A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Seq2Seq models speed up epidemic model predictions.
Koopman-PINN framework improves epidemic model parameter inference and forecasting
GPR models epidemic spread on logarithmic scale.
Model for valuing options 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.
Deep models forecast epidemics with uncertainty quantification.
Model predicts COVID-19 cases with high accuracy using mobility data.
New method combines neural nets with epidemic models for better prediction.
Paper introduces a new method to model epidemic dynamics with varying parameters.
Study on stock market volatility and return dispersion during COVID-19.
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…
Unified model bridges mechanistic and non-mechanistic epidemic approaches.
Paper uses RL to optimize ICU load during COVID-19.
Unified model forecasts epidemics with spatial and temporal dynamics.
New method for epidemic model inference using multinomial approximations.
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…
Robust model detects outliers in spatiotemporal epidemic data.
We study epidemic forecasting on real-world health data by a graph-structured recurrent neural network (GSRNN). We achieve state-of-the-art forecasting accuracy on the benchmark CDC dataset. To improve model efficiency, we sparsify the network weights via transformed- penalty and maintain prediction accuracy at…
PySR method automates discovering equations from data in chaotic dynamics and epidemics.
Study historical cholera epidemics and simulate long-term mortality impacts.
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…
Graph Mixture Density Networks model multimodal data on graphs.
In this paper, a unified susceptible-exposed-infected-susceptible-aware (SEIS-A) framework is proposed to combine epidemic spreading with individuals' on-line self-consultation behaviors. An epidemic spreading prediction model is established based on the SEIS-A framework. The prediction process contains two phases. In …
New method for inferring Markov chains from large state spaces, applied to epidemic models.
This paper analyzes complex equilibria in a networked bivirus epidemic model.
EGDL predicts TB outbreaks with deep learning, integrating epidemiological models.
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…
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.…
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…
Optimizes control of infectious disease spread using stochastic methods.
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…
WISER detects watermarked segments in text via epidemic change-point analysis.
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…
CovidCare uses EMR data to predict patient outcomes in emerging epidemics.
Robust -learning for mean-field control under Wasserstein uncertainty
Algorithm estimates COVID-19 cases from phone calls.
NPE trains neural networks to approximate posterior distributions in SIR models from final outcome data.
Accurate real-time tracking of influenza outbreaks helps public health officials make timely and meaningful decisions that could save lives. We propose an influenza tracking model, ARGO (AutoRegression with GOogle search data), that uses publicly available online search data. In addition to having a rigorous statistica…
Financial contagion from liquidity shocks has being recently ascribed as a prominent driver of systemic risk in interbank lending markets. Building on standard compartment models used in epidemics, in this work we develop an EDB (Exposed-Distressed-Bankrupted) model for the dynamics of liquidity shocks reverberation be…
New method models stopping times that can be equal with non-zero probability.
We consider the problem of learning the weighted edges of a balanced mixture of two undirected graphs from epidemic cascades. While mixture models are popular modeling tools, algorithmic development with rigorous guarantees has lagged. Graph mixtures are apparently no exception: until now, very little is known about wh…
Study forecasts cholera outbreaks in Malawi using dynamic models.
Deep learning predicts opioid use disorder risk in patients.
Simulations of infectious disease spread have long been used to understand how epidemics evolve and how to effectively treat them. However, comparatively little attention has been paid to understanding the fairness implications of different treatment strategies -- that is, how might such strategies distribute the expec…
We consider the problem of learning the weighted edges of a graph by observing the noisy times of infection for multiple epidemic cascades on this graph. Past work has considered this problem when the cascade information, i.e., infection times, are known exactly. Though the noisy setting is well motivated by many epide…
STNN models forecast COVID-19 spread with improved accuracy.
Study shows frequent 'stock' mentions on Twitter correlate with stock market declines.