Frontiers in Physics

With significant population fractions in many societies who refuse vaccines, it is important to reconsider how vaccination is incorporated into compartmental epidemiology models. It is still most common to apply the vaccination rate to the entire class of susceptibles, rather than to use the more realistic assumption that the vaccination rate function should depend only on the population of susceptibles who are willing and able to receive a vaccination. This study uses a simple generic disease model to address two questions: (1) How much error is introduced in key model outcomes by neglecting vaccine unwillingness?, and (2) Can the error be reduced by incorporating vaccine unwillingness into the vaccination rate constant rather than the rate diagram? The answers depend greatly on the time scale of interest. For the endemic time scale, where longterm behavior is studied with equilibrium point analysis, the error in neglecting unwillingess is large and cannot be improved upon by decreasing the vaccination rate constant. For the epidemic time scale, where the first big epidemic wave is studied with numerical simulations, the error can still be significant, particularly for diseases that are relatively less infectious and vaccination programs that are relatively slow.

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.

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.

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.

Visceral leishmaniasis (VL) is considerably more severe among individuals infected with human immunodeficiency virus (HIV), leading to higher parasite loads, frequent relapse, and increased mortality. To examine the epidemiological interaction between the two diseases, we develop a comprehensive VL-HIV co-infection model that incorporates transmission pathways, treatment effects, and relapse dynamics. The model is parameterized using real-time data from Bihar, India, including monthly VL-only and VL-HIV co-infected cases and annual HIV prevalence data. Our analysis shows that HIV infection drives the resurgence and persistence of VL even in settings where VL alone would not sustain transmission, underscoring the amplifying effect of HIV-induced immunosuppression on VL dynamics. We further demonstrate that increasing HIV treatment coverage substantially reduces co-infection prevalence and lowers VL relapse rates. Numerical simulations and optimal control analysis highlight the effectiveness of integrated intervention strategies that combine awareness, treatment enhancement, and vector control. Overall, this study emphasizes the need for coordinated VL and HIV control programs and provides data-driven guidance for designing sustainable intervention strategies in endemic regions.

Schistosomiasis is a neglected parasitic disease caused by various trematode species of the genus Schistosoma for which 251 million people needed treatment in 2021. Many mathematical models of Schistosoma mansoni transmission incorporate the effect of chemoprophylaxis on parasite burden within the human host. While praziquantel is the most commonly implemented pharmaceutical used to control schistosomiasis, due to its applicability over several species and its negligible side effects, it is not very effective against juvenile schistosomes in humans. This limited efficacy on the juvenile life-stage of the parasite may be an important factor in the persistence of the disease. The demographic consequences of praziquantel use on schistosome population age and sex composition within the human host may obfuscate the effectiveness of these chemoprophylactic control strategies. Furthermore, the effectiveness of this treatment is heavily dependent on the force of infection to humans and the frequency at which these pharmaceuticals are administered. Using a stochastic mechanistic model, we investigated the effects of inconsistent drug efficacy among parasite life stages, varying parasite population structure within the human host, and alternative treatment regimes to the prevailing once-yearly strategy. This allowed us to identify the reduction in infection prevalence under differing infection risk scenarios, parasite population structures at the time of treatment, and treatment schedules. Our results indicate that if elimination is the goal, then widespread (>75% of the population) treatment should be the target and that more frequent treatment schedules are useful up to several treatments a year.

Nipah virus (NiV) is a sporadic yet extremely deadly zoonotic pathogen, with reported case fatality rates of 40%-75% in impacted areas. Prolonged incubation, documented relapse, and delayed-onset encephalitis following apparent recovery indicate that NiV dynamics are influenced by intricate temporal processes. However, mechanistic contributions of these processes to epidemic persistence remain poorly understood. In this study, we develop and analyze a delay differential equation model for NiV transmission that explicitly incorporates incubation delay, relapse, and post-recovery delay effects. We compute a primary-transmission reproduction threshold (R0), characterize the disease-free and endemic equilibria, and analyze their stability, including delay-induced Hopf bifurcations. We show that relapse modifies the endemic-equilibrium existence condition, so an endemic equilibrium is not determined solely by the classical threshold criterion R0 = 1. We calibrate the model to NiV incidence data from Bangladesh (2001-2024) and perform simulations and sensitivity analyses to evaluate the effects of relapse and delays across epidemiological scenarios. Results indicate that sustained oscillations occur only under hypothetical parameter regimes, suggesting that delay-induced periodic outbreaks are unlikely under empirically informed conditions. Scenario analyses demonstrate that relapse and encephalitis-related delays predominantly influence post-peak dynamics, while incubation delay alters the time and intensity of the epidemic peak. We also introduce a relapse-driven replenishment fraction to quantify contribution of relapse to continued transmission, demonstrating its growing significance following the first outbreak peak. Overall, our results identify relapse as a key mechanism for epidemic persistence and underscore the importance of incorporating relapse and biological time delays into epidemiological modeling and public health strategies.

