Search Results (161)

This study introduces a novel Fuzzy Piecewise Fractional Derivative (FPFD) framework to enhance epidemiological modeling, specifically for the multi-phasic co-infection dynamics of Omicron and malaria. We address the limitations of traditional models by incorporating two key realities. First, we use fuzzy set theory to manage the inherent uncertainty in biological parameters. Second, we employ piecewise fractional operators to capture the dynamic, phase-dependent nature of epidemics. The framework utilizes a fuzzy classical derivative for initial memoryless spread and transitions to a fuzzy Atangana–Baleanu–Caputo (ABC) fractional derivative to capture post-intervention memory effects. We establish the mathematical rigor of the FPFD model through proofs of positivity, boundedness, and stability of equilibrium points, including the basic reproductive number (R0). A hybrid numerical scheme, combining Fuzzy Runge–Kutta and Fuzzy Fractional Adams–Bashforth–Moulton algorithms, is developed for solving the system. Simulations show that the framework successfully models dynamic shifts while propagating uncertainty. This provides forecasts that are more robust and practical, directly informing public health interventions. Full article

Background and Objectives: Laryngeal cancer imposes a disproportionate burden on speech, airway protection and long-term quality of life. Contemporary population-based data for Central and Eastern Europe remain scarce, and the post-pandemic trajectory is uncertain. Materials and Methods: We performed a nationwide, retrospective ecological time-series study using Romanian mortality registers and hospital-discharge files for 2017–2023. Crude and age-standardised mortality rates (ASMRs) were calculated, county-level indirect standardisation and spatial autocorrelation assessed and joinpoint regression quantified temporal trends. Forecasts to 2040 combined Holt–Winters/ARIMA models with Elliott-wave heuristics anchored to Fibonacci retracements. Results: In 2023, 798 laryngeal cancer deaths yielded a crude mortality of 3.65/100,000 (95% CI 3.41–3.91). Male mortality (7.07/100,000) exceeded female mortality 18-fold. Rural residents experienced a higher rate than urban counterparts (4.26 vs. 3.04/100,000), a difference unchanged after indirect age standardisation. National ASMR fell by 3.7% annually (p < 0.01), yet five counties formed a high-risk corridor (standardised mortality ratios 1.59–1.82); Moran’s I = 0.27 (p < 0.01) indicated significant spatial clustering. Pandemic-era surgical throughput collapsed by 48%, generating a backlog projected to persist beyond 2030. Ensemble forecasting anticipates a doubling of discharges and mortality between 2034 and 2037 unless smoking prevalence falls by ≥30% and radon exposure is curtailed. Conclusions: Although overall laryngeal cancer mortality in Romania is declining, the pace lags behind Western Europe and is threatened by geographic inequities and pandemic-related care delays. Aggressive tobacco control, radon-remediation policies and expansion of surgical and radiotherapeutic capacity are required to avert a forecasted surge in the next decade. Full article

Background/Objectives: Measles is a highly contagious viral disease that continues to have a profound effect on morbidity in Romania. Identifying temporal and spatial trends in how the disease spreads among the country’s counties and regions, both in the same disease generation as well as one generation apart (2-week case lag), aided by forecasting tools, could provide valuable insights into tailoring public health interventions. Methods: A big data analysis has been performed on notified measles cases from January 2020 to December 2024 using Python v3.13 grouping cases based on location (using the Nomenclature of territorial units for statistics) and time of the onset of the disease. Results: Feedback loops among both counties and macroregions have been identified (for example Centru-Brașov and București-Ilfov with a correlation factor of 0.77) while monthly forecasting for 2025 and 2026, explored using both the SARIMA and the Holt-Winters models (MAE 1616.74 and 1281.99, respectively), shows the measles might continue to be a burden, with the Holt–Winters models exhibiting slightly more reliable monthly forecast data nationwide, helping to define a solid basis for future predictions and decisions. Conclusions: The spatial feedback loops, both interregional or within the same region, coupled with the trend of lowering vaccination rates, contribute to the persistent emergence of new measles cases which might continue throughout 2025 and 2026 based on the forecasting, distinct from previous outbreaks which followed a specific cadence. Full article

