Infectious Diseases of Poverty

BackgroundDengue remains a major public health challenge in Thailand despite decades of vector control implementation. While mathematical models have explored dengue transmission dynamics, systematic evaluation of current control strategies under realistic operational conditions remains limited. MethodsWe developed a temperature-dependent, multi-serotype dengue transmission model that explicitly incorporates three primary vector control strategies: reduction in mosquito biting rates through personal protection measures, further reduction in mosquito birth rates beyond current larval control efforts, and further increase in adult mosquito mortality beyond current adulticide application levels. Using Approximate Bayesian Computation with Sequential Monte Carlo (ABC-SMC), we fitted the model to dengue hemorrhagic fever (DHF) surveillance data from nine province-year combinations representing high (Rayong), moderate (Ratchaburi), and low (Phrae) transmission settings across three years (2006, 2015, and 2017). The model accounts for four dengue serotypes, temperature-dependent mosquito dynamics, and temporary cross-protective immunity between serotypes. ResultsThe model closely reproduced observed monthly DHF case counts across all nine province-year combinations. Estimated reporting proportions ranged from 1.4% to 16.7%, with the highest values occurring in high-transmission provinces during the 2015 outbreak year. When each strategy was independently intensified by 50% relative to fitted baseline levels, reducing mosquito biting rates and increasing adult mosquito mortality consistently produced greater reductions in transmission than reducing mosquito birth rates. In the highest-transmission scenario (Rayong, 2015), a 50% reduction in biting rate from the baseline level yielded a 96.4% reduction in cumulative infections (95% CrI: 95.4-97.3%), compared with 94.3% (95% CrI: 91.8-95.6%) for a 50% increase in adult mosquito mortality and 77.0% (95% CrI: 58.6-84.6%) for a 50% reduction in mosquito birth rate. Analysis of the time-varying reproduction number (Rt) confirmed that interventions targeting adult mosquito-human contact achieved the greatest sustained epidemic suppression, although the relative ranking between bite prevention and adulticide application varied by epidemiological setting. ConclusionsUnder the uniform 50% intensification scenario tested, interventions that directly disrupt adult mosquito-human contact, whether through personal protection or adulticide application, substantially outperformed larval control in reducing dengue transmission across diverse Thai settings. These findings support prioritizing personal protection and adulticide application, while the generalizability of this ranking to other intensification levels and settings warrants further investigation.

Against the backdrop of global climate change and accelerating population mobility in 2025, chikungunya fever (CHIKF) exhibited a trend of worldwide spread, significantly increasing the difficulty of controlling tropical mosquito-borne diseases. To enhance the precision of intervention strategies, this study developed an age- and sex-structured human-mosquito interaction dynamic model based on data from the largest CHIKF outbreak ever recorded in China, and conducted a targeted analysis of prevention and control strategies. By decomposing the basic reproduction number and examining population heterogeneity, asymptomatic males aged 15-59 years were identified as the core transmission group. Optimal control analysis revealed that the synergistic implementation of three measures-- reducing the effective human-to-mosquito transmission rate, reducing the effective mosquito-to-human transmission rate, and suppressing mosquito population density--could reduce the overall infection rate by 95.7586%. Among these, mosquito population suppression should be prioritized as a universal core strategy; however, its protective effect on females aged 60 years and above was relatively weak, warranting particular attention. The study further demonstrated that asymmetric intensity combinations targeting these three intervention pathways--such as intensity profiles of "10%, 90%, 90%" or "60%, 80%, 90%"--could achieve effective outbreak control. This research elucidates population-specific transmission patterns and key pathways for intervention intensity, providing a theoretical and strategic foundation for the precise control of mosquito-borne diseases. It also provides actionable operational insights to support rapid response and strategy optimization for future emerging outbreaks. Author summaryCHIKF is a mosquito-borne viral disease that is gradually spreading from tropical regions to other areas. To achieve more precise control of this disease, we developed an age- and sex-structured analytical model based on the largest CHIKF outbreak in China, aiming to provide a scientific basis for responding to potential future outbreaks with inherent uncertainties. The study found that asymptomatic males aged 15-59 years were the primary drivers of transmission and should be prioritized as a key population for reducing viral spread in prevention efforts. When evaluating the effectiveness of different intervention strategies, females aged 60 years and above were the least affected by the implemented measures, indicating that this group should strengthen personal protection to lower their infection risk. Among all control measures, mosquito suppression was the most effective, suggesting that vector control strategies should be prioritized in future outbreak responses.

