Surveillance of Dengue Fever Virus: A Review of Epidemiological Models and Early Warning Systems
Hu W (2012) Surveillance of Dengue Fever Virus: A Review of Epidemiological Models and Early Warning Systems. PLoS Negl
Trop Dis 6(5): e1648. doi:10.1371/journal.pntd.0001648
Surveillance of Dengue Fever Virus: A Review of Epidemiological Models and Early Warning Systems
Vanessa Racloz 0
Rebecca Ramsey 0
Shilu Tong 0
Wenbiao Hu 0
Assaf Anyamba, NASA Goddard Space Flight Center, United States of America
0 1 School of Population Health, University of Queensland , Brisbane, Queensland , Australia , 2 School of Public Health and Institute of Health and Biomedical Innovation, Queensland University of Technology , Kelvin Grove Campus, Kelvin Grove, Queensland , Australia
Dengue fever affects over a 100 million people annually hence is one of the world's most important vector-borne diseases. The transmission area of this disease continues to expand due to many direct and indirect factors linked to urban sprawl, increased travel and global warming. Current preventative measures include mosquito control programs, yet due to the complex nature of the disease and the increased importation risk along with the lack of efficient prophylactic measures, successful disease control and elimination is not realistic in the foreseeable future. Epidemiological models attempt to predict future outbreaks using information on the risk factors of the disease. Through a systematic literature review, this paper aims at analyzing the different modeling methods and their outputs in terms of acting as an early warning system. We found that many previous studies have not sufficiently accounted for the spatio-temporal features of the disease in the modeling process. Yet with advances in technology, the ability to incorporate such information as well as the socioenvironmental aspect allowed for its use as an early warning system, albeit limited geographically to a local scale.
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Funding: This study was supported by a grant of the National Health and Medical Research Council, Australia (no. 1002608) and grants from Queensland
University of Technology (2008BAI56B02, 2009ZX10004-201). The funders had no role in study design, data collection and analysis, decision to publish, or
preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
Dengue fever virus
Dengue fever (DF) is one of the most common widespread
vector borne diseases in the world [1,2,3,4]. There are currently
2.5 billion people living in areas at risk of DF transmission, with
100 million cases reported annually [5,6]. DF is a flaviviral disease
caused by one of four serotypes of dengue virus (DEN 14) which
are transmitted by mosquito vectors, in particular the peridomestic
species Aedes aegypti [2,7], and Ae. albopictus, which has recently been
expanding its geographic distribution as seen in several outbreaks
[8].
Infection by one serotype will provide lifelong immunity to that
particular strain but not to the remaining three [1,9]. Cross-strain
infections are common and can have severe consequences, with
extreme cases leading to death [10]. Over the past 40 years the
incidence and geographic distribution of DF has increased in
many countries, particularly in those with tropical and sub-tropical
climates [6,11,12,13,14]. DF has strong spatial and temporal
patterns which have been linked to climatic and environmental
conditions [15]. Thus the inclusion of spatial and temporal data in
analytic processes may potentially allow for the identification of
DF characteristics linked to these parameters and have significant
applications in the prevention and control of this disease.
Additionally, as discussed in the Intergovernmental Panel on
Climate Change report [16], with global temperatures likely to
increase, it is predicted that the endemic range of DF will expand
geographically [17,18,19,20,21]. Altered extrinsic incubations
periods (EIP), biting rates hence transmission levels [18,22] of
the disease will increase its capacity as a vector, more specifically
its competence and activity, and is linked to climate and
environment, amongst other factors [23].
Surveillance of vector borne diseases
Several surveillance system methods exist for a variety of vector
borne diseases [24], [25], yet successful early warning strategies
are limited due to the complex and dynamic nature of the disease,
environmental factors, the vectors and the hosts involved as well as
the necessary health system infrastructure needed to combine all
the factors in an integrated manner. In Europe, the VBORNET
network which combines knowledge from entomologists and
public health experts [26] was recently developed with aim at
building an integrated approach to surveillance of vector borne
diseases. The report highlights the different parameters and
methods needed to establish surveillance activities, as well as the
various data types and collection strategies (www.vbornet.eu).
Sentinel surveillance is a type of risk based surveillance which
can serve (...truncated)