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Many studies have found associations between climatic conditions and dengue transmission. However, there is a debate about the future impacts of climate change on dengue transmission. This paper reviewed epidemiological evidence on the relationship between climate and dengue with a focus on quantitative methods for assessing the potential impacts of climate change on global dengue transmission. A literature search was conducted in October 2012, using the electronic databases PubMed, Scopus, ScienceDirect, ProQuest, and Web of Science. The search focused on peer-reviewed journal articles published in English from January 1991 through October 2012. Sixteen studies met the inclusion criteria and most studies showed that the transmission of dengue is highly sensitive to climatic conditions, especially temperature, rainfall and relative humidity. Studies on the potential impacts of climate change on dengue indicate increased climatic suitability for transmission and an expansion of the geographic regions at risk during this century. A variety of quantitative modelling approaches were used in the studies. Several key methodological issues and current knowledge gaps were identified through this review. It is important to assemble spatio-temporal patterns of dengue transmission compatible with long-term data on climate and other socio-ecological changes and this would advance projections of dengue risks associated with climate change.
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R E S E A R C H A R T I C L E Open Access
Climate change and dengue: a critical and
systematic review of quantitative modelling
approaches
Suchithra Naish
1*
, Pat Dale
2
, John S Mackenzie
3
, John McBride
4
, Kerrie Mengersen
5
and Shilu Tong
1
Abstract
Background: Many studies have found associations between climatic conditions and dengue transmission.
However, there is a debate about the future impacts of climate change on dengue transmission. This paper
reviewed epidemiological evidence on the relationship between climate and dengue with a focus on quantitative
methods for assessing the potential impacts of climate change on global dengue transmission.
Methods: A literature search was conducted in October 2012, using the electronic databases PubMed, Scopus,
ScienceDirect, ProQuest, and Web of Science. The search focused on peer-reviewed journal articles published in
English from January 1991 through October 2012.
Results: Sixteen studies met the inclusion criteria and most studies showed that the transmission of dengue is
highly sensitive to climatic conditions, especially temperature, rainfall and relative humidity. Studies on the potential
impacts of climate change on dengue indicate increased climatic suitability for transmission and an expansion of
the geographic regions at risk during this century. A variety of quantitative modelling approaches were used in the
studies. Several key methodological issues and current knowledge gaps were identified through this review.
Conclusions: It is important to assemble spatio-temporal patterns of dengue transmission compatible with long-term
data on climate and other socio-ecological changes and this would advance projections of dengue risks associated with
climate change.
Keywords: Climate, Dengue, Models, Projection, Scenarios
Background
Dengue is a major public health concern for over half of
the worlds population and is a leading cause of hospital-
isation and death, particularly for children in endemic
countries [1]. Recent studies estimate that 3.6 billion
people are at risk, with over 230 million infections,
millions of cases with dengue fever, over 2 million cases
with severe disease, and 21,000 deaths [1-5]. A 30-fold
increase in the number of dengue cases over the past
50 years has been recorded with nearly 119 countries en-
demic for dengue [4]. As part of global climate changes,
temperature has increased by a global average of 0.75°C
over the past 100 years. Temperature increases such as
these are potentially associated with substantial increases
in dengue outbreaks. Apart from climate factors other
important issues that potentially contribute to global
changes in dengue incidence and distribution include
population growth, urbanisation, lack of sanitation, in-
creased human travel, ineffective mosquito control, and
increased reporting capacity [3-9].
Dengue is primarily transmitted by Aedes aegypti and
secondarily by Aedes albopictus. Both mosquitoes have
adapted to local human habitation with oviposition and
larval habitats in natural (e.g., rock pools, tree holes and
leaf axis) and artificial (e.g., water tanks, blocked drains,
pot plants and food and beverage containers) collections
in the urban and peri-urban environment. These mosqui-
toes may be infected with any of the four dengue viruses
with an incubation period of 314 days [10]. Dengue
affects people of all ages, including infants irrespective of
* Correspondence: s.naish@qut.edu.au
1
School of Public Health and Social Work & Institute of Health and
Biomedical Innovation, Queensland University of Technology, Victoria Park
Road, Brisbane, Queensland, Australia
Full list of author information is available at the end of the article
© 2014 Naish et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly credited.
Naish et al. BMC Infectious Diseases 2014, 14:167
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gender. Dengue viruses cause a spectrum of disease, with
symptoms from mild influenza-like symptoms to severe
or fatal haemorrhage fever [11].
The ecology of dengue and vector
The epidemiological triangle of dengue includes host,
pathogen and mosquito vectors (including Ae. aegypti
and Ae. Albopictus) together with their interactions in
the environment. Dengue is climate sensitive as the virus
has to complete part of its development in the mosquito
vectors that transmit the disease [5]. The major vector is
Ae. aegypti whose life cycle is directly influenced by ambi-
ent temperature and rainfall [12]. Increased temperature
could increase dengue risk by increasing the rate of mos-
quito development and reducing virus incubation time in
areas where the vector presently exists, thereby increasing
the rate of transmission [13-16]. Conversely, extreme hot
temperatures may also increase the rate of mosquito mor-
tality and thus decrease dengue risk [17]. Similarly, rainfall
can have non-linear contrasting effects on dengue risk
[6,17]. Heavy rainfall may flush away eggs, larvae, and
pupae from containers in the short term but residual
water can create breeding habitats in the longer term [18].
A dry climate can lead to human behaviour of saving
water in water storage containers, which may become
breeding sites for Ae. Aegypti [19].Thus, climatic condi-
tions may affect the virus, the vector and/or human be-
haviour both directly and indirectly [20]. Studies have
demonstrated that the ecology of virus is intrinsically tied
to the ecology of dengue vectors [21]. Recent studies have
also elaborated on the impacts of climate change on the
vector, for example, how the extreme climatic events drive
mosquito outbreaks [22-25]. However, empirical relation-
ships have been demonstrated between climate variables,
dengue and dengue vectors, casual relationships have not
been strongly established.
