Research

All EHP content is accessible to individuals with disabilities. A fully accessible (Section 508–compliant)
HTML version of this article is available at http://dx.doi.org/10.1289/ehp.1307799.

Estimated Effects of Projected Climate Change on the Basic Reproductive
Number of the Lyme Disease Vector Ixodes scapularis
Nicholas H. Ogden,1* Milka Radojević,1* Xiaotian Wu,2,3* Venkata R. Duvvuri,2 Patrick A. Leighton,4
and Jianhong Wu 2,3
1Zoonoses Division, Centre for Food-borne, Environmental and Zoonotic Infectious Diseases, Public Health Agency of Canada,
Saint-Hyacinthe, Quebec, Canada; 2Centre for Disease Modelling, York Institute of Health Research, Toronto, Ontario, Canada;
3Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada; 4Faculty of Veterinary Medicine, Université de
Montréal, Saint-Hyacinthe, Quebec, Canada

*These authors are equal first authors.

Background: The extent to which climate change may affect human health by increasing risk from
vector-borne diseases has been under considerable debate.
Objectives: We quantified potential effects of future climate change on the basic reproduction
number (R0) of the tick vector of Lyme disease, Ixodes scapularis, and explored their importance for
Lyme disease risk, and for vector-borne diseases in general.
Methods: We applied observed temperature data for North America and projected temperatures
using regional climate models to drive an I. scapularis population model to hindcast recent, and
project future, effects of climate warming on R0. Modeled R0 increases were compared with R0
ranges for pathogens and parasites associated with variations in key ecological and epidemiological
factors (obtained by literature review) to assess their epidemiological importance.
Results: R0 for I. scapularis in North America increased during the years 1971–2010 in spatiotemporal patterns consistent with observations. Increased temperatures due to projected climate
change increased R0 by factors (2–5 times in Canada and 1.5–2 times in the United States), comparable to observed ranges of R0 for pathogens and parasites due to variations in strains, geographic
locations, epidemics, host and vector densities, and control efforts.
Conclusions: Climate warming may have co-driven the emergence of Lyme disease in northeastern North America, and in the future may drive substantial disease spread into new geographic
regions and increase tick-borne disease risk where climate is currently suitable. Our findings highlight the potential for climate change to have profound effects on vectors and vector-borne diseases,
and the need to refocus efforts to understand these effects.
Citation: Ogden NH, Radojević M, Wu X, Duvvuri VR, Leighton PA, Wu J. 2014. Estimated
effects of projected climate change on the basic reproductive number of the Lyme disease vector
Ixodes scapularis. Environ Health Perspect 122:631–638;  http://dx.doi.org/10.1289/ehp.1307799

Introduction
Considerable attention has been devoted
to the possibility that climate change will
exacerbate the burden of mosquito-borne
diseases such as malaria and dengue, with
important impacts on public health (Githeko
et al. 2000). Early assessments of the effects of
climate change on malaria and dengue used
simplistic models to assess possible effects of
climate change on their basic reproductive
numbers (R0, the universally recognized metric of the capacity of a parasite or pathogen
to reproduce given particular environmental
conditions) (Martens et al. 1995; Patz et al.
1998). However, these assessments were criticized for giving weight to future increases in
R0 whether or not such increases resulted in
R0 rising above the critical threshold of > 1
for disease persistence (Rogers and Randolph
2000) and for being oversimplistic by only
accounting for climate effects rather than the
full range of nonclimatic factors that impact
the occurrence of these diseases (Reiter 2001;
Rogers and Randolph 2000). Any impact of
climate on R0 of malaria and dengue is limited by the effects of variations in human host
density, mosquito control, infection prevention and treatment in humans, and human

management of the environment (e.g., agriculture, forest management, logging) that
affect the ecology and epidemiology of the
vectors, pathogens, and diseases (Githeko
et al. 2012). Consequently, the strength of
evidence for recent climate warming effects
on malaria risk has been questioned and
much debated (Reiter et al. 2004; Tanser
et al. 2003).
Many vector-borne diseases of public
health significance (e.g., Lyme disease, West
Nile virus) are, however, maintained in transmission cycles that involve wild animal hosts.
These cycles are independent of human cases,
and the spatio-temporal risk of human disease is less dependent on the direct effects
of human activities than is the risk from
malaria and dengue. Nevertheless, despite
some assessments (Gubler et al. 2001), the
effects of climate change on vector-borne
zoonoses have also been downplayed mostly
on the basis of limited evidence for recent
effects of climate change (Kilpatrick and
Randolph 2012). Lyme disease emerged (or
likely reemerged) in the northeastern United
States in the late 1970s due to the expansion of tick populations, which was generally
thought to have been associated with changes

Environmental Health Perspectives  •  volume 122   number 6   June 2014 

in land use over some decades that resulted in
reforestation and expansion of the population
of the deer that are key hosts for the ticks
(Wood and Lafferty 2013). Lyme disease is
now emerging in Canada and some northern
U.S. states due to the northward expansion of
the geographic range of the tick vector Ixodes
scapularis (I. scapularis) (Hamer et al. 2010;
Ogden et al. 2009), which is dispersed from
source populations by migratory birds and
terrestrial hosts (Leighton et al. 2012).
A mechanistic simulation model of the
I. scapularis life cycle has identified temperature effects on I. scapularis population
survival in order to assist in assessment of
current and future on-the-ground Lyme
disease risk in Canada (Ogden et al. 2005,
2006b). Prospective field studies and retrospective analyses of surveillance data on
tick and pathogen emergence in southeastern Canada validated the model findings and
identified temperature as a statistically significant determinant and possible driver of emergence of the tick in Canada (Bouchard et al.
2013a, 2013b; Leighton et al. 2012; Ogden
et al. 2008, 2010). The I. scapularis model
Address correspondence to N.H. Ogden, Centre for
Food-borne, Environmental and Zoonotic Infectious
Diseases, Public Health Agency of Canada, 3200
Sicotte, C.P. 5000, Saint-Hyacinthe, Quebec, J2S
7C6 Canada. Telephone: +1 450-773-8521, ext.
8643. E-mail: nicholas.ogden@phac-aspc.gc.ca
Supplemental Material is available online (http://
dx.doi.org/10.1289/ehp.1307799).
We thank the Canadian Centre for Climate
Modelling and Analysis, Environment Canada,
and the Meteorological Service of Canada, Quebec
region for providing Canadian Global Climate
Model CGCM3.1 data and for allowing us to
use their servers for computational purposes. We
also thank the Ouranos Consortium (Climate
Simulation team) for Canadian Regional Climate
Model CRCM4.2.3 data, and the North American
Regional Climate Change Assessment Program for
WRF (Weather Research and Forecasting Model)
and MM5I (Fifth-Generation Penn State/NCAR
Mesoscale Model) data.
This work was funded by the Public Health Agency
of Canada, the Natural Sciences and Engineering
Research Council of Canada, and the Canada
Research Chairs Program.
The authors declare they have no actual or potential
competing financial interests.
Received: 24 October 2013; Accepted: 10 March
2014; Advance Publication: 14 March 2014; Final
Publication: 1 June 2014.

