TY - JOUR
T1 - Bayesian inference for the onset time and epidemiological characteristics of emerging infectious diseases
AU - Shi, Benyun
AU - Yang, Sanguo
AU - Tan, Qi
AU - Zhou, Lian
AU - Liu, Yang
AU - Zhou, Xiaohong
AU - Liu, Jiming
N1 - Funding Information:
The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by the National Natural Science Foundation of China and the Research Grants Council (RGC) of Hong Kong Joint Research Scheme (Nos. 62261160387, N_HKBU222/22) and the General Research Fund of Hong Kong (Nos. RGC/HKBU12202220 and RGC/HKBU12203122). The funders had no role in study design, data collection and analysis, decision to publish or manuscript preparation.
Publisher Copyright:
© 2024 Shi, Yang, Tan, Zhou, Liu, Zhou and Liu.
PY - 2024/5/17
Y1 - 2024/5/17
N2 - Background: Emerging infectious diseases pose a significant threat to global public health. Timely detection and response are crucial in mitigating the spread of such epidemics. Inferring the onset time and epidemiological characteristics is vital for accelerating early interventions, but accurately predicting these parameters in the early stages remains challenging.Methods: We introduce a Bayesian inference method to fit epidemic models to time series data based on state-space modeling, employing a stochastic Susceptible-Exposed-Infectious-Removed (SEIR) model for transmission dynamics analysis. Our approach uses the particle Markov chain Monte Carlo (PMCMC) method to estimate key epidemiological parameters, including the onset time, the transmission rate, and the recovery rate. The PMCMC algorithm integrates the advantageous aspects of both MCMC and particle filtering methodologies to yield a computationally feasible and effective means of approximating the likelihood function, especially when it is computationally intractable.Results: To validate the proposed method, we conduct case studies on COVID-19 outbreaks in Wuhan, Shanghai and Nanjing, China, respectively. Using early-stage case reports, the PMCMC algorithm accurately predicted the onset time, key epidemiological parameters, and the basic reproduction number. These findings are consistent with empirical studies and the literature. Conclusion: This study presents a robust Bayesian inference method for the timely investigation of emerging infectious diseases. By accurately estimating the onset time and essential epidemiological parameters, our approach is versatile and efficient, extending its utility beyond COVID-19.
AB - Background: Emerging infectious diseases pose a significant threat to global public health. Timely detection and response are crucial in mitigating the spread of such epidemics. Inferring the onset time and epidemiological characteristics is vital for accelerating early interventions, but accurately predicting these parameters in the early stages remains challenging.Methods: We introduce a Bayesian inference method to fit epidemic models to time series data based on state-space modeling, employing a stochastic Susceptible-Exposed-Infectious-Removed (SEIR) model for transmission dynamics analysis. Our approach uses the particle Markov chain Monte Carlo (PMCMC) method to estimate key epidemiological parameters, including the onset time, the transmission rate, and the recovery rate. The PMCMC algorithm integrates the advantageous aspects of both MCMC and particle filtering methodologies to yield a computationally feasible and effective means of approximating the likelihood function, especially when it is computationally intractable.Results: To validate the proposed method, we conduct case studies on COVID-19 outbreaks in Wuhan, Shanghai and Nanjing, China, respectively. Using early-stage case reports, the PMCMC algorithm accurately predicted the onset time, key epidemiological parameters, and the basic reproduction number. These findings are consistent with empirical studies and the literature. Conclusion: This study presents a robust Bayesian inference method for the timely investigation of emerging infectious diseases. By accurately estimating the onset time and essential epidemiological parameters, our approach is versatile and efficient, extending its utility beyond COVID-19.
KW - Bayesian inference
KW - emerging infectious disease (EID)
KW - epidemiological characteristics
KW - Particle Markov chain Monte Carlo
KW - SEIR compartmental model
UR - http://www.scopus.com/inward/record.url?scp=85194864503&partnerID=8YFLogxK
U2 - 10.3389/fpubh.2024.1406566
DO - 10.3389/fpubh.2024.1406566
M3 - Journal article
C2 - 38827615
AN - SCOPUS:85194864503
SN - 2296-2565
VL - 12
JO - Frontiers in Public Health
JF - Frontiers in Public Health
M1 - 1406566
ER -
