Environmental variability and the transmission of haemorrhagic fever with renal syndrome in Changsha, People's Republic of China

Abstract

The transmission of haemorrhagic fever with renal syndrome (HFRS) is influenced by climatic, reservoir and environmental variables. The epidemiology of the disease was studied over a 6-year period in Changsha. Variables relating to climate, environment, rodent host distribution and disease occurrence were collected monthly and analysed using a time-series adjusted Poisson regression model. It was found that the density of the rodent host and multivariate El Niño Southern Oscillation index had the greatest effect on the transmission of HFRS with lags of 2–6 months. However, a number of climatic and environmental factors played important roles in affecting the density and transmission potential of the rodent host population. It was concluded that the measurement of a number of these variables could be used in disease surveillance to give useful advance warning of potential disease epidemics.

How environmental conditions impact mosquito ecology and Japanese encephalitis: An eco-epidemiological approach

“…Our results were consistent with previous studies in India that rice fields are stable ecosystems and sudden increases in mosquito populations as a resultant of climate are uncommon (Das et al, 2006). Absolute humidity, rather than relative humidity was used in this study (Shaman and Kohn, 2009;Xiao et al, 2013b), as absolute humidity is the actual water vapor content of air irrespective of temperature (Xiao et al, 2013a). The absolute humidity is found to be a key factor to affect JEV transmission.…”

A Space-Time Study of Hemorrhagic Fever with Renal Syndrome (HFRS) and Its Climatic Associations in Heilongjiang Province, China

“…To the best of our knowledge, very few studies have used analytical methods to describe the space-time HFRS spread in Heilongjiang province. On the other hand, the associations between HFRS incidence and climate factors have been always assessed in terms of numerical modeling, for example, autoregressive integrated moving average models (ARIMA), seasonal ARIMA (SARIMA), ecological niche models (ENM), Poisson regression models, multiple regression, conditional logistic regression, and principal components regression (PCR) models [25,34,[45][46][47][48]. Interestingly, none of these studies explored the association between HFRS incidence and climatic factors in the context of their co-variation.…”

Transmission of Haemorrhagic Fever with Renal Syndrome in China and the Role of Climate Factors: A Review

“…Land surface temperature directly influences the distribution and abundance of A. agrarius populations, 27 whereas ambient temperature considers impacts on both mice and human behaviour. Associations between temperature and HFRS incidence were both positive 11,15,18,21,[25][26][27][28] and negative. 7 Also found was an association between temperature and rodent density 11,12 but no significant association with incidence.…”

“…3,11,15,[27][28][29] Liu et al (2006) identified that relative humidity was an important factor in all four seasons. 26 The highest incidence of disease was reported to be in semi-humid areas 8 or in mountainous regions with a humid or semi-humid climate.…”

“…9,11,28 An indicator of ENSO is the Southern Oscillation Index (SOI) which is the normalised atmospheric pressure difference between sea level pressures between Tahiti and Darwin, Australia. 9 Several studies used SOI to measure possible associations with HFRS incidence as it may be more appropriate than using other variables such as averaged rainfall or temperature data when analysing data for large regions.…”