Spatiotemporal variation of the association between climate dynamics and HFRS outbreaks in Eastern China during 2005-2016 and its geographic determinants

• Published in: PLoS neglected tropical diseases 2018-06, Vol.12 (6), p.e0006554-e0006554
• Main Authors: He, Junyu, Christakos, George, Wu, Jiaping, Cazelles, Bernard, Qian, Quan, Mu, Di, Wang, Yong, Yin, Wenwu, Zhang, Wenyi
• Format: Article
• Language: English
• Subjects: Biology and Life Sciences; Cities; Climate; Climate change; Climate effects; Coasts; Computer and Information Sciences; Countries; Disease control; Disease hot spots; Disease prevention; Distance; Domestic animals; Dynamics; Earth sciences; Ecology and Environmental Sciences; Fever; Geography; Global climate; Grasslands; Hantavirus; Hantaviruses; Health; Health aspects; Health policy; Hemorrhage; Hemorrhagic fever with renal syndrome; Hemorrhagic fevers; Hot spots; Humidity; Infections; Infectious diseases; Knowledge bases (artificial intelligence); Laboratories; Latitude; Learning algorithms; Machine learning; Mathematical analysis; Mathematical models; Medicine and Health Sciences; Outbreaks; People and Places; Physical Sciences; Policies; Precipitation; Public health; Remote sensing; RNA viruses; Rodents; Software; Spatial dependencies; Spatial distribution; Strength; Synthesis; Time series; Trends; Tropical diseases; Variation; Viruses; Woodlands; Zoonoses
• ISSN: 1935-2735; 1935-2727; 1935-2735
• DOI: 10.1371/journal.pntd.0006554

Hemorrhagic fever with renal syndrome (HFRS) is a rodent-associated zoonosis caused by hantavirus. The HFRS was initially detected in northeast China in 1931, and since 1955 it has been detected in many regions of the country. Global climate dynamics influences HFRS spread in a complex nonlinear way. The quantitative assessment of the spatiotemporal variation of the "HFRS infections-global climate dynamics" association at a large geographical scale and during a long time period is still lacking. This work is the first study of a recently completed dataset of monthly HFRS cases in Eastern China during the period 2005-2016. A methodological synthesis that involves a time-frequency technique, a composite space-time model, hotspot analysis, and machine learning is implemented in the study of (a) the association between HFRS incidence spread and climate dynamics and (b) the geographic factors impacting this association over Eastern China during the period 2005-2016. The results showed that by assimilating core and city-specific knowledge bases the synthesis was able to depict quantitatively the space-time variation of periodic climate-HFRS associations at a large geographic scale and to assess numerically the strength of this association in the area and period of interest. It was found that the HFRS infections in Eastern China has a strong association with global climate dynamics, in particular, the 12, 18 and 36 mos periods were detected as the three main synchronous periods of climate dynamics and HFRS distribution. For the 36 mos period (which is the period with the strongest association), the space-time correlation pattern of the association strength indicated strong temporal but rather weak spatial dependencies. The generated space-time maps of association strength and association hotspots provided a clear picture of the geographic variation of the association strength that often-exhibited cluster characteristics (e.g., the south part of the study area displays a strong climate-HFRS association with non-point effects, whereas the middle-north part displays a weak climate-HFRS association). Another finding of this work is the upward climate-HFRS coherency trend for the past few years (2013-2015) indicating that the climate impacts on HFRS were becoming increasingly sensitive with time. Lastly, another finding of this work is that geographic factors affect the climate-HFRS association in an interrelated manner through local climate or by means of HFRS infect