Abstract
Background: Hemorrhagic fever with renal syndrome (HFRS) is highly endemic in China, especially in Heilongjiang province (90% of all reported HFRS cases worldwide occur in China). The dynamic identification of high HFRS incidence spatiotemporal regions and the quantitative assessment of HFRS associations with climate change in Heilongjiang province can provide valuable guidance for HFRS monitoring, preventing and control. Yet, so far there exist very few and of limited scope quantitative studies of the spatiotemporal HFRS spread and its climatic associations in Heilongjiang province. Method: To address this need, the well-known Bayesian maximum entropy (BME) method of space-time modeling and mapping together with its recent proposed variant, the projected BME (P-BME) method, were employed in this work to perform a composite space-time analysis and mapping of HFRS incidence in Heilongjiang province during the years 2005-2013. Also, using multivariate El Nino-Southern Oscillation index as a proxy, we employed Hilbert-Huang transform and wavelet analysis to study the “HFRS incidence-climate change” associations. Results: We identified three core areas with high spatially distributed HFRS incidences and biomodal temporal patterns in the eastern, western and southern parts of Heilongjiang province. Furthermore, it was found that there exists a considerable association between HFRS incidence and climate change, particularly, an approximately 6 months period coherency was clearly detected. Conclusions: The combination of modern space-time modeling and mapping techniques (P-BME theory, Hilbert-Huang spectrum analysis and wavelet analysis) used in this work led to valuable quantitative findings concerning the spatiotemporal spread of HFRS incidence in Heilongjiang province and its association with climate change. Our findings include the identification of three core areas with high HFRS incidences in Heilongjiang province, and evidence was also found that HFRS incidence is closely related to climate change.
Highlights
Hemorrhagic fever with renal syndrome (HFRS) is a rodentborne zoonosis caused by Hantavirus
Where 72 × 103 (m) and 2.6 are, respectively, the spatial and temporal correlation ranges of the HFRS incidence distribution
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]
Summary
Hemorrhagic fever with renal syndrome (HFRS) is a rodentborne zoonosis caused by Hantavirus (belonging to the Bunyaviridae family). Complications, like adverse kidney effects and subsequent pulmonary edema, shock, renal insufficiency, encephalopathy, hemorrhages, and cardiac complications, can cause death [5, 6]. Hemorrhagic fever with renal syndrome (HFRS) is highly endemic in China, especially in Heilongjiang province (90% of all reported HFRS cases worldwide occur in China). There exist very few and of limited scope quantitative studies of the spatiotemporal HFRS spread and its climatic associations in Heilongjiang province. Making up for this lack of quantitative studies is the reason for the development of the present work
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