Search Results (213)

One difficulty that arises in tracking and forecasting real-time epidemics is reporting delays, which are defined as the inherent delay between the time of symptom onset and the time a case is reported. Reporting delays can be caused by delays in case detection, symptom onset after infection, seeking medical care, or diagnostics, and they distort the accurate forecasting of diseases during epidemics and pandemics. To address this, we introduce a practical nowcasting approach grounded in survival analysis and actuarial science, explicitly allowing for non-stationarity in reporting delay patterns to better capture real-world variability. Despite its relevance, no flexible and accessible toolbox currently exists for non-stationary delay adjustment. Here, we present Nowcast-It, a tutorial-based toolbox that includes two components: (1) an R code base for delay adjustment and (2) a user-friendly R-Shiny application to enable interactive visualization and reporting delay correction without prior coding expertise. The toolbox supports daily, weekly, or monthly resolution data and enables model performance assessment using metrics such as mean absolute error, mean squared error, and 95% prediction interval coverage. We demonstrate the utility of Nowcast-It toolbox using publicly available weekly Ebola case data from the Democratic Republic of Congo. We and others have adjusted for reporting delays in real-time analyses (e.g., Singapore) and produced early COVID-19 forecasts; here, we package those delay adjustment routines into an accessible toolbox. It is designed for researchers, students, and policymakers alike, offering a scalable and accessible solution for addressing reporting delays during outbreaks. Full article

Accurate, efficient, and explainable modeling of the dynamics of acute respiratory infections (ARIs) remains, in many aspects, a significant challenge. While compartmental models such as SIR (Susceptible–Infected–Recovered) remain widely used for that purpose due to their simplicity, they cannot capture the complicated multiscale nature of disease progression which unites individual-level interactions affecting the initial phase of an outbreak and mass action laws governing the disease transmission in its general phase. Individual-based models (IBMs) offer a detailed representation capable of capturing these transmission nuances but have high computational demands. In this work, we explore hybrid and surrogate approaches to accelerate forecasting of acute respiratory infection dynamics performed via detailed epidemic models. The hybrid approach combines IBMs and compartmental models, dynamically switching between them with the help of statistical and ML-based methods. The surrogate approach, on the other hand, replaces IBM simulations with trained autoencoder approximations. Our results demonstrate that the usage of machine learning techniques and hybrid modeling allows us to obtain a significant speed–up compared to the original individual-based model—up to 1.6–2 times for the hybrid approach and up to ${10}^4$ times in case of a surrogate model—without compromising accuracy. Although the suggested approaches cannot fully replace the original model, under certain scenarios they make forecasting with fine-grained epidemic models much more feasible for real-time use in epidemic surveillance. Full article

Background. West Nile Virus (WNV) represents a significant public health concern in Europe, with Italy—particularly its northern regions—experiencing recurrent outbreaks. Climate variables and vector dynamics are known to significantly influence transmission patterns, highlighting the need for reliable predictive models to enable timely outbreak detection and response. Methods. We integrated epidemiological data on human WNV infections in Italy (2012–2024) with high-resolution climate variables (temperature, humidity, and precipitation). Using advanced feature engineering and a gradient boosting framework (XGBoost), we developed a predictive model optimized through time-series cross-validation. Results. The model achieved high predictive accuracy at the national level (R² = 0.994, MAPE = 5.16%) and maintained robust performance across the five most affected provinces, with R² values ranging from 0.896 to 0.996. SHAP analysis identified minimum temperature as the most influential climate predictor, while maximum temperature and rainfall demonstrated considerably weaker associations with case incidence. Conclusions. This machine learning approach provides a reliable framework for forecasting WNV outbreaks and supports evidence-based public health responses. The integration of climate and epidemiological data enhances surveillance capabilities and enables informed decision-making at regional and local levels. Full article

