Analyzing hemorrhagic fever with renal syndrome in Hubei Province, China: a space–time cube-based approach. Issue 7 (July 2019)

• Record Type: Journal Article
• Authors: Zhao, Youlin; Ge, Liang; Liu, Junwei; Liu, Honghui; Yu, Lei; Wang, Ning; Zhou, Yijun; Ding, Xu
• Is Part Of: Journal of international medical research. Volume 47:Issue 7(2019)
• Page Start: 3371
• Page End: 3388
• Publication Date: 2019-07
• Subjects: Hemorrhagic fever with renal syndrome -- space-time cube -- emerging hot spots -- local outlier analysis -- spatial gravity center -- influencing factors; Medicine -- Periodicals; Pharmacology -- Periodicals; 610.5
• DOI: 10.1177/0300060519850734
• Languages: English
• ISSNs: 0300-0605

Abstract

Objective: Hemorrhagic fever with renal syndrome (HFRS), a natural–focal infectious disease caused by hantaviruses, resulted in 37 deaths between 2011 and 2015 in Hubei Province, China. HFRS outbreaks are seasonally distributed, exhibiting heterogeneity in space and time. We aimed to identify the spatial and temporal characteristics of HFRS epidemics and their probable influencing factors. Methods: We used the space–time cube (STC) method to investigate HFRS epidemics in different space–time locations. STC can be used to visualize the trajectories of moving objects (or changing tendencies) in space and time in three dimensions. We applied space–time statistical methods, including space–time hot spot and space–time local outlier analyses, based on a calculated STC model of HFRS cases, to identify spatial and temporal hotspots and outlier distributions. We used the space–time gravity center method to reveal associations between possible factors and HFRS epidemics. Results: In this research, HFRS cases for each space–time location were defined by the STC model, which can present the dynamic characteristics of HFRS epidemics. The STC model delivered accurate and detailed results for the spatiotemporal patterns of HFRS epidemics. Conclusion: The methods in this paper can potentially be applied for infectious diseases with similar spatial and temporal patterns.