Literature DB >> 26799511

Bayesian estimation of the dynamics of pandemic (H1N1) 2009 influenza transmission in Queensland: A space-time SIR-based model.

Xiaodong Huang1, Archie C A Clements2, Gail Williams3, Kerrie Mengersen4, Shilu Tong1, Wenbiao Hu5.   

Abstract

BACKGROUND: A pandemic strain of influenza A spread rapidly around the world in 2009, now referred to as pandemic (H1N1) 2009. This study aimed to examine the spatiotemporal variation in the transmission rate of pandemic (H1N1) 2009 associated with changes in local socio-environmental conditions from May 7-December 31, 2009, at a postal area level in Queensland, Australia.
METHOD: We used the data on laboratory-confirmed H1N1 cases to examine the spatiotemporal dynamics of transmission using a flexible Bayesian, space-time, Susceptible-Infected-Recovered (SIR) modelling approach. The model incorporated parameters describing spatiotemporal variation in H1N1 infection and local socio-environmental factors.
RESULTS: The weekly transmission rate of pandemic (H1N1) 2009 was negatively associated with the weekly area-mean maximum temperature at a lag of 1 week (LMXT) (posterior mean: -0.341; 95% credible interval (CI): -0.370--0.311) and the socio-economic index for area (SEIFA) (posterior mean: -0.003; 95% CI: -0.004--0.001), and was positively associated with the product of LMXT and the weekly area-mean vapour pressure at a lag of 1 week (LVAP) (posterior mean: 0.008; 95% CI: 0.007-0.009). There was substantial spatiotemporal variation in transmission rate of pandemic (H1N1) 2009 across Queensland over the epidemic period. High random effects of estimated transmission rates were apparent in remote areas and some postal areas with higher proportion of indigenous populations and smaller overall populations.
CONCLUSIONS: Local SEIFA and local atmospheric conditions were associated with the transmission rate of pandemic (H1N1) 2009. The more populated regions displayed consistent and synchronized epidemics with low average transmission rates. The less populated regions had high average transmission rates with more variations during the H1N1 epidemic period.
Copyright © 2016 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Pandemic (H1N1) 2009 influenza; Spatial conditional autoregressive model; Susceptible-Infected-Removed model; Transmission rate

Mesh:

Year:  2016        PMID: 26799511     DOI: 10.1016/j.envres.2016.01.013

Source DB:  PubMed          Journal:  Environ Res        ISSN: 0013-9351            Impact factor:   6.498


  6 in total

1.  Cross-sectional analysis and data-driven forecasting of confirmed COVID-19 cases.

Authors:  Nan Jing; Zijing Shi; Yi Hu; Ji Yuan
Journal:  Appl Intell (Dordr)       Date:  2021-07-05       Impact factor: 5.019

2.  Impact of PM2.5 and ozone on incidence of influenza in Shijiazhuang, China: a time-series study.

Authors:  Xue Wang; Jianning Cai; Xuehui Liu; Binhao Wang; Lina Yan; Ran Liu; Yaxiong Nie; Yameng Wang; Xinzhu Zhang; Xiaolin Zhang
Journal:  Environ Sci Pollut Res Int       Date:  2022-09-08       Impact factor: 5.190

3.  A novel Bayesian geospatial method for estimating tuberculosis incidence reveals many missed TB cases in Ethiopia.

Authors:  Debebe Shaweno; James M Trauer; Justin T Denholm; Emma S McBryde
Journal:  BMC Infect Dis       Date:  2017-10-02       Impact factor: 3.090

4.  Estimation of time-varying reproduction numbers underlying epidemiological processes: A new statistical tool for the COVID-19 pandemic.

Authors:  Hyokyoung G Hong; Yi Li
Journal:  PLoS One       Date:  2020-07-21       Impact factor: 3.240

5.  Generalized SIR (GSIR) epidemic model: An improved framework for the predictive monitoring of COVID-19 pandemic.

Authors:  Pushpendra Singh; Anubha Gupta
Journal:  ISA Trans       Date:  2021-02-15       Impact factor: 5.911

6.  Modeling analysis revealed the distinct global transmission patterns of influenza A viruses and their influencing factors.

Authors:  Chaoyuan Cheng; Jing Li; Wenjun Liu; Lei Xu; Zhibin Zhang
Journal:  Integr Zool       Date:  2020-08-06       Impact factor: 2.083

  6 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.