Suicide is one of the worlds leading public health problems, with more than 720,000 deaths annually. Suicide has traditionally been studied from an individual perspective. However, research has increasingly highlighted the influence of community-level factors on suicide risk. This study aimed to (1) analyse the spatial distribution of suicide mortality at the provincial level in Spain (2018-2022); (2) perform stratified analyses by sex and age group; and (3) compare suicide risk across different phases of the COVID-19 pandemic. We used data from the Spanish National Institute of Statistics on 19,381 suicide deaths in 47 peninsular provinces between 2018 and 2022. Covariates included sociodemographic (e.g. aging rate, population density), economic (e.g. unemployment, GDP), and environmental (e.g. temperature) indicators. Bayesian hierarchical spatial Poisson regression models were fitted to estimate suicide risk and identify significant contextual variables. The general spatial model revealed a higher risk of suicide in provinces with lower population density, higher aging rates, and lower health expenditure. Other covariates such as gross domestic product, unemployment, or temperature were associated with specific sex or age groups. Suicide risk was highest in the northwestern provinces and lowest in the central regions. Stratified analyses showed similar patterns across gender and age groups, and between time periods, with some variations in spatial distribution. This study reveals significant spatial heterogeneity in suicide risk across Spanish regions, influenced by socio-demographic, economic, and environmental factors. These findings underline the importance of regionally tailored suicide prevention policies, especially in aging and low-density areas with low health investment. Key MessagesWe examined spatial patterns and socioeconomic and environmental determinants of suicide mortality in 50 Spanish provinces between 2018 and 2022. We found persistent geographical inequalities in suicide rates, with higher mortality in low-density provinces and those with older populations, and protective effects associated with health expenditure. These findings highlight the importance of place-based suicide prevention strategies that consider regional disparities and socioeconomic vulnerabilities.