BackgroundBoth hemorrhagic fever with renal syndrome (HFRS) and scrub typhus (ST) are acute zoonotic infectious diseases. There is an overlap in their epidemiological characteristics and clinical manifestations, posing challenges for early differential diagnosis. This study aims to identify predictive factors for these two diseases to provide a basis for early diagnosis. Method/FindingsA retrospective analysis was conducted on the clinical data of patients diagnosed with HFRS and ST at the First Affiliated Hospital of Dali University. Logistic regression analysis was employed to explore independent risk factors for the early differential diagnosis of these two diseases, and a nomogram model was constructed based on these risk factors. The performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC). The nomogram was utilized to visually present the predictive variables. Decision curve analysis (DCA) was performed to assess the clinical utility of the model. ResultsA total of 235 patients each with HFRS and ST were included in this study. After adjusting for confounding factors, the results of multivariate logistic regression analysis revealed that sex (male) (adjusted odds ratio [ajOR]: 2.093, 95% confidence interval [CI]: 1.107 - 3.957, P = 0.018), positive proteinuria (ajOR: 4.937, 95% CI: 2.427 - 10.042, P < 0.001), creatinine (CREA) (ajOR: 1.009, 95% CI: 1.003 - 1.015, P = 0.005), heart rate (ajOR: 0.981, 95% CI: 0.966 - 0.997, P = 0.018), and conjunctival congestion (ajOR: 16.167, 95% CI: 5.326 - 49.072, P < 0.001) were independent risk factors for differentiating HFRS from ST. The AUC of the model constructed based on these five independent risk factors was 0.856. ConclusionSex (male), positive proteinuria, elevated CREA, decreased heart rate, and conjunctival congestion are effective predictive factors.

BackgroundStigma - a procedures with label, stereotype, prejudice, status loss, and discrimination -has long played a role in the spread of HIV since the beginning of the epidemic. However, few researchers conducted on the HIV-related stigma and discrimination for general population in China. Consequently, we introduced translated and adapted the English version of HIV/AIDS Stigma and Discrimination Scale applied for undergraduates in China. ObjectiveThis study aimed to adapt the HIV/AIDS Public Stigma and Discrimination Scale (HPSDS) in China and to investigate its psychometric properties (e.g., reliability and validity). MethodsUsing translation, back-translation, quality evaluation, cross-cultural adaption and pre-assessment, a Chinese draft version of the HPSDS was obtained. From April 2022 to July 2022, the scale was distributed to179 universities and colleges and 2,333 college students filled out the translated and adapted questionnaires. Finally, we collected 1,604 valid questionnaires. The results were recruited to assess the psychometric characteristics of the CV-HPSDS. ResultThe CV-PHSDS consists of 3 dimensions and 14 items with Cronbachs alpha coefficient, McDonalds omega coefficient and test-retest reliability of the scale are 0.869, 0.883 and 0.857 respectively, manifesting good internal consistency and stability. To construct validity of adapted scale, an exploratory factor analysis was conducted with the cumulative variance contribution rate of 76.6% was obtained. For confirmatory factor analysis, the CFI, GFI, TLI, and IFI showed excellent fitness to the structure, with fitness indices of 0.972, 0.949, 0.965, and 0.972, respectively. Finally, a valid and reliable instrument to measure the HIV/AIDS stigma and discrimination level is obtained. ConclusionThe translated and adapted version of HPSDS shows to be a reliable and valid instrument for assessing stigma and discrimination level for undergraduates.

Effective public health planning and intervention strategies necessitate an understanding of the temporal and geographic distribution of disease incidences. This requires robust frameworks for disease incidence forecasting. However, due to variations in cases and temporal dynamics, grasping the distinct patterns of climate-sensitive diseases poses significant challenges, including identifying hotspots, trends, and seasonal variations in disease incidence. Furthermore, although most studies focus on directly predicting future incidence using historical patterns and covariates, a significant gap remains between methodological proliferation marked by diverse architectures, where models are trained and validated on benchmark datasets that are standardized and statistically stable, and epidemiological reality, which is often characterized by irregular, sparse, and highly skewed data, as well as rare but high-magnitude or bimodally distributed incidences. Hence, traditional end-to-end approaches that directly map climate and disease data often fail in these data-scarce settings due to overfitting and poor generalization. To understand disease epidemiology and mitigate the impact of incidence, we analyzed a decade of retrospective datasets in Ethiopia to examine how climate and weather conditions influence the incidence or spread of climate-sensitive diseases, including malaria and dysentery. In this study, we proposed a two-stage hybrid framework, a climate-informed disease prediction model, to forecast the likelihood of disease incidences using decades of climate and weather data. First, deep learning was applied to capture latent weather dynamics. Then, a hurdle model using Extreme Gradient Boosting (XGB) was designed for zero-inflated incidence data, combining XGBClassifier to predict incidence and XGBRegressor to estimate its size, based on weather dynamics to forecast disease incidence. Our proposed multivariate climate-driven disease incidence model incorporates both spatial (elevation, coordinates) and temporal (year, month) factors, along with key weather parameters (precipitation, sunlight, wind, relative humidity, temperature) to predict the likelihood of multiple diseases occurring in each area, serving as a foundation for future disease incidence predictions in the region. Out of 72 evaluated experiments across four categories and six targets, we found that the Transformer model showed highest number of statistically significant wins (n=18, 25.0%) comparison with Long Short-Term Memory (LSTM) (n=9, 12.5%) and the Temporal Convolutional Neural Network (TCN) (n=5, 6.9%) at climate variable forecasting using Pairwise Model Comparison Diebold-Mariano Test. The hurdle model that combines XGBClassifier and XGBRegressor outperformed the baseline in both Malaria and Dysentery forecasting. Error stratification revealed that the hurdle model provided the greatest benefit during incidence periods, as indicated by a substantially lower Mean Average Error (MAE) in both incidence and non-incidence periods than the baseline. Our proposed modular pipeline first forecasts climate variables, then predicts disease incidence, thereby enhancing interpretability and generalization in data-sparse settings. Overall, this approach provides a scalable, climate-aware forecasting tool for public health planning, particularly in regions where these diseases are endemic or where climate change may affect their prevalence, as well as in data-scarce settings.