631

 Ogden et al.

was modified to permit the direct calculation of R0 for I. scapularis via the next generation operator approach (Wu et al. 2013),
which, given the universal use of R0 and its
estimation for a wide range of parasites and
pathogens under many different conditions,
allowed comparison of R0 variations in the
present study with observed variations for
other parasites and pathogens.
Here, we have estimated projected effects
of climate change on R0 of an arthropod vector using a model that has been extensively
ground-truthed, and we have assessed the ecological and epidemiological significance of the
projected changes in R0 by comparing them
with ranges of R0 values observed for other
parasites and pathogens.

Methods
We estimated R0 under current and future
projected climatic conditions at 30 sites in
Canada that formed two roughly south–
north transects in Ontario and Quebec, two
Canadian provinces where I. scapularis ticks
are becoming established. These transects
were chosen to capture the climate variability that exists in the region. For simplicity
in data presentation, the sites were grouped
into clusters [Southern Ontario, Huron
Ontario, Upper Southern Ontario, SouthWestern Quebec, and the Boreal region
(see Supplemental Material, Table S1 and
Figure S1)] according to geographic proximity and similarity in temperature conditions (see Supplemental Material, “Variation
in temperature and R 0 amongst sites,”
pp. 2–6, Figures S2–S4), and mean values
for clusters are presented. We also estimated
R0 for two sites in the United States where
Lyme disease is endemic in the Northeast
and upper Midwest, respectively: Old Lyme
(Connecticut), where the human Lyme disease cases were first recognized (Wood and
Lafferty 2013), and Fort McCoy (Wisconsin)
(Anderson et al. 1987).
Modeling R0. The I. scapularis model is a
deterministic model consisting of 12 ordinary
differential equations as described by Wu et al.
(2013), based on the mechanistic simulation
model described by Ogden et al. (2005). This
model captures the effects of temperature on
host-seeking activity and the rates of development from one life stage to the next (effects
common to, but variable among, all arthropod
vectors), parame­terized from field and laboratory studies on I. scapularis. Mortality rates
of nonfeeding I. scapularis in Canada and the
northeastern United States are similar in summer and winter, presumably due to the insulating effects of the litter layer in woodland
habitats (Brunner et al. 2012; Lindsay et al.
1995). Our analyses operated on the hypothesis that the effects of ambient temperature on
I. scapularis population survival are indirect

632 

via effects on temperature-dependent rates of
development of ticks from one life stage to the
next. The lower the temperature, the longer is
the tick life cycle and, due to constant daily
per capita mortality, the fewer larval ticks survive to become mated adult female ticks. At
a threshold temperature, mortality outstrips
reproduction and the tick populations die
out (or fail to become established)—that is,
at this temperature, threshold R0 falls below
unity (Ogden et al. 2005). At the latitudes
under study here, effects of climate change
on I. scapularis are expected to be the effect
of climate warming on shortening the life
cycle, resulting in increasing R0 (Ogden et al.
2006b). Quadratic effects of temperature on
arthropod vector life history traits are common (Mordecai et al. 2013), and quadratic
effects of temperature on tick activity are
included in the model. High temperatures
may impact tick survival, causing a northward
contraction of the southern range of I. scapularis, resulting in a northward shift, rather
than overall expansion, of the geographic
range of climatic suitability for I. scapularis
(Brownstein et al. 2005). Here we confined
our study to Canada and the main regions of
Lyme disease risk in the United States north
of 40°N (Diuk-Wasser et al. 2012). Impacts of
rainfall on off-host tick survival and on hostseeking activity are considered accounted for
in the model in assuming a) tick populations
only become established in woodlands where
the micro­climate is suitable for tick survival,
and b) most temperate woodlands types occur
where rainfall is sufficient for I. scapularis
survival, which is supported by studies in
Canada (Lindsay et al. 1995). Future projections for increased precipitation across much
of Canada with climate warming are already
being seen to occur (Environment Canada
2013), so rainfall changes are not expected to
limit the northward spread of I. scapularis. For
the present study, we modified the simulation model of Ogden et al. (2005) in order to
calculate R0 by the next generation operator
as described by Wu et al. (2013). Apart from
the temperature values used to calculate tick
development and host-finding rates, the values
for host numbers (20 deer and 200 rodents)
and all other parameter values were those used
as starting values by Wu et al. (2013). R0 was
estimated using mean monthly temperature
data for each year and location as described
in the following sections. Variations in host
abundance affect the final size of the tick
population but not the temperature threshold (Ogden et al. 2005). The temperature
threshold would be affected by variations in
the mortality rates of ticks in the environment, and slight variations in this have been
observed in the field (Ogden et al. 2006a).
For the sensitivity analysis of R0 to variations
in model parameter values, see Supplemental
volume

