AUTHOR=He Junyu , Christakos George , Zhang Wenyi , Wang Yong 

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

JOURNAL=Frontiers in Applied Mathematics and Statistics

VOLUME=3

YEAR=2017

URL=https://www.frontiersin.org/journals/applied-mathematics-and-statistics/articles/10.3389/fams.2017.00016

DOI=10.3389/fams.2017.00016

ISSN=2297-4687

ABSTRACT=<p><bold>Background:</bold> 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. Making up for this lack of quantitative studies is the reason for the development of the present work.</p><p><bold>Method:</bold> To address this need, the well-known Bayesian maximum entropy (BME) method of space-time modeling and mapping together with its recently 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 Niño-Southern Oscillation index as a proxy, we proposed a combination of Hilbert-Huang transform and wavelet analysis to study the “HFRS incidence-climate change” associations.</p><p><bold>Results:</bold> The main results of this work were two-fold: (1) three core areas were identified with high HFRS incidences that were spatially distributed and exhibited distinct biomodal temporal patterns in the eastern, western, and southern parts of Heilongjiang province; and (2) there exists a considerable association between HFRS incidence and climate change, particularly, an ~6 months period coherency was clearly detected.</p><p><bold>Conclusions:</bold> 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 essential findings include the identification of three core areas with high HFRS incidences in Heilongjiang province, and considerable evidence that HFRS incidence is closely related to climate change.</p>