Dengue and climate change
It is established that climate change is happening and it
is likely to expand the geographical distribution of sev-
eral mosquito-borne diseases [26]. The mounting evi-
dence around climate-disease relationships raises many
important issues about the potential effects of global cli-
mate changes on the transmission of infectious diseases,
particularly dengue [5,27-29]. There is evidence indicat-
ing that dengue epidemics have been associated with
temperature [30-32], rainfall [33,34] and relative humidity
[35-37]. Few studies have included spatial data in climate-
based predictive models [33,38].
As global climate change is predicted to accelerate
over the next a few decades at least [1,3-6,27], an in-
creased frequency, intensity and duration of extreme
climatic events are more likely, so affecting dengue
transmission. This is a global public health priority. A
better understanding of the relationship between climate
and disease is an important step towards finding ways
to mitigate the impact of disease on communities, for
example, malaria [39]. Successful future management of
dengue requires an understanding of the dynamics of the
virus, host, vector, and environmental factors especially
in the context of a changing climate [21].
Quantitative modelling of dengue and climate
The influences drawn about the relationships between
dengue and climate, and the predictions of dengue
under future climate change scenarios may depend on
the analytical approaches used. The aim of this paper is
to review the relevant literature on dengue disease and
climate with a focus on quantitative models of the impact
of climate change; to address methodological issues in
this challenging field and then to indicate future oppor-
tunities and research directions.
Methods
Search strategy
A literature search was conducted in October 2012 using
the electronic databases PubMed, Scopus and Science-
Direct, ProQuest and Web of Science to obtain the infor-
mation on the impact of climate variables (and climate
change) on dengue disease transmission. The search period
included January 1991 (the commencement of the report-
ing of dengue in Queensland, Australia) to October 2012.
We limited the literature search to journal articles pub-
lished in English, available in full-text/ pdf. The key words
used were dengue disease transmission, climate and/or cli-
mate change, projection /forecast and scenario but not
diagnostic tests, vector/ virus type. References and citations
of the articles identified were checked to ensure that all
relevant articles were included (Figure 1).
Selection criteria
Three selection criteria were used to select articles from
the search results for the detailed consideration. First,
in order to obtain authoritative information, this review
included only peer-reviewed journal articles. Second, ar-
ticles had to include larger geographical areas, climate
data including climate parameters and statistical analytical
methods and at least one climate-based projection of fu-
ture dengue disease transmission. Finally, we included only
quantitative studies based on statistical models (climate-
based) because qualitative studies used an entirely different
research design and analytic approach.
We assessed the strengths and limitations of analytical
models and their use of established relationship between
climate change and dengue transmission. Finally, we pro-
vide recommendations for future research directions to-
wards model parameters and predictions of climate change
on dengue transmission.
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Results
The initial search yielded 531 studies of which 456
were deemed to be potentially relevant and were sub-
jected to further perusal. This led to 75 studies con-
sidered in detail and 16 that strictly met the inclusion
criteria. Table 1 shows the characteristics of the 16 studies
that addressed the climate variables (climate change) and
future risk of dengue transmission, using climate change
scenarios.
Dengue data
For the purpose of this study, we have considered Dengue
Fever (DF) or Dengue Hemorrhagic Fever (DHF) as a
single entity dengueas such kind of information was
not available from many studies. Eight studies used
monthly confirmed and reported dengue notified case data
[8,31,33,34,40-43], two studies used annual cases [44,45],
three studies used weekly confirmed case data [9,32,36]
aggregated from daily surveillance and one study used
daily cases [30]. Two studies combined dengue-specific
(entomological) parameters [38,46]. Most of the studies
used dengue laboratory-confirmed case data (notified) ob-
tained from health departments (Table 1) and some used
reported cases [36,47,48].
Covariate data
Most data were aggregated to monthly estimates from daily,
weekly and annual data obtained from meteorological
stations. With the exception of a few studies that used
global gridded projection data sets [34,38], all other stud-
ies obtained climate variables from local/ national me-
teorological stations [31,32,36,40-43,45,49,50]. Data on
socio-economic heterogeneity, climatic diversity includ-
ing both tropical and subtropical, and un-observed con-
founders such as social behaviour were described in
individual studies.
Analytical approaches
Among the selected studies, six used SARIMA- time
series or wavelet time series models [34,36,40,42,45,50],
four used different types of regression analysis [9,32,33,41]
and other studies have variously employed a general
additive mixed (GAM) model [30], a spatial model [43],
a non-linear model [31], a multivariate model [46], and
global circulation models (GCM) towards projections
[33,38]. Most studies used a combination of the different
analytical models but to varying degrees. For example,
among studies that used regression analysis, some intro-
duced autoregressive terms. A few studies used Poisson
regression models to allow for autocorrelation and over
dispersion. A number of studies incorporated the prob-
ability of risk of seasonal forecasts as covariates in their
analysis [31,36] and only few studies demonstrated the
potential use of forecasting in the development of climate-
driven models [33,38,44]. The details of these models are
explained below.
Records identified through
database searching
(n = 551)
Screening
Included Eligibility Identification
Additional records identified
through other sources
(n = 34)
Records after duplicates removed
(n = 561)
Records screened
(n = 531)
Records excluded
(n = 456)
Full-text articles
assessed for eligibility
(n = 75)
Full-text articles
excluded, with reasons
(n = 59)
Studies included in
qualitative synthesis
(n = 16)
Figure 1 Flowchart of literature search.