Material, “Model sensitivity analysis,”
pp. 7–11, Table S2 and Figures S5 and S6.
Modeling R0 under current climate. For
observed temperatures, we used Australian
National University Splines (ANUSPLIN)
(Hutchinson et al. 2009) of 10-km gridded
daily time-series data, which were obtained
by thin-plate smoothing spline interpolation
of daily climate station observations while
accounting for latitude, longitude, and elevation. ANUSPLIN data cover the 40 years
(1971–2010) that encompass the period of
Lyme disease emergence in North America,
have coverage across northern North America,
and account for missing data by temporal and spatial interpolation. A mean daily
near-­surface temperature was assigned to
the 32 study sites, which were weather stations located over a wide range of the orographic and forest eco­systems of Ontario and
Quebec or interpolations of ANUSPLIN data
for locations in the United States. Monthly
mean near-surface air temperatures were used
to parameterize the I. scapularis population
model for estimating annual values of R0 for
each site, for each year from 1971 to 2010
(see Supplemental Material, “Variation in
temperature and R0 amongst sites,” pp. 2–6).
Values for annual cumulative degree days
> 0°C (DD > 0°C), contemporaneous for
each estimated R0 value, were computed as
the accumulation of daily temperature > 0°C
for each year for each site. The tick model
calculated R0 for each year using temperature
data for that year, but in reality R0 will depend
on the temperature conditions over the 2- to
3-year life cycle of the tick. Therefore, we used
the moving average of R0 over 3 years (that
year, the previous year, and the subsequent
year) to describe R0 for each year for each site;
so for each site, we obtained a time series for
R0 and DD > 0°C for 38 years (1972–2009)
and DD > 0°C for 40 years (1971–2010).
Modeling R 0 using projected climate
data. An ensemble of modeled temperature data available from three regional climate model (RCM) and two global climate
model (GCM) runs were used to estimate
future changes in R 0 (see Supplemental
Material, “Validation of climate model output,” pp. 11–14; Table S3). We extracted
time series of daily temperature for the 30
Canadian sites from each model. Simulated
temperature at a given site was defined as the
mean of the closest grid values to that site,
increasing confidence in the physical representativeness (Gachon and Dibike 2007).
We chose bias-corrected output from
Canadian RCM CRCM4.2.3, version 4
(Laprise et al. 1997; Music and Caya 2007)
to provide temperature data for the I. scapularis model because it relatively accurately
and conservatively hindcasted observed
ANUSPLIN data in comparison with other

122   number 6   June 2014  •  Environmental Health Perspectives

 Climate change and R0 of arthropod vectors

climate models and because it provided projected temperatures at a local scale. For full
details justifying the climate model selection,
see Supplemental Material, “Validation of
climate model output,”pp. 11–14, Figures
S7 and S8. Like other RCMs, CRCM4.2.3
dynamically downscales output from a coarser
resolution GCM and produces data at a horizontal resolution of approximately 50 km.
CRCM4.2.3 is driven by initial and boundary
conditions of the Canadian GCM CGCM3.1
T47 (McFarlane et al. 2005; Scinocca et al.
2008). Hindcasting up to 2000 used green­
house gas emissions for CGCM3.1 T47 as in
the Coupled Model Intercomparison Project
(CMIP) 20th century experiment (Meehl
et al. 2000). For future projections starting
in 2001, we chose the A2 scenario (midhigh Green-House-Gas emission scenario)
of the Intergovernmental Panel on Climate
Change Special Report on Emission Scenarios
(Nakicenovic and Swart 2000) because of the
availability of regional climate model output
using this scenario and because current actual
trajectory of emissions corresponded best to
this emissions scenario.
Mapping R0 under current and future climate. We mapped 30-year mean values of
R0 for I. scapularis using observed and projected values. Maps of DD > 0°C for North
America north of 40°N and east of the Rocky
Mountains were generated using observed
data for 1971–2000, and using DD > 0°C
projected by bias-corrected CRCM4.2.3
output for the period 2001–2070. We
then computed R0 for each year from the
gridded DD > 0°C data using the formula
R0 = 1.072 × 10–6 DD > 0°C–4.658 × 10–3
DD > 0°C2 + 5.556, obtained using observed
temperature data as described in Supplemental
Material, “Mapping R0,”pp. 15–16, Figure S9
(the threshold for R 0 ≥ 1 was DD  > 0°C
≥ 2859.6°C). We then mapped mean 30-year
values for R 0 for the periods 1971–2000,
2011–2040, and 2041–2070 (Figure 2).
Regions west of the Rocky Mountains were
masked because it was assumed that I. scapularis will not cross the Rocky Mountains, west
of which Lyme disease risk will continue to
depend on transmission of Borrelia burgdorferi
by the tick I. pacificus (Ogden et al. 2009).
Literature search on R0 ranges for parasite and pathogen systems. There are, to our
knowledge, no equivalent estimates of how
environmental changes may affect R0 of vectors. However, to better comprehend the
ecological or epidemiological importance of
projected changes of R0 of I. scapularis, we
performed a literature review to obtain published estimates of how R0 for parasites and
pathogens varies due to changes in factors
already recognized as having ecological or
epidemiological importance. These include
variations in geographic location, host density,

strain or genotype, disease control effort, and
variations among different epidemics. Articles
were searched in the National Centre for
Biotechnology Information PubMed search
site (http://www.ncbi.nlm.nih.gov/pubmed/)
using search terms a) “basic reproduction
number,” without specifying pathogens or
parasites, and then b) “basic reproductive
number,” repeated with one of the following terms: tick, mosquito, chagas, malaria,
dengue, nematode, “seasonal influenza,” “pandemic influenza”, pH1N1, “avian influenza,”
measles, HIV (human immunodeficiency
virus), and fluke. Abstracts were reviewed, and
relevant articles were reviewed in full. Relevant
articles were those in which R0 for parasites
and pathogens was calculated to explicitly
estimate its value under field, rather than
theoretical, conditions. This meant articles
that employed simulation models using field
data, fitting of epidemiological data (e.g., age–­
seroprevalence or age–­infection prevalence),
or other methods such as estimates from phylogenetic analysis. We did not review and use
R0 ranges obtained in model-based sensitivity analyses, variations in R0 associated with
seasonal variations in mosquito abundance
in one location (which may vary from zero
to very high values), estimates where control
methods effectively eradicated disease resulting in almost infinite values for changes in
R 0 , or model-predicted variations across
whole potential geographic ranges that range
from a theoretical high to zero values (e.g.,
Estrada-Peña et al. 2013). We also did not
use some very high modeled ranges of R0 for
malaria [e.g., 1–11,000 (Smith et al. 2007)]
when the modeling of empirical age–infection
prevalence data produced strongly contrasting single-digit estimates of R 0 (Hagmann
et al. 2003; Hay et al. 2005). The goal of the
literature search was to provide an illustration of how important projected changes in
R0 of I. scapularis could be, compared with
R0 of other parasites and pathogens; it was
not intended to be an exhaustive catalog­ing
of all literature in this field. Further, we recognized that R0 estimates are not precise and
vary according to the estimation method used
(Heffernan et al. 2005; Li et al. 2011).

