Parameters estimation in Ebola virus transmission dynamics model based on machine learning

DOI10.1016/j.physa.2019.122604OpenAlexW2971922862WikidataQ127319677 ScholiaQ127319677MaRDI QIDQ2164308

Yong-Ping Wu, Li Li, Jing Gong

Publication date: 12 August 2022

Published in: Physica A (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.physa.2019.122604

zbMATH Keywords

kernel functionEbolamulti-output Gaussian processprobabilistic machine learning

Mathematics Subject Classification ID

Statistical mechanics, structure of matter (82-XX)

Uses Software

• L-BFGS
• MCMCglmm
• emcee

Cites Work

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• On the limited memory BFGS method for large scale optimization
• Transmission dynamics of ebola virus disease with human mobility in Sierra Leone
• Machine learning of linear differential equations using Gaussian processes
• Preferential imitation can invalidate targeted subsidy policies on seasonal-influenza diseases
• Effects of awareness diffusion and self-initiated awareness behavior on epidemic spreading -- an approach based on multiplex networks
• Analysis of transmission dynamics for Zika virus on networks
• Componentwise adaptation for high dimensional MCMC
• Approximation with polynomial kernels and SVM classifiers
• Inverse problems: A Bayesian perspective
• A Stochastic Newton MCMC Method for Large-Scale Statistical Inverse Problems with Application to Seismic Inversion
• Estimating the parameters of gravitational waves from neutron stars using an adaptive MCMC method
• Suppression of epidemic spreading in complex networks by local information based behavioral responses
• Vaccination and the theory of games
• Automata, Languages and Programming
• Theory of Reproducing Kernels
• An adaptive Metropolis algorithm