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Table 1 Studies included in the review of climate impact on dengue
References Study area
(Period)
Dengue data Covariate data Spatial resolution Analytical approaches Key findings Comments
Earnest et al. [32] Singapore
(20012008)
Weekly laboratory
confirmed notified
dengue cases
Weekly climate (mean/
minimum/maximum
temperature, mean rainfall,
mean/minimum/maximum
relative humidity, mean
hours of sunshine and
meanhoursofcloud)data
Local meteorological
station data
Poisson regression,
Sinusoidal function
Temperature, relative humidity
and SOI associated with dengue
cases.
Temporal trends of dengue
were noticeable.
Descloux et al.
[31]
Noumea
(New Caledonia)
(19712010)
Monthly confirmed
cases of DF/ DHF
Monthly climate (temperature,
precipitation, relative humidity,
wind force, potential
evapo-transpiration, hydric
balance sheet) data and
ENSO indices
Local meteorological
station data
Non-linear models Significant inter-annual
correlations were observed
between dengue outbreaks
and summertime temperature,
precipitation, relative humidity
but not ENSO.
The epidemic dynamics of
dengue were driven by
climate.
Chen et al. [30] Taiwan
(19942008)
Daily confirmed
cases of notified DF
Daily climate (temperature,
rainfall) data, socio-
demographic factors
Local meteorological
station data
GAM Rainfall was correlated with
dengue cases. Lag effects were
observed.
A climatic change does have
influence on dengue
outbreaks.
Hu et al. [43] Australia
(20022005)
Monthly confirmed
cases of notified
dengue
Monthly weather, SEIFA, pop
(LGA)
Local meteorological
station data
Bayesian CAR Increase in dengue cases of 6%
in association with a 1-mm
increase in average monthly
rainfall and a 1°C increase in
average monthly maximum
temperature, respectively was
observed.
Socio-ecological factors
appear to influence dengue.
The drivers may differ for
local and overseas cases.
Spatial clustering of dengue
cases was evident.
Chowell [45] Peru
(19942008)
Annual confirmed
cases
Time series of annual
population size and density,
altitude and climate data
Local meteorological
station data
Wavelet time series A significant difference in the
timing of epidemics between
jungle and coastal regions was
observed.
The differences in the timing
of dengue epidemics
between jungle and coastal
regions were significantly
associated with the timing of
the seasonal temperature
cycle.
Thai et al. [50] Vietnam
(19942009)
Monthly confirmed
cases
Monthly climate (mean
temperature, rainfall and
relative humidity) data
and ENSO indices
Local meteorological
station data
Wavelet time series ENSO indices and climate
variables were significantly
associated with dengue
incidence.
Climate variability and ENSO
impact dengue outbreaks.
Colon-Gonzalez
et al. [41]
Mexico
(19852007)
Monthly confirmed
cases
Monthly climate (minimum
and maximum temperature
and rainfall) and ENSO
indices
Local meteorological
station data
Linear regression,
PhillipsPerron and
JarqueBera test
tests
Incidence was higher during
El-Nino. Incidence was
associated with El-Nino and
temperature during cool and
dry times.
Temperature was an
important factor in the
dengue incidence in Mexico.
Pinto et al. [9] Singapore
(20002007)
Weekly confirmed
notified DF cases
Weekly climate (maximum
and minimum temperature,
maximum and minimum
relative humidity) data
Local meteorological
station data
Poisson regression,
Principal component
anlaysis
For every 210 degrees of
maximum and minimum
temperature variation, an
increase of cases of 22-184%
and 26-230% respectively, was
observed.
Temperature was the best
predictor for the dengue
increase in Singapore.
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Table 1 Studies included in the review of climate impact on dengue (Continued)
Gharbi et al. [36] French West
Indies
(20002007)
Weekly laboratory
confirmed cases
from hospitals or
not
Weekly climate (cumulative
rainfall, relative humidity,
minimum, maximum and
average temperature) data
Local meteorological
station data
Time series (SARIMA),
RMSE and Wilcoxon
signed-ranks test
Temperature was significantly
associated with dengue
forecasting but not humidity.
Temperature improves
dengue outbreaks better
than humidity and rainfall.
Hu et al. [42] Australia
(19932005)
Monthly confirmed
cases of notified DF
cases
Monthly SOI, rainfall and
annual population
Local meteorological
station data
Cross-correlations,
Time series (SARIMA)
A decrease in the SOI was
significantly associated with an
increase in the dengue cases.
Climate variability is directly
and/or indirectly associated
with dengue. SOI based
epidemic forecasting is
possible.
Johansson et al.
[48]
Puerto Rico,
Mexico, Thailand
(19862006)
Monthly reported
cases of DF/ DHF
Monthly climate (precipitation,
minimum, maximum and
mean average temperature)
data and ENSO indices
Global climate
surfaces (0.5 × 0.5°)
local meteorological
station data
Wavelet time series Temperature, rainfall and dengue
incidence were strongly
associated in all three countries
for the annual cycle. The
associations with ENSO varied
between countries in the
multi-annual cycle.
The role of ENSO may be
obscured by local climate
heterogeneity, insufficient
data, randomly coincident
outbreaks, and other,
potentially stronger, intrinsic
factors regulating dengue
transmission dynamics.
Bambrick et al.
[44]
Australia
(19912007)
Annual incidence
notified cases of DF
Annual Temperature, vapour
pressure and population
Local meteorological
station data
Climate change
scenarios
Geographic regions with
climates that are favourable
to dengue could expand to
include large population centres
in a number of currently
dengue-free regions.
An eight-fold increase in
the number of people living
in dengue prone regions in
Australia will occur unless
greenhouses gas es are
reduced.
Bulto et al. [46] Cuba (19611990) Dengue-specific
parameters of
DF/ DHF
Monthly climate (maximum
and minimum temperature,
precipitation, atmospheric
pressure, vapour pressure,
relative humidity, thermal
oscillation and solar radiation)
data
Local meteorological
station data
Multivariate (Empiric
orthogonal function)
Strong associations between
climate anomalies and dengue
Climate variability has
influence on dengue.