BackgroundDengue fever is a major neglected tropical disease with a rapidly rising global burden, and localized outbreaks are increasingly reported in southern subtropical China. Fujian Province, a coastal subtropical region with favorable ecological conditions for Aedes albopictus breeding and frequent cross-border exchanges with dengue-endemic areas, has had continuous local dengue cases for over a decade, raising concerns about the establishment of a stable natural endemic focus. Sustained local dengue transmission is defined by four core criteria, but no systematic assessment of these criteria has been conducted for Fujian using long-term multi-dimensional surveillance data. We aimed to evaluate whether a natural endemic focus for sustained local dengue transmission has been established in Fujian Province from 2014 to 2024 using four core evidence dimensions. MethodsWe extracted data on imported and locally acquired dengue cases in Fujian from 2014 to 2024 from Chinas National Notifiable Disease Reporting System (NNDRS). Serological surveillance for dengue IgG antibodies and virological surveillance for dengue virus in Aedes albopictus were conducted at seven sentinel sites. The study period was stratified into three phases based on the impact of COVID-19 non-pharmacological interventions: pre-pandemic (2014-2019), pandemic(2020-2022), and post-pandemic(2023-2024). Descriptive epidemiological analysis and data visualization were performed using R software (version 4.4.1), with t-tests for continuous variables and {chi}{superscript 2} tests for categorical variables. ResultsA total of 3,606 dengue cases were reported in Fujian during the study period, including 1,229 imported and 2,377 locally acquired cases. Key findings were as follows: (1) Temporal distribution: Local dengue transmission was completely interrupted during the 2020-2022 COVID-19 pandemic (0 local cases, only 26 imported cases), and resumed at a low level in 2023-2024 (160 local cases). (2) Serology: The overall population dengue IgG antibody positivity rate was 4.2% (66/15,736), with no statistically significant difference between pre-epidemic (3.8%, 30/7,835) and post-epidemic seasons (4.5%, 36/7,901; P=0.48), and no year with a positivity rate exceeding 10%. (3) Vector surveillance: Only one dengue virus-positive sample was detected among 385,000 Aedes albopictus mosquitoes collected during routine surveillance (Taijiang District, Fuzhou, October 2017), with no viral nucleic acid detected in all other samples. (4) Age distribution: The mean age of locally acquired cases (46.1{+/-}19.8 years) was significantly higher than that of imported cases (35.8{+/-}11.2 years, P<0.001), and local cases were concentrated in the middle-aged group (40-60 years) with no child-dominant pattern observed. ConclusionsFujian Province has not established a stable natural endemic focus for sustained local dengue transmission, and imported cases are the primary driver of local outbreaks in the region. Strengthened surveillance and early management of imported cases, integrated vector control targeting Aedes albopictus, and targeted public health education are critical and essential strategies to prevent the establishment of a dengue natural endemic focus in Fujian and other subtropical coastal regions with similar epidemiological characteristics. Author SummaryDengue fever is a rapidly spreading neglected tropical disease worldwide, and southern China faces persistent threats of local transmission due to favorable ecological conditions for mosquito breeding and frequent cross-border travel. Fujian Province, a subtropical coastal region in southeastern China, has reported annual local dengue cases for over a decade, raising public health concerns about the potential establishment of a stable natural endemic focus--where the virus circulates sustainably without relying on imported cases. To address this critical question, we conducted a comprehensive 11-year assessment (2014-2024) of dengue transmission in Fujian using four key evidence dimensions defined for identifying dengue endemic foci: the continuity of local cases independent of imported sources, population antibody levels, dengue virus detection in local mosquitoes (Aedes albopictus), and the age distribution of infected patients. We also leveraged the COVID-19 pandemic(2020-2022) as a unique natural experiment, during which strict travel restrictions drastically reduced imported dengue cases, to test whether local transmission could persist on its own. Our findings showed that local dengue transmission in Fujian completely stopped during the COVID-19 pandemic and only resumed when cross-border travel and imported cases recovered, confirming local transmission is entirely dependent on imported virus sources. Additionally, the local population had a very low dengue antibody positivity rate (4.2%), dengue virus was detected in only one mosquito sample over 11 years of surveillance, and local cases were concentrated in middle-aged adults (not children--the typical group affected in endemic areas). Together, these results confirm that Fujian Province has not established a stable natural endemic focus for dengue fever. While no endemic focus exists yet, Fujian remains at high risk of imported-driven local outbreaks due to its climate and cross-border exchanges. Our study highlights three critical strategies to prevent the future establishment of a dengue endemic focus in Fujian and other similar subtropical coastal regions: strengthening surveillance and early response for imported dengue cases, implementing targeted mosquito control measures during peak transmission seasons, and conducting public health education to raise awareness of dengue prevention. These evidence-based interventions are key to blocking the formation of sustained local dengue transmission and protecting regional population health.

BackgroundTraumatic events, such as terror attacks and war, are expected to impact mental health. These potential effects can be explored by assessing the mental health of the general population of Israel, from before the events of October 7, 2023 and over the course of the Swords of Iron war. MethodsGeneral population data were collected from Jewish adults in Israel before October 7 (April 2022), after October 7 (December 2023), and over the course of the ongoing war (March 2024, June 2024, February 2025), in a series of repeated cross-sectional samples with longitudinal data on a subset of the respondents. Among a subset of the sample including individuals who were surveyed in April 2022 and at least one follow-up time point (N=1,368), we used regression analysis to model trajectories over time in prevalence of problematic non-medical use of alcohol, tobacco, cannabis, sedatives, prescription stimulants, and prescription opioid painkillers; problematic use of internet, social media, electronic gaming, gambling, pornography, and compulsive sexual behavior; and post-traumatic stress disorder (PTSD), depression, and anxiety. Trajectories were modeled overall and moderation analysis was used to determine if trajectories differed by gender or age. ResultsDifferent patterns were observed by outcome. Different trajectories were observed before and after December 2023, suggesting that the events of October 7th and the early war may have been a key transition point, for problematic use of alcohol, tobacco, sedatives, opioid painkillers, gambling, sexual behaviors, internet and PTSD. Smooth changes over time were observed for problematic use of gaming and social media, and anxiety and depression. No changes over time were observed for problematic use of cannabis, stimulants, and pornography. Many outcomes showed different trajectories by gender and age. ConclusionsFindings suggest possible effects of ongoing trauma and war and suggest that outcome-specific and group-specific strategies may be warranted. Monitoring the prevalence of addictions and other common mental health issues in the general population during and after nationally traumatic events is important to understand the evolving mental health of the population and provide information and resources for potential interventions. Awareness of the potentially harmful effects of such life events, as well as the consequences of maladaptive coping styles on health and well-being, should be increased.

