Frontiers in Applied Mathematics and Statistics · 2017 · 21 citations · 56 references
Veterinary MedicineEarth SciencesNephrologyClimate ScienceSpatial EpidemiologyTime Series AnalysisBayesian Disease MappingDisease MappingDynamic ClimatologySpace-time EpidemiologyTime-series Analysis In EpidemiologyTime-series SurveillanceEl Niño-southern Oscillation VariabilityHilbert-huang TransformSpatial-temporal Disease ModelingSpatiotemporal Disease AnalysisSpatiotemporal Disease MappingSpatiotemporal Spread PatternsWavelet AnalysisWavelet-based Analysis
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 Niño-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.
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Norden E. Huang, Steven Long, Man‐Li C. Wu et al. · Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences · 1998 · 23.3K citations · Full text
Computing, Computational Engineering, Biomedical Signal Processing +16