Cazelles et al. [40] Thailand
(19831997)
Monthly confirmed
cases of DHF
Monthly climate (temperature
and rainfall) data and ENSO
indices
Local meteorological
station data
Wavelet time series Strong association between
dengue incidence and El-Nino
events was observed.
Temperature had greater
influence on dengue than
rainfall.
The association is
non-stationary and have
a major influence on the
synchrony of dengue
epidemics.
Hales et al. [33] Global
(19751996)
Monthly reported
cases of DF
Monthly climate (maximum,
minimum and mean
temperature, rainfall and
vapour pressure) and
population and projections
(future climate and
population) data
Region-specific and
GCM projections
Logistic regression
and IPCC scenarios
In 2085, under climate and
population projections, 50-60%
of the population would be at
dengue risk.
There is a potential increase
in the dengue risk areas
under climate change
scenarios, if the risk factors
remain constant.
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Table 1 Studies included in the review of climate impact on dengue (Continued)
Patz et al. [38] Global
(19311980)
Dengue-specific
parameters
Monthly climate data Site-specific GCM GCM output to
vectorial capacity
Among the three GCMs, the
average projected temperature
elevation was 1.16°C, expected
by the year 2050.
Epidemic potential increased
with a relatively small
temperature rise, indicating
that lower mosquitoes
infestation values would be
necessary to maintain or
spread dengue in a
vulnerable population.
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Linear regression models
Colon-Gonzalez et al. [41] used multiple linear regression
models to examine the associations between changes in
the climate variability and dengue incidence in the warm
and humid regions of Mexico for the years 19852007.
Their results showed that the incidence was higher dur-
ing El- Niño events and in the warm and wet season.
Their study demonstrated that dengue incidence was
positively associated with the strength of El-Niño and the
minimum temperature, especially during the cool and dry
season.
Time series/wavelet time series models
Time series modelling approaches have been extensively
applied in assessing the impact of climate variables on
dengue incidence. For example, Gharbi et al. [36] fit-
ted a seasonal autoregressive integrated moving aver-
age (SARIMA) model of dengue incidence and climate
variables including temperature, rainfall and relative
humidity in French West Indies for the period 2000
2006. They found that temperature significantly improved
the ability of the model to forecast dengue incidence
but this was not so for humidity and rainfall. They also
found that minimum temperature at 5 weeks lag time
was the best climatic variable for predicting dengue
outbreaks. Similarly, Hu et al. [42] used a time series
SARIMA model to examine the impact of El-Niño on
dengue in Queensland, Australia for the period 1993
2005. They suggested that a lower Southern Oscillation
Index (SOI) was related to increased dengue cases.
Wavelet time series analysis has been applied to exam-
ine the associations between El-Niño Southern Oscillation
(ENSO), local weather, and dengue incidence in Puerto
Rico, Mexico, and Thailand [34] particularly, with the aim
of identifying time- and frequency-specific associations. In
all three countries, temperature, rainfall, and dengue inci-
dence were strongly associated on an annual scale. On a
multiyear scale, ENSO was associated with temperature
and with dengue incidence in Puerto Rico, but only for
part of the study period. Only local rainfall was associated
with the incidence of dengue in that country. The lack of a
direct association between ENSO and weather variables to
dengue incidence suggests that the ENSO-dengue associ-
ation may be a spurious result. In Thailand, ENSO was as-
sociated with both temperature and rainfall, and rainfall
was associated with dengue incidence. However, detailed
analysis suggested that this latter association was also
probably spurious. The authors concluded that there was
no significant association between any of the variables in
Mexico on the multiyear scale. In another study, Cazelles
et al. [40] used a wavelet time series analysis to demon-
strate a strong non-stationary association between dengue
incidence and El-Niño in Thailand for the years 1986
to 1992. They suggested that under certain conditions,
interannual variation in local or regional climate linked to
El-Niño may determine the temporal and spatial dynamics
of dengue. Thai et al. [50] used a wavelet time series ana-
lysis to investigate the associations between climate vari-
ables including mean temperature, humidity and rainfall,
and ENSO indices and dengue incidence in Vietnam
during the period 1994 to 2009. Their results showed
that the ENSO indices and climate variables were sig-
nificantly associated with dengue incidence in the 2 to
3-year periodic band, although the associations were
transient in time. Chowell [45] used wavelet time series
analysis to determine the relationship between climatic
factors including mean, maximum and minimum tem-
perature and rainfall and dengue incidence for the period
19942008 in jungle and coastal regions of Peru. They
revealed that incidence was highly associated with sea-
sonal temperature and suggested that dengue was fre-
quently imported into coastal regions through infective
sparks from endemic jungle areas and/or cities of other
neighbouring endemic countries.
Poisson regression models
Poisson regression models have been applied in deter-
mining the relationship between climate and dengue.
For example, Earnest et al. [32] used this approach
to determine the association between climate variables
(temperature, humidity, rainfall), ENSO indices and den-
gue in Singapore. They found that temperature, relative
humidity and ENSO were significantly and independently
associated with dengue cases. No one set of climate vari-
ables was superior to the others, so they suggested that
all the climate variables had a similar predictive ability.
Pinto et al. [9] used Poisson regression model to deter-
mine the impact of climate variables (temperature, rain-
fall and relative humidity) on dengue cases in Singapore.
They found that for every 2-10°C of variation of the
maximum temperature, dengue cases were increased by
22.2-184.6%. For the minimum temperature, for the same
variation, they observed that there was an average in-
crease of 26.1-230.3% in the number of the dengue
cases from April to August. Their study concluded that
the variable temperature (maximum and minimum) was
the best predictor for the increased number of dengue
cases.