This study investigates how cycling behaviour in urban and tourism areas of Slovenia responded to the COVID-19 pandemic and its aftermath, with implications for forecasting and sustainable mobility planning. Using high-frequency daily data from January 2020 to August 2024 across Ljubljana (urban) and Rateče (tourism), we model the interdependence between weather conditions, cycling volume, and reported COVID-19 cases. The results reveal contrasting dynamics: in Ljubljana, higher cycling activity correlates with fewer infections, supporting cycling as a low-risk commuting mode, whereas in Rateče, tourism-driven cycling coincides with higher variability in infections. Regression and vector autoregressive (VAR(2)) models highlight the significant roles of precipitation and sunlight in shaping these patterns and enable short-term forecasts of COVID-19 cases up to January 2025. Machine learning methods complemented the VAR model, improving forecasting accuracy and revealing nonlinear interactions. These findings demonstrate the value of integrating behavioural and environmental indicators into public health forecasting and support region-specific strategies for resilient, sustainable mobility during future health or climate disruptions. Full article

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

Recent advances in plant electrophysiology and machine learning suggest that bioelectric signals in plants may encode environmentally relevant information beyond physiological processes. In this study, we present a novel framework to analyse waveforms from real-time bioelectrical potentials recorded in vascular plants. Using a multi-channel electrophysiological monitoring system, we acquired continuous data from Vitis vinifera samples in a vineyard plantation under natural conditions. Plants were in different health conditions: healthy; under the infection of Flavescence dorée; plants in recovery from the same disease; and dead stumps. These signals were used as input features for an ensemble of complex machine learning models, including recurrent neural networks, trained to infer short-term meteorological parameters such as temperature and humidity. The models demonstrated predictive capabilities, with accuracy comparable to sensor-based benchmarks between one and two degree Celsius for temperature, particularly in forecasting rapid weather transitions. Feature importance analysis revealed plant-specific electrophysiological patterns that correlated with ambient conditions, suggesting the existence of biological pre-processing mechanisms sensitive to microclimatic fluctuations. This bioinspired approach opens new directions for developing plant-integrated environmental intelligence systems, offering passive and biologically rooted strategies for ultra-local forecasting—especially valuable in remote, sensor-sparse, or climate-sensitive regions. Our findings contribute to the emerging field of plant-based sensing and biomimetic environmental monitoring, expanding the role of flora to biosensors, useful in Earth system observation tasks. Full article

This research provides valuable insights into the application of mathematical modeling to real-world scenarios, as exemplified by the COVID-19 pandemic. After data collection, the preparation stage included exploratory analysis, standardization and normalization, computation, and validation. A mathematical model initially developed for COVID-19 dynamics in Romania was subsequently applied to data from Italy and Switzerland during the same time interval. The model is structured as a multiple-input single-output (MISO) system, where the inputs underwent a neural network-based training stage to address inconsistencies in the acquired data. In parallel, an ARMAX model was employed to capture the stochastic nature of the epidemic process. Results demonstrate that the Romanian-based model generalized effectively across the three countries, achieving a strong predictive accuracy (forecast accuracy > 98.59%). Importantly, the model maintained robust performance despite significant cross-country differences in testing strategies, policy measures, timing of initial cases, and imported infections. This work contributes a novel perspective by showing that a unified data-driven modeling framework can be transferable across heterogeneous contexts. More broadly, it underscores the potential of integrating mathematical modeling with predictive analytics to support evidence-based decision-making and strengthen preparedness for future global health crises. Full article

Herpesvirus infections, including herpes simplex virus (HSV), Epstein–Barr virus (EBV), and cytomegalovirus (CMV), present significant challenges in diagnosis, treatment, and transmission control. Despite advances in medical technology, managing these infections remains complex due to the viruses’ ability to establish latency and their widespread prevalence. Artificial Intelligence (AI) has emerged as a transformative tool in biomedical science, enhancing our ability to understand, predict, and manage infectious diseases. In veterinary virology, AI applications offer considerable potential for improving diagnostics, forecasting outbreaks, and implementing targeted control strategies. This review explores the growing role of AI in advancing our understanding of herpesvirus infection, particularly those caused by MDV, through improved detection, transmission modeling, treatment strategies, and predictive tools. Employing AI technologies such as machine learning (ML), deep learning (DL), and natural language processing (NLP), researchers have made significant progress in addressing diagnostic limitations, modeling transmission dynamics, and identifying potential therapeutics. Furthermore, AI holds the potential to revolutionize personalized medicine, predictive analytics, and vaccine development for herpesvirus-related diseases. The review concludes by discussing ethical considerations, implementation challenges, and future research directions necessary to fully integrate AI into clinical and veterinary practice. Full article

