期刊
TROPICAL MEDICINE & INTERNATIONAL HEALTH
卷 9, 期 2, 页码 298-304出版社
WILEY
DOI: 10.1046/j.1365-3156.2003.01175.x
关键词
climate change; cross-correlation function; Ross River virus; seasonal auto-regressive integrated moving average; time series
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.
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