Vector-borne parasitic diseases (VBPDs), including malaria, schistosomiasis, leishmaniasis, Chagas disease, African trypanosomiasis, lymphatic filariasis, and onchocerciasis, impose a significant global health burden. This study analyzes the global disease burden of VBPDs from 1990 to 2021 using Global Burden of Disease (GBD) 2021 data and projects trends to 2036. Metrics include prevalence, deaths, disability-adjusted life years (DALYs), and age-standardized rates (ASRs) across regions, sexes, age groups, and Socio-demographic Index (SDI) levels. Key findings reveal persistent disparities: malaria dominated the burden (42% of cases, 96.5% of deaths), disproportionately affecting sub-Saharan Africa. Schistosomiasis ranked second in prevalence (36.5%). While African trypanosomiasis, Chagas disease, lymphatic filariasis, and onchocerciasis declined significantly, leishmaniasis showed rising prevalence (EAPC = 0.713). Low-SDI regions bore the highest burden, linked to environmental, socioeconomic, and healthcare access challenges. Males exhibited greater DALY burdens than females, attributed to occupational exposure. Age disparities were evident: children under five faced high malaria mortality and leishmaniasis DALY peaks, while older adults experienced complications from diseases like Chagas and schistosomiasis. ARIMA modeling forecasts divergent trends: lymphatic filariasis prevalence nears elimination by 2029, but leishmaniasis burden rises across all metrics. Despite overall progress, VBPDs remain critical public health threats, exacerbated by climate change, drug resistance, and uneven resource distribution. Targeted interventions are urgently needed, prioritizing vector control in endemic areas, enhanced surveillance for leishmaniasis, gender- and age-specific strategies, and optimized resource allocation in low-SDI regions. This analysis provides a foundation for evidence-based policy and precision public health efforts to achieve elimination targets and advance global health equity. Full article

This study addresses the critical challenge of emergency material distribution during atypical public health crises, using the COVID-19 pandemic in Hubei Province as a representative case. An innovative internal–external coupled dispatching framework is proposed by integrating regional medical resource allocation with cross-regional supply chain networks. Our methodology employs the SEIR epidemiological model to forecast infection rates and corresponding material demands, then incorporates bidirectional dispatching efficiency as a key determinant of demand urgency. Through systematic risk stratification of affected areas, we develop a dual-objective optimization model that simultaneously minimizes logistical time and cost, solved by the NSGA-II algorithm. The results demonstrate that the internal–external coupled emergency material dispatching approach significantly enhances demand satisfaction in affected regions and improves overall dispatching effectiveness. This study offers practical recommendations and valuable references for emergency material dispatching during public health crises. Full article

Objectives: Diabetes in pregnancy represents a significant public health concern with established impacts on both maternal and fetal health outcomes. Our aim was to evaluate the epidemiology of diabetes mellitus in pregnancy (DMP) and specific fetal and neonatal transient metabolic disorders (FNTMDs) in Hungary between 2010 and 2024, as well as to project future trends through to 2035. Methods: We carried out a quantitative, retrospective study using nationwide real-world data from the Hungarian ‘Pulvita’ Health Data Warehouse. ICD-10 codes O24.0–O24.9 (DMP) and P70.0–P70.9 (FNTMDs) were included. Annual patient numbers, the number of hospital days, and the number of DMP patients per 1000 women aged 15–49, as well as the number of FNTMD patients per 1000 live births, were analyzed with joinpoint regression analysis and different forecasting models to project future prevalence up to 2035. Results: Despite a 14.2% decrease in live births, DMP cases increased significantly (54.9% inpatient, 26.6% outpatient), with GDM incidence per thousand reproductive-age women rising by 85.7%. FNTMD cases showed similar trends, with GDM-related infant syndromes more than doubling (154% increase). Projections indicate that DMP prevalence could reach 4.60 per 1000 reproductive-age women by 2035, while FNTMD cases show varying trends between inpatient (increasing) and outpatient (stabilizing) care. Conclusions: These findings demonstrate a continuing upward trend in diabetes-related pregnancy complications, despite shorter hospital stays, suggesting an urgent need for enhanced preventive programs and specialized care service planning. Full article

