Viral zoonoses have emerged as the key drivers of recent pandemics. Human infection by zoonotic viruses are either spillover events -- isolated infections that fail to cause a widespread contagion -- or species jumps, where successful adaptation to the new host leads to a pandemic. Despite expensive bio-surveillance ef…
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48 results for “Population Surveillance”
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…
Optimizes COVID-19 testing policy using a Multi-Armed Bandit approach.
problem Balancing discovery of positive cases with population surveillance.
method Risk scoring and random sampling based on Multi-Armed Bandit theory.
result Effective prioritization captures 65-92% of positive cases with varying testing capacity.
Machine learning has been an emerging tool for various aspects of infectious diseases including tuberculosis surveillance and detection. However, WHO provided no recommendations on using computer-aided tuberculosis detection software because of the small number of studies, methodological limitations, and limited genera…
MutaGAN predicts mutations of evolving protein populations using GANs.
problem Predicting mutations in evolving protein populations.
method Generative adversarial networks (GANs) with recurrent neural networks (RNNs).
result MutaGAN generates complete protein sequences with mutations.
Syndromic surveillance detects and monitors individual and population health indicators through sources such as emergency department records. Automated classification of these records can improve outbreak detection speed and diagnosis accuracy. Current syndromic systems rely on hand-coded keyword-based methods to parse…
New method tackles anomaly detection in video surveillance using continual learning.
problem Challenges in continual learning for high-dimensional applications like video surveillance.
method Transfer learning and continual learning for online anomaly detection.
result Significantly reduces training complexity and continual learning from recent data.
Classifiers and beamforming algorithms improved audio surveillance detection accuracy.
problem Detecting surveillance sound events with high accuracy and efficiency.
method Evaluated seven classifiers and two beamforming algorithms; used data augmentation and tested with varying SNR levels.
result SVM and Delay-and-Sum (DaS) combination achieved the highest accuracy (86.0%), but had high computational cost.
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.
Conventional surveillance systems for monitoring infectious diseases, such as influenza, face challenges due to shortage of skilled healthcare professionals, remoteness of communities and absence of communication infrastructures. Internet-based approaches for surveillance are appealing logistically as well as economica…
Proposes a neural network method to combine nonprobability and probability survey samples.
problem Combining nonprobability and probability survey samples for accurate population mean estimation.
method Uses a deep neural network to estimate sampling scores from nonprobability samples and combines them with probability sample information.
result Proposed estimators improve robustness to parametric propensity-score misspecification, especially for nonlinear selection mechanisms.
New algorithm rSVDdpd improves robustness and scalability for video surveillance background modeling.
problem Camera tampering and noisy videos make background separation challenging.
method Introduces rSVDdpd, a robust singular value decomposition technique for scalable video surveillance.
result Demonstrates superior performance on benchmark and real-life datasets.
This paper explores using a Long short-term memory (LSTM) based sequence autoencoder to learn interesting features for detecting surveillance aircraft using ADS-B flight data. An aircraft periodically broadcasts ADS-B (Automatic Dependent Surveillance - Broadcast) data to ground receivers. The ability of LSTM networks …
Online anomaly detection in surveillance videos with false alarm rate bounds.
problem Lack of theoretical performance analysis and online decision making in anomaly detection.
method Proposes an online anomaly detection method with asymptotic bounds on false alarm rate.
result Demonstrates effectiveness on publicly available data sets, outperforming state-of-the-art algorithms.
Italy's daily COVID-19 cases stratified by age groups.
problem Analyzing the spread and impact of COVID-19 across different age groups.
method Stratified daily data of COVID-19 cases by age classes.
result Detailed insights into how age affects COVID-19 outcomes.
Systemic Risk Surveillanceecon.EM
Online surveillance detects systemic risk in financial markets.
problem Detecting and monitoring systemic risk in financial markets.
method Online monitoring procedures for multiple series, controlling for false rejections.
result Procedures allow timely detection of financial distress.
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.
Much recent research aims to identify evidence for Drug-Drug Interactions (DDI) and Adverse Drug reactions (ADR) from the biomedical scientific literature. In addition to this "Bibliome", the universe of social media provides a very promising source of large-scale data that can help identify DDI and ADR in ways that ha…
Multiple Document Representations from News Alerts for Automated Bio-surveillance Event Detectioncs.IR
Due to globalization, geographic boundaries no longer serve as effective shields for the spread of infectious diseases. In order to aid bio-surveillance analysts in disease tracking, recent research has been devoted to developing information retrieval and analysis methods utilizing the vast corpora of publicly availabl…
Maximising the detection of intrusions is a fundamental and often critical aim of perimeter surveillance. Commonly, this requires a decision-maker to optimally allocate multiple searchers to segments of the perimeter. We consider a scenario where the decision-maker may sequentially update the searchers' allocation, lea…
Predicting epidemic dynamics is of great value in understanding and controlling diffusion processes, such as infectious disease spread and information propagation. This task is intractable, especially when surveillance resources are very limited. To address the challenge, we study the problem of active surveillance, i.…
Estimates population size using capture-recapture designs with binary indicators.
problem Estimating population size from capture-recapture data with binary indicators.
method Proposes a modern method using undersmoothed lasso model to estimate the target parameter of interest.
result The choice of constraint on the K-dimensional distribution significantly impacts the value of the estimand.
