Shilu Tong1, Wenbiao Hu, A J McMichael. 1. School of Public Health, Queensland University of Technology, Kelvin Grove, Australia. s.tong@qut.edu.au
Abstract
BACKGROUND: How climate variability affects the transmission of infectious diseases at a regional level remains unclear. We assess the impact of climate variation on the Ross River virus (RRv) transmission in the Townsville region, Queensland, north-east Australia. METHODS: We obtained population-based information on monthly variations in RRv cases, climatic factors, sea level, and population growth between 1985 and 1996. Cross-correlations were computed for a series of associations between climate variables (rainfall, maximum temperature, minimum temperature, relative humidity and high tide) and the monthly incidence of RRv disease over a range of time lags. We assessed the impact of climate variability on RRv transmission using the seasonal auto-regressive integrated moving average (SARIMA) model. RESULTS: There were significant correlations of the monthly incidence of RRv to rainfall, maximum temperature, minimum temperature and relative humidity, all at a lag of 2 months, and high tide in the current month. The results of SARIMA models show that monthly average rainfall (beta = 0.0007, P = 0.01) and high tide (beta = 0.0089, P = 0.04) were significantly associated with RRv transmission and maximum temperature was also marginally significantly associated with monthly incidence of RRv (beta = 0.0412, P = 0.07), although relative humidity did not seem to have played an important role in the Townsville region. CONCLUSIONS: Rainfall, high tide and maximum temperature were likely to be key determinants of RRv transmission in the Townsville region.
BACKGROUND: How climate variability affects the transmission of infectious diseases at a regional level remains unclear. We assess the impact of climate variation on the Ross River virus (RRv) transmission in the Townsville region, Queensland, north-east Australia. METHODS: We obtained population-based information on monthly variations in RRv cases, climatic factors, sea level, and population growth between 1985 and 1996. Cross-correlations were computed for a series of associations between climate variables (rainfall, maximum temperature, minimum temperature, relative humidity and high tide) and the monthly incidence of RRv disease over a range of time lags. We assessed the impact of climate variability on RRv transmission using the seasonal auto-regressive integrated moving average (SARIMA) model. RESULTS: There were significant correlations of the monthly incidence of RRv to rainfall, maximum temperature, minimum temperature and relative humidity, all at a lag of 2 months, and high tide in the current month. The results of SARIMA models show that monthly average rainfall (beta = 0.0007, P = 0.01) and high tide (beta = 0.0089, P = 0.04) were significantly associated with RRv transmission and maximum temperature was also marginally significantly associated with monthly incidence of RRv (beta = 0.0412, P = 0.07), although relative humidity did not seem to have played an important role in the Townsville region. CONCLUSIONS: Rainfall, high tide and maximum temperature were likely to be key determinants of RRv transmission in the Townsville region.
Authors: Suchithra Naish; Wenbiao Hu; Neville Nicholls; John S Mackenzie; Anthony J McMichael; Pat Dale; Shilu Tong Journal: Environ Health Perspect Date: 2006-05 Impact factor: 9.031
Authors: Weiwei Yu; Kerrie Mengersen; Pat Dale; John S Mackenzie; Ghasem Sam Toloo; Xiaoyu Wang; Shilu Tong Journal: Am J Trop Med Hyg Date: 2014-05-05 Impact factor: 2.345
Authors: Edgar E Lara-Ramírez; Mario A Rodríguez-Pérez; Miguel A Pérez-Rodríguez; Monsuru A Adeleke; María E Orozco-Algarra; Juan I Arrendondo-Jiménez; Xianwu Guo Journal: PLoS Negl Trop Dis Date: 2013-02-14
Authors: Shilu Tong; Pat Dale; Neville Nicholls; John S Mackenzie; Rodney Wolff; Anthony J McMichael Journal: Environ Health Perspect Date: 2008-07-24 Impact factor: 9.031