Background: Pan-drug-resistant (PDR) Acinetobacterinfections are an escalating ICU threat, demanding both patient-level triage and facility-wide forecasting. Objective: The aim of this study was to build a dual-scale AI framework that (i) predicts PDR status at infection onset and (ii) forecasts hospital-level PDR burden through 2027. Methods: We retrospectively analyzed 270 Acinetobacter infection episodes (2020–2024) with 65 predictors spanning demographics, timelines, infection type, resistance-class flags, and a 25-drug antibiogram. TabTransformer and XGBoost were trained on 2020–2023 episodes (n = 210), evaluated by stratified 5-fold CV, and externally tested on 2024 episodes (n = 60). Metrics included AUROC, AUPRC, accuracy, and recall at 90% specificity; AUROC was optimism-corrected via 0.632 + bootstrap and DeLong-tested for drift. SHAP values quantified feature impact. Weekly PDR incidence was forecast with an attention–LSTM model retrained monthly (200 weekly origins, 4-week horizon) and benchmarked against seasonal-naïve, Prophet, and SARIMA models (MAPE and RMSE). Quarterly projections (TFT-lite) extended forecasts to 2027. Results: The CV AUROC was 0.924 (optimism-corrected 0.874); an ensemble of TabTransformer + XGBoost reached 0.958. The 2024 AUROC fell to 0.586 (p < 0.001), coinciding with a PDR prevalence drop (75→38%) and three covariates with PSIs > 1.0. Isotonic recalibration improved the Brier score from 0.326 to 0.207 and yielded a net benefit equivalent to 26 unnecessary isolation-days averted per 100 ICU admissions at a 0.20 threshold. SHAP highlighted Ampicillin/Sulbactam resistance, unknown acquisition mode, and device-related infection as dominant drivers. The attention–LSTM achieved a median weekly MAE of 0.10 (IQR: 0.028–0.985) vs. 1.00 for the seasonal-naïve rule, outperforming it on 48.5% of weeks and surpassing Prophet and SARIMA (MAPE = 6.2%, RMSE = 0.032). TFT-lite projected a ≥ 25% PDR tipping point in 2025 Q1 with a sustained rise in 2027. Conclusions: The proposed framework delivers explainable patient-level PDR risk scores and competitive 4-week and multi-year incidence forecasts despite temporal drift, supporting antimicrobial stewardship and ICU capacity planning. Shrinkage and bootstrap correction were applied to address the small sample size (EPV = 2.1), which poses an overfitting risk. Continuous recalibration and multi-center validation remain priorities. 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

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

Powdery mildew, caused by Erysiphe quercicola, is one of the primary diseases responsible for the reduction in natural rubber production in China. This disease is a typical airborne pathogen, characterized by its ability to spread via air currents and rapidly escalate into an epidemic under favorable environmental conditions. Accurate prediction and determination of the prevention and control period represent both a critical challenge and key focus area in managing rubber-tree powdery mildew. This study investigates the effects of spore concentration, environmental factors, and infection time on the progression of powdery mildew in rubber trees. By employing six distinct machine learning model construction methods, with the disease index of powdery mildew in rubber trees as the response variable and spore concentration, temperature, humidity, and infection time as predictive variables, a preliminary predictive model for the disease index of rubber-tree powdery mildew was developed. Results from indoor inoculation experiments indicate that spore concentration directly influences disease progression and severity. Higher spore concentrations lead to faster disease development and increased severity. The optimal relative humidity for powdery mildew development in rubber trees is 80% RH. At varying temperatures, the influence of humidity on the disease index differs across spore concentration, exhibiting distinct trends. Each model effectively simulates the progression of powdery mildew in rubber trees, with predicted values closely aligning with observed data. Among the models, the Kernel Ridge Regression (KRR) model demonstrates the highest accuracy, the R² values for the training set and test set were 0.978 and 0.964, respectively, while the RMSE values were 4.037 and 4.926, respectively. This research provides a robust technical foundation for reducing the labor intensity of traditional prediction methods and offers valuable insights for forecasting airborne forest diseases. Full article