A field trial was conducted using 10% lambda-cyhalothrin microcapsule suspension to provide a method for killing ticks and preventing diseases in outdoor gatherings of people or temporary resettlement places after disasters. In this study, three field experimental sites were selected, and each experimental site was set up with a test area and a control area. Before pesticide application, the tick density in three test areas and three control areas was surveyed using the flagging method. Subsequently, two methods were used for pesticide spraying: motorized fogging and electric constant-volume spraying (with the pesticide diluted 300 times). The relative density decline rate of ticks was calculated in three test sites on days 1, 7, 14, 21, and 28 after spraying, and all experimental areas achieved good tick-killing effects. Even without prohibiting wild animals, grazing sheep, and dogs (which are often infested with ticks and not treated) from entering the trial sites, spraying 10% lambda-cyhalothrin microcapsule suspension could maintain a tick-free (low-density) state for approximately 3-4 weeks. Our study provides an idea for controlling epidemics through tick elimination during the high incidence period of tick-borne diseases.

Mainland China experienced multiple waves of COVID-19 pandemic during 2020-2022, driven by emerging variants and changes in public health and social measures (PHSMs). We developed a hypergraph-based Susceptible-Vaccinated-Exposed-Infectious-Recovered-Susceptible (SVEIRS) model to reconstruct epidemic dynamics across 31 provinces, capturing transmission heterogeneity associated with clustered contacts. We assessed key characteristics of transmission at national and provincial levels during four outbreak periods: initial, localized pre-delta, Delta, and widespread Omicron, which accounted for 96.7% of all infections. We found significant diversity in transmission contributions across cluster sizes, with a small fraction of larger clusters responsible for a disproportionate share of infections. Counterfactual analyses showed that reducing cluster-size heterogeneity, while holding overall exposure constant, could have lowered national infections by 11.70-30.79%, with the largest effects during Omicron period. Ascertainment rates increased over time but remained spatially heterogeneous with a range: (14.40, 71.93)%. Population susceptibility declined following mass vaccination (to 42.49% in Aug 2021, nationally) and rebounded (to 89.89% in Nov 2022) due to waning immunity with variations across the provinces. Effective reproduction numbers displayed marked temporal and spatial variability, with higher estimates during Omicron. Overall, these results highlight critical role of group contact heterogeneity in shaping epidemic dynamics.

Malawis HIV treatment monitoring system faces serious challenges because of a shortage of experts and reliance on viral load testing every 3 to 12 months. The process causes dangerous delays in identifying treatment failure. This leads to a higher risk of disease progression, transmission, and death. To tackle this issue, this study used a machine learning model based on association rules and combined it with clustering analysis to create a machine learning framework to identify key factors and risk profiles for virological failure among children living with HIV (CLHIV) in Malawi. The methodology combines a Random Forest classifier for feature importance, association rule mining to find predictive rules, and k-Prototype clustering for risk profiling among CLHIV. The random forest feature importance results show that Body Mass Index (BMI), CD4 count, TB status, ART regimen, gender, ART adherence, and treatment duration are major drivers of virological failure. In addition to these individual factors, the analysis produced highly reliable association rules with over 90% confidence. This establishes a framework for identifying complex risk profiles and informing focused clinical interventions. The high lift values of 4.9 across the most significant rules demonstrate the models effectiveness by revealing strong, non-random associations. Clustering analysis also identified two distinct risk profiles associated with virological failure. The k-prototype clustering model performed optimally with a cluster purity of 100% and a silhouette score of 79%.