This study presents an extended approach to compartmental modeling of infectious disease spread, focusing on regional heterogeneity within affected areas. Using classical SIS, SIR, and SEIR frameworks, we simulate the dynamics of COVID-19 across two major regions of Ukraine—Dnipropetrovsk and Kharkiv—during the period 2020–2024. The proposed mathematical model incorporates regionally distributed subpopulations and applies a system of differential equations solved using the classical fourth-order Runge–Kutta method. The simulations are validated against real-world epidemiological data from national and international sources. The SEIR model demonstrated superior performance, achieving maximum relative errors of 4.81% and 5.60% in the Kharkiv and Dnipropetrovsk regions, respectively, outperforming the SIS and SIR models. Despite limited mobility and social contact data, the regionally adapted models achieved acceptable accuracy for medium-term forecasting. This validates the practical applicability of extended compartmental models in public health planning, particularly in settings with constrained data availability. The results further support the use of these models for estimating critical epidemiological indicators such as infection peaks and hospital resource demands. The proposed framework offers a scalable and computationally efficient tool for regional epidemic forecasting, with potential applications to future outbreaks in geographically heterogeneous environments. Full article

In this paper, the authors introduce the transmuted geometric integer-valued autoregressive model with periodicity, designed specifically to analyze epidemiological and public health time series data. The model uses a transmuted geometric distribution as a marginal distribution of the process. It also captures varying tail behaviors seen in disease case counts and health data. Key statistical properties of the process, such as conditional mean, conditional variance, etc., are derived, along with estimation techniques like conditional least squares and conditional maximum likelihood. The ability to provide k-step-ahead forecasts makes this approach valuable for identifying disease trends and planning interventions. Monte Carlo simulation studies confirm the accuracy and reliability of the estimation methods. The effectiveness of the proposed model is analyzed using three real-world public health datasets: weekly reported cases of Legionnaires’ disease, syphilis, and dengue fever. Full article

Consider an evolving epidemic in which each person is either (S) susceptible and healthy; (E) exposed, contagious but asymptomatic; (I) infected, symptomatic, and quarantined; or (R) recovered, healthy, and susceptible. The inference problem, given (i) who is showing symptoms (I) and who is not (S, E, R) and (ii) the distribution of meetings among people each day, is to predict the number of infected people (state I) in future days (e.g., 1 through 20 days out into the future) for the purpose of planning resources (e.g., needles, medicine, staffing) and policy responses (e.g., masking). Each prediction horizon has different uses. For example, staffing may require forecasts of only a few days, while logistics (i.e., which supplies to order) may require a two- or three-week horizon. Our algorithm and system EpiInfer is a non-Markovian approach to forecasting infection rates. It is non-Markovian because it looks at infection rates over the past several days in order to make predictions about the future. In addition, it makes use of the following information: (i) the distribution of the number of meetings per person and (ii) the transition probabilities between states and uses those estimates to forecast future infection rates. In both simulated and real data, EpiInfer performs better than the standard (in epidemiology) differential equation approaches as well as general-purpose neural network approaches. Compared to ARIMA, EpiInfer is better starting with 6-day forecasts, while ARIMA is better for shorter forecast horizons. In fact, our operational recommendation would be to use ARIMA (1,1,1) for short predictions (5 days or less) and then EpiInfer thereafter. Doing so would reduce relative Root Mean Squared Error (RMSE) over any state of the art method by up to a factor of 4. Predictions of this accuracy could be useful for people, supply, and policy planning. Full article

