Twitter system detects unexpected epidemics.
problem Detecting sudden, unexpected epidemic outbreaks in Twitter.
method Dynamic classification, alert generation, and ranking/recommendation.
result Empirical evaluation and validation with domain experts.
Study bounds deepfake detection error probability using robust statistics.
problem Limiting error in deepfake detection.
method Formulated as hypothesis testing, uses robust statistics and Euclidean approximation.
result Established relationships between error probability and network thresholds.
Robust model detects outliers in spatiotemporal epidemic data.
problem Outliers in epidemic data can mislead public health decisions.
method RST-GAM with mean-shift, adaptive Lasso, splines, and proximal algorithm.
result Demonstrates effectiveness in real-world COVID-19 data analysis.
WISER detects watermarked segments in text via epidemic change-point analysis.
problem Identifying watermarked segments in mixed-source texts.
method Epidemic change-point perspective, WISER algorithm.
result WISER outperforms state-of-the-art methods in accuracy and speed.
Method reconstructs networks and identifies communities from dynamic data.
problem Reconstructing networks and identifying communities from dynamic data.
method Nonparametric Bayesian approach that simultaneously infers network structure and community membership.
result Joint reconstruction and community detection improve each other's accuracy.
Paper introduces a new method to model epidemic dynamics with varying parameters.
problem Capturing discontinuous variations in epidemic model parameters.
method Total variation regularization with Iterated Nelder--Mead optimization.
result The method accurately models epidemic dynamics with instant changes.
Algorithm estimates COVID-19 cases from phone calls.
problem Delay in confirming COVID-19 cases.
method Modeling calls as background plus signal, fitting data with high R2. result Algorithm estimates cases days before lab results.
This article reviews datasets for COVID-19 detection using ML.
problem Lack of accessible datasets for COVID-19 detection research.
method Analyzed 96 papers on COVID-19 detection from January 2020 to June 2020.
result Identified and summarized datasets used in COVID-19 detection studies.
Seq2Seq models speed up epidemic model predictions.
problem Complex epidemic models are computationally expensive.
method Used deep seq2seq models as surrogates for complex models.
result Surrogates predict scenarios up to several thousand times faster.
Algorithm detects anomalies in large, complex data sets.
problem Detecting shifts in data distribution, especially in high-dimensional, non-uniform data.
method Adversarial autoencoder for gaussianization of data.
result Effective detection of soft anomalies in various domains.
Koopman-PINN framework improves epidemic model parameter inference and forecasting
problem Epidemic model parameter inference and forecasting
method Combining Koopman operator theory and physics-informed learning
result More accurate parameter estimation, trajectory reconstruction, and long-term forecasting
GPR models epidemic spread on logarithmic scale.
problem Modeling and predicting epidemic spread for policy decisions.
method Gaussian process regression (GPR) on logarithmic scale of infected cases.
result GPR predictions have high probability of being within 95% confidence interval for 94.29% of data.
Study improves epidemic forecasting with a sparsified GSRNN.
problem Epidemic forecasting on real-world health data.
method Graph-structured recurrent neural network (GSRNN) with sparsification via transformed-ℓ1 penalty. result Maintained prediction accuracy with 70% of network weights being zero.
Model for valuing options on epidemic spread.
problem Valuation of options on epidemic spread during an outbreak.
method Stochastic differential SIR model for epidemic dynamics.
result Parsimonious model for option valuation on epidemic spread.
Recently, the behavior of different epidemic models and their relation both to different types of geometries and to some biological models has been revisited . Path equations representing the behavior of epidemic models and their corresponding deviation vectors are examined. A comparison between paths and their deviati…
New model captures long-term memory effects in epidemic dynamics.
problem Identifying memory effects in disease progression and recovery.
method Physics-informed neural networks (PINN) with fractional SEIRD model.
result Fractional memory order α improves predictive performance over classical models. Study on stock market volatility and return dispersion during COVID-19.
problem Impact of COVID-19 on stock market volatility and return dispersion.
method Used Google index to proxy epidemic impact, modeled volatility, and analyzed influencing factors of log-return.
result Volatility significantly affected by epidemic and cross-sectional return dispersion, with positive coefficients.