Human immunodeficiency virus (HIV) persistence remains a major barrier to cure due to the existence of long-lived latent reservoirs that evade immune clearance and persist despite combination antiretroviral therapy (ART). Although ART effectively suppresses viral replication, treatment interruption often leads to rapid viral rebound originating from these latent reservoirs. In this study, we develop a deterministic mathematical model describing the in vivo dynamics of HIV infection incorporating uninfected CD4+ T cells, infected cells, latent reservoirs, deep latent reservoirs, and infectious and non-infectious virions, while explicitly accounting for the therapeutic effects of reverse transcriptase inhibitors (RTIs), protease inhibitors (PIs), and Tat transcription inhibitors. Analytical results establish positivity and boundedness of solutions and derive the effective reproduction number Re using the next-generation matrix approach. Stability analysis shows that the virus-free equilibrium is locally asymptotically stable when Re < 1, while viral persistence occurs when Re > 1. Numerical simulations were performed to investigate therapy interactions, viral rebound following treatment interruption, and the impact of drug efficacy on viral set-points and latent reservoir dynamics. To further explore therapy interactions, three-dimensional viral set-point surfaces and heat maps were generated to examine how combinations of infection inhibition, viral production inhibition, and transcriptional inhibition influence viral dynamics. The simulations reveal that Tat inhibition suppresses viral transcription, thereby reducing the transition of infected cells into productive infection and limiting viral propagation when combined with conventional ART mechanisms. The therapy parameter planes further demonstrate that strong transcriptional inhibition promotes the transition of infected cells into deep latency, supporting the emerging block-and-lock strategy for functional HIV cure. In addition, a three-dimensional eradication boundary surface and therapy cube were constructed to identify regions of parameter space where Re < 1, corresponding to successful viral control. These visualizations show that viral eradication is unlikely when therapies act independently but becomes achievable when multiple therapeutic mechanisms act simultaneously. Overall, the results highlight the critical role of transcriptional inhibition through Tat-targeting therapies in complementing existing ART regimens. By simultaneously suppressing viral replication and promoting deep latency, Tat-based combination strategies may significantly reduce viral rebound and contribute to long-term functional control of HIV infection.

In recent years, detection of local dengue cases in Florida have increased in both frequency and geographical extent. From 2022 to 2024, consecutive outbreaks in Miami-Dade County were mainly caused by a single lineage of dengue virus (DENV) serotype 3, prompting questions about changing epidemiology and a transition towards endemicity. In this study, we used mathematical modeling and genomic epidemiology to reveal the spatiotemporal dynamics and drivers of local dengue cases in Florida. We found that annual clusters and outbreaks were caused by frequent short-lived DENV introductions, primarily from the Caribbean, and did not find evidence for local trans-seasonal DENV lineage persistence. Further, we show that the climate-driven increases in local suitability for Aedes aegypti transmission and travel-associated cases were the greatest risk factors for outbreaks in Miami-Dade and the geographic expansion of dengue in Florida. Overall, while we do not yet find evidence for endemicity, we demonstrate how climatic trends are enhancing the local public health risk caused by dengue in Florida.

Based on the 2023 Global Burden of Disease (GBD) database, this study analyzed the global burden of preterm birth from 1990 to 2023 and predicted its development trend by 2050, while exploring the disparities in disease burden across regions with different Socio-demographic Index (SDI) levels, income groups and countries. A retrospective trend analysis was conducted to collect data on preterm birth incidence, prevalence, death and disability-adjusted life years (DALYs) in 204 countries and regions worldwide from 1990 to 2023 from the GBD 2023 database. ARIMA model (p=2,d=1,q=1) and grey prediction model (GM(1,1)) were combined to predict the preterm birth burden from 2023 to 2050. In 2023, preterm birth was the primary cause of the global neonatal disease burden, with its four core indicators significantly higher than other neonatal diseases. From 1990 to 2023, the global incidence, death and DALYs of preterm birth decreased to 0.91, 0.44 and 0.52 times of the 1990 levels respectively, while the prevalence increased to 1.54 times of the baseline. Projection results showed that by 2050, the incidence, death and DALYs of preterm birth would drop to 0.79, 0.08 and 0.32 times of the 2023 levels, and the prevalence would rise to 1.23 times of 2023. Low SDI regions, lower-middle income countries, as well as India and Nigeria, bore the heaviest disease burden. Over the past three decades, the global acute health burden of preterm birth such as death has decreased notably, but the continuous rise in prevalence and severe regional and age disparities remain prominent public health challenges. The 0-6 days and 6-11 months age groups are the key time windows for preterm birth intervention. It is urgent to implement targeted prevention and control measures for low SDI regions and lower-middle income countries to reduce the global burden of preterm birth.

