical cancer incidence was stable, between 11 and 12 per 100,000 women. From 2021 and onwards the incidence is declining and was 7,7/100,000 in 2024. The decline is stronger among women younger than 30 (-60%) but is strong in all ages (-27%). ConclusionsA rapid introduction of primary HPV self-sampling at home in 2020-2021 was followed by a rapid decline in invasive cervical cancer incidence.