The protracted Rohingya refugee crisis continues to deteriorate with approximately 1.2 million refugees currently sheltering in Coxs Bazar, Bangladesh. This study aimed to estimate the prevalence and identify factors associated with psychosocial distress among Rohingya refugees. Data were sourced from the 2023 Joint Multi-Sectoral Needs Assessment - a representative cross-sectional household survey conducted across the 33 Rohingya refugee camps of Coxs Bazar. Households were selected using stratified (by camp) random sampling. Psychosocial distress was assessed via proxy report by an adult household respondent and defined as the presence of at least one of eleven symptoms in the two weeks preceding the survey. Binary logistic regression was conducted to investigate household characteristics and individual factors associated with psychosocial distress status. The prevalence of psychosocial distress was 14.9% (95%CI: 14.1%-15.7%) among 16,455 Rohingya refugees sampled from 3,400 households. After adjustment, psychosocial distress was associated with individuals from aid-dependent households (aOR= 1.42 [95%CI: 1.21-1.67]), stress livelihood coping strategies (aOR= 3.03 [95%CI: 1.94-4.74]), crisis livelihood coping strategies (aOR= 4.40 [95%CI: 2.81-6.89]), emergency livelihood coping strategies (aOR= 4.15 [95%CI: 2.58-6.66]), individuals who required and received healthcare (aOR= 1.27 [95%CI: 1.12-1.43]), individuals who required and did not receive healthcare (aOR=1.49 [95%CI: 1.16-1.91]), individuals aged 18-34 years (aOR= 8.38 [95%CI: 6.99-10.04]), aged 35-59 years (aOR= 10.33 [95%CI: 8.44-12.65]), and aged 60+ years (aOR= 13.31 [95%CI: 10.25-17.30]). Psychosocial distress among Rohingya refugees was highly prevalent and associated with increasing age groups, aid dependency, negative livelihood coping strategies, and healthcare needs. The findings emphasise the need for comprehensive mental health and psychosocial support services in protracted humanitarian emergencies. Additional validation studies may be required to measure both the prevalence and severity of psychosocial distress to better inform humanitarian programming.

BackgroundStrikingly low allocation of SARS-CoV-2 vaccine to the African Continent limits its capacity to control transmission. Characterizing the trajectory of vaccination efforts and their impact on the expected burden of SARS-CoV-2 will help planning vaccine delivery strategies, and public health interventions more broadly. As the burden is strongly age-dependent, this requires an understanding of the age-structured dynamics of susceptible individuals, accounting for the combined effects of vaccination and infection induced immunity. Methods and FindingsWe illustrate with projections for diverse African LMIC demographics. To this end, we develop an age-structured mathematical model with vaccination to assess the likely time-horizon to reach target vaccine coverage of high-risk groups, and how susceptibility patterns across age will shift as a result of both infection, and the broadening of vaccination targets from a focus on risk groups to efforts to reach the general population. We base our assessment on the demography, contact patterns and public health capacity of 16 African countries with diverse age pyramids. We identify a considerable divergence in the projected horizon of expanded targeting from prioritized age groups to general vaccination, with longer time among those with higher mean age and lower vaccination capacity. We parameterize the model using realistic demographies and contact patterns to project the changing age profile of susceptibles. We demonstrate that contacts and vaccination jointly drive the early age profile; while immune duration contributes to the transition of age-susceptibility profile in the intermediate future. ConclusionsOur model framework provides a flexible and critical preparedness-tools to inform decision making against future epidemic waves and beyond Covid-19.

