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.
GPR models epidemic spread on logarithmic scale.
Model for valuing options on epidemic spread.
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…
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 …
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…
Optimizes control of infectious disease spread using stochastic methods.
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.…
STNN models forecast COVID-19 spread with improved accuracy.
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…
Unified model forecasts epidemics with spatial and temporal dynamics.
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…
Study reveals how dengue spread patterns vary across different years in Recife, Brazil.
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.
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…
Study forecasts COVID-19 cases in Iran using deep learning.
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…
Algorithm estimates COVID-19 cases from phone calls.
Algorithm optimizes lockdown policies balancing health and economy.
Paper models COVID-19 spread as spatio-temporal point processes.
Seq2Seq models speed up epidemic model predictions.
Simplicial complexes are increasingly used to study complex system structure and dynamics including diffusion, synchronization and epidemic spreading. The spectral dimension of the graph Laplacian is known to determine the diffusion properties at long time scales. Using the renormalization group here we calculate the s…
Study forecasts cholera outbreaks in Malawi using dynamic models.
Koopman-PINN framework improves epidemic model parameter inference and forecasting
Enhances inference of spreading processes using neural-network priors.
Study examines cryptocurrency risk spillover effects before and after pandemic.
Model predicts COVID-19 spread with better accuracy than existing methods.
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…
A new machine learning model forecasts COVID-19 incidence at county level in the USA.
Study on stock market volatility and return dispersion during COVID-19.
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 uses RL to optimize ICU load 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…
The latest financial crisis has painfully revealed the dangers arising from a globally interconnected financial system. Conventional approaches based on the notion of the existence of equilibrium and those which rely on statistical forecasting have seen to be inadequate to describe financial systems in any reasonable w…
The increasing integration of world economies, which organize in complex multilayer networks of interactions, is one of the critical factors for the global propagation of economic crises. We adopt the network science approach to quantify shock propagation on the global trade-investment multiplex network. To this aim, w…
Paper introduces a new method to model epidemic dynamics with varying parameters.
Unified model bridges mechanistic and non-mechanistic epidemic approaches.
New algorithm estimates past and future diffusion processes on networks.
Graph neural networks detect structural perturbations from time series data.
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…
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…
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…
CRISP predicts individual-level COVID-19 risk based on contact data.