The global ambition to end the human immunodeficiency virus (HIV) epidemic requires understanding which system-level policy levers, enacted under the framework of Universal Health Coverage (UHC), are most effective in achieving both transmission reduction and diagnostic coverage. This study addresses an important evidence gap by quantifying the within-country association between measurable UHC policy indicators and the estimated rate of new HIV infections across nine Southeast Asian countries between 2013 and 2022. Employing a Fixed-Effects panel data methodology, the analysis controls for time-invariant national heterogeneity, ensuring reliable estimates of policy impact. We found that marginal changes in total current health expenditure (CHE) as a percentage of gross domestic product (GDP) were not statistically significantly associated with changes in HIV incidence. However, increases in the UHC Infectious Disease Service Coverage Index were statistically significantly associated with concurrent reductions in HIV incidence (p < 0.001), suggesting the efficacy of targeted service implementation as the principal driver of curbing new HIV infections. In addition, the UHC Reproductive, Maternal, Newborn, and Child Health Service Coverage Index exhibited a statistically significant positive association with changes in HIV incidence (p < 0.01), which is interpreted as a vital surveillance artefact resulting from expanded detection and reporting of previously undiagnosed HIV cases. Furthermore, out-of-pocket (OOP) health expenditure as a percentage of CHE showed a counter-intuitive negative association with changes in HIV incidence (p < 0.01), suggesting this metric primarily shows ongoing indirect cost burdens on the established patient cohort, or, alternatively, presents a diagnostic access barrier that results in lower case finding. These findings suggest that policymakers should prioritise investment in targeted infectious disease service efficacy over aggregate fiscal commitment and utilise integrated sexual health platforms for strengthened HIV surveillance and case identification.

IntroductionViral load (VL) testing is the recommended approach for monitoring antiretroviral therapy (ART) effectiveness, while guidelines recommend targeted CD4 testing after ART initiation. This study examined trends in VL and CD4 testing frequencies, as well as the relationship with AIDS diagnosis and mortality among people with HIV in the Asia-Pacific region. MethodsWe included adults enrolled in the Treat Asia HIV Observational Database (TAHOD) between 2003-2018 who had been on ART for [&ge;]1 year. VL and CD4 testing rates were analysed using Poisson regression models. Associations between testing frequency and AIDS diagnosis or mortality were evaluated using Fine and Gray competing risk regression. ResultsAmong 8,446 patients, VL testing rates remained steady at 1 per person-year (PYS) between 2003-2018. Increased VL testing was associated with more frequent CD4 testing (>2 tests in the previous year; IRR=1.57, 95%CI 1.53-1.60), later follow-up years (2008-2012: IRR=1.15, 95%CI 1.12-1.18; 2013-2015: IRR=1.07, 95%CI 1.04-1.10), older age (31-40 years: IRR=1.06, 95%CI 1.03-1.08; 41-50 years: IRR=1.08, 95%CI 1.05-1.11; >50 years: IRR=1.07, 95%CI 1.03-1.11), higher current VL (401-1000 copies/mL: IRR=1.16, 95%CI 1.09-1.24; >1000 copies/mL: IRR=1.07, 95%CI 1.04-1.11), initial ART regimen (NRTI+PI: IRR=1.07, 95%CI 1.04-1.10; other combinations: IRR=1.11, 95%CI 1.05-1.17), and higher country income levels (upper-middle: IRR=2.17, 95%CI 2.11-2.23; high: IRR=3.14, 95%CI 3.03-3.26). CD4 testing rates decreased from 2.04 to 1.06/PYS over the same period. Lower CD4 testing frequency was associated with HIV exposure mode (MSM: IRR=0.94, 95%CI 0.92-0.96; IDU: IRR=0.93, 95%CI 0.90-0.97; other/unknown: IRR=0.90, 95%CI 0.87-0.93), higher current CD4 (201-350 cells/{micro}L: IRR=0.95, 95%CI 0.93-0.97; 351-500 cells/{micro}L: IRR=0.89, 95%CI 0.87-0.91; >500 cells/{micro}L: IRR=0.85, 95%CI 0.83-0.87) and receiving an NRTI+PI first-line combination (IRR=0.96, 95% CI 0.94-0.98). VL and CD4 testing frequencies were not significantly associated with AIDS diagnosis. However, having > 2 CD4 tests in the previous year was associated with higher mortality risk. ConclusionThe trends in the rates for CD4 and VL testing in the region between 2003-2018 were significantly affected by demographic, clinical and socio-economic factors. Recognizing these factors is critical to optimizing differentiated monitoring strategies and improving outcomes for PWH in the region.