Chen et al. [49] applied Poisson regression using a
GAM model to examine the relationship between pre-
cipitation and dengue in Taiwan for the period 1994
2008. The GAM allows a Poisson regression to be fit as
a sum of nonparametric smooth functions of predictor
variables. They found that differential lag effects follow-
ing precipitation were statistically associated with in-
creased risk of dengue. Poisson regression, using a GAM
model was used to evaluate the multiple-lag effects of
stratified precipitation levels on specific diseases. All
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models were adjusted for the multiple-lag effects of daily
temperature, month, and township for evaluating the as-
sociations between categorized extreme precipitation and
diseases, with further trend tests performed to examine
linear associations between levels of precipitation and
outbreaks of each disease.
Bayesian models
Bayesian spatial conditional autoregressive modelling
approaches have been used to demonstrate the impact
of climatic, social and ecological factors on dengue in
Queensland, Australia [43]. The authors suggested that
6% increase in locally acquired dengue was observed in
association with a 1-mm increase in average monthly
rainfall and a 1°C increase in average monthly maximum
temperature. They also reported that overseas-acquired
dengue cases were increased by 1% in association with a
1-mm increase in average monthly rainfall and a 1-unit
increase in average socioeconomic index, respectively.
Non-linear models
Descloux et al. [31] developed an early warning system
using a long-term data set (39 years) including dengue
cases and meteorological data (mean temp, min and
max temperature, relative humidity, precipitation and
ENSO indices) in New Caledonia, using multivariate
non-linear models. They observed a strong seasonality
of dengue epidemics and found significant inter-annual
correlations between epidemics and temperature, pre-
cipitation and relative humidity. Bulto et al. [46] applied
empiric orthogonal function (EOF) to estimate the asso-
ciation between climate data including monthly max-
imum and minimum mean temperatures, precipitation,
atmospheric pressure, vapour pressure, relative humid-
ity, thermal oscillation, days with precipitation, solar
radiation in, and isolation and dengue in Cuba during
the period 19611990. The EOF is designed to obtain
the dominant variability patterns from sets of fields of
any type, synthetic indicators or indexes, and summar-
ise the variability observed in a group of variables. Cli-
matic anomalies included were multivariate ENSO index,
quasi-biennial oscillation and North Atlantic Oscillation.
They found a strong association between climate anom-
alies and dengue which demonstrated significant climate
variability.
In summary, the quantitative models employed for
evaluating the relationship between climate variables and
dengue have been typically different with respect to the
distributional assumptions (e.g., normal, Poisson), the na-
ture of the relationship (linear and non-linear) and the
spatial and/or temporal dynamics of the response. Overall,
the models consistently reveal variability in the relationship
between dengue and climate variables, related to country,
but the methods identified an association with temperature
(except for [49]) followed by rainfall in the majority of
research.
Projections of climate change impacts on dengue
Patz et al. [38] examined the potential risk posed by global
climate change on dengue transmission. They used vector-
ial capacity equation that was modified to estimate the epi-
demics of dengue. The model used project climate change
data from global circulation model (GCM) at 250 km x
250 km resolution project future risk of dengue globally.
Their findings suggest that increased incidences have pre-
dominantly occurred in regions bordering endemic zones
in latitude or altitude. They found that epidemic activity
increased with a small rise in temperature, indicating
that fewer mosquitoes would be necessary to maintain or
spread dengue in a population at risk of dengue. They con-
cluded that transmission may be saturated in hyper-
endemic tropical regions and human migration patterns of
susceptible individuals are likely to be more important to
overall transmission than are climatic factors. Endemic lo-
cations may be at higher risk from dengue if transmission
intensity increases. Hales et al. [33] estimated the changes
in the geographical limits of dengue transmission from
1975 to 1996 and the size of population at risk, using logis-
tic regression. Monthly averages of vapour pressure, rain-
fall, and temperature recorded between 1961 and 1990,
with or without statistical interaction terms between vari-
ables were included in their statistical models. Based on
data from GCM projections and human demographics,
they predicted that dengue would increase and include a
larger total population and higher percent of the popula-
tion. Although their studies suggested that the dengue dis-
tribution was climate dependent, other factors needed to
be considered in addition to climate during epidemics.
Bambrick et al. [44] highlighted the potential for cli-
mate change to affect the safety and supply of blood glo-
bally through its impact on vector-borne disease, using
the example of dengue in Australia as a case-study. They
modelled geographic regions that were suitable for den-
gue transmission over the coming century under four
climate change scenarios, estimated changes to the pop-
ulation at risk and effect on blood supply. They applied
logistic regression models using climate change scenar-
ios to the observed geographic distribution of dengue in
Australia. The most important predictor variables in the
models were vapour pressure and temperature. Their re-
sults indicated that geographic regions with climates that
are favourable to dengue transmission could expand to
include large population centres in a number of cur-
rently dengue-free regions in Australia.
Methodological issues
Several methodological issues emerged when we con-
ducted this literature review on climate change and
Naish et al. BMC Infectious Diseases 2014, 14:167 Page 8 of 14
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dengue. These issues include the study design, analytical
model, time period, scale of analysis, exposure variables
and other factors associated with dengue transmission as
illustrated below.
Study designs
Several study designs have been considered by various
authors while studying the relationships between climate
and dengue (Table 1). For example, Hales et al. [47] used
a mixed ecological study design and long-term data to
determine the relationship between the annual number
of dengue cases, ENSO, temperature and rainfall using
global atmospheric analyses climate-based data. Descloux
et al. [31] used long-term observational data and examined
inter-annual correlations between ENSO, local climate
and dengue.
Analytical models
Time series modelling approaches have been applied to
estimate the baseline relationships between climate and
dengue [9,36,40,42,50,51] (Table 1). SARIMA models are
potentially useful when there are time dependences be-
tween each observation [36,52]. The assumption that
each observation is correlated to previous ones makes it
possible to model a temporal structure, with more reli-
able predictions, especially for climate-sensitive diseases
(e.g., mosquito-borne diseases), than those obtained by
other statistical methods. SARIMA models have been
successfully used in epidemiology to predict the evolu-
tion of infectious diseases. Moreover, these models allow
the integration of external factors, such as climatic vari-
ables, that may increase the predictive power and ro-
bustness of predictive models due to longer time periods
of data.
