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ISDS 2018 Conference Abstracts

Analysing Trends of Guillain-Barre Syndrome (GBS)
and Dengue cases in Hong Kong
Xin Wang*
Shenzhen Center for Disease Control and Prevention, Shenzhen, China

Objective
To study the trends of GBS and dengue in Hong Kong, the ecological 

associations between GBS, dengue, and local meteorological factors. To 
examine the non-stationary oscillating association among these factors.

Introduction
Guillain-Barre Syndrome (GBS) is a severe paralytic neuropathy 

associated with virus infections such as Zika virus and Chikungunya 
virus. There were also case reports of dengue fever preceding GBS. 
With the aim to understand the mechanisms of GBS and dengue 
outbreaks, this ecological study investigates the relationships between 
GBS, dengue, meteorological factors in Hong Kong and global 
climatic factors from January 2000 to June 2016.

Methods
The correlations between GBS, dengue, Multivariate El Nino 

Southern Oscillation Index (MEI) and local meteorological data were 
explored by Spearman’s Rank correlations and cross-correlations. 
Three Poisson regression models were fitted to identify non-linear 
associations among GBS, dengue and MEI. Cross wavelet analyses 
were applied to infer potential non-stationary oscillating associations 
among GBS, dengue and MEI.

Results
We found a substantial increasing of local GBS and dengue cases 

(mainly imported) in recent year in Hong Kong. The seasonalities of 
GBS and dengue are different, in particular, GBS is low while dengue 
is high in the summer. We observed weak but significant correlations 
between GBS and local meteorological factors. MEI could explain 
over 17% of dengue’s variations based on Poisson regression 
analyses. We report a possible non-stationary oscillating association 
between dengue fever and GBS cases in Hong Kong.

Conclusions
We report increasing patterns of both local GBS cases and 

imported dengue cases in Hong Kong, and investigate the possible 
mechanism behind these patterns. This study has led to an improved 
understanding about the timing and ecological relationships between 
MEI, GBS and dengue.

Fig 1. Trends and seasonality of GBS and dengue cases (scaled by number of 
population in Hong Kong). Panel (a), Annual cases of GBS and dengue cases 
show a sudden increase in recent years. Panel (b), Monthly cases of GBS and 
dengue cases. The grey shaded area of panel (a,b) is MEI. Panel (c), Boxplot of 
GBS cases per day. Panel (d), Boxplot of dengue cases per day.

Fig 2. Poisson regression results among dengue, MEI and GBS. Panel (a) 
shows regression coefficients between dengue and MEI, panel (b) shows 
regression coefficients between GBS and MEI and panel (c) shows regression 
coefficients between GBS and dengue. In all three panels, we consider time 
lags from 0 to 11 months. The vertical black bars are 95% confidence intervals 
and the squares in the middle are the mean estimate of regression coefficients. 
The blue dotted line is p-value of each correlation coefficient. The horizontal 
dashed light blue lines on all panels indicate the 0.05 significance level. The 
red dotted line is R2 of each regression coefficient. The horizontal dashed pink 
lines represent the median level of all R2.

Fig 3. Wavelet coherence and phase plots of dengue and GBS data from 
2000-15 in Hong Kong. Panel (a) is dengue time series with peaks shaded in 
grey. Panel (b) are phase plots of dengue and GBS. Data are shown in red and 
blue, and the black dashed line shows phase difference. Panel (c) shows cross 
wavelet average power level and wavelet coherence plots of dengue and GBS. 
The horizontal axis labels of 5, 10 and 15 represent year 2005, 2010 and 2015.

Keywords
Guillain-Barre Syndrome; Dengue; survaillance

*Xin Wang
E-mail: szwxin@163.com

Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * 10(1):e93, 2018