2014.ISDS.Abstracts.Final.pdf


ISDS Annual Conference Proceedings 2014. This is an Open Access article distributed under the terms of the Creative Commons Attribution-
Noncommercial 3.0 Unported License (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, 
and reproduction in any medium, provided the original work is properly cited.

ISDS 2014 Conference Abstracts

Spatial Clustering of ILI in Yunnan Province, China, 
Based on a Geographical Information System
Xia Xiao*1, 4, Chunrui Luo2, Xiaoxiao Song1, 3, 4, Wei Liu1, 4, Le Cai1, 4, Yan Li1, 4, Lin Lu4, 2 and 
Qiongfen Li2, 4

1School of Public Health, Kunming Medical University, Kunming, China; 2Yunnan Province Center for Disease Control and 
Prevention, Kunming, China; 3Fudan University, Shanghai, China; 4Yunnan Provincial Collaborative Innovation Center for Public 
Health and Disease Prevention and Control, Yunnan, China

Objective
The purpose of the study was to determine spatial clustering of the 

spread of influenza like illness (ILI) epidemic in Yunnan province, 
China with the aim of producing useful information for prevention 
and control measures.

Introduction
Influenza is a highly contagious, acute respiratory disease that 

causes periodic seasonal epidemics and global pandemics[1]. Yunnan 
Province is characterized by poverty, multi-ethnic, and cross-border 
movement, which maybe be susceptible of influenza (Fig-1). Finding 
from spatial patter of ILI will promote to control and prevent the 
respiratory diseases epidemic.

Methods
Data was obtained from the sentinel surveillance of illness like 

influenza (ILI) in Yunnan Center of Disease Control and Prevention 
from 2009 to 2013. The characteristics of the ILI clustering will 
be assessed by ‘Global’ and ‘Local’ Moran’s I using Monte Carlo 
simulation by GeoDa. The spatial weights methods based on Queen-
contiguity (polygons are adjacent if they share a border or corner)[2].

Results
A total of 49139 ILI cases were reported from sentinel surveillance 

data, which accounted for 3.35% of the total outpatient visit. Two 
incidence peaks occurred in spring and autumn. Among the positive 
samples, the top was Victoria (accounted for 31.98%), and the follow 
rank was influenza A (H1N1) (accounted for 26.03%).

From the Fig-2, we got the global Moran’s I=0.256(p<0.05). 
It indicated clustering was actually apparent throughout Yunnan 
Province. The four quadrants in the scatter plot correspond to 
different types of spatial correlation. However, the Global of Moran’s 
I assume that the spatial process under investigation is stationary and 
fail to know ‘where was cluster of disease’[3]. So we turn to look the 
Local measures of spatial autocorrelation (LISA, local Moran’s I). 
Examination of the LISA map showed that most of the counties were 
no statistical significant differences in 0.05, except only 4 counties. 
You can add the results of the LISA analysis to the LISA map (Fig-
3). From the above studying, we concluded that the areas susceptible 
to influenza featured mostly in poorer surrounding districts, or be 
neighboring with Vietnam or/and Laos.

Conclusions
General spatial autocorrelation indicated that influenza incidence 

was aggregated at the provincial level, and local spatial autocorrelation 
analyses found that border- area with poorer living-level were 
evidence for hotspots of high incidence of ILI. An approach base 
on Moran’s I statistic complemented with GeoDa for visualization 
facilitates decision-making regarding various options such as isolation 
according to districts and months, and implement specific control 
measures in high risk districts to control the spread of ILI.

Fig-1 The map of research site -Yunnan Province- in China

Fig-2 The Moran’s I scatter plot of virus-strain positive rates

Fig-3 LISA cluster map of Influenza virus-strain positive rates for p<0.05

Keywords
Influenza like illness; geographical information systems; spatial 
autocorrelation

References

1. Kimura Y, Saito R, Tsujimoto Y, Ono Y, Nakaya T, et al. (2011)
Geodemographics profiling of influenza A and B virus infections in
community neighborhoods in Japan. BMC infectious diseases 11: 36.

2. Anselin L (2005) Exploring Spatial Data With GeoDa: A Work
Book. Spatial Analysis Laboratory, University of Illinois. Center for
Spatially Integrated Social Science.

3. Stevenson M, Stevens K, Rogers D, Clements A, Pfeiffer D, et al.
(2008) Spatial analysis in epidemiology. Oxford University Press,
New York.

*Xia Xiao
E-mail: xxkmyn@gmail.com    

Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * (1):e176, 201