Background The Hump-nosed pit viper is a recognized but neglected medically significant species causing morbidity and mortality, with non-availability of a specific antivenom. There are many gaps in our understanding of its envenomation, including burden, clinical syndrome, complications and management. Methodology The study is a retrospective sub analysis of the Prospective VENOMS registry and hospital records of Hump Nosed Pit Viper envenomation from a single tertiary care center in coastal Karnataka from May 2018 to March 2024. Epidemiology, syndrome, complications and treatment strategies have been described. A linear mixed model analysis was conducted to study the effect of different therapeutic interventions in combating venom induced consumptive coagulopathy (VICC) Principal Findings Of 46 cases, 24 patients had VICC. The most common complications were AKI (21.7%), TMA (10.9%) and stroke (4.4%). Anaphylaxis to ASV (23.9%) was the most common therapeutic complication. Therapeutic interventions included ASV, administration of blood products and therapeutic plasma exchange along with supportive care. The linear mixed model revealed that administration of blood products (p=<0.001) had the strongest influence on the INR value, however, often resulting in a transient decline in INR value. ASV (p=0.052) caused only marginally significant change in INR. The role of TPE could not be statistically inferred, however, individual cases with severe VICC improved without complications, therefore it required further study but can be considered in critical cases. Conclusions/Significance This study describes the syndrome of hump-nosed pit viper envenomation, while highlighting the urgent need for a species-specific antivenom, recommends treatment strategies that can be used in the interim. Additionally, geo-spatial mapping draws attention to hotspots and the hypothesis that HNPV in coastal Karnataka have regionally distinct toxicity trends.

Wastewater is increasingly being recognized as an important data stream that can contribute to infectious disease surveillance and forecasting. With this recognition, a growing number of statistical inference approaches are being developed to use wastewater data to provide quantitative insights into epidemiological dynamics. However, few existing approaches have allowed for systematic integration of data streams for inference, for example by combining case incidence data and/or serological data with wastewater data. Furthermore, only a subset of existing approaches have been able to handle missing data without imputation and to handle datasets with different sampling times or intervals. Here, we develop a statistically rigorous, yet lightweight, approach to infer and forecast time-varying effective reproduction numbers (Rt values) using longitudinal wastewater virus concentrations either alone or jointly with additional data streams including case incidence data and serological data. Our approach relies on a state-space modeling approach for inference and forecasting, within the context of a simple bootstrap particle filter. We first describe the structure of our underlying disease transmission process model as well as our observation models. Using a mock dataset, we then show that Rt can be accurately estimated by interfacing this model with case incidence data, wastewater data, or a combination of these two data streams using the bootstrap particle filter. Of note, we show that these data streams alone do not allow for reconstruction of underlying infection dynamics due to structural parameter unidentifiability. We then apply our particle filter to a previously analyzed SARS-CoV-2 dataset from Zurich that includes case data and wastewater data. Our analyses of these real-world datasets indicate that incorporation of process noise (in the form of environmental stochasticity) into the state space model greatly improves our ability to reconstruct the latent variables of the model. We further show that underlying infection dynamics can be made identifiable through the incorporation of serological data and that the bootstrap particle filter can be used to make forecasts of Rt, case incidence, and wastewater virus concentrations. We hope that the inference approach presented here will lead to greater reliance on wastewater data for disease surveillance and forecasting that will aid public health practitioners in responding to infectious disease threats.

ObjectiveThis study explored the recovery experiences of individuals who report having (largely) recovered from long covid and who attributed their improvement to mind-body approaches. Design, setting and participantsWe conducted an explorative qualitative study using purposive recruitment through social media and snowball sampling. Eighteen adult women (aged 37-62 years), who self-identified as having had long covid and having substantially recovered through mind-body approaches participated in semi-structured interviews. Data were analysed using Saunders practical thematic analysis. ResultsDespite variation in personal narratives, a common trajectory emerged: participants moved away from a biomedical explanatory model towards one centred on nervous system dysregulation. This shift, sometimes following initial scepticism, was often described as a turning point, sparking hope and motivation to engage in self-directed strategies. Recovery was not linear but an iterative process, involving cycles of practice, reflection (especially when progress stagnated) and adaptation of mind-body techniques. Over time, participants gained insights into contributing factors and, in many cases, made intentional life changes to support ongoing recovery. These patterns echo findings from previous research on mind-body approaches in chronic pain and chronic fatigue, and align with neuroscientific perspectives on symptom generation. Most participants navigated this process without formal clinical support, relying instead on online communities and actively avoiding sources of (biomedical) information that conflicted with their new understanding. ConclusionsWhile causal inferences cannot be drawn from qualitative data, this study highlights potential mechanisms that may underpin recovery for people with long covid using mind-body approaches. Further research is needed to develop structured interventions, and to evaluate their efficacy and safety. Future research should also explore how prevailing narratives within healthcare and society influence treatment engagement and recovery trajectories. STRENGTHS AND LIMITATIONS OF THIS STUDYO_LIThis is the first study exploring experiences of recovery from long covid using mind-body approaches. C_LIO_LIIn-depth, real-world accounts capture the lived-experiences over time and allow in-depth exploration if the recovery process, while the semi-structured design facilitates the emergence of themes rarely captured in clinical research. C_LIO_LIGeneralisability is limited due to self-identified long covid status, lack of formal diagnostic verification, absence of strict definitions of mind-body approaches and recovery, and a relatively homogenous sample (mostly highly educated women). C_LI

