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PLoS One. 2015 Nov 25;10(11):e0143411. doi: 10.1371/journal.pone.0143411. eCollection 2015.

Visualized Exploratory Spatiotemporal Analysis of Hand-Foot-Mouth Disease in Southern China.

Author information

1
College of Forestry, Beijing Forestry University, Beijing, China.
2
State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing, China.
3
State Key Laboratory of Resources and Environmental Information System, Chinese Academy of Sciences, Beijing, China.
4
Key Laboratory of Infectious Disease Surveillance and Early-Warning, Chinese Center for Disease Control and Prevention, Beijing, China.
5
Beijing Research Institute of Water Affair Automation, Beijing, China.

Abstract

OBJECTIVES:

In epidemiological research, major studies have focused on theoretical models; however, few methods of visual analysis have been used to display the patterns of disease distribution.

DESIGN:

For this study, a method combining the space-time cube (STC) with space-time scan statistics (STSS) was used to analyze the pattern of incidence of hand-foot-mouth disease (HFMD) in Guangdong Province from May 2008 to March 2009. In this research, STC was used to display the spatiotemporal pattern of incidence of HFMD, and STSS were used to detect the local aggregations of the disease.

SETTING:

The hand-foot-mouth disease data were obtained from Guangdong Province from May 2008 to March 2009, with a total of 68,130 cases.

RESULTS:

The STC analysis revealed a differential pattern of HFMD incidence among different months and cities and also showed that the population density and average precipitation are correlated with the incidence of HFMD. The STSS analysis revealed that the most likely aggregation includes the Shenzhen, Foshan and Dongguan populations, which are the most developed regions in Guangdong Province.

CONCLUSION:

Both STC and STSS are efficient tools for the exploratory data analysis of disease transmission. STC clearly displays the spatiotemporal patterns of disease. Using the maximum likelihood ratio, the STSS model precisely locates the most likely aggregation.

PMID:
26605919
PMCID:
PMC4659604
DOI:
10.1371/journal.pone.0143411
[Indexed for MEDLINE]
Free PMC Article

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