Results
Using observed (ANUSPLIN) temperature
data, R0 for I. scapularis in the late 1970s—
when Lyme disease emerged in the northeastern United States (Wood and Lafferty
2013)—was estimated at approximately
3 and 1.9 in Old Lyme and Fort McCoy,
respectively; at between 2 and 3 in Southern
Ontario; approximately 1.5 in Huron Ontario
and South-Western Quebec, but mostly < 1
in Upper Southern Ontario and the Boreal
region (Figure 1). In Old Lyme, R0 increased
almost linearly to approximately 3.5 by 1999

Environmental Health Perspectives  •  volume 122   number 6   June 2014 

during the first period of expansion of I. scapularis in the northeastern United States. In Fort
McCoy, R0 increased slightly, but this increase
was small compared with interannual variations. In Southern Ontario, R0 increased to 4
by the early 2000s; during which time I. scapularis populations emerged at a number of
locations in this region (Point Pelee National
Park, Turkey Point, Rondeau Provincial Park;
Figure 1A). In Huron Ontario and SouthWestern Quebec, R0 increased from 1.5 to
2.5 by the early 2000s; and subsequent to this
(mostly from 2000 onward), I. scapularis populations began to emerge in South-Western
Quebec (Figure 1D). In Upper Southern
Ontario, R 0 increased to > 1 in the late
1990s, but in the Boreal region R0 remained
below unity for the whole 1971–2010 period
(Figure 1).
R 0 values for I. scapularis obtained in
model simulations using projected climate
data were similar for an ensemble of climate
models, and we used bias-corrected output
from the regional climate model CRCM4.2.3
as a representative of the ensemble because
of its spatial resolution and predictive accuracy. R0 for I. scapularis in Canada was projected to increase 1.5 to 2.3 times from the
period 1971–2000 to 2001–2050, and 2.2
to 4.6 times from the period 1971–2000 to
2051–2069 depending on location (Figure 1,
Table 1); and in the United States, R0 was
predicted to approximately double to 7.1 and
5.2 in Old Lyme (Figure 1) and Fort McCoy,
respectively, by 2051–2069 (Table 1).
Increases in R0 to values > 1 predicted in
regions where R0 was < 1 during the period
1970–2000 would be expected to facilitate
range expansion of I. scapularis northward and
possibly westward (Figure 2).
The projected increases in R0 are equivalent, for the most part, to ranges of values of R0
estimated for other globally important parasites
and pathogens associated with variations in
major determinants of their ecology and epidemiology such as geographic location, pathogen
genotype, different epidemics, reservoir host or
vector density, and control efforts (Table 1).

Discussion
These findings suggest that increasing temperatures in northern North America that support an R0 for I. scapularis of > 1.5 have been
coincident with, or in advance of, but not subsequent to, expanding numbers of locations
where I. scapularis populations have become
established. In Canada, where we have tracked
the spread of I. scapularis, temperature has
remained a statistically significant determinant
of I. scapularis occurrence in field studies and
analyses of surveillance data that accounted
for alternative environmental determinants
(e.g., host abundance, altitude, rainfall, habitat
types, tick immigration rates) (Bouchard et al.

633

 Ogden et al.

9.00

8.00
7.00

6.00

6.00

5.00

5.00

R0

7.00

4.00

4.00

3.00

3.00

2.00

2.00

1.00

1.00

0.00
1970
9.00
8.00

R0

9.00

Southern Ontario

1980

1990

2000

2010

2020

2030

2040

2050

2060

0.00
1970

2070

9.00

Upper Southern Ontario

8.00

7.00

7.00

6.00

6.00

5.00

5.00

R0

R0

8.00

4.00

4.00

3.00

3.00

2.00

2.00

1.00

1.00

0.00
1970

1980

1990

2000

2010

2020

2030

2040

2050

2060

0.00
1970

2070

9.00

8.00

7.00

7.00

6.00

6.00

5.00

5.00

4.00

4.00

3.00

3.00

2.00

2.00

1.00

1.00

0.00
1970

Huron Ontario

1980

1990

2000

2010

2020

2030

2040

2050

2060

2070
120

South-Western Quebec

100
80
60
40
20

1980

1990

2000

2010

2020

2030

2040

2050

2060

0
2070

2000

2010

2020

2030

2040

2050

2060

2070

2000

2010

2020

2030

2040

2050

2060

2070

9.00
Boreal Region

R0

R0

8.00

the United States (Barbour and Fish 1993).
Forest fragmentation may enhance Lyme
disease risk for a variety of reasons; however,
I. scapularis ticks are invading Canada where
forest fragmentation occurred over time scales

I. scapularis population expansion in Canada
is occurring despite an overall deforestation
(Natural Resources Canada 2013) rather
than the reforestation thought to have driven
the initial reemergence of Lyme disease in

No. of CSDs with I. scapularis

2013a, 2013b; Leighton et al. 2012; Ogden
et al. 2008; 2010). These observations supported a key role for temperature in I. scapularis populations becoming established at
the northern edge of the tick’s range. Also,

