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

Xiaodong Huang; Archie C A Clements; Gail Williams; Kerrie Mengersen; Shilu Tong; Wenbiao Hu

doi:10.1016/j.envres.2016.01.013

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

Environ Res. 2016 Apr:146:308-14. doi: 10.1016/j.envres.2016.01.013. Epub 2016 Jan 19.

Authors

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

Affiliations

¹ School of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia.
² Research School of Population Health, The Australian National University, Canberra, ACT, Australia.
³ School of Public Health, the University of Queensland, Brisbane, Queensland, Australia.
⁴ Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia.
⁵ School of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia. Electronic address: w2.hu@qut.edu.au.

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

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.

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

MeSH terms

Bayes Theorem
Humans
Influenza A Virus, H1N1 Subtype / physiology*
Influenza, Human / epidemiology*
Influenza, Human / transmission*
Influenza, Human / virology
Models, Theoretical*
Pandemics*
Queensland / epidemiology
Regression Analysis
Socioeconomic Factors
Weather