In retrospective assessments, internet news reports have been shown to capture early reports of unknown infectious disease transmission prior to official laboratory confirmation. In general, media interest and reporting peaks and wanes during the course of an outbreak. In this study, we quantify the extent to which med…
The early detection of infectious disease outbreaks is a crucial task to protect population health. To this end, public health surveillance systems have been established to systematically collect and analyse infectious disease data. A variety of statistical tools are available, which detect potential outbreaks as abber…
PHIBP predicts infectious disease outbreaks in sparse data regions.
problem Predicting outbreaks in regions with no historical data.
method Poisson Hierarchical Indian Buffet Process (PHIBP) framework.
result PHIBP provides accurate outbreak predictions and meaningful insights in sparse data settings.
Method predicts disease outbreaks using search logs, overcoming instability.
problem Predicting disease outbreaks from search logs is challenging due to short-term and long-term instability.
method Seasonal-adjustment method decomposes logs into seasonal, trend, and irregular components; feature selection method selects relevant search terms.
result Proposed method outperforms comparative methods in prediction accuracy for seven of ten diseases.
Machine learning predicts COVID-19 activity in China.
problem Real-time forecasting of COVID-19 activity in Chinese provinces.
method Combines mechanistic disease models with digital traces (internet searches, news alerts). Uses clustering and data augmentation techniques.
result Stable and accurate forecasts 2 days ahead of current time, outperforming baseline models in 27 out of 32 provinces.
Processes such as disease propagation and information diffusion often spread over some latent network structure which must be learned from observation. Given a set of unlabeled training examples representing occurrences of an event type of interest (e.g., a disease outbreak), our goal is to learn a graph structure that…
Several problems such as network intrusion, community detection, and disease outbreak can be described by observations attributed to nodes or edges of a graph. In these applications presence of intrusion, community or disease outbreak is characterized by novel observations on some unknown connected subgraph. These prob…
New methods solve graph sparsity optimization problems faster.
problem Complex graph sparsity optimization problems in disease outbreak monitoring and social network analysis.
method Stochastic variance-reduced gradient-based methods GraphSVRG-IHT and GraphSCSG-IHT.
result Our methods achieve linear convergence speed.
TLRF improves timely COVID-19 outbreak detection with small sample size counties.
problem Balancing accuracy and speed in estimating COVID-19 case growth rates.
method Transfer Learning Random Forest (TLRF) framework for growth rate estimation.
result TLRF outperforms existing methods in predicting case growth rates and timely outbreak detection.
Study forecasts cholera outbreaks in Malawi using dynamic models.
problem Cholera transmission forecasting in developing countries.
method Qualitative dynamics, Monte Carlo Markov Chain, sensitivity analysis, machine learning.
result Enhanced cholera forecasting models improve future trends prediction.
Study links public concern in Italy to financial markets worldwide.
problem Understanding public concern's impact on financial markets during pandemics.
method Used Google Trends data from YouTube, News, and Search to measure public concern and correlate it with stock index returns.
result Public concern in Italy drives concerns in other countries and explains stock index returns of multiple nations.
Framework extracts symptoms from EHRs for rapid disease outbreak detection.
problem Extracting relevant data from unstructured medical texts.
method Conformal active learning for efficient data mining.
result Framework achieves strong performance with minimal manual labeling.
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…
Model predicts COVID-19 spread with better accuracy than existing methods.
problem Limited daily samples in time for data-driven methods.
method Integrated spatiotemporal model combining epidemic differential equations and RNN.
result Model outperforms existing methods in forecasting cases.
Forecasting influenza-like illness (ILI) is of prime importance to epidemiologists and health-care providers. Early prediction of epidemic outbreaks plays a pivotal role in disease intervention and control. Most existing work has either limited long-term prediction performance or lacks a comprehensive ability to captur…
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.
New anomaly estimator reduces bias in MLE for normally distributed data.
problem Bias in Maximum Likelihood Estimation of structured anomalies.
method Derive a new anomaly estimator using a mixture model.
result New estimator is asymptotically unbiased regardless of anomaly family size.