In Kenya, Rift Valley fever (RVF) outbreaks pose significant challenges, being one of the most severe climate-sensitive zoonoses. While machine learning (ML) techniques have shown superior performance in time series forecasting, their application in predicting disease outbreaks in Africa remains underexplored. Leveraging data from the International Livestock Research Institute (ILRI) in Kenya, this study pioneers the use of ML techniques to forecast RVF outbreaks by analyzing climate data spanning from 1981 to 2010, including ML models. Through a comprehensive analysis of ML model performance and the influence of environmental factors on RVF outbreaks, this study provides valuable insights into the intricate dynamics of disease transmission. The XGB Classifier emerged as the top-performing model, exhibiting remarkable accuracy in identifying RVF outbreak cases, with an accuracy score of 0.997310. Additionally, positive correlations were observed between various environmental variables, including rainfall, humidity, clay patterns, and RVF cases, underscoring the critical role of climatic conditions in disease spread. These findings have significant implications for public health strategies, particularly in RVF-endemic regions, where targeted surveillance and control measures are imperative. However, this study also acknowledges the limitations in model accuracy, especially in scenarios involving concurrent infections with multiple diseases, highlighting the need for ongoing research and development to address these challenges. Overall, this study contributes valuable insights to the field of disease prediction and management, paving the way for innovative solutions and improved public health outcomes in RVF-endemic areas and beyond. Full article

Background/Objectives: The COVID-19 pandemic highlighted the critical need for accurate predictive models to guide public health interventions and optimize healthcare resource allocation. This study evaluates how the complexity of compartmental infectious disease models influences their forecasting accuracy and utility for pandemic resource planning. Methods: We analyzed a range of compartmental models, including simple susceptible-infected-recovered (SIR) models and more complex frameworks incorporating asymptomatic carriers and deaths. These models were calibrated and tested using real-world COVID-19 data from the United States to assess their performance in predicting symptomatic and asymptomatic infection counts, peak infection timing, and resource demands. Both adaptive models (updating parameters with real-time data) and non-adaptive models were evaluated. Results: Numerical results show that while more complex models capture detailed disease dynamics, simpler models often yield better forecast accuracy, especially during early pandemic stages or when predicting peak infection periods. Adaptive models provided the most accurate short-term forecasts but required substantial computational resources, making them less practical for long-term planning. Non-adaptive models produced stable long-term forecasts useful for strategic resource allocation, such as hospital bed and ICU planning. Conclusions: Model selection should align with the pandemic stage and decision-making horizon. Simpler models are effective for rapid early-stage interventions, adaptive models excel in short-term operational forecasting, and non-adaptive models remain valuable for long-term resource planning. These findings can inform policymakers on selecting appropriate modeling approaches to improve pandemic response effectiveness. Full article

Reliable short-term forecasts of hepatitis B incidence are indispensable for sizing national vaccine and antiviral procurement. However, predictive modelling is complicated when surveillance streams experience reporting delays and episodic under-reporting, as has occurred in Ukraine since 2022. We address this challenge by training a deliberately compact two-layer long short-term memory (LSTM) network on 72 monthly observations (January 2018–December 2023) drawn from the Public Health Center electronic registry and evaluating performance on a strictly held-out 12-month horizon (January–December 2024). Grid-search optimisation selected a 12-month sliding input window, 64 hidden units per layer, 0.20 dropout, the Adam optimiser, and early stopping. Walk-forward validation showed that the network attained mean squared errors of 411 for acute infection and 76 for chronic infection on the monthly series. When forecasts were aggregated to the cumulative scale, the mean absolute percentage error remained below 1%. This study presents the first peer-reviewed hepatitis B forecasts calibrated on Ukraine’s registry during a period of pronounced reporting instability, demonstrating that robust accuracy is attainable without missing-value imputation. Full article