BackgroundTo anticipate the impact of new pneumococcal vaccines and guide future updates, accurate forecasts of changes in non-vaccine serotypes (NVTs) are needed. We developed and evaluated three models that incorporated different assumptions about the way in which NVTs will increase and generated ensemble predictions for the frequency of NVTs in different post-pneumococcal conjugate vaccines (PCV) periods. MethodsWe analyzed age- and serotype-specific invasive pneumococcal disease (IPD) cases from the United States CDCs Active Bacterial Core surveillance during the pre-PCV (1998-1999), early post-PCV7 (2000-2004), late post-PCV7/pre-PCV13 (2005-2009), early post-PCV13 (2010-2014), and late post-PCV13 (2015-2019) periods. These data were augmented with IPD cases from several countries and combined with serotype-specific invasiveness to infer serotype-specific carriage prevalence. Three models ("Ranking", "Proportionate", "NFDS-lite") generated independent predictions of post-PCV IPD frequencies, which were integrated using an accuracy-weighted ensemble. Model performance was evaluated using the normalized root mean square error (NRMSE). ResultsA total of 23,959 non-PCV7 and 15,580 non-PCV13 cases were analyzed. NVT cases increased from the pre-PCV7 to the late post-PCV7 and post-PCV13 periods. The accuracy of predictions across age groups and models was consistent and high during the post-PCV13 periods but varied during the post-PCV7 periods. The Proportionate model (NRMSE=0.70-3.95) outperformed the NFDS-lite (NRMSE=0.93-8.91) and Ranking (NRMSE=1.51-5.37) models during the early-post-PCV7 period, whereas the NFDS-lite model (NRMSE=1.55-9.82) was superior to the Proportionate (NRMSE=1.45-10.22) and Ranking (NRMSE=1.86-11.35) models during the late post-PCV7 period. The Ensemble model improved on these individual models. ConclusionsThe Ensemble model offers a tool for forecasting serotype patterns to inform pneumococcal vaccines impact and future pneumococcal vaccine formulation.

Sylvatic Yellow Fever (YF) is an infectious mosquito-borne disease with significant epidemiological relevance due to its widespread distribution and high lethality for human and non-human primates, particularly in tropical regions of the planet such as in Brazil. Identifying regions and periods of high environmental suitability for the occurrence of YF is essential for preventing or mitigating its burden, as it enables the efficient allocation of surveillance efforts, prevention, and implementation of control measures. Environmental modeling of YF occurrence has proven to be an effective approach toward this goal; however, its effectiveness strongly depends on the modeling framework's capabilities as well as the spatial and temporal precision of all associated data. We propose a fine-scale geospatial modeling of YF environmental suitability that is based on a generative machine-learning ensemble method built on a large set of high-resolution environmental covariates. First, we take the spatiotemporal statistical description of the environment of each of the 545 YF cases from 2019--2024 up to 30 m/monthly resolution at three buffer scales: 100 m, 500 m, and 1000 m ratios. Then, we perform a feature selection and train hundreds of One-Class Support Vector Machine submodels to form a robust ensemble model, whose predictions are projected to a 1x1 km resolution grid of Brazil under several metrics, exceeding seven million ensemble evaluations. The predictions ranked the Southern Brazil region with the highest mean suitability for YF, with a level of 0.64; Southeast comes next with 0.46, followed closely by Central-West region (0.44), North (0.39), and finally Northeast (0.28). The model exhibited high uncertainty for the North region, indicating that data collection efforts are much needed in this region. As for the environmental covariates, a feature analysis pointed out that Land use and cover accounts for the largest influence in the model output.

