Dengue in Southeast Asia: epidemiological characteristics and strategic challenges in disease prevention

Ooi, Eng Eong; Gubler, Duane J.

doi:10.1590/s0102-311x2009001300011

Cited by 174 publications

(162 citation statements)

References 35 publications

(54 reference statements)

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“…We did not consider potentially relevant local factors, such as population movements, the occurrence of household water tanks, vegetation cover, and distance to rivers or other water bodies. Further, although our results point to climate factors associated with increased risk of dengue transmission and outbreaks, human activities and their impact on local ecology may be more important than climate factors in driving the long-term trends (29)(30)(31). Moreover, the role of climate is likely to vary among locations and even over time in the same location (32).…”

Section: Significancementioning

confidence: 65%

Climate variation drives dengue dynamics

Stige

Chan

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

175

147

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Dengue, a viral infection transmitted between people by mosquitoes, is one of the most rapidly spreading diseases in the world. Here, we report the analyses covering 11 y (2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015) from the city of Guangzhou in southern China. Using the first 8 y of data to develop an ecologically based model for the dengue system, we reliably predict the following 3 y of dengue dynamics-years with exceptionally extensive dengue outbreaks. We demonstrate that climate conditions, through the effects of rainfall and temperature on mosquito abundance and dengue transmission rate, play key roles in explaining the temporal dynamics of dengue incidence in the human population. Our study thus contributes to a better understanding of dengue dynamics and provides a predictive tool for preventive dengue reduction strategies.D engue is one of the most rapidly spreading diseases in the world (1), including within the Guangdong province of southern China (2). During the last 50 y, the incidence of dengue has increased 30-fold with increasing geographic expansion to new countries (1). In 2010, an estimated 390 million dengue infections occurred, of which around 96 million showed symptoms (3). Dengue outbreaks in China were previously thought to be imported and initiated by people traveling to China from dengue-endemic areas elsewhere (4); however, recent studies suggest that dengue may now be endemic to China as well (2). The epidemiological triangle of both dengue fever and dengue hemorrhagic fever, which is the more serious form of dengue, includes hosts (humans), pathogens (one or more of five dengue virus serotypes) (5), and mosquito vectors (Aedes albopictus and Aedes aegypti) with their ecological interactions (6). The dengue outbreaks are qualitatively known to be strongly influenced by temperature (7), humidity, rainfall, and socioeconomic factors like urbanization (8). However, a full understanding of the quantitative nature of such effects is largely lacking. With this paper, we provide such a quantitative understanding of dengue dynamics.In 2014, an extensive dengue outbreak hit China, with 47,127 dengue cases diagnosed, a new record since 1986 (9). Since the 1990s, dengue epidemics have gradually spread from Guangdong, Hainan, and Guangxi provinces (9). We present here a time series analysis of dengue dynamics, using dengue surveillance data for the years 2005−2015 from Guangzhou, the largest city in Guangdong and the city with the most dengue cases in China. We split the main analysis by using the first 8 y to develop a model, and the three remaining years to test that model, as these latter years encompass exceptionally extensive dengue outbreaks.Monthly human dengue incidence data (number of diagnosed cases) were obtained from the China National Notifiable Disease Surveillance System (10) (Fig. 1). Monthly surveillance data of A. albopictus density, the only dengue vector species in Guangzhou, were obtained from local Centers for Disease Control and Prevention (11) (Metho...

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Section: Significancementioning

confidence: 65%

Climate variation drives dengue dynamics

Stige

Chan

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

175

147

View full text Add to dashboard Cite

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“…The Most targeted regions of the disease are SE Asia [6] and the Western Pacific ( Fig. 1), but within past few years, a rising trend is witnessed in South America and the Caribbean [3].…”

Section: Dengue Statistics Around the Worldmentioning

confidence: 99%

“…Only in India, 37 million dengue cases have been reported with 227 500 hospitalizations [2], [6]. Health authorities in Brazil have reported more than 200,000 dengue infected people within first seven weeks of 2013 as compared with 70,000 in the same period last year, thus, indicating a steep rise in confirmed cases.…”

Section: Dengue Statistics Around the Worldmentioning

confidence: 99%

Effective Use of ICT and Emerging Cellular Technologies for Health Care: E-Govt Reshaping Governance into Good Governance

Zafar¹,

Hassan²,

Mahmood³

2014

JOEBM

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Abstract-This paper highlights best practices of technological influenced e-government using ICT, emerging mobile and web 2-0 technology for the effective health Care & disease management system. Modern technology has given a whole new dimension to the concept of good governance, thus embarking a strong and effective influence on citizens and cultural trends. Authors have investigated successful case studies, which are taken from existing practice of egovernment health care projects within Pakistan.Pakistan was hit by the epidemic of dengue, affecting over 350,000 people in 2010. Due to the unprecedented scale of epidemic, the basic health infrastructure was not ready to address the issue. However by 2012 a monitoring mechanism was devised as an e-Govt health care activity, to log all field activities related to prevention/eradication of the disease. The project focused mobile reporting using android phones, EDGE services, and Google maps. 650 smart phones users reported 43,000 activities. The information received was extrapolated to bring all stakeholders of anti-dengue campaign on a single platform and DIMS (Dengue Information Management System) was conceived. Features of DIMS include:A central dashboard to view and dispose of complaints on real-time coming in from the Helpline and Hospitals e-CRS (Electronic Complaint Routing System). 2 Vector Surveillance System for tracking and reporting all indoor / outdoor larvaciding activities. 3 A Disease Reporting System to manage the clinical and lab diagnosis records of dengue affected patients.Utilization of this system has resulted in 0 % deaths reported due to dengue in the past 1.5 years. Careful investigation of step by step usage, reporting to DMIS with Android phone and working behavior of ICT infrastructure are elaborated in this research.

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“…2) Changes in land cover, particularly rapid unplanned expansion of urban areas with inadequate housing and infrastructure [5][6][7][8][9][10][11]; and 3) High population density [12].…”

Section: Introductionmentioning

confidence: 99%

Assessing the Temporal and Spatial Dynamics of the Dengue Epidemic in Northern Sri Lanka using Remote Sensing Data, GIS and Statistical Analysis

Anno¹,

Imaoka²,

Tadono³

et al. 2014

J Geophys Remote Sens

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Dengue outbreaks are affected by biological, ecological, socio-economic and demographic factors that vary with time and space. These spatial and temporal variables have been examined separately with some success, but still elude systematic understanding. The present study investigates the covariance of spatial and temporal factors for dengue outbreaks in the northern region of Sri Lanka. The relations identified herein demonstrate spatio-temporal dynamics of the disease and can inform surveillance and control strategies. Multi-satellite remote sensing (RS) data were used to construct an index comprising rainfall, humidity and temperature data. RS data gathered by ALOS/ AVNIR-2 and a digital land cover map were used to extract land usage information. Other data on relevant factors and dengue outbreaks were collected through institutions and public databases. RS and other data were integrated and analysed for spatial association analysis and spatial statistics. Our findings show that a combination of ecological, socio-economic and demographic factors can predict spatial and temporal trends in dengue outbreaks.

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Dengue in Southeast Asia: epidemiological characteristics and strategic challenges in disease prevention

Abstract: Dengue emerged as a public health burden in

Cited by 174 publications

References 35 publications

Climate variation drives dengue dynamics

Climate variation drives dengue dynamics

Effective Use of ICT and Emerging Cellular Technologies for Health Care: E-Govt Reshaping Governance into Good Governance

Assessing the Temporal and Spatial Dynamics of the Dengue Epidemic in Northern Sri Lanka using Remote Sensing Data, GIS and Statistical Analysis

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