Early warning forecasts for COVID-19 in Korea using Bayesian estimation of the transmission rate

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참고문헌 (42)

1. Welding, J., "Real time analysis of epidemic data", arXiv preprint arXiv:1909.11560, ~ (2019)
2. Nemeth, C., "Parameter estimation for state space models using sequential Monte Carlo algorithms", Doctor, pp.2014
3. Zhu, J., "Big learning with Bayesian methods", National Science Review, 4(4), pp.627~651 (2017)
4. Adams, BM, "Hiv dynamics: modeling, data analysis, and optimal treatment protocols", J. Comput. Appl. Math, 184, pp.10~49 (2005)
5. Adda, P., "General models of host-parasite systems", Global analysis, Discrete Contin. Dyn. Syst. Ser. B, 8, pp.1~17 (2007)
6. Becker, Niels G, "On a general stochastic epidemic model", Theoretical Population Biology, 11(1), pp.23~36 (1977)
7. Watson, Ray, "An application of a martingale central limit theorem to the standard epidemic model", Stochastic Processes and Their Applications, 11(1), pp.79~89 (1981)
8. Sudbury, Aidan, "The proportion of the population never hearing a rumour", Journal of applied probability, pp.443~446 (1985)
9. Andersson, H., "Lecture notes in statistics", Stochastic epidemic models and their statistical analysis, 151 (2000)
10. Roberts, Gareth O., "On inference for partially observed nonlinear diffusion models using the Metropolis–Hastings algorithm", Biometrika, 88(3), pp.603~621 (2001)
11. Jandarov, Roman, "Emulating a gravity model to infer the spatiotemporal dynamics of an infectious disease", Journal of the Royal Statistical Society: Series C: Applied Statistics, pp.423~444 (2014)
12. McKinley, Trevelyan, "Inference in epidemic models without likelihoods", The International Journal of Biostatistics, 5(1) (2009)
13. Toni, Tina, "Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems", Journal of the Royal Society Interface, 6(31), pp.187~202 (2009)
14. McKinley, Trevelyan J., "Simulation-based Bayesian inference for epidemic models", Computational Statistics & Data Analysis, 71, pp.434~447 (2014)
15. Andrieu, Christophe, "Particle markov chain monte carlo methods", Journal of the Royal Statistical Society: Series B (Statistical Methodology), 72(3), pp.269~342 (2010)
16. Ionides, Edward L., "Iterated filtering", The Annals of Statistics, 39(3), pp.1776~1802 (2011)
17. Dukic, Vanja, "Tracking epidemics with Google flu trends data and a state-space SEIR model", Journal of the American Statistical Association, 107(500), pp.1410~1426 (2012)
18. Koepke, Amanda A., "Predictive modeling of cholera outbreaks in Bangladesh", The annals of applied statistics, 10(2), pp.575~ (2016)
19. Auranen, Kari, "Transmission of pneumococcal carriage in families: a latent Markov process model for binary longitudinal data", Journal of the American Statistical Association, 95(452), pp.1044~1053 (2000)
20. H¨ohle, Michael, "Estimating parameters for stochastic epidemics" (2002), The Royal Veterinary and Agricultural University
21. Cauchemez, Simon, "A Bayesian MCMC approach to study transmission of influenza: application to household longitudinal data", Statistics in medicine, 23(22), pp.3469~3487 (2004)
22. Neal, Peter J., "Statistical inference and model selection for the 1861 Hagelloch measles epidemic", Biostatistics, 5(2), pp.249~261 (2004)
23. O’Neill, Philip D., "Bayesian inference for stochastic multitype epidemics in structured populations using sample data", Biostatistics, 10(4), pp.779~791 (2009)
24. Camacho, Anton, "Temporal changes in Ebola transmission in Sierra Leone and implications for control requirements: a real-time modelling study", PLoS currents, 7 (2015)
25. Funk, Sebastian, "Real-time forecasting of infectious disease dynamics with a stochastic semi-mechanistic model", Epidemics, 22, pp.56~61 (2018)
26. Thompson, R. N., "Improved inference of time-varying reproduction numbers during infectious disease outbreaks", Epidemics, 29, pp.100356~ (2019)
27. Cori, Anne, "A new framework and software to estimate time-varying reproduction numbers during epidemics", American journal of epidemiology, 178(9), pp.1505~1512 (2013)
28. Dureau, Joseph, "Capturing the timevarying drivers of an epidemic using stochastic dynamical systems", Biostatistics, 14(3), pp.541~555 (2013)
29. Endo, Akira, "Introduction to particle Markovchain Monte Carlo for disease dynamics modellers", Epidemics, 29, pp.100363~ (2019)
30. Bret´o, Carles, "Compound markov counting processes and their applications to modeling infinitesimally over-dispersed systems", Stochastic Processes and their Applications, 121(11), pp.2571~2591 (2011)
31. Martcheva, M., "An introduction to mathematical epidemiology (Vol. 61)" (2015), Springer
32. King, Aaron A., "Statistical inference for partially observed Markov processes via the R package pomp", arXiv preprint arXiv:1509.00503, ~ (2015)
33. 최선화, "Estimating the reproductive number and the outbreak size of COVID-19 in Korea", Epidemiology and Health, 42, pp.1~10 (2020), 한국역학회[DOI]
34. 기모란, "Epidemiologic characteristics of early cases with 2019 novel coronavirus (2019-nCoV) disease in Korea", Epidemiology and Health, 42, pp.1~7 (2020), 한국역학회[DOI]
35. Doucet, Arnaud, "Handbook of nonlinear filtering 12", pp.656~704 (2009), Handbook of nonlinear filtering
36. Sch¨on, Thomas B., "Sequential Monte Carlo methods for system identification", IFAC-PapersOnLine, 48(28), pp.775~786 (2015)
37. Salmond, David, "Advances in Missile Guidance, Control, and Estimation" (2012)
38. Gustafsson, Fredrik, "Particle filter theory and practice with positioning applications", IEEE Aerospace and Electronic Systems Magazine, 25(7), pp.53~82 (2010)
39. Chen, Zhe, "Bayesian filtering: From Kalman filters to particle filters, and beyond", Statistics, 182(1), pp.1~69 (2003)
40. Michaud, Nicholas, "Sequential Monte Carlo methods in the nimble R package", arXiv preprint arXiv:1703.06206, ~ (2017)
41. "KDCA briefing report"[URL]
42. Jeyanathan, Mangalakumari, "Immunological considerations for COVID-19 vaccine strategies", Nature Reviews Immunology, pp.1~18 (2020)

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