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

Abstract

BackgroundHemorrhagic 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.Methods and findingsThis 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 infections. In particular, location (latitude, distance to coastline and longitude), grassland and woodland are the geographic factors exerting the most noticeable effects on the climate-HFRS association (e.g., low latitude has a strong effect, whereas distance to coastline has a wave-like effect).ConclusionsThe proposed synthetic quantitative approach revealed important aspects of the spatiotemporal variation of the climate-HFRS association in Eastern China during a long time period, and identified the geographic factors having a major im...

Comparative infection modeling and control of COVID-19 transmission patterns in China, South Korea, Italy and Iran

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“…g . COVID-19 cases and temperature) were used to implement wavelet coherency analysis ( He et al, 2018 ). Larger coherency value implies stronger association.…”

Environmental Determinants of Hemorrhagic Fever with Renal Syndrome in High-Risk Counties in China: A Time Series Analysis (2002–2012)

“…Given that small-scale studies will yield more precise and a better quantification of the local characteristics of HFRS transmission, the present study chose four high-risk HFRSaffected counties from Heilongjiang and Shaanxi provinces, where the largest number of HFRS cases are documented in China. [9][10][11][12] It was found that HFRS variation exhibits multiannual cycles (especially around 1 year cycle) in various locations, for example, macroscopically, HFRS cases in China shows bimodal seasonal peaks, that is, May-June and November 8,11,13 ; microscopically, two main cycles (1 and 3-4 years) were detected by wavelet analysis in Changsha 4 ; similarly, HFRS cases in Xi'an also have a period of 0.8-1.2 years. 10 Moreover, it is reported that HFRS transmission is closely associated with the environmental factors, such as climatic variability and land-cover characteristics because the activity and population of virus hosts, which play an important role in HFRS transmission, are sensitive to these local environmental factors.…”