New method detects close contacts to prevent SARS-CoV-2 spread.
problem Detecting close contacts to prevent SARS-CoV-2 spread.
method Machine learning approach using BLE data.
result Reliable detection of close contacts reduces SARS-CoV-2 risk.
Paper presents a spatio-temporal Bayesian model for early detection of COVID-19 hotspots.
problem Understanding spatio-temporal dynamics of COVID-19 hotspots to prevent outbreaks.
method Spatio-temporal Bayesian framework with a zero-mean Gaussian process and non-stationary kernel function enhanced by deep neural networks.
result Model demonstrates superior hotspot-detection performance compared to baseline methods.
Many methods have been proposed for detecting emerging events in text streams using topic modeling. However, these methods have shortcomings that make them unsuitable for rapid detection of locally emerging events on massive text streams. We describe Spatially Compact Semantic Scan (SCSS) that has been developed specif…
We approach the development of models and control strategies of susceptible-infected-susceptible (SIS) epidemic processes from the perspective of marked temporal point processes and stochastic optimal control of stochastic differential equations (SDEs) with jumps. In contrast to previous work, this novel perspective is…
This paper compares ML models for predicting COVID-19 trends.
problem Forecasting the spread of COVID-19 to reduce its impact.
method Applied ARDL method to identify relationships, then used ML models (SVM, RF, KNN, ANN) for prediction.
result Models accurately forecasted COVID-19 cases with low MAPE.
Study on financial impacts of zombie outbreak on economy.
problem Financial and economic consequences of a zombie epidemic.
method Epidemiological modeling and financial computation.
result GDP losses of 23.44% and financial market drop of 29.30% in a major industrialized nation.
Epidemiologists use a variety of statistical algorithms for the early detection of outbreaks. The practical usefulness of such methods highly depends on the trade-off between the detection rate of outbreaks and the chances of raising a false alarm. Recent research has shown that the use of machine learning for the fusi…
Stochastic optimization algorithms update models with cheap per-iteration costs sequentially, which makes them amenable for large-scale data analysis. Such algorithms have been widely studied for structured sparse models where the sparsity information is very specific, e.g., convex sparsity-inducing norms or ℓ0-n…
Artificial intelligence has provided us with an exploration of a whole new research era. As more data and better computational power become available, the approach is being implemented in various fields. The demand for it in health informatics is also increasing, and we can expect to see the potential benefits of its a…
Social media services such as Twitter are a valuable source of information for decision support systems. Many studies have shown that this also holds for the medical domain, where Twitter is considered a viable tool for public health officials to sift through relevant information for the early detection, management, an…
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.
Russia-Ukraine conflict impacts global agricultural futures and spot markets' extreme risks.
problem Impact of Russia-Ukraine conflict on global agricultural futures and spot markets' extreme risks.
method Analytical framework for tail dependence, Copula-CoVaR method, ARMA-GARCH-skewed Student-t model.
result The outbreak of the conflict intensified risks in the wheat market the most and showed significant asymmetries in extreme risk spillovers.
This paper considers the problem of predicting the number of events that have occurred in the past, but which are not yet observed due to a delay. Such delayed events are relevant in predicting the future cost of warranties, pricing maintenance contracts, determining the number of unreported claims in insurance and in …
Accurate real-time monitoring systems of influenza outbreaks help public health officials make informed decisions that may help save lives. We show that information extracted from cloud-based electronic health records databases, in combination with machine learning techniques and historical epidemiological information,…
Market structure changed dramatically in US during COVID-19, mirroring 2008 crisis.
problem Impact of COVID-19 on market structure.
method Observation of market structure changes during the outbreak.
result Market structure resembles 2008 crisis but may evolve into a new structure.
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.