The rapid expansion of infectious diseases in urban environments presents a significant public health challenge, as traditional surveillance methods rely on delayed case reporting, limiting proactive response capabilities. With the increasing availability of real-time health data, artificial intelligence (AI) has emerged as a powerful tool for disease monitoring, anomaly detection, and outbreak prediction. This study proposes SmartHealth-Track, an AI-powered real-time infectious disease monitoring framework that integrates machine learning models with IoT-enabled surveillance, smart pharmacy analytics, wearable health tracking, and wastewater surveillance to enhance early outbreak detection and predictive forecasting. The system leverages time series forecasting with long short-term memory (LSTM) networks, logistic regression for outbreak probability estimation, anomaly detection with isolation forests, and natural language processing (NLP) for extracting epidemiological insights from public health reports and social media trends. Experimental validation using real-world datasets demonstrated that SmartHealth-Track achieves high accuracy, with an outbreak detection accuracy of 92.4%, wearable-based fever detection accuracy of 93.5%, AI-driven contact tracing precision of 91.2%, and AI-enhanced wastewater pathogen classification accuracy of 94.1%. The findings confirm that AI-driven real-time surveillance significantly improves outbreak detection and forecasting, enabling timely public health interventions. Future research should focus on federated learning for secure data collaboration and reinforcement learning for adaptive decision making. Full article

Accurate forecasting of COVID-19 case numbers is critical for timely and effective public health interventions. However, epidemiological data’s irregular and noisy nature often undermines the predictive performance. This study examines the influence of four smoothing techniques—the rolling mean, the exponentially weighted moving average, a Kalman filter, and seasonal–trend decomposition using Loess (STL)—on the forecasting accuracy of four models: LSTM, the Temporal Fusion Transformer (TFT), XGBoost, and LightGBM. Weekly case data from Ukraine, Bulgaria, Slovenia, and Greece were used to assess the models’ performance over short- (3-month) and medium-term (6-month) horizons. The results demonstrate that smoothing enhanced the models’ stability, particularly for neural architectures, and the model selection emerged as the primary driver of predictive accuracy. The LSTM and TFT models, when paired with STL or the rolling mean, outperformed the others in their short-term forecasts, while XGBoost exhibited greater robustness over longer horizons in selected countries. An ANOVA confirmed the statistically significant influence of the model type on the MAPE (p = 0.008), whereas the smoothing method alone showed no significant effect. These findings offer practical guidance for designing context-specific forecasting pipelines adapted to epidemic dynamics and variations in data quality. Full article

Background: Pancreatic cancer (PC), the third leading cause of cancer-related mortality globally, exhibits a persistently low five-year survival rate (13%). While the global burden of PC among individuals aged 15–59 years has declined, trends in China remain understudied. This study evaluates global and national trends in PC incidence, mortality, and disability-adjusted life years (DALYs) from 1990 to 2021 and projects trajectories to 2045. Methods: Using data from the Global Burden of Disease (GBD) 2021 study, we calculated age-standardized rates (ASRs) for 204 countries/territories. Joinpoint (version: 5.3.0.0) regression identified temporal trends via average annual percentage changes (AAPCs), and Bayesian age-period-cohort (BAPC) modeling forecasted future burdens. Results: Globally, PC burden declined among 15–59-year-olds (AAPC for incidence: −0.8%, 95% UI: −1.2 to −0.4). However, China experienced a significant reversal after 2009, with incidence rising by 1.5% annually (95% UI: 0.9–2.1), disproportionately affecting males. Smoking (contributing to 22.2% of DALYs in China) and high fasting plasma glucose (15%) emerged as key modifiable risk factors, while elevated BMI exacerbated burdens in high SDI regions (3.1% of DALYs). Projections indicate a continued surge in China’s PC burden by 2045, particularly among males (incidence projected to increase by 50% from 2010 to 2045). Conclusions: High SDI regions exhibit concentrated PC burdens linked to lifestyle factors, whereas China’s rising trends align with healthcare expansion and metabolic disease proliferation. Targeted interventions—smoking cessation, glycemic control, and weight management—are imperative to mitigate growing burdens in younger populations. This study highlights the urgent need for region-specific strategies to address evolving epidemiological challenges in PC prevention and control. Full article