Some studies have used Fourier analysis to analyse rela-
tionships between oscillating time series. This technique
decomposes time series into their periodic components
that can then be compared between time series. Since
Fourier analysis cannot take into account temporal
changes in the periodic behaviour of time series (i.e. non-
stationarity), this method, and others such as generalized
linear models, may be inadequate for investigating the de-
terminants of transmission dynamics of dengue [53].
Wavelet analysis is suitable for investigating time
series data from non-stationary systems and for infer-
ring associations between such systems [53]. This ap-
proach reveals how the different scales (i.e. the periodic
components) of the time series change over time. Wavelet
analysis is able to measure associations between two time
series at any period. Wavelet analyses have been used
to compare time series of disease incidence across lo-
calities and countries for the characterisation of the
evolution of epidemic periodicity and the identification of
synchrony. Wavelet analyses have been used in analysing
dengue [34,40,50]. Although annual periodic patterns
are a common phenomenon in dengue endemic areas,
the identification of a periodic multi-annual (e.g., 2 to
3 years) cycle differs between countries as well as in ana-
lyses used. Cazelles et al. [40] used wavelet approaches to
demonstrateahighlysignificantbutdiscontinuousassoci-
ation between ENSO, precipitation and dengue epidemics
in Thailand.
A continuous annual mode of oscillation with a non-
stationary 2 to 3-year multi-annual cycle was found with
strong irregular associations between dengue incidence
and ENSO indices and climate variables in Vietnam [50].
Although these wavelet analyses have provided important
contribution to the cyclical dynamics of dengue transmis-
sion, the associations with ENSO have been irregular and
temporary, which reduce the potential for estimating fu-
ture predictions based on these climate anomalies.
Time period
Choosing a baseline time period for climate data is also
important. Climate and dengue relationships in the same
city can be very different between the 1960s and the
2010s. Differences could be due to socioeconomic and,
demographic changes and urbanisation. Differences in
the time periods used to estimate the historical climate
and dengue relationships also make it difficult to com-
pare projections across studies. Therefore, it has been
recommended that long-term (generally, at least many
decades) baseline climate data be used for modelling
climate-based diseases to calculate an average that is not
influenced by climate variability [54,55].
Spatial and temporal scales
Another issue to be considered when modelling is the
spatio-temporal scale of analysis. This is because spatial
and temporal characteristics may provide useful infor-
mation on risk assessments to be used by local or national
dengue prevention and control programs to prepare for
and respond to dengue epidemics in endemic settings.
Dengue may be sensitive to differences in climatic con-
ditions at a local, regional or global level. At the global
level, there is the potential for climate-related spread of
dengue. There are areas that are currently only at risk
and not endemic for dengue, but may become endemic
as climate changes, especially related to temperature
change. For example, Patz et al. [38] used a process-
based model considering an entomological variable, i.e.,
the vectorial capacity (VC). They included temperature
effects over VC and predicted the potential global den-
gue spread using climate change scenarios for 2050.
Therefore, we suggest to develop models at a local and/or
regional scale that can increase their predictive capacity
[56] and incorporate important local factors that affect dis-
ease transmission [54].
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Climate variables
The choice of climate variables in the modelling is
an important issue to consider. The following climate
variables have been considered in the studies: max-
imum, minimum and mean temperature, rainfall, relative
humidity, ENSO indices and sunshine. Among these,
temperature and ENSO indices have been found to be
important in any study. Authors have identified that
other variables such as river levels are also included along
with climate variables [57], however, these studies are out
of the study scope. Hales et al. [33] developed an empir-
ical model fitting logistic regression and modelled dengue
outbreak (presence/absence) considering climate baseline
data for the period 19611990. They found that the
vapour pressure was the only explanatory climate vari-
able responsible for global dengue transmission. Using
this model, applying climate projections for 2085 from a
GCM, Hales et al. predicted a limited extension of den-
gue. In both the projection studies, climate projections
were based on historical exposure-response functions of
climate variables and dengue incidence that are applied
to climate change models and emissions scenarios to esti-
mate and predict future dengue distribution. Therefore,
it is important to consider which climate variables are
the best predictors of dengue transmission and the re-
search reported here indicates that temperature is con-
sistently important but that vapour pressure or relative
humidity are also significant contributors.
Other factors associated with dengue transmission
There are other environmental, socio-economic and demo-
graphic factors associated with dengue transmission and
the relative contribution of these factors may differ be-
tween settings (scales, countries, regions). These include,
but are not limited to attenuators (e.g., mosquito manage-
ment, screening dwellings, using personal insect repellents
and bed nets) or exacerbators (e.g., actively storing of water
in open containers, passively allowing water to remain in
bins, garden accoutrements) [58]. These are outside the
scope of our current review. However, socio-demographic
change is important and we have briefly considered this
below.
Associations between socio-demographic changes
and dengue
Other etiologic factors that impact dengue transmission
include social and demographic changes, economic sta-
tus, human behaviour and education [7,43,59-61]. Reiter
et al. [62] evidenced that dengue virus transmission was
limited by human life style in Texas. Other important
human related factors such as exceptional population
growth, unplanned urbanisation and air travel needs to be
considered in modelling studies [63]. Other factors driven
by economic growth such as animals and commodities
should also be considered. For example, Gubler et al. [63]
claimed that a dramatic urban growth has occurred in the
past 40 years providing the suitable ecological conditions
for Ae. aegypti to increase in close association with large
and crowded human populations in tropical areas, creating
ideal conditions for dengue transmission.