Wastewater-based epidemiology provides a low-cost, scalable view of community infection dynamics, but converting these signals into actionable epidemiological insights remains difficult. Mechanistic models offer interpretability, yet, assumptions such as a constant transmission rate limit realism over long simulation horizons and heterogeneous settings. We present a susceptible-exposed-infectious-recovered (SEIR) universal differential equation (UDE) that links wastewater viral loads to case counts and embeds neural networks to represent time-varying parameters. Parameter and prediction uncertainties are quantified using an ensemble method. We assessed the method using newly collected data for Bonn, Germany, as well as published data for five cities in Rhineland-Palatinate, Germany. The proposed approach produces realistic out-of-sample projections of case counts over an up to 50-week test horizon, and it learns city-specific mappings to prevalence that generalise within each location. Compared to SEIR models with fixed transmission rates, the UDE captures non-stationary drivers (policy, behaviour, seasonality) without sacrificing epidemiological structure, while propagating observation and model uncertainty into the projections. Accordingly, the approach facilitates a scalable interpretation and exploitation of wastewater data for the monitoring of infectious diseases.

IntroductionNational trends in youth suicide risk may mask significant regional variations within a country. This article attempts to account for spatio-temporal trends through a comparative analysis across South America and Europe. This paper analyzes the spatiotemporal patterns in suicide mortality among young people (10-29 years) in Argentina, Chile, Spain, and Uruguay during the period 1997-2021. MethodsOfficial data from vital statistics and population censuses of the four countries were analyzed. Spatiotemporal clusters were detected using Poisson-based scan statistics. Sociodemographic characteristics of high-and low-mortality clusters were compared with the rest of each country using Kruskal-Wallis and Wilcoxon tests. ResultsWith the exception of Chile, each country showed the emergence of spatiotemporal suicide clusters extending through 2021. Indicators of social fragmentation and lower socioeconomic status were most consistently associated with the formation of high-risk youth suicide clusters. ConclusionRecent national increases in youth suicide rates appear to be concentrated in specific sub-national regions, underscoring the need to target resources toward improving living conditions and mental healthcare access for young people in these areas.

Work-related stress is a major public health issue affecting workers across various sectors. Individuals experiencing work-related stress are more likely to consume psychoactive substances, primarily alcohol, tobacco, and cannabis, as well as psychoactive medications, which may be used as coping mechanisms. Work-related stress is also associated with adverse outcomes such as burnout, depression, anxiety, and sleep disorders. In France, early childhood professionals, including "ATSEMs", "animateurs", and "agents dentretien", play a crucial role in the education, care, and well-being of children but are exposed to high levels of occupational stress due to the emotionally demanding nature of their work and the associated physical strain, making them vulnerable to substance use, burnout, depression, anxiety, and sleep disorders. This cross-sectional epidemiological study, conducted at a single time point, will be carried out among early childhood professionals working in schools for children in Orleans Metropole, Communaute de Communes des Terres du Val de Loire (CCTVL), and Fleury-les-Aubrais. Ethical approval for this study was obtained from the Ethics Committee of the Centre Hospitalier Universitaire dOrleans (assigned reference number is CERO 2511-02). The study aims to provide a better understanding of the relationship between work-related stress and the use of psychoactive substances and medications among early childhood professionals, as well as the association between work-related stress and burnout, depression, anxiety, and sleep disorders. Data will be collected anonymously using self-administered online questionnaires, accessed via a QR code printed on flyers distributed to participants. The same QR code will also provide access to an information sheet explaining that the study complies with ethical guidelines and that proceeding implies non-objection to participation. Based on calculations performed using BiostaTGV, a sample size of 265 participants is required. Statistical analysis will be conducted using SPSS software. Studying these associations is essential for informing the development of targeted interventions and prevention.