Wastewater-based epidemiology (WBE) has emerged as a transformative approach for community-level health monitoring, particularly during the COVID-19 pandemic. This review critically examines the methodological framework of WBE systems through the following three core components: (1) sampling strategies that address spatial–temporal variability in wastewater systems, (2) comparative performance of different platforms in pathogen detection, and (3) predictive modeling integrating machine learning approaches. We systematically analyze how these components collectively overcome the limitations of conventional surveillance methods through early outbreak detection, asymptomatic case identification, and population-level trend monitoring. While highlighting technical breakthroughs in viral concentration methods and variant tracking through sequencing, the review also identifies persistent challenges, including data standardization, cost-effectiveness concerns in resource-limited settings, and ethical considerations in public health surveillance. Drawing insights from global implementation cases, we propose recommendations for optimizing each operational phase and discuss emerging applications beyond pandemic response. This review highlights WBE as an indispensable tool for modern public health, whose methodological refinements and cross-disciplinary integration are critical for transforming pandemic surveillance from reactive containment to proactive population health management. Full article

This study analyzes cancer trends in Canada using machine learning techniques to extract insights from extensive cancer data sourced from the Canadian Cancer Society and Statistics Canada. It aims to enhance the understanding of cancer epidemiology and inform better prevention, diagnosis, and treatment strategies. Data preprocessing addressed issues like missing values and normalization, ensuring reliability. The findings indicate a steady increase in new cancer cases, with estimates reaching 248,700 in 2026, up from 244,000 in 2022. Male incidence rates are projected to rise slightly to 602.3 per 100,000, while female rates may decline to 530.6. Regions such as Alberta, British Columbia, Ontario, and Quebec show rising incidence rates, contrasted by declines in Newfoundland and Labrador, Nunavut, and Yukon. Notably, this research reveals significant increases in cancer cases among individuals aged 60 and older, particularly those 70+. The hybrid ARIMA-LSTM model demonstrated superior forecasting accuracy compared with the other selected models. These findings offer valuable insights for health policymakers and highlight the potential of machine learning in public health forecasting, providing a framework for future research in other disease areas. Full article

The trematodes Fasciola hepatica and F. gigantica are transmitted by lymnaeid snails and cause fascioliasis in livestock and humans. Human infection is emerging in southern and southeastern Asia. In India, the number of case reports has increased since 1993. This multidisciplinary study analyzes the epidemiological scenario of human infection. The study reviews the total of 55 fascioliasis patients, their characteristics, and geographical distribution. Causes underlying this emergence are assessed by analyzing (i) the climate change suffered by India based on 40-year-data from meteorological stations, and (ii) the geographical fascioliasis hotspots according to archeological–historical records about thousands of years of pack animal movements. The review suggests frequent misdiagnosis of the wide lowland-distributed F. gigantica with F. hepatica and emphasizes the need to obtain anamnesic information about the locality of residence and the infection source. Prevalence appears to be higher in females and in the 30–40-year age group. The time elapsed between symptom onset and diagnosis varied from 10 days to 5 years (mean 9.2 months). Infection was diagnosed by egg finding (in 12 cases), adult finding (28), serology (3), and clinics and image techniques (12). Climate diagrams and the Wb-bs forecast index show higher temperatures favoring the warm condition-preferring main snail vector Radix luteola and a precipitation increase due to fewer rainy days but more days of extreme rainfall, leading to increasing surface water availability and favoring fascioliasis transmission. Climate trends indicate a risk of future increasing fascioliasis emergence, including a seasonal infection risk from June–July to October–November. Geographical zones of high human infection risk defined by archeological–historical analyses concern: (i) the Indo-Gangetic Plains and corridors used by the old Grand Trunk Road and Daksinapatha Road, (ii) northern mountainous areas by connections with the Silk Road and Tea-Horse Road, and (iii) the hinterlands of western and eastern seaport cities involved in the past Maritime Silk Road. Routes and nodes are illustrated, all transhumant–nomadic–pastoralist groups are detailed, and livestock prevalences per state are given. A baseline defining areas and seasons of high infection risk is established for the first time in India. This is henceforth expected to be helpful for physicians, prevention measures, control initiatives, and recommendations for health administration officers. Full article