Dengue-infected human movement should be consid-
ered as another important factor [64] since Aedes gen-
erally has a short flight range so is unlikely to spread
dengue over large distances. For example, Rabbai et al.
[65] have demonstrated dengue viral exchange between
the urban areas of Ho Chi Minh City and other prov-
inces of southern Vietnam and suggested that human
movementbetweenurbanandruralareasmayplayakey
role in the transmission of dengue virus across southern
Vietnam.
Challenges
The fundamental challenge for predicting dengue trans-
mission is how to best model future climate at a regional
and/or local level. In another words, how can we appro-
priately downscale the GCM modelling outcomes to a
regional and/or local level? The Intergovernmental Panel
on Climate Change (IPCC) has developed 40 Special
Reports on Emissions Scenarios (SRES) covering a wide
range of main driving forces of future green house gas
emissions [26]. These scenarios were categorised into
four classes: A1, A2, B1 and B2. A1 characterises rapid
economic growth, population growth by 2050, introduc-
tion of new and efficient technologies. A2 characterises
high population growth, slow economic development
and technological changes. B1 characterises the similar
population growth like A1 but with rapid economic
changes. B2 characterises medium population and eco-
nomicgrowthwithlocalised solutions to economic,
social and environmental sustainability. These scenar-
ios can be used to project future climate based on GCM
models [26].
Selecting climate models is not a trivial task, con-
sidering the strengths and limitations of various GCM
models. The IPCC recommended that no single GCM
can be considered the best and that various GCMs
should be applied to account for climate modelling un-
certainties [26]. These do not necessarily indicate errors
in the modelling and should therefore not be used to
conclude that a model is inaccurate. As the global tem-
perature increases, tropical insects may spread their habi-
tats into more northern or southern latitudes which can
result in higher transmission. This intriguing idea was
first suggested by Shope [12] and was considered further
by other researchers [15,66]. However, there is still de-
bate whether the increased pattern of dengue arose from
climate change [16] or from socio-economic changes
in combination with ecologic and demographic changes
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[63,67]. Finally, other etiological factors must ultimately
be incorporated into integrated modelling to determine
human risk to dengue [68].
Discussion
Climatic factors play a significant role in the mosquito
biology, the viruses they transmit, and more broadly,
dengue transmission cycles. Higher temperatures in-
crease the rate of larval development and shorten the
emergence of adult mosquitoes, increase the biting rate
of mosquito and reduce the time required for virus rep-
lication within the mosquito. Extreme higher tempera-
tures may reduce mosquito survival time, which could
offset the positive effect on mosquito abundance [69].
Evidence had accrued to show the link between tem-
perature and dengue incidence rates [9,34,36,47]. These
studies have used a range of statistical approaches con-
sidering different temperature parameters (e.g. mean,
maximum and minimum temperatures), and the results
are generally consistent, indicating that the epidemics of
dengue are driven by climate to some extent.
Relative humidity is another key factor that influences
mosquitoeslife cycle at different stages. The combined
effect of temperature and humidity significantly influ-
ences the number of blood meals and can also affect the
survival rate of the vector, and the probability that it will
become infected and able to transmit dengue [70]. In
the literature, relative humidity and temperature are the
two most important variables with potential impact
on dengue transmission. Vapour pressure or relative
humidity is affected by a combination of rainfall and
temperature and influences the mosquito lifespan and
thus the potential for transmission of the virus. Hales et al.
found that annual average vapour pressure was the most
important climatic predictor of global dengue occurrence
[33].Therefore,temperature,rainfallandrelativehu-
midity are important determinants of the geographic
limits within which dengue transmission can be expected
to continue, primarily through their effects on the Aedes
vector. Furthermore, within areas where minimum thresh-
olds of these climate parameters are sufficient to maintain
dengue transmission, seasonal fluctuations in these pa-
rameters will be important determinants of the duration
and potentially the intensity of transmission.
Several studies have shown that the increased temper-
atures and relative humidity are determining factors in
predicting changes of the dengue transmission [31,32].
Contrasting, rainfall did not appear to play a significant
role because many breeding sites of Ae. aegypti were
more dependent on human behaviour than on rainfall
for their development and survival [71-73]. This might
partly explain the lower impact of rainfall compared
to other climatic variables on the dengue incidence.
However, Chaves et al. [74] suggested that a series of
rainfall followed by low/ lack of rainfall could intimi-
date sharp decrease in mosquito populations, for example,
in Puerto Rico.
Dengue transmission in endemic settings is charac-
terised by non-linear dynamics, with strong seasonality,
multi-annual oscillations and non-stationary temporal
variations. Seasonal and multi-annual cycles in dengue
incidence vary over time and space. Besides the sea-
sonality of dengue transmission, periodic epidemics and
more irregular intervals of outbreaks are commonly
observed [34,75-77].
Evidence suggests that inter-annual and seasonal
climate variability have a direct influence on the transmis-
sion of dengue [17,32,34,41,78,79]. This evidence has been
assessed at the country level in order to determine the pos-
sible consequences of the expected future climate change
[33,38]. These studies have highlighted that many climatic
variables play a key role in dengue transmission as dis-
cussed above.
Many studies have highlighted the importance of lag
time, at monthly scales. For example, in Taiwan, there
was a significant positive correlation with the maximum
temperature at lag 14 months, the minimum tem-
perature at lag 13 months and the relative humidity at
lag 13 months [49]. In New Caledonia, significant asso-
ciations were found between temperature and dengue
transmission at lag-0 [31].
The delayed effect (or time lag) of climatic variables on
dengue incidence could be explained by climatic factors
which do not directly influence the incidence of dengue
but do so indirectly. This is through their effect on the life-
cycle dynamics of both vector and virus. This starts with
mosquito hatching, larval and pupal development, adult
emergence and virus amplification, incubation in humans
culminating in a dengue outbreak and results in a cumula-
tive time lag [36,70]. Depending on the respective lag be-
tween the biological cycle or mosquito life-stage and the
clinical symptoms, the lag between climate data and inci-
dence data will differ. The lag is expected to be shorter for
minimum temperatures that are usually associated with
adult mosquitos mortality, longer for high relative humid-
ity, both related to adult survival and hatching. On the
other side, the mean temperature is involved in all bio-
logical cycles of Ae. aegypti that take more time to influ-
ence the dengue incidence [5,36,78].
Strengths and limitations of studies
Several statistical analytical methods have been used
to determine the relationship between climate variables
(and climate change) and dengue, including cross corre-
lations, Poisson, logistic and multivariate regression,
SARIMA-time series and wavelet time series (Table 1).
Many have been successful [31,34,41,49,50] in establishing
climate and dengue relationships and developing predictive
Naish et al. BMC Infectious Diseases 2014, 14:167 Page 11 of 14
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models of dengue based on climate relationships. Mini-
mum, maximum and mean temperatures, relative humidity
and rainfall were the most important climate variables that
predict the dengue. However, these variables are predictive
at specific lags of time.
Remarkably, due to data constraints, modelling ana-
lyses were conducted using aggregated data over large
spatial scales or long time periods [34]. Studies based on
long time scales and large geographic areas may be un-
successful in describing the influences that happen over
daily or weekly periods and climate changes that occur
at country level [45].
In general, the predictive power and model robustness
would be better improved with large data over longer
periods. For example, Gharbi et al. [36] developed statis-
tical predictive models that were build-up on <10 years
of data and could only be validated over a 1-year period.
Hence, it is difficult to say whether the relationships they
found will restrain in time.
The dengue cases/incidence reported could be over-
and under reported. These may change over time and
geographical area. Additionally, the reported dengue cases
may be influenced by case definitions, availability of public
health systems and subclinical cases documented.
Therefore, it is important to consider all these factors
before identifying the relationships between climate and
dengue disease transmission. Patz et al. [38] provided a
good framework for future research on climate change
and dengue transmission. However, these estimates
should be updated based on better improved resolution
with current GCM projections.
Recommendations for future research
We recommend the following five directions for future
research: 1) Disease surveillance need to be improved
for effective dengue prevention and control programs.
The new approach to surveillance lays emphasis on the
inter-epidemic period, as information on the dengue en-
demicity is important in predicting dengue epidemics.
The surveillance of active dengue virus activity during
inter-epidemic periods provides information on preva-
lent virus serotypes in the area. Any subsequent intro-
duction of another serotype would then require control
measures to prevent the increase in transmission of the
virus, thereby helping in containing the impending epi-
demic and reducing the incidence of dengue. 2) Better un-
derstanding of dengue ecology is required to predict the
climate-biological relationships on dengue transmission.
3) Application of advanced spatio-temporal modelling ap-
proaches in dengue research is required to more fully
understand the complex relationship between climate and
dengue and thereby obtain better prediction. Moreover,
these approaches, or at least the outcomes of these models
need to be better integrated. 4) Uncertainties due to
confounding effects of urbanisation, population growth
and tourism development are required to develop scenar-
ios based on future projections of population growth and
socio-economic development, including human behaviour.
5) There is a clear need for inter-disciplinary collabora-
tions with ecologists, sociologists, micro-biologists, bio-
statisticians and epidemiologists. Two areas need special
attention: one is in the area of climate modelling to ad-
dress issues of spatial and temporal scale and analytical
methods, and the other relates to dengue incidence data
quality control with the reporting agency (e.g., laboratories,
hospitals, health centres) addressing issues such as under-
reporting and misdiagnosis, dengue case definition, clinical
or lab-confirmed diagnosis, inpatient and outs report-
ing, specific ages and/or dengue severity reported. From
an analytical and modelling perspective, analyses need to
be able to consider localised long-term time series demo-
graphic, socio-economic and environmental conditions.
Finally, we recommend that caution should be taken
when estimating the relationships between climate vari-
ables (and climate change) and dengue in the following as-
pects: use of time lags, the analysis of extreme climatic
events, the differences between seasonal and long term
trends, nonlinear effects and threshold effects in the associ-
ations. In addition, more emphasis should be given to data
quality and the use of information for decision-making.
Conclusion
The weight of evidence about climate change impacts on
dengue indicates that dengue transmission is sensitive to
climate variability and change. We believe that it is import-
ant to develop, employ and integrate different quantitative
modelling approaches for dengue transmission compatible
with long-term data on climate and other socio-ecological
changes and this would advance projections of the impact
of climate on dengue transmission.
Competing interests
The authors declare that they have no competing interests.
Authorscontributions
SN participated in data extraction, analysis and drafted the manuscript. PD,
JM, JB, KM and ST critically revised the manuscript. All authors read and
approved the manuscript.
Acknowledgements
Authors would like to acknowledge ARC grant (#DP 110 100 651).
Author details
1
School of Public Health and Social Work & Institute of Health and
Biomedical Innovation, Queensland University of Technology, Victoria Park
Road, Brisbane, Queensland, Australia.
2
Environmental Futures Centre,
Australian Rivers Institute, Griffith School of Environment Griffith University,
Brisbane, Queensland, Australia.
3
Faculty of Health Sciences, Curtin University,
Perth, Australia.
4
School of Medicine and Dentistry, James Cook University,
Cairns, Queensland, Australia.
5
Mathematical Sciences, Statistical Science,
Queensland University of Technology, George Street, Brisbane, Queensland,
Australia.
Naish et al. BMC Infectious Diseases 2014, 14:167 Page 12 of 14
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Received: 15 February 2013 Accepted: 20 March 2014
Published: 26 March 2014
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doi:10.1186/1471-2334-14-167
Cite this article as: Naish et al.:Climate change and dengue: a critical
and systematic review of quantitative modelling approaches. BMC
Infectious Diseases 2014 14:167.
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