Spatio-temporal dynamics of outbreak on a lattice with quenched mobility patterns
We have designed a computational model of a virus spread near the outbreak threshold. Using computer simulation we studied the Susceptible - Infected - Recovered (SIR) process where in consequence of a force of habit that is manifested by the population mobility patterns, the recovered persons create the spatio-temporal patterns as the barriers to a virus transmission. The results show a spontaneous stopping of the virus spread without a need to infect the whole population, a non-trivial random noise of daily count of infected cases, and power laws of a cumulative count of infected cases. Outbreak evolution strongly depends on the initial conditions thus we concluded that the model has the features of chaotic systems that makes it difficult to predict its behaviors.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
A random-walk based epidemiological model
Random walkers on a two-dimensional square lattice are used to explore the spatio-temporal growth of an epidemic. We have found that a simple random-walk system generates nontrivial dynamics compared with traditional we…
2D Human Pose EstimationmodelSpatio-Temporal Multi-step Prediction of Influenza Outbreaks
Flu circulates all over the world. The worldwide infection places a substantial burden on people's health every year. Regardless of the characteristic of the worldwide circulation of flu, most previous studies focused on…
PredictionTG-PhyNN: An Enhanced Physically-Aware Graph Neural Network framework for forecasting Spatio-Temporal Data
Accurately forecasting dynamic processes on graphs, such as traffic flow or disease spread, remains a challenge. While Graph Neural Networks (GNNs) excel at modeling and forecasting spatio-temporal data, they often lack …
Graph Neural NetworkPredictionMachine learning spectral functions in lattice QCD
We study the inverse problem of reconstructing spectral functions from Euclidean correlation functions via machine learning. We propose a novel neural network, SVAE, which is based on the variational autoencoder (VAE) an…
BIG-bench Machine LearningGraph neural network force fields for adiabatic dynamics of lattice Hamiltonians
Scalable and symmetry-consistent force-field models are essential for extending quantum-accurate simulations to large spatiotemporal scales. While descriptor-based neural networks can incorporate lattice symmetries throu…
Graph Neural Network