Dengue

Surveillance of Dengue Fever Virus: A
Review of Epidemiological Models and
Early Warning Systems
Vanessa Racloz,1,* Rebecca Ramsey,2 Shilu Tong,2 and Wenbiao Hu1
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Abstract
Dengue fever affects over a 100 million people annually hence is one of the world's most
important vector-borne diseases. The transmissionarea 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 notrealistic 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 thespatio-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.

Author Summary
Despite mass vaccination campaigns and large scaled improvements in global surveillance,
infectiousdiseases are a worldwide problem. In recent years, the ability to use models as a
tool to help visualize, understand and combat infectious diseases has become more feasible
and reliable. In this context, modelling focuses on transmission patterns between the
different animal, human or vector components as well as including parameters which affect
these pathways such as environmental, climatic orgeographic ones. The output of these
models can help in decision making processes concerning control purposes, surveillance
methods and hopefully also as good predictive tools. Prediction forms part of surveillance
systems, and more specifically in early warning systems. It is the timely collection and
analysis of data as well as the use of risk-based assessments in order to aid in prompt healthinterventions such as movement control, vaccination campaigns or the distribution of
important information. Early warning systems for vector borne diseases are especially
complex due to the involvement of various factors originating from the human, animal and
insect sector as well the disease itself. The authors investigate the variety and depth of
available models for dengue feversurveillance and their use as early warning tools.

Introduction
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 denguevirus (DEN 1–4) 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 commonand 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...