BackgroundTuberculosis (TB) remains a critical public health challenge, with two-thirds of the global TB burden in ten Asian countries. Social vulnerabilities, comorbidities, health inequity, multi-dimensional poverty, malnutrition, and barriers to healthcare access continue to fuel TB epidemic. Inability to detect asymptomatic and sub-clinical TB, combined with passive approach in service delivery and overreliance on smear microscopy, leads to delayed diagnosis, a substantial burden of undetected cases, and continuing TB transmission in the communities. In such a context, the introduction and scale-up of active case-finding approaches - including community-based TB screening using highly sensitive screening tools and novel rapid diagnostics - becomes a strategic priority to interrupt transmission. The growing availability of multiple screening and diagnostic options makes evidence-based decision-making increasingly complex. MethodsTo estimate the potential epidemiological impact and cost implications of scaling up TB diagnostics and community-based screening in ten high-burden Asian countries, we constructed a mathematical model and evaluated multiple intervention scenarios. We then assessed and compared four service delivery models: 1) digital ultraportable chest x-ray (UPCXR) & Xpert/Truenat in community, 2) digital UPCXR in community and Xpert/Truenat at health facilities, 3) digital UPCXR in community and near point of care (nPOC) at health facilities, 4) nPOC in community & Xpert/Truenat at health facilities - for total investment required and projected health benefits for their cost-effectiveness. Results and conclusionsThe modelling study indicated that strengthening health facility capacity (with enhanced TB screening, expanded molecular diagnostics, reduced loss to follow-up, private sector standard of care, leading to increased treatment coverage & quality of active disease treatment and reduced post-treatment relapse, scale-up of TB preventive treatment (TPT), and provision of nutritional support to 80% of TB patients and their household contacts) can significantly reduce TB incidence and mortality; however, community-wide mass screening remains essential to achieving TB elimination targets. Targeted screening of vulnerable populations demonstrated greater cost-effectiveness than untargeted screening approaches. Achieving the End TB goals will ultimately require an effective TB vaccine with high population-level coverage. AI-enabled digital UPCXR-based screening combined with Xpert/Truenat testing at the community level demonstrated maximum epidemiological impact potential, while the most cost-efficient model is Digital UPCXR in the community combined with nPOC testing at health facilities. An investment of USD 12.7 billion over the next five years in community-level implementation of digital UPCXR and molecular diagnostics could avert an additional 9.8 million TB cases and 1.9 million deaths across ten Asian countries over a ten-year horizon

Female sex workers (FSW) in sub-Saharan Africa experience disproportionately high risks of HIV infection. Mathematical models are widely used to assess the contribution of sex workers and other key populations to HIV transmission dynamics and to inform targeted programmes. However, many rely on simplifying assumptions, such as stable sex worker characteristics and constant HIV transmission risk over time. These assumptions may be unrealistic and could bias modelled estimates. We used the South African Thembisa model to assess how alternative assumptions about FSW age, duration of sex work, and client-to-FSW transmission risk affect modelled HIV outcomes. We compared six scenarios that combined constant and increasing FSW age and sex work duration with constant and early-epidemic declining (exponentially or exposure-dependent) transmission risk. Each scenario was calibrated to HIV prevalence data from population-based and sex worker-specific surveys. Scenarios that allowed both FSW characteristics and transmission risk to vary over time showed the best agreement with external data, most closely reproducing HIV incidence, prevalence, and viral suppression estimates from a 2019 national sex worker survey (incidence [~]5 per 100 person-years, prevalence 61-62%, viral suppression [~]60%), and producing incidence rate ratios more consistent with estimates from the broader eastern and southern Africa region. By contrast, the scenario assuming constant FSW characteristics and transmission risk overestimated HIV incidence and underestimated prevalence and viral suppression. At the same time, this time-invariant specification attributed a much larger share of new HIV infections to sex work, with commercial sex work accounting for more than 20% of new infections in 2025, compared with 9-13% under time-varying assumptions. Overall, our findings show that HIV model estimates for sex workers are highly sensitive to modelling assumptions. Incorporating time-varying FSW parameters yields estimates that are more consistent with empirical data and support more reliable programme planning and evaluation. Author SummaryFemale sex workers in sub-Saharan Africa face much higher risks of HIV infection than other women. Mathematical models are often used to understand why and to guide prevention programmes. Yet many of these models make simple assumptions about sex workers - for example, that their average age stays the same over time, that they spend a fixed number of years in sex work, or that the chance of HIV passing from a client to a sex worker never changes. In reality, these factors changed over time. In this study, we used South Africas national HIV model to test how changing these assumptions affects the results. We compared different versions of the model and checked which ones best matched national sex worker survey data. We found that the model worked better when we allowed sex workers to become older over time, to spend longer in sex work, and the risk of passing on HIV to decline. Our findings show that mathematical models can give very different answers depending on how they represent the lives and experiences of sex workers. More realistic assumptions lead to more accurate estimates and can help ensure that programmes focus support where it is most needed.