Adaptive sequential testing optimizes epidemic control by learning optimal test strategies.
problem Optimizing test allocation in epidemics with network and temporal dependence.
method Adaptive sequential design with Online Super Learner for optimal test strategies.
result Superior performance in simulated university COVID-19 pandemic.
Ensemble classifier detects pneumonia patterns in chest CT images.
problem Manual and time-consuming diagnosis of pneumonia in chest CT images.
method Probabilistic Support Vector Machine (SVM) ensemble, kernel PCA, patch-based classification.
result 97.86% accuracy in pneumonia detection.
Incremental machine learning models predict COVID-19 cases more efficiently than traditional methods.
problem Predicting the spread of COVID-19 cases in real-time across multiple countries.
method Comparison of online incremental machine learning algorithms against traditional LSTM models.
result Incremental machine learning models are more efficient and computationally cheaper than traditional methods.
The detection of anomalous activity in graphs is a statistical problem that arises in many applications, such as network surveillance, disease outbreak detection, and activity monitoring in social networks. Beyond its wide applicability, graph structured anomaly detection serves as a case study in the difficulty of bal…
Study shows how China's stock market reflects economic demand changes during COVID-19.
problem Understanding how stock market volatility is influenced by economic demand changes.
method Divided industries into demand-oriented groups and analyzed spillover networks.
result Spillover effects from demand-oriented sectors to consumption-oriented sectors increased during the outbreak.
Proposes efficient sensitivity analysis for complex Bayesian models.
problem Inefficiency of sensitivity analyses in complex Bayesian models.
method SA-ABI: weight sharing and neural network rapid inference.
result Efficiently integrates sensitivity analyses into Bayesian inference.
DeCom predicts post-COVID RSV timing and intensity with NPI consideration.
problem Predicting RSV timing and intensity post-COVID with NPI impact.
method Deep coupled tensor factorization machine (DeCom) leveraging tensor factorization and residual modeling.
result DeCom achieves up to 46% lower RMSE and 49% lower MAE compared to baselines.
GraphXCOVID uses deep semi-supervised learning to identify COVID-19 from chest X-rays with minimal labels.
problem Identifying COVID-19 on chest X-rays with limited labelled data.
method Graph-based deep semi-supervised framework with pseudo-labeling and attention maps.
result Outperforms current supervised models with a tiny fraction of labelled examples.
New algorithm tracks COVID-19 outbreak phases.
problem Decision-making in pandemic data.
method Developed a new algorithm (BLLR) based on decision theory.
result Demonstrated ability to track different phases of the COVID-19 outbreak.
Online learning algorithms update models via one sample per iteration, thus efficient to process large-scale datasets and useful to detect malicious events for social benefits, such as disease outbreak and traffic congestion on the fly. However, existing algorithms for graph-structured models focused on the offline set…
Unsupervised learning identifies key factors for NYC COVID-19 spread.
problem Identify key factors for NYC COVID-19 spread.
method Unsupervised machine learning framework to cluster ZIP code areas.
result 9 interpretable categories of similar outbreak patterns identified.
Proposes a test for shared information between time series and events.
problem Detecting extreme events in time series data.
method Non-parametric statistical test using multiple two-sample testing at increasing lags.
result Outperforms or matches related tests on various datasets.
Study uses social media to analyze COVID-19 impact.
problem Understanding the global impact of COVID-19.
method Machine learning and linguistic tools to analyze social media posts.
result Automatic detection of positive reports of COVID-19.
Graph neural networks predict future COVID-19 cases based on human mobility.
problem Predicting future COVID-19 cases using human mobility data.
method Created a graph with regions as nodes and human mobility as edge weights. Used graph neural networks to capture diffusion patterns and transfer learning for limited data.
result Graph neural networks outperform traditional methods in predicting future cases.
The paper investigates model misspecification in Bayesian inference using neural networks.
problem Detecting model misspecification in Bayesian inference with neural networks.
method Conceptualized types of model misspecification and proposed an augmented optimization objective with MMD.
result MMD can detect potentially catastrophic misspecifications in Bayesian inference.