1980

1990

2000

2010

2020

2030

2040

2050

2060

0.00
1970

2070

9.00
R0 using observed temperature
R0 using CRCM4 output
No. CSDs with I. scapularis

8.00

Old Lyme

1980

1990

Fort McCoy

7.00

R0

6.00
5.00
4.00
3.00
2.00
1.00
0.00
1970

1980

1990

Figure 1. Mean values for R0 of the tick I. scapularis obtained in tick model simulations using observed temperature data (ANUSPLIN: 1971-2010), and projected temperature data obtained from the RCM CRCM4.2.3 [according to Special Report on Emissions Scenarios (SRES) A2 emissions scenario] for (A)
Southern Ontario, (B) Huron Ontario, (C) Upper Southern Ontario, (D) South-Western Quebec, (E) the Boreal region of central Ontario and Quebec, (F) Old Lyme
(Connecticut), and (G) Fort McCoy (Wisconsin). The black arrows in each panel reference the first identification of Lyme disease in the United States (Wood and
Lafferty 2013). The green arrows indicate the year of first field detection of I. scapularis populations within the Canadian clusters. (A) In Southern Ontario, these
dates are 1976 for Long Point (Watson and Anderson 1976), 1996 for Point Pelee (Lindsay et al. 1999a), 1999 for Rondeau Park (Morshed et al. 2003), and 2001
for Turkey Point (Scott et al. 2004). (D) The date is 2007 for a number of sites in South-Western Quebec (Ogden et al. 2008); the estimated numbers of Census
Subdivisions (CSDs) with established I. scapularis populations in South-Western Quebec, based on passive surveillance data (Leighton et al. 2012), is shown
as the green dashed line. The range of R0 values produced in simulations for 2020–2069 of CRCM4.2.3 and five other GCMs and RCMs is indicated by the error
bar to the right of each panel except for the U.S. sites (F,G), for which only output from CRCM4.2.3 was available. Full details of all simulations are presented in
Supplemental Material, Figure S6.

634 

volume

122   number 6   June 2014  •  Environmental Health Perspectives

 Climate change and R0 of arthropod vectors

long predating current I. scapularis invasion
(Elliott 1998). Together, these findings suggest that even if recent warming in the region
(5–10% increases in DD > 0°C; Figure 1) was
not associated with global warming, a future
warming climate will increase R0 of I. scapularis in northern North America. Increases
in R0 may drive increased Lyme disease risk
where it is already endemic (within limits
determined by density-dependent regulation of the tick) and drive range expansion
into more northern regions where it is currently absent. Furthermore, they support
the hypothesis that climate warming in
northeastern North America may have codriven the emergence of Lyme disease risk,
alongside other hypothe­sized factors such as

reforestation and burgeoning deer populations
(Wood and Lafferty 2013), by facilitating the
spread of I. scapularis from refuges. Expansion
of I. scapularis in the northern United States
has then provided the source of ticks to fuel its
northward expansion into Canada.
The immediate importance of future
increased R0 of I. scapularis in the northeast
and upper Midwest of North America is that
a) regions currently climatically unsuitable
become suitable for I. scapularis establishment
(i.e., R0 changes from < 1 to > 1; Figure 2);
b) in regions currently suitable for I. scapularis
(where R0 > 1), tick invasion speed will accelerate as the likelihood of stochastic tick population fade-out reduces (May et al. 2001), and
tick-borne pathogen invasion speed increases

due to increasing tick abundance (Ogden
et al. 2007); and c) risk from I. scapularis–
transmitted pathogens may increase where
the tick and pathogen are already established
due to increased tick abundance up to a point
at which this is limited by density-dependent
regulation (Ogden et al. 2007). To date,
I. scapularis invasion in the northern United
States and Canada has been followed by invasion of the agent of Lyme disease, B. burgdorferi sensu stricto (Hamer et al. 2010; Ogden
et al. 2013), hence we assume northward
I. scapularis range expansion is synonymous
with expansion in Lyme disease risk.
The magnitude of projected increases in R0
of I. scapularis in the present study is of importance for the ecology and epidemiology of

Table 1. R0 values quantified for infectious diseases, arthropod vectors, and vector-borne diseases.
Pathogen or parasite
Directly transmitted infectious diseases
Cholera in Zimbabwe
1918–1919 A/H1N1 Pandemic influenza
1957–1958 A/H2N2 Pandemic influenza
2009 A/H1N1
Low-pathogenic influenza A viruses in turkey flocks
H5N1 influenza A in poultry
H7N7 influenza A in poultry
Seasonal influenza
HIV (human immunodeficiency virus)
SARS (severe acute respiratory syndrome)
Measles
Polio
Canine rabies
African swine fever
Foot and Mouth disease in cattle
Arthropod vectors: Ixodes scapularis in Canada
Boreal region, 1971–2000 vs. 2001–2050
Boreal region, 1971–2000 vs. 2051–2069
Huron Ontario, 1971–2000 vs. 2001–2050
Huron Ontario, 1971–2000 vs. 2051–2069
Southern Ontario, 1971–2000 vs. 2001–2050
Southern Ontario, 1971–2000 vs. 2051–2069
Upper Southern Ontario, 1971–2000 vs. 2001–2050
Upper Southern Ontario, 1971–2000 vs. 2051–2069
South-Western Quebec, 1971–2000 vs. 2001–2050
South-Western Quebec, 1971–2000 vs. 2051–2069
Old Lyme, CT, USA, 1971–2000 vs. 2001–2050
Old Lyme, CT, USA, 1971–2000 vs. 2051–2069
Fort McCoy, WI, USA, 1971–2000 vs. 2001–2050
Fort McCoy, WI, USA, 1971–2000 vs. 2051–2069
Vector-borne diseases
Dengue in Columbia
Dengue in Brazil
Dengue in Brazil
Chikungunya in Italy
Leishmaniasis (Leishmania infantum) in dogs
Bluetongue virus
African horse sickness in zebra
Endoparasites
Nematodes of sheep
Oncherciasis

R0 range estimate

Factors associated with variation

References

1–2.72 (× 2.7)