BackgroundImplementing proven vehicle safety standards recommended by the UN World Forum for Harmonization of Vehicle Regulations is among the most cost-effective strategies to reduce road traffic deaths. In 2022, Mexico approved updated vehicle safety standards, including side pole testing, electronic stability control, seatbelts, airbags, side structures, and anchorage child restraint systems. However, pedestrian protection and advanced driver-assistance technologies, such as autonomous emergency braking systems (AEBS), were excluded. These exclusions are critical, given that more than half of road traffic deaths involve vulnerable road users. Local evidence on the expected benefits of implementing comprehensive vehicle safety standards is needed to guide policy decision-making. ObjectiveTo estimate the potential public health impact of increasing the availability of recommended vehicle safety technologies in Mexico. MethodsWe conducted a comparative risk assessment analysis to estimate the impact of improving vehicle safety standards on road traffic deaths, injuries, and disability-adjusted life years. Counterfactual analyses were defined using traffic statistics for 2019 as baseline, relative risk estimates associated with each safety technology, and technology penetration within Mexicos vehicle fleet. Three scenarios were modeled: (1) full implementation of Mexicos 2022 standards; (2) addition of crashworthiness, AEBS, and motorcycle ABS/ESC; and (3) inclusion of expanded AEBS crash configurations, lane departure warning (LDW), and lane keeping assistance (LKA) systems. ResultsScenario 1 reduced deaths by 18%, injuries by 16%, and DALYs by 18%, with the greatest benefits for car occupants. Scenario 2 reduced deaths by 29%, injuries by 27%, and DALYs by 30%, benefiting motorcyclists and pedestrians the most. Scenario 3 reduced deaths, injuries, and DALYs by 41%, 38%, and 41%, respectively, benefiting car occupants and motorcyclists. ConclusionsCurrent vehicle safety standards in Mexico are expected to reduce deaths, injuries, and disabilities, yet existing guidelines focus largely on protecting car occupants. Mexico should strive to update and strengthen its current legislation by adding technologies that protect vulnerable road users, such as pedestrians and cyclists, and to focus on technologies for motorcycle users to further reduce the burden of road traffic injuries.

Background Malaria remains a critical global health challenge, with over 68% of Ethiopias population living in at-risk areas. While Long-Lasting Insecticidal Nets (LLINs) are a cornerstone of prevention, their effectiveness depends on consistent use. This study aimed to assess LLIN ownership and utilization patterns and identify socio-behavioral and physical determinants of their effectiveness in endemic communities. Methods A community-based, cross-sectional survey was conducted from October 2024 to January 2025 across 11 administrative regions in Ethiopia. Using a two-stage stratified cluster sampling technique, data were collected from 9,222 households (34,427 individuals) through face-to-face interviews and direct physical observations. Data analysis was performed using the SPSS Complex Samples module and hierarchical multivariable logistic regression. Results The survey found a household LLIN ownership rate of 71.5%, while the proportion of sufficient LLINs for every two people was 58.3%. Among those who owned nets, the overall utilization rate was 59.9%, with significantly higher rates in rural areas (72.7%) than in urban areas. Vulnerable groups achieved higher usage levels, specifically pregnant women (78.5%) and children under five (67.2%). Multivariable analysis indicated that age and pregnancy status were the strongest predictors of LLIN use, with ORs of 0.258 (p < 0.001) and 0.662 (p < 0.001), respectively. Major barriers identified included a 60.5% lack of confidence in hanging nets (p < 0.001) and a widespread misconception (64.1%) that malaria risk is restricted to the rainy season. Conclusion Although Ethiopia has made strides in LLIN ownership and prioritized protection for vulnerable demographics, overall utilization remains below the 80% threshold required for community-wide protection. To bridge the gap between ownership and consistent use, national strategies should transition toward skill-based interventions and targeted communication to address practical barriers and seasonal misconceptions.

BackgroundChronic Kidney Disease of unknown etiology is a growing health concern in low-and middle-income countries. While occupational heat stress is recognized as a potential contributor to kidney dysfunction among agricultural workers, the causal relationship between heat stress, core body temperature (Tc), and kidney function remains unclear. MethodsWe conducted an observational study over two harvest seasons in Guatemala, following 148 male sugarcane workers across six months. Heat stress was measured using heat index (HI) and Tc with ingestible telemetric temperature pills. Particulate matter (PM) exposure was measured using personal breathing zone samplers worn during the work shift. We evaluated changes in kidney function using pre-and post-shift estimated glomerular filtration rate (eGFR). We applied G-computation to estimate causal effects and modeled hypothetical policy interventions reducing HI, Tc, and PM exposure, simulating occupational heat reduction strategies. ResultsThe average daily HI was 37.4 {degrees}C (SD: 2.0) with an average Tc increase of 1.16 {degrees}C (SD: 0.48) per shift. Both HI and Tc were associated with declines in eGFR across the work shift. At an HI of 34 {degrees}C, workers experienced an average eGFR decline of about 5 mL/min/1.73 m{superscript 2}, while at 40 {degrees}C the decline exceeded 16 mL/min/1.73 m{superscript 2}. High HI early in the season and elevated Tc later in the season contributed to kidney decline. A simulated intervention reducing HI exposure by 5% improved eGFR change by 1.46 mL/min/1.73 m{superscript 2}. PM exposure did not have a significant impact on eGFR decline. ConclusionReducing workday heat exposure may mitigate acute kidney function decline. These findings support the development of policy interventions aimed at reducing external heat exposure and internal heat strain to protect kidney health. More research is needed to investigate the potential contribution of other environmental factors, including PM exposure.