Search Results (220)

Significant earthquakes can cause widespread infrastructure damage, social implications, and substantial economic losses. To mitigate these impacts, earthquake forecasting models have been developed to estimate earthquake occurrences and improve recovery efforts, with the Epidemic-Type Aftershock Sequence (ETAS) model being the most informative statistical framework for characterizing earthquake sequences. In this study, the ETAS model is used to estimate the model parameters for seismicity in Canada using the historical earthquake catalogue and to forecast long-term seismicity for seven different regions in Canada. Furthermore, the model is used to generate synthetic earthquake catalogues in order to assess its ability to replicate observed seismic patterns. The study identifies the southwestern region of Canada, associated with the coastal area of British Columbia, as being at the highest seismic risk, with a 66% exceedance probability for M7.5 events or above to occur in 30 years. In contrast, Alberta features the least seismic risk, with a 4% exceedance probability for events above 6.5 magnitude. For southeastern Canada, associated with Eastern Ontario and Southern Quebec, an exceedance probability of 74% for events above 6.0 magnitude poses the potential for significant damage due to the larger exposed population. Moreover, the resulting seismicity maps show the model’s capability for real-events analysis, but improvements are needed for further applications. Full article

In recent years, the complexity and suddenness of infectious disease transmission have posed significant limitations for traditional time-series forecasting methods when dealing with the nonlinearity, non-stationarity, and multi-peak distributions of epidemic scale variations. To address this challenge, this paper proposes a forecasting framework based on diffusion models, called CNL-Diff, aimed at tackling the prediction challenges in complex dynamics, nonlinearity, and non-stationary distributions. Traditional epidemic forecasting models often rely on fixed linear assumptions, which limit their ability to accurately predict the incidence scale of infectious diseases. The CNL-Diff framework integrates a forward–backward consistent conditioning mechanism and nonlinear data transformations, enabling it to capture the intricate temporal and feature dependencies inherent in epidemic data. The results show that this method outperforms baseline models in metrics such as Mean Absolute Error (MAE), Continuous Ranked Probability Score (CRPS), and Prediction Interval Coverage Probability (PICP). This study demonstrates the potential of diffusion models in complex-distribution time-series modeling, providing a more reliable probabilistic forecasting tool for public health monitoring, epidemic early warning, and risk decision making. Full article

Seasonal influenza continues to pose a substantial and recurrent public health challenge in Queensland, driven by annual variability in transmission and uncertainty in climatic, demographic, and behavioural determinants. Predictive modelling is constrained by data limitations and parameter uncertainty. In response, this study developed a Bayesian network (BN) model to estimate the probability of influenza epidemics in Queensland, Australia. The model integrated diverse inputs, including international and local influenza surveillance data, demographic health statistics, and expert and stakeholder insights to capture the complex multifactorial causal relationships underlying epidemic risk. Scenario-based simulations revealed that Southeast Asian viral origin, severe global influenza seasons, peak season timing, increasing international travel, absence of control measures, and low immunisation rates substantially elevate the likelihood of influenza epidemics. Southeast Queensland was identified as particularly vulnerable under high-risk conditions. Model evaluation demonstrated good discriminative performance (AUC = 0.6974, accuracy = 70%) with appropriate uncertainty quantification through credible intervals and sensitivity analysis. Its modular design and capacity for integrating various data sources make it a practical decision-making support tool for public health preparedness and responding to evolving climatic and epidemiological conditions. Full article

Background/Objectives: Tuberculosis (TB) remains a major global health challenge influenced by social determinants of health (SDHs) such as poverty, overcrowding, malnutrition, and limited healthcare access. Although Central Asia (CA) has achieved progress through vaccination, screening, and treatment, the region continues to face severe disease consequences, unstable incidence patterns, and an escalating challenge of TB resistant to first-line drugs. This study aimed to analyze TB incidence dynamics in Kazakhstan, Tajikistan, the Kyrgyz Republic, Turkmenistan, and Uzbekistan from 2000–2023, forecast trends to 2030, and identify key SDHs shaping the epidemic. Methods: Data on TB incidence were obtained from the World Bank DataBank for 2000–2023. Of 61 socioeconomic, environmental, and health-related indicators, 29 were included in the analysis. Statistical procedures in SPSS (v24.0) involved time-series forecasting through 2030, calculation of average annual percentage change (AAPC), correlation testing, and linear regression, with significance set at p < 0.05. Results: TB incidence generally declined across CA during 2000–2023, though trends varied by country. Forecasts suggest continued decreases in Turkmenistan and Uzbekistan, while Kazakhstan, Tajikistan, and the Kyrgyz Republic display fluctuating or nonsignificant patterns, likely influenced by SDHs. Regression analyses indicated that anemia, undernourishment, and population density showed a positive relationship with TB incidence, while clean fuel access, physician density, and Gross Domestic Product per capita (GDP) were inversely related. Conclusions: The findings highlight the heterogeneous nature of TB dynamics in CA and the possible role of SDHs. Enhanced surveillance, nutritional and social interventions are required to sustain progress toward End TB targets. Full article

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

Poland, as an important transit hub in Europe, has experienced a dynamic increase in the significance of air transport in recent years. However, the outbreak of the COVID-19 pandemic in 2020 led to a severe collapse of passenger aviation worldwide. The aim of this study was to assess the condition of the passenger air transport market in Poland against the background of selected European countries in connection with the disruptions caused by the pandemic. The Holt–Winters models, based on pre-pandemic data, made it possible to forecast passenger transport volumes in the absence of the crisis and compare them with actual values to estimate losses and the extent of recovery. In the first three months after the outbreak, passenger losses ranged from 7.8 million in Sweden to 13.5 million in Portugal, while Poland recorded 10.9 million; after one year, cumulative losses in Poland reached 44.5 million. In addition, the pace of restoration of selected markets was evaluated. In 2022, Poland reached levels of up to 145% of its reference value, indicating one of the strongest restoration dynamics among the analyzed countries. The results show that all markets experienced sharp declines followed by a comparable rate of growth. The findings confirm Poland’s strengthening position in the European air transport system and highlight the need to build resilience to potential future crises. 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

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

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

Background: Emerging pandemics can rapidly overwhelm hospital capacity, leading to increased mortality and healthcare costs. Objective: We develop an optimization-based framework that dynamically schedules hospital beds across multiple facilities to minimize total healthcare costs, including patient rejections and logistical expenses, under resource constraints. Methods: The model integrates several real-world flexibilities: standard hospital beds, buffer capacity from non-pandemic wards, in situ field hospitals, and inter-hospital patient transfers. To capture demand uncertainty, we link the model with an SEIRD epidemic forecasting approach and further extend it with a robust optimization variant that safeguards against worst-case surges. Recognizing computational challenges, we reformulate the problem to significantly reduce solution times and derive structural properties that provide guidance on when to open field hospitals, allocate buffer beds, and prioritize patients across facilities. Results: A case study based on COVID-19 data from Northern Virginia shows that the proposed framework reduces healthcare costs by more than 50% compared with current practice, mainly by lowering patient rejection rates. Conclusions: These results highlight the value of combining epidemic forecasting with prescriptive optimization to improve resilience and inform healthcare policy during crises. Full article

This study aimed at proposing a predictive model for real-time monitoring of epidemic dynamics at the municipal scale in Lombardy region, in northern Italy, leveraging Emergency Medical Services (EMS) dispatch data and Geographic Information Systems (GIS) methodologies. Unlike traditional epidemiological models that rely on official diagnoses and offer limited spatial granularity, our approach uses EMS call data (rapidly collected, geo-referenced, and unbiased by institutional delays) as an early proxy for outbreak detection. The model integrates spatial filtering and machine learning (random forest classifier) to categorize municipalities into five epidemic scenarios: from no diffusion to active spread with increasing trends. Developed in collaboration with the Lombardy EMS agency (AREU), the system is designed for operational applicability, emphasizing simplicity, speed, and interpretability. Despite the complexity of the phenomenon and the use of a five-class output, the model shows promising predictive capacity, particularly for identifying outbreak-free areas. Performance is affected by changing epidemic dynamics, such as those induced by widespread vaccination, yet remains informative for early warning. The framework supports health decision-makers with timely, localized insights, offering a scalable tool for epidemic preparedness and response. 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

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 spread of COVID-19 is influenced by multiple factors, including control policies, virus characteristics, individual behaviors, and environmental conditions, exhibiting highly complex nonlinear dynamic features. The time series of new confirmed cases shows significant nonlinearity and non-stationarity. Traditional prediction methods that rely solely on one-dimensional case data struggle to capture the multi-dimensional features of the data and are limited in handling nonlinear and non-stationary characteristics. Their prediction accuracy and generalization capabilities remain insufficient, and most existing studies focus on single-step forecasting, with limited attention to multi-step prediction. To address these challenges, this paper proposes a multi-module fusion prediction model—TSMixer-BiKSA network—that integrates multi-feature inputs, Variational Mode Decomposition (VMD), and a dual-branch parallel architecture for 1- to 3-day-ahead multi-step forecasting of new COVID-19 cases. First, variables highly correlated with the target sequence are selected through correlation analysis to construct a feature matrix, which serves as one input branch. Simultaneously, the case sequence is decomposed using VMD to extract low-complexity, highly regular multi-scale modal components as the other input branch, enhancing the model’s ability to perceive and represent multi-source information. The two input branches are then processed in parallel by the TSMixer-BiKSA network model. Specifically, the TSMixer module employs a multilayer perceptron (MLP) structure to alternately model along the temporal and feature dimensions, capturing cross-time and cross-variable dependencies. The BiGRU module extracts bidirectional dynamic features of the sequence, improving long-term dependency modeling. The KAN module introduces hierarchical nonlinear transformations to enhance high-order feature interactions. Finally, the SA attention mechanism enables the adaptive weighted fusion of multi-source information, reinforcing inter-module synergy and enhancing the overall feature extraction and representation capability. Experimental results based on COVID-19 case data from Italy and the United States demonstrate that the proposed model significantly outperforms existing mainstream methods across various error metrics, achieving higher prediction accuracy and robustness. Full article