AbstractsO_ST_ABSBackgroundC_ST_ABSAccurate dengue forecasting is vital for public health preparedness. Despite a surge in forecasting approaches, a quantitative ranking of the relative performance and practical utility of dengue forecasting is lacking. MethodsA systematic review and Network Meta-Analysis (NMA) of studies comparing dengue forecasting methods (2014-2024) was conducted. Models were categorised into five groups: Time Series, Deep Learning (DL), Machine Learning (excluding DL), Hybrid, and Ensembles. NMA was applied to the logarithm of the most common forecast error metric to rank relative performance--an "Implementability Score" quantified analyst and data requirements, and computational costs. Results59 studies were included. NMA of Root Mean Squared Error identified k-Nearest Neighbour (k-NN) models as achieving the highest predictive accuracy, followed closely by Vector Autoregression, Kalman Filtering, Generalised Linear Model and Autoregressive Neural Network (ARNN). While DL models showed high potential, they scored lowest in implementability due to poor interpretability and high data requirements. Most studies utilised meteorological covariates, with significant gaps in the use of socio-economic and entomological predictors. ConclusionsAlthough there was some trade-off between accuracy and implementability, traditional statistical models were often comparable in accuracy to machine learning approaches, with advantages in interpretability and data needs. Under-explored areas for future research include the use of ensemble models and the use of socio-economic and entomological data. RegistrationPROSPERO CRD420251016662. Author SummaryDengue is a critical global health threat affecting the worlds population. While many forecasting models exist to help officials prepare for outbreaks, there has been no standardised way to compare their performance. This leaves health experts in resource-limited areas uncertain about which tools are truly reliable or easy to use under their specific local conditions. We conducted a network meta-analysis of studies comparing dengue forecasting methods accuracy, grouping them into five categories: Machine Learning, Deep Learning, Time Series, Ensemble, and Hybrid. Beyond ranking their accuracy, we developed an "Implementability Score" to evaluate the practical feasibility of each model, accounting for technical complexity, data requirements, and software accessibility. Our analysis identified the top-performing models. Notably, traditional statistical models often performed as well as complex Deep Learning algorithms. While advanced models show potential, they are often difficult to implement or explain to decision-makers. There is no "one-size-fits-all" solution; the best model depends on capacity and data in each setting. This study provides a roadmap for public health officials to select tools that are both accurate and feasible.

IntroductionHemophagocytic lymphohistiocytosis (HLH) is a serious condition induced by Dengue virus which becomes fatal if not detected early and treated appropriately. So objectives of the present study are to observe the different patterns of presentations, clinical features and outcome of HLH induced by Dengue. MethodsIn this observational study, 14 patients admitted and diagnosed HLH as per diagnostic criteria, were included after informed written consent. Study conducted in a period of six months from 01/07/2025 to 31/12/2025. All patients were followed up till discharge. After collection, all data were analyzed by Microsoft Excel 2010. Ethical clearance was taken from Ethical Review Board of the Medical College. ResultsAmong 14 cases, male were more affected then the female (78.6% VS 21.4%) and majority were in between 20 to 50 years age groups. Clinical data showed, all 14 cases had fever for >7 days, joint pain 3(21.4%), headache 11(78.6%), skin rashes 10(71.4%), retro-orbital pain 2(14.3%), vomiting 11(78.6%),bleeding 10(71.4%), cough 4(28.6%), loose motion 9(64.3%), abdominal pain 7(50.0%), anorexia 2(14.3%), Melaena 2(14.3%), jaundice 4(28.6%) and spleenomegaly 9(64.3%). One(7.1%) case had history of Hypertension. Laboratory data showed different level of Bi or Pancytopenia, high ferritin, high TG, low fibrinogen, raised liver enzymes and low sodium. Dengue RT PCR and serology results showed 8(42.9%) cases were both IG M and Ig G dengue antibody positive, 6 cases were RT PCR positive, 2 cases were IgM and another 4 cases were IgG positive. Outcome of patients revealed, among all 14 cases12(85.8%) patients improved uneventfully and 2 were shifted to ICU where one improved and one died. ConclusionDengue is prevailing for long time and different complications are evolving and HLH is a relatively newer incident among the dengue patients. Infection by different serotypes at different time or multiple dengue serotype infection may be related with HLH and it might be a future subject to explore and to evaluate.