Environment, socio­economic conditions, Mukandavire et al. 2011
and cultural practices
1.5–7.5 (× 5)
Human population density
Chowell et al 2007; Massad et al. 2007;
Mills et al. 2004; Vynnycky et al. 2007
1.4–1.7 (× 1.2)
Country
Longini et al. 2004; Nishiura 2010b
1.3–1.7 (× 1.4)
Country, community, human population Fraser et al. 2009; Pourbohloul et al. 2009;
density
Tuite et al. 2010; White et al. 2009; Yang
et al. 2009
0.6–5.5 (× 9.2)
Virus strain and farm
Comin et al. 2011
1–3 (× 3)
Different global epidemics
Zhang et al. 2012
1.2–6.5 (× 5.4)
With and without control
Stegeman et al. 2004
1.6–3 (× 1.9)
Locations, years, and viral strains
Gran et al. 2010; Truscott et al. 2012
1.1–3.7 (× 3.4)
Country and subepidemic
Nishiura 2010a; Stadler et al. 2012; Xiao
et al. 2013
1.2–8 (× 6.7)
Modeling methods and human
Bauch et al. 2005
population demography
1.2–9.5 (× 7.9)
Vaccination, different schools
Mossong and Muller 2000; Plans Rubio 2012
2–14 (× 7)
Levels of hygiene
Fine and Carneiro 1999
1.05–2.44 (× 2.3)
Location across the world
Fitzpatrick et al. 2012; Kitala et al. 2002
2–3 (between farms: × 1.5), and Location in Russian federation
Gulenkin et al. 2011
8–11 (within farms: × 1.4)
1.6–4.5 (× 2.8)
With and without control
Ferguson et al. 2001
0.3–0.7 (× 2.3)
0.3–1.4 (× 4.6)
1.8–3.0 (× 1.6)
1.8–5.3 (× 2.9)
3.0–4.5 (× 1.5)
3.0–6.7 (× 2.2)
0.9–1.7 (× 1.9)
0.9–3.3 (× 3.6)
1.7–2.8 (× 1.6)
1.7–4.3 (× 2.5)
3.1–4.8 (× 1.5)
3.1–7.1 (× 2.3)
2.3–3.4 (× 1.4)
2.1–5.2 (× 2.2)
0.88–3.87 (× 4.4)
1.5–2.75 (× 1.8)
1.6–22.9 (× 14.3)
1.8–6.0 (× 3.3)
5.9–11 (× 1.9)
1.8–11 (× 6.1)
10–23 (× 2.3)
6–16 (× 2.7)
5.3–7.7 (× 1.5)

Environmental Health Perspectives  •  volume 122   number 6   June 2014 

Climate change
Climate change
Climate change
Climate change
Climate change
Climate change
Climate change
Climate change
Climate change
Climate change
Climate change
Climate change
Climate change
Climate change

This study
This study
This study
This study
This study
This study
This study
This study
This study
This study
This study
This study
This study
This study

Human and mosquito density
With and without adult mosquito control
City and year
Local variations in mosquito abundance
Countries
Geographic regions of the Netherlands
Virus strain

Padmanabha et al. 2012
Pinho et al. 2010
Degallier et al. 2009
Poletti et al. 2011
Dye et al. 1992; Quinnell et al. 1997
Santman-Berends et al. 2013
Lord et al. 1997

Nematode species
Countries

Kao et al. 2000
Filipe et al. 2005

635

 Ogden et al.

respond to the central tendency of increasing temperatures due to a) the long periods
of inter-stadial development that take place
in the surface layers of the soil where fluctuating air temperature are buffered, b) latency in
responses of development rates to temperature
changes minimizing effects of very short-term
temperature fluctuations (Ogden et al. 2004),

vector-borne diseases in general. This was illustrated by R0 ranges estimated for other globally
important parasites and pathogens associated
with variations in known key determinants
of their ecology and epidemiology (Table 1).
These ranges were of a similar magnitude to
projected increases in R0 of I. scapularis with
climate change. Tick species are likely to

c) their ability to return to soil-level refugia
during extremes of heat, cold, drought, or rainfall while host seeking, d) their associations
with woodland habitats within which a microclimate is buffered from extremes of temperature occurring in treeless areas (Lindsay et al.
1999b; Morecroft et al. 1998), and e) ticks
have no nonparasitic immature feeding stages
whose survival is susceptible to short-term
changes in weather as do dipteran vectors such
as mosquitoes. Therefore, the increases in R0
projected here represent a possible magnitude
of increase in mean R0 values arthropod vectors
may experience with climate change. Around
this mean, dipteran population R0 may fluctuate seasonally and annually over a much wider
range due to the rapid effects of rainfall and
temperature on reproduction and mortality
rates. Abundance and geographic distributions
of many mosquito-borne diseases are currently
driven primarily by control efforts that superimpose on any climate effects. Large increases
in vector R0 may, however, render current vector and vector-borne disease control methods
ineffective as vector multiplication outstrips
control efforts (Massad, 2008; Reithinger et al.
2003; Smith et al. 2007).
R0 increases associated with climate change
may be limited in some circumstances. Host
population densities and habitat do not seem
to be currently limiting on I. scapularis range
expansion, but they may be in the future.
Mosquitoes and other dipteran vectors can be
dispersed by wind (Service 1997), but ticks
need hosts for their dispersal to effect range
expansion. I. scapularis are dispersed over long
distances by migratory birds, but ticks that
are not carried by migratory animals would
be expected to have less capacity to invade
climatically suitable environments. Ticks of
public health importance such as I. ricinus and
I. scapularis are mostly host and woodland habitat generalists, which facilitates range changes,
whereas the more highly specialized the niche
of a species, the less likely it will be to be dispersed and/or capable of becoming established
in new locations (Morin and Chuine 2006).

Conclusions

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

Figure 2. Maps of values of R0 estimated from ANUSPLIN observations (1971–2000; A) and projected climate obtained from the CRCM4.2.3 driven by CGCM3.1 T47, following the SRES A2 GHG emission scenario
for 2011–2040 (B) and 2041–2070 (C). The color scale indicates R0 values. Within the zones where R0 of
I. scapularis is > 1, geographic occurrence of Lyme disease risk is also limited by other environmental
variables (Diuk-Wasser et al. 2012).