Zero-shot understanding of accidents from surveillance videos using vision-language models
problem Accident understanding from surveillance videos
method Three-stage pipeline with vision-language similarity, metadata-driven multi-prompt reasoning, and entropy-gated pairwise adjudicator
result Substantial improvement in harmonic-mean score over baseline
According to the definition of the London Interbank Offered Rate (LIBOR), contributing banks should give fair estimates of their own borrowing costs in the interbank market. Between 2007 and 2009, several banks made inappropriate submissions of LIBOR, sometimes motivated by profit-seeking from their trading positions. …
Plasmodium falciparum malaria still poses one of the greatest threats to human life with over 200 million cases globally leading to half-million deaths annually. Of these, 90% of cases and of the mortality occurs in sub-Saharan Africa, mostly among children. Although malaria prediction systems are central to the 2016-2…
Flusion combines multiple data sources to improve flu forecasts.
problem Accurate flu predictions to improve public health actions.
method Ensemble model combining gradient boosting quantile regression and Bayesian autoregressive models.
result Flusion was the top-performing model in the CDC's influenza prediction challenge.
Mobile apps and machine learning improve malaria prevention and treatment.
problem High malaria cases and deaths in low-income countries.
method Adaptive interventions using mobile health apps and machine learning.
result Increased malaria testing, adherence, and provider skills.
Traditional disease surveillance can be augmented with a wide variety of real-time sources such as, news and social media. However, these sources are in general unstructured and, construction of surveillance tools such as taxonomical correlations and trace mapping involves considerable human supervision. In this paper,…
Due to the growing demand for improving surveillance capabilities in smart cities, systems need to be developed to provide better monitoring capabilities to competent authorities, agencies responsible for strategic resource management, and emergency call centers. This work assumes that, as a complementary monitoring so…
Objectives: Most cancer data sources lack information on metastatic recurrence. Electronic medical records (EMRs) and population-based cancer registries contain complementary information on cancer treatment and outcomes, yet are rarely used synergistically. To enable detection of metastatic breast cancer (MBC), we appl…
Method detects insider trading using trading data and dimensionality reduction.
problem Identifying insider trading in large datasets.
method Unsupervised machine learning, principal component analysis, autoencoders.
result Identifies suspicious trading behavior based on reconstruction errors.
Person re-identification (re-id), an emerging problem in visual surveillance, deals with maintaining entities of individuals whilst they traverse various locations surveilled by a camera network. From a visual perspective re-id is challenging due to significant changes in visual appearance of individuals in cameras wit…
In this paper we describe a video surveillance system able to detect traffic events in videos acquired by fixed videocameras on highways. The events of interest consist in a specific sequence of situations that occur in the video, as for instance a vehicle stopping on the emergency lane. Hence, the detection of these e…
In financial markets, abnormal trading behaviors pose a serious challenge to market surveillance and risk management. What is worse, there is an increasing emergence of abnormal trading events that some experienced traders constitute a collusive clique and collaborate to manipulate some instruments, thus mislead other …
The Centers for Disease Control and Prevention (CDC) coordinates a labor-intensive process to measure the prevalence of autism spectrum disorder (ASD) among children in the United States. Random forests methods have shown promise in speeding up this process, but they lag behind human classification accuracy by about 5%…
Two machine learning methods detect insider trading from investor activity data.
problem Detecting insider trading from trading activity data is challenging.
method Two unsupervised machine learning methods: clustering and group identification.
result Identifies potential insider trading rings around price sensitive events.
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,…
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.
We propose a method for recognizing moving vehicles, using data from roadside audio sensors. This problem has applications ranging widely, from traffic analysis to surveillance. We extract a frequency signature from the audio signal using a short-time Fourier transform, and treat each time window as an individual data …
The technique of Formal Concept Analysis is applied to a dataset describing the traits of rodents, with the goal of identifying zoonotic disease carriers,or those species carrying infections that can spillover to cause human disease. The concepts identified among these species together provide rules-of-thumb about the …
A new ML framework for RF fingerprinting across various applications.
problem Extracting unique RF fingerprints for specific emitter identification.
method Generic machine learning framework for automatic RF fingerprinting.
result Framework achieves superior performance compared to traditional methods.
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…
A new model synthesizes population with fewer structural and sampling zeros.
problem Synthesizing a feasible and diverse synthetic population from limited data.
method A deep generative model with two regularizations to minimize structural zeros and preserve sampling zeros.
result The model significantly improves feasibility and diversity of synthetic populations.
Aims to describe neural network training dynamics using two-time-scale models.
problem Lack of a general mathematical description of neural network training.
method Introduces a theoretical framework based on two-time-scale population dynamics.
result Derives selection-mutation equations and effective fitness for hyperparameters.
NeuPL learns diverse policies in strategy games efficiently.
problem Iterative training of policies in strategy games leads to under-trained good-responses and wasteful repetition.
method NeuPL uses a single conditional model to represent a population of policies, offering convergence guarantees and transfer learning.
result NeuPL achieves better performance and efficiency across various domains, enabling access to novel strategies.
Fragmentation, integration and macroprudential surveillance of the US financial industry: Insights from network sciencephysics.soc-ph
Drawing on recent contributions inferring financial interconnectedness from market data, our paper provides new insights on the evolution of the US financial industry over a long period of time by using several tools coming from network science. Following [1] a Time-Varying Parameter Vector AutoRegressive (TVP-VAR) app…
Targeting Underrepresented Populations in Precision Medicine: A Federated Transfer Learning Approachstat.ML
Proposes a federated transfer learning method to improve precision medicine models for underrepresented populations.
problem Underrepresentation of minorities in precision medicine research leads to underperforming risk prediction models.
method Two-way federated transfer learning strategy integrating diverse populations and healthcare institutions.
result Improves risk prediction models for underrepresented populations, reducing performance gaps.
New framework for online control in evolving populations.
problem Control of evolving populations in real-world conditions.
method Online control framework for linear and non-linear dynamical systems.
result Near-optimal regret bounds for gradient-based controllers.