Deep models forecast epidemics with uncertainty quantification.
problem Accurate probabilistic forecasting of epidemics is challenging due to nonlinear temporal dependencies and spatial interactions.
method Deep spatiotemporal engression methods with geometric ergodicity and asymptotic stationarity.
result Proposed methods outperform benchmarks in point and probabilistic forecasting.
Model predicts COVID-19 cases with high accuracy using mobility data.
problem Accurate forecasting of COVID-19 cases for resource management.
method Heterogeneous infection rate model with human mobility, linearization, weighted least squares.
result Extremely accurate predictions of confirmed cases at country and state levels.
Paper proposes a graph neural network for accurate long-term ILI prediction.
problem Limited long-term prediction performance and spatio-temporal dependency in existing models.
method Cross-location attention based graph neural network (Cola-GNN) for time series embeddings and location aware attentions.
result Proposed method shows strong predictive performance and interpretable results for long-term epidemic predictions.
New control strategy minimizes infected individuals in SIS epidemics.
problem Developing effective control strategies for SIS epidemics.
method Stochastic optimal control of SDEs with jumps, using treatment intensities.
result Control strategy consistently outperforms alternatives in synthetic data.
New method combines neural nets with epidemic models for better prediction.
problem Improving epidemic prediction and forecasting using deep neural networks.
method Integrates machine learning with compartmental disease models for data-driven analysis.
result Data augmentation strategy improves neural network reliability for epidemic forecasting.
Paper uses RL to optimize ICU load during COVID-19.
problem Optimizing ICU load during a pandemic.
method Combines epidemic model, Bayesian inference, and RL for adaptive intervention levels.
result RL policies reduce ICU burden compared to historical interventions.
Paper reproduces a kernel-based scan B-statistic for online change-point detection.
problem Continuous detection of distribution changes in online data streams.
method Efficient kernel-based scan B-statistic for online change-point detection.
result Scan B-statistic outperforms parametric methods in challenging scenarios.
Proposes a method to identify key components for predicting epidemic dynamics with limited resources.
problem Predicting epidemic dynamics with limited surveillance resources.
method Developed a group sparse Bayesian learning algorithm to identify sentinel components for monitoring.
result The proposed algorithm effectively predicts epidemic dynamics using partial data from sentinel components.
Unified model bridges mechanistic and non-mechanistic epidemic approaches.
problem Understanding the dynamics of epidemics with flexibility and interpretability.
method A mixture-based model representing time series as Gaussian mixtures, derived from a networked SIR framework.
result The model provides interpretable parameters and low prediction error, validating its use in understanding interventions.
LAD detects anomalies in dynamic graphs using Laplacian matrix.
problem Anomaly detection in temporal graphs for real-world applications.
method LAD uses the spectrum of the Laplacian matrix to model graph snapshots and temporal dependencies.
result LAD outperforms state-of-the-art methods in synthetic and real-world datasets.
Unified model predicts disease spread using EMD and ensemble learning.
problem Predicting fluctuating disease spread and individual behavior.
method SEIS-A framework, EMD decomposition, ensemble learning, on-line query data.
result The method outperforms other methods in predicting HFMD consultation rates.
Unified model forecasts epidemics with spatial and temporal dynamics.
problem Limited accuracy in traditional models and lack of interpretability in deep learning models.
method CSTGNN integrates Spatio-Contact SIR model with Graph Neural Networks.
result Effective spatiotemporal epidemic forecasting with interpretability.
We solve learning mixtures of graphs from epidemic cascades, establishing conditions and algorithms.
problem Learning the weighted edges of a balanced mixture of two undirected graphs from epidemic cascades.
method Established necessary and sufficient conditions for polynomial-time solvability, provided efficient algorithms with optimal sample complexity.
result First rigorous conditions and algorithms for learning graph mixtures from epidemic cascades.
New method for epidemic model inference using multinomial approximations.
problem Inference in stochastic epidemic models with partial observations.
method Recursive multinomial approximations to integrate over unobserved variables.
result Accuracy demonstrated through real and simulated data.