636 

volume

We estimated the degree to which projected
climate change may impact the ecology of
arthropod vectors and, by inference, vectorborne diseases. The emergence of Lyme
disease in North America may itself have
been partly driven by recent climate change.
Confidence in our projections is increased
by observed changes in temperature and
estimated R0 for the vector associated with
the actual emergence of the vector and the
vector-borne diseases it transmits. Our findings suggest that effort should be refocused on
assessing the health risks due to vector-borne
disease, particularly vector-borne zoonoses,
associated with our changing climate.

122   number 6   June 2014  •  Environmental Health Perspectives

 Climate change and R0 of arthropod vectors

References
Anderson JF, Duray PH, Magnarelli LA. 1987. Prevalence
of Borrelia burgdorferi in white-footed mice and
Ixodes dammini at Fort McCoy, Wis. J Clin Microbiol
25(8):1495–1497.
Barbour AG, Fish D. 1993. The biological and social phenome­
non of Lyme disease. Science 260(5114):1610–1616.
Bauch CT, Lloyd-Smith JO, Coffee MP, Galvani AP. 2005.
Dynamically modeling SARS and other newly emerg­
ing respiratory illnesses: past, present, and future.
Epidemiology 16(6):791–801.
Bouchard C, Beauchamp G, Leighton PA, Lindsay R, Bélanger D,
Ogden NH. 2013a. Does high biodiversity reduce the
risk of Lyme disease invasion? Parasit Vectors 6(1):195;
doi:10.1186/1756-3305-6-195.
Bouchard C, Leighton PA, Beauchamp G, Nguon S, Trudel L,
Milord F, et  al. 2013b. Harvested white-tailed deer as
sentinel hosts for early establishing Ixodes scapularis
popu­la­tions and risk from vector-borne zoonoses in south­
eastern Canada. J Med Entomol 50(2):384–393.
Brownstein JS, Holford TR, Fish D. 2005. Effect of climate change
on Lyme disease risk in North America. Ecohealth 2(1):38–46.
Brunner JL, Killilea M, Ostfeld RS. 2012. Overwintering survival
of nymphal Ixodes scapularis (Acari: Ixodidae) under
natural conditions. J Med Entomol 49(5):981–987.
Chowell G, Nishiura H, Bettencourt LM. 2007. Comparative
estimation of the reproduction number for pandemic
influenza from daily case notification data. J R Soc
Interface 4(12):155–166.
Comin A, Klinkenberg D, Marangon S, Toffan A, Stegeman A.
2011. Transmission dynamics of low pathogenicity
avian influenza infections in Turkey flocks. PLoS One.
6(10):e26935; doi:10.1371/journal.pone.0026935.
Degallier N, Favier C, Boulanger JP, Menkes C. 2009. Imported
and autochthonous cases in the dynamics of dengue
epidemics in Brazil. Rev Saude Publica 43(1):1–7.
Diuk-Wasser MA, Hoen AG, Cislo P, Brinkerhoff R, Hamer SA,
Rowland M, et  al. 2012. Human risk of infection with
Borrelia burgdorferi, the Lyme disease agent, in eastern
United States. Am J Trop Med Hyg 86(2):320–327.
Dye C, Killick-Kendrick R, Vitutia MM, Walton R, KillickKendrick M, Harith AE, et  al. 1992. Epidemiology of
canine Leishmaniasis: prevalence, incidence and basic
reproduction number calculated from a cross-sectional
serological survey on the island of Gozo, Malta.
Parasitology 105(pt 1):35–41.
Elliott KA. 1998. The forests of southern Ontario. Forest Chron
74(6):850–854.
Environment Canada. 2013. Plots and Animations. Canadian
Centre for Climate Modelling and Analysis. Available:
http://www.cccma.ec.gc.ca/diagnostics/canrcm4/
canrcm4.shtml [accessed 6 December 2013].
Estrada-Peña A, Ruiz-Fons F, Acevedo P, Gortazar C, de la
Fuente J. 2013. Factors driving the circulation and possible
expansion of Crimean-Congo haemorrhagic fever virus in
the western Palearctic. J Appl Microbiol 114(1):278–286.
Ferguson NM, Donnelly CA, Anderson RM. 2001. The foot-andmouth epidemic in Great Britain: pattern of spread and
impact of interventions. Science 292(5519):1155–1160.
Filipe JA, Boussinesq M, Renz A, Collins RC, Vivas-Martinez S,
Grillet ME, et  al. 2005. Human infection patterns and
heteroge­neous exposure in river blindness. Proc Natl Acad
Sci USA 102(42):15265–15270.
Fine PE, Carneiro IA. 1999. Transmissibility and persistence
of oral polio vaccine viruses: implications for the global
poliomyelitis eradication initiative. Am J Epidemiol
150(10):1001–1021.
Fitzpatrick MC, Hampson K, Cleaveland S, Meyers LA,
Townsend JP, Galvani AP. 2012. Potential for rabies
control through dog vaccination in wildlife-abundant
communities of Tanzania. PLoS Negl Trop Dis 6(8):e1796;
doi:10.1371/journal.pntd.0001796.
Fraser C, Donnelly CA, Cauchemez S, Hanage WP, Van
Kerkhove MD, Hollingsworth TD, et al. 2009. Pandemic
potential of a strain of influenza A (H1N1): early findings.
Science 324(5934):1557–1561.
Gachon P, Dibike Y. 2007. Temperature change signals
in northern Canada: convergence of statistical down­
scaling results using two driving GCMs. Int J Climatology
27(12):1623–1641.
Githeko AK, Lindsay SW, Confalonieri UE, Patz JA. 2000.
Climate change and vector-borne diseases: a regional
analysis. Bull World Health Organ 78(9):1136–1147.

