Literature DB >> 31587877

Introduction to particle Markov-chain Monte Carlo for disease dynamics modellers.

Akira Endo1, Edwin van Leeuwen2, Marc Baguelin3.   

Abstract

The particle Markov-chain Monte Carlo (PMCMC) method is a powerful tool to efficiently explore high-dimensional parameter space using time-series data. We illustrate an overall picture of PMCMC with minimal but sufficient theoretical background to support the readers in the field of biomedical/health science to apply PMCMC to their studies. Some working examples of PMCMC applied to infectious disease dynamic models are presented with R code.
Copyright © 2019 The Authors. Published by Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Hidden Markov process; Particle Markov-chain Monte Carlo; Particle filter; Sequential Monte Carlo; State-space models

Year:  2019        PMID： 31587877     DOI： 10.1016/j.epidem.2019.100363

Source DB:  PubMed          Journal:  Epidemics        ISSN： 1878-0067            Impact factor:   4.396


  16 in total

1.  Reproducible parallel inference and simulation of stochastic state space models using odin, dust, and mcstate.

Authors:  Richard G FitzJohn; Edward S Knock; Lilith K Whittles; Pablo N Perez-Guzman; Sangeeta Bhatia; Fernando Guntoro; Oliver J Watson; Charles Whittaker; Neil M Ferguson; Anne Cori; Marc Baguelin; John A Lees
Journal:  Wellcome Open Res       Date:  2021-06-10

2.  Estimating the introduction time of highly pathogenic avian influenza into poultry flocks.

Authors:  Peter H F Hobbelen; Armin R W Elbers; Marleen Werkman; Guus Koch; Francisca C Velkers; Arjan Stegeman; Thomas J Hagenaars
Journal:  Sci Rep       Date:  2020-07-24       Impact factor: 4.379

3.  Evaluation of the Secondary Transmission Pattern and Epidemic Prediction of COVID-19 in the Four Metropolitan Areas of China.

Authors:  Longxiang Su; Na Hong; Xiang Zhou; Jie He; Yingying Ma; Huizhen Jiang; Lin Han; Fengxiang Chang; Guangliang Shan; Weiguo Zhu; Yun Long
Journal:  Front Med (Lausanne)       Date:  2020-05-07

Review 4.  Tooling-up for infectious disease transmission modelling.

Authors:  Marc Baguelin; Graham F Medley; Emily S Nightingale; Kathleen M O'Reilly; Eleanor M Rees; Naomi R Waterlow; Moritz Wagner
Journal:  Epidemics       Date:  2020-05-13       Impact factor: 4.396

5.  Maximum likelihood-based extended Kalman filter for COVID-19 prediction.

Authors:  Jialu Song; Hujin Xie; Bingbing Gao; Yongmin Zhong; Chengfan Gu; Kup-Sze Choi
Journal:  Chaos Solitons Fractals       Date:  2021-04-02       Impact factor: 5.944

6.  Estimating effects of intervention measures on COVID-19 outbreak in Wuhan taking account of improving diagnostic capabilities using a modelling approach.

Authors:  Jingbo Liang; Hsiang-Yu Yuan; Lindsey Wu; Dirk Udo Pfeiffer
Journal:  BMC Infect Dis       Date:  2021-05-05       Impact factor: 3.090

7.  Estimating chikungunya virus transmission parameters and vector control effectiveness highlights key factors to mitigate arboviral disease outbreaks.

Authors:  Frédéric Jourdain; Henriette de Valk; Harold Noël; Marie-Claire Paty; Grégory L'Ambert; Florian Franke; Damien Mouly; Jean-Claude Desenclos; Benjamin Roche
Journal:  PLoS Negl Trop Dis       Date:  2022-03-04

8.  Infection kinetics of Covid-19 and containment strategy.

Authors:  Amit K Chattopadhyay; Debajyoti Choudhury; Goutam Ghosh; Bidisha Kundu; Sujit Kumar Nath
Journal:  Sci Rep       Date:  2021-06-02       Impact factor: 4.379

9.  Fitness Estimation for Viral Variants in the Context of Cellular Coinfection.

Authors:  Huisheng Zhu; Brent E Allman; Katia Koelle
Journal:  Viruses       Date:  2021-06-23       Impact factor: 5.048

10.  Effects of Anti-Diabetic Drugs on Fracture Risk: A Systematic Review and Network Meta-Analysis.

Authors:  Yu-Sheng Zhang; Yan-Dan Zheng; Yan Yuan; Shi-Chun Chen; Bao-Cheng Xie
Journal:  Front Endocrinol (Lausanne)       Date:  2021-10-14       Impact factor: 5.555
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