Spectral clustering is widely used to partition graphs into distinct modules or communities. Existing methods for spectral clustering use the eigenvalues and eigenvectors of the graph Laplacian, an operator that is closely associated with random walks on graphs. We propose a new spectral partitioning method that exploi…
It is the main purpose of this paper to introduce a graph-valued stochastic process in order to model the spread of a communicable infectious disease. The major novelty of the SIR model we promote lies in the fact that the social network on which the epidemics is taking place is not specified in advance but evolves thr…
Algorithm learns graph structure and weights from noisy epidemic cascade data.
problem Learning graph structure and weights from noisy infection times of multiple epidemics.
method Developed algorithms for two noisy settings: limited-noise and extreme-noise, with polynomial time complexity.
result Optimal sample complexity and efficient algorithms for various graph types.
Study fairness in vaccine allocation in social networks.
problem Fairness implications of vaccine allocation strategies in social networks.
method Defined precision disease control problem, used ML Fairness Gym to simulate and analyze.
result Different treatment strategies distribute disease burden differently across subgroups.
Improved outbreak detection using machine learning fusion of statistical algorithms.
problem Balancing detection of outbreaks with false alarms.
method Train a fusion classifier using p-values and additional features.
result Fusion classifier using p-values and additional features improves outbreak detection.
PySR method automates discovering equations from data in chaotic dynamics and epidemics.
problem Discovering equations from complex data in dynamical systems.
method Symbolic regression methods, focusing on PySR.
result PySR method efficiently infers equations from chaotic dynamics and epidemic models, matching original forms.
Graph neural networks detect structural perturbations from time series data.
problem Detecting structural causes of disturbances in complex systems.
method Graph neural network approach to infer structural perturbations from functional time series.
result Data-driven approach outperforms typical reconstruction methods and meets Bayesian inference accuracy.
CNNs improve ECG noise detection for better heart condition monitoring.
problem Signal noise in mobile ECGs hinders accurate diagnosis.
method Developed and trained a 16-layer CNN on a noise-annotated dataset.
result 16-layer CNN achieves 0.977 AUC on ECG noise detection.
Study historical cholera epidemics and simulate long-term mortality impacts.
problem Long-term impacts of mortality shocks on longevity.
method Historical analysis of cholera epidemics and mathematical modeling of stochastic Individual-Based models.
result Simulated long-term mortality impacts following a mortality shock.
Graph Mixture Density Networks model multimodal data on graphs.
problem Challenging conditional density estimation problems with structured data.
method Combining mixture models and graph representation learning.
result Significant improvement in likelihood of epidemic outcomes.
New method for inferring Markov chains from large state spaces, applied to epidemic models.
problem Challenging to compute matrix exponentials and derivatives for large state spaces.
method Differentiated uniformization method for continuous-time Markov chains.
result Estimation of infection and recovery rates during the first wave of COVID-19 in Austria.
Neural network detects sex trafficking ads from escort websites.
problem Manual identification of sex trafficking ads from escort websites is inefficient and resource-intensive.
method Ordinal regression neural network with a modified cost function and deep learning enhancements.
result Significantly improved detection accuracy on Trafficking-10K dataset.
EGDL predicts TB outbreaks with deep learning, integrating epidemiological models.
problem Predicting TB outbreaks with complex spatiotemporal dynamics.
method Modified MN-SIR model with Bayesian inference, deep neural networks.
result EGDL delivers robust and accurate TB outbreak predictions.
This paper analyzes complex equilibria in a networked bivirus epidemic model.
problem Identify conditions for coexistence equilibria in a networked bivirus model.
method Employ Poincaré-Hopf Theorem with modifications and Morse inequalities.
result Establish properties on the local stability/instability of coexistence equilibria.
We consider the problem of finding the graph on which an epidemic cascade spreads, given only the times when each node gets infected. While this is a problem of importance in several contexts -- offline and online social networks, e-commerce, epidemiology, vulnerabilities in infrastructure networks -- there has been ve…
Market trade-routes can support infectious-disease transmission, impacting biological populations and even disrupting causal trade. Epidemiological models increasingly account for reductions in infectious contact, such as risk-aversion behaviour in response to pathogen outbreaks. However, market dynamics clearly differ…
The credit crisis roiling the world's financial markets will likely take years and entire careers to fully understand and analyze. A short empirical investigation of the current trends, however, demonstrates that the losses in certain markets, in this case the US equity markets, follow a cascade or epidemic flow like m…