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.
New method combines neural nets with epidemic models for better prediction.
New model captures long-term memory effects in epidemic dynamics.
Koopman-PINN framework improves epidemic model parameter inference and forecasting
GPR models epidemic spread on logarithmic scale.
Unified model forecasts epidemics with spatial and temporal dynamics.
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…
Deep models forecast epidemics with uncertainty quantification.
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…
Graph Mixture Density Networks model multimodal data on graphs.
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…
Seq2Seq models speed up epidemic model predictions.
Unified model bridges mechanistic and non-mechanistic epidemic approaches.
This paper analyzes complex equilibria in a networked bivirus epidemic model.
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.…
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…
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…
Model for valuing options on epidemic spread.
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…
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…
Study on stock market volatility and return dispersion during COVID-19.
Model predicts COVID-19 cases with high accuracy using mobility data.
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…
EGDL predicts TB outbreaks with deep learning, integrating epidemiological models.
Paper uses RL to optimize ICU load during COVID-19.
Paper introduces a new method to model epidemic dynamics with varying parameters.
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…
NPE trains neural networks to approximate posterior distributions in SIR models from final outcome data.
STNN models forecast COVID-19 spread with improved accuracy.
Study shows frequent 'stock' mentions on Twitter correlate with stock market declines.
New method for epidemic model inference using multinomial approximations.
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…
Adaptive sequential testing optimizes epidemic control by learning optimal test strategies.
PySR method automates discovering equations from data in chaotic dynamics and epidemics.
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 …
Infectious diseases are studied to understand their spreading mechanisms, to evaluate control strategies and to predict the risk and course of future outbreaks. Because people only interact with a small number of individuals, and because the structure of these interactions matters for spreading processes, the pairwise …
Robust model detects outliers in spatiotemporal epidemic data.
Study historical cholera epidemics and simulate long-term mortality impacts.
Deepfake detection is formulated as a hypothesis testing problem to classify an image as genuine or GAN-generated. A robust statistics view of GANs is considered to bound the error probability for various GAN implementations in terms of their performance. The bounds are further simplified using a Euclidean approximatio…
The negative externalities from an individual bank failure to the whole system can be huge. One of the key purposes of bank regulation is to internalize the social costs of potential bank failures via capital charges. This study proposes a method to evaluate and allocate the systemic risk to different countries/regions…
Percolation on complex networks has been used to study computer viruses, epidemics, and other casual processes. Here, we present conditions for the existence of a network specific, observation dependent, phase transition in the updated posterior of node states resulting from actively monitoring the network. Since tradi…
We present a scalable nonparametric Bayesian method to perform network reconstruction from observed functional behavior that at the same time infers the communities present in the network. We show that the joint reconstruction with community detection has a synergistic effect, where the edge correlations used to inform…
New method for inferring Markov chains from large state spaces, applied to epidemic models.
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…
Study forecasts cholera outbreaks in Malawi using dynamic models.
Deep learning predicts opioid use disorder risk in patients.
Study examines cryptocurrency risk spillover effects before and after pandemic.
Algorithm estimates COVID-19 cases from phone calls.
WISER detects watermarked segments in text via epidemic change-point analysis.