Githeko AK, Ototo EN, Guiyun Y. 2012. Progress towards
understanding the ecology and epidemiology of malaria
in the western Kenya highlands: opportunities and chal­
lenges for control under climate change risk. Acta Trop
121(1):19–25.
Gran JM, Iversen B, Hungnes O, Aalen OO. 2010. Estimating
influenza-related excess mortality and reproduction
numbers for seasonal influenza in Norway, 1975–2004.
Epidemiol Infect 138(11):1559–1568.
Gubler DJ, Reiter P, Ebi KL, Yap W, Nasci R, Patz JA. 2001.
Climate variability and change in the United States:
potential impacts on vector- and rodent-borne diseases.
Environ Health Perspect 109(suppl 2):223–233.
Gulenkin VM, Korennoy FI, Karaulov AK, Dudnikov SA. 2011.
Cartographical analysis of African swine fever outbreaks
in the territory of the Russian Federation and computer
modeling of the basic reproduction ratio. Prev Vet Med
102(3):167–174.
Hagmann R, Charlwood JD, Gil V, Ferreira C, do Rosário V,
Smith TA 2003. Malaria and its possible control on the
island of Príncipe. Malar J 2:15; doi:10.1186/1475-2875-2-15.
Hamer SA, Tsao JI, Walker ED, Hickling GJ. 2010. Invasion of
the Lyme disease vector Ixodes scapularis: implications
for Borrelia burgdorferi endemicity. Ecohealth 7(1):47–63.
Hay SI, Guerra CA, Tatem AJ, Atkinson PM, Snow RW. 2005.
Urbanization, malaria transmission, and disease burden in
Africa. Nat Rev Microbiol 3(1):81–90.
Heffernan JM, Smith RJ, Wahl LM. 2005. Perspectives on the
basic reproductive ratio. J R Soc Interface 2(4):281–293.
Hutchinson M, McKenney DW, Lawrence K, Pedlar PJ. 2009.
Development and testing of Canada-wide interpolated
spatial models of daily minimum-maximum temperature
and precipitation for 1961–2003. J Appl Meteor Climatol
48(4):725–741.
Kao RR, Leathwick DM, Roberts MG, Sutherland IA. 2000.
Nematode parasites of sheep: a survey of epidemiological
parameters and their application in a simple model.
Parasitology 121(pt 1):85–103.
Kilpatrick AM, Randolph SE. 2012. Drivers, dynamics, and
control of emerging vector-borne zoonotic diseases.
Lancet 380(9857):1946–1955.
Kitala PM, McDermott JJ, Coleman PG, Dye C. 2002. Comparison
of vaccination strategies for the control of dog rabies in
Machakos District, Kenya. Epidemiol Infect 129(1):215–222.
Laprise R, Caya D, Bergeron G, Giguère M. 1997. The formulation
of André Robert MC2 (Mesoscale Compressible
Community) model. Atmos-Ocean 35(suppl 1):195–220.
Leighton PA, Koffi JK, Pelcat Y, Lindsay LR, Ogden NH. 2012.
Predicting the speed of tick invasion: an empirical model
of range expansion for the Lyme disease vector Ixodes
scapularis in Canada. J Appl Ecol 49(2):457–464.
Li J, Blakeley D, Smith RJ. 2011. The failure of R0. Comput Math
Methods Med 2011:527610; doi:10.1155/2011/527610.
Lindsay LR, Akwar TH, Barker IK, Reive D. 1999a. Recent
Establishment of an Isolated Population of Ixodes scapularis,
the Vector of Lyme borreliosis, at Point Pelee National Park,
Ontario [Abstract]. PRFO Proceedings, 500. Available: http://
casiopa.mediamouse.ca/wp-content/uploads/2010/05/PRFO1999-Proceedings-p500-Lindsay-Akwar-Barker-and-ReiveAbstract.pdf [accessed 13 February 2014].
Lindsay LR, Barker IK, Surgeoner GA, McEwen SA, Gillespie TJ,
Robinson JT, et  al. 1995. Survival and development of
Ixodes scapularis (Acari: Ixodidae) under various climatic
conditions in Ontario, Canada. J Med Entomol 32(2):143–152.
Lindsay LR, Mathison SW, Barker IK, McEwen SA, Gillespie TJ,
Surgeoner GA. 1999b. Microclimate and habitat in relation
to Ixodes scapularis (Acari: Ixodidae) populations on Long
Point, Ontario, Canada. J Med Entomol 36(3):255–262.
Longini IM, Halloran ME, Nizam A, Yang Y. 2004. Containing
pandemic influenza with antiviral agents. Amer J
Epidemiol 159(7):623–633.
Lord CC, Woolhouse ME, Barnard BJ. 1997. Transmission and
distribution of virus serotypes: African horse sickness in
zebra. Epidemiol Infect 118(1):43–50.
Martens WJ, Niessen LW, Rotmans J, Jetten TH,
McMichael AJ. 1995. Potential impact of global climate
change on malaria risk. Environ Health Perspect
103:458–464.
Massad E. 2008. The elimination of Chagas’ disease from
Brazil. Epidemiol Infect 136(9):1153–1164.
Massad E, Burattini MN, Coutinho FA Lopez, LF. 2007. The 1918
influenza A epidemic in the city of São Paulo, Brazil. Med
Hypotheses 68(2):442–445.
May RM, Gupta S, McLean AR. 2001. Infectious disease

Environmental Health Perspectives  •  volume 122   number 6   June 2014 

dynamics: What characterizes a successful invader?
Philos Trans R Soc Lond B Biol Sci 356(1410):901–910.
McFarlane NA Scinocca JF, Lazare M, Harvey R, Verseghy D,
Li  J. 2005. The CCCma Third Generation Atmospheric
General Circulation Model (AGCM3). CCCMA, Internal
Report. Available: http://www.cccma.ec.gc.ca/papers/
jscinocca/AGCM3_report.pdf [accessed 6 August 2013].
Meehl GA, Boer GJ, Covey C, Latif M, Stouffer RJ. 2000. The
Coupled Model Intercomparison Project (CMIP). Bull Amer
Meteor Soc 81(2):313–318.
Mills CE, Robins JM, Lipsitch M. 2004. Transmissibility of 1918
pandