Predicting Unprecedented Dengue Outbreak Using Imported Cases and Climatic Factors in Guangzhou, 2014

Abstract

IntroductionDengue is endemic in more than 100 countries, mainly in tropical and subtropical regions, and the incidence has increased 30-fold in the past 50 years. The situation of dengue in China has become more and more severe, with an unprecedented dengue outbreak hitting south China in 2014. Building a dengue early warning system is therefore urgent and necessary for timely and effective response.Methodology and Principal FindingsIn the study we developed a time series Poisson multivariate regression model using imported dengue cases, local minimum temperature and accumulative precipitation to predict the dengue occurrence in four districts of Guangzhou, China. The time series data were decomposed into seasonal, trend and remainder components using a seasonal-trend decomposition procedure based on loess (STL). The time lag of climatic factors included in the model was chosen based on Spearman correlation analysis. Autocorrelation, seasonality and long-term trend were controlled in the model. A best model was selected and validated using Generalized Cross Validation (GCV) score and residual test. The data from March 2006 to December 2012 were used to develop the model while the data from January 2013 to September 2014 were employed to validate the model. Time series Poisson model showed that imported cases in the previous month, minimum temperature in the previous month and accumulative precipitation with three month lags could project the dengue outbreaks occurred in 2013 and 2014 after controlling the autocorrelation, seasonality and long-term trend.ConclusionsTogether with the sole transmission vector Aedes albopictus, imported cases, monthly minimum temperature and monthly accumulative precipitation may be used to develop a low-cost effective early warning system.

The Driving Force for 2014 Dengue Outbreak in Guangdong, China

“…In recent two years, some researches on 2014 Guangzhou outbreak data (only including the symptomatic data) were published [31–34]. Sang et al [31, 32] claimed that the number of imported cases, minimum temperature with a one-month lag and cumulative precipitation with a three month lag predicted the outbreak in 2013 and 2014 by using a multivariate Poisson regression analysis of the Guangzhou outbreak data. Cheng et al [33] used a mathematical model to obtain that climate and the timing of imported cases as the causal factors of dengue outbreak in Guangzhou (The authors assumed that only one case was imported to Guangzhou in the model, this assumption was actually wrong).…”

Severe Dengue Fever Outbreak in Taiwan

“…9,10 It is essential to examine the association between climatic variables and DF epidemic. Our data show that precipitation peaked in August and the highest temperature was measured during the months of July-September in Kaohsiung.…”

Synergistic China–US Ecological Research is Essential for Global Emerging Infectious Disease Preparedness

“…At a macro-scale, anthropogenic activities have influenced microbial transmission dynamics, particularly for vector-borne pathogens (Jones et al 2008;Goklany 2009;Woodward et al 2014;Murray and Daszak 2013;Morse 1995). For example, climate variation has been documented to drive transmission of dengue virus dynamics in Guangdong, China Xu et al 2017;Sang et al , 2015Xiang et al 2017). This region experienced no outbreaks of dengue-like illness from the period of 1950-1977, followed by a relatively low level of incidence until a large-scale outbreak occurred in 2014, infecting over 45,000 people .…”