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

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. Making up for this lack of quantitative studies is the reason for the development of the present work.Method: 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. Results: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. 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 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.

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

“…Future work could also focus on employing the dynamic transmission rate to forecast any trends in the numbers of COVID-19 cases, or to model patterns in future epidemics. Further, spatiotemporal disease characteristics (including the composite space-time disease dependencies and spread patterns using modern geostatistics methods, and their inter-association or tele-connection with climatic factors using time series, time-frequency methods ( Christakos and Olea, 2005 ; Christakos et al, 2005 ; Chirstakos, 2017 ; He et al, 2017 , He et al, 2018a , He et al, 2018b , He et al, 2019a , He et al, 2019b , He et al, 2019c ; Xiao et al, 2019 ; Jahangiri et al, 2020 ; Qi et al, 2020 ; Shi et al, 2020 ) could be explored by considering various levels of data, such as county-level or even individual-level disease data. More specifically, as was documented in the above literature, the geostatistics methods can help detect the trends, spread directions and core areas of the infectious disease.…”

Probabilistic logic analysis of the highly heterogeneous spatiotemporal HFRS incidence distribution in Heilongjiang province (China) during 2005-2013

“…lower HFRS incidences), whereas Wusuli River, Songhua River and Mudan River locate in the eastern part of the province (i.e. higher HFRS incidences) [15]. Moreover, the eastern and southeastern parts of Heilongjiang Province exhibit mixed land types with forests, favoring rodents’ reproduction.…”

“…During this period, the monthly rainfall, temperature and relative humidity ranged from 0.23 to 221.4 mm , -23.12 to 23.12 ° C and 38.77 to 83.74%, respectively. The Heilongjiang Province has 38.98, 26.29 and 16.92% croplands, mixed forests and cropland/natural vegetation mosaic, respectively [15]. In addition, the GDP of Heilongjiang Province increased from 551.4 to 1445.5 billion yuan .…”

“…In a more recent study, Li et al [13] applied a geographically weighted regression model to Chinese data during the period 2005–2009, and concluded that temperature, precipitation, humidity, NDVI, land use and elevation significantly impacted the spatiotemporal heterogeneity of HFRS incidences. Also, Tian et al [14] and He et al [15, 16] used wavelet analysis to study how the dynamics of HFRS are linked to rainfall, temperature, rodents’ density and the multivariate El Niño-Southern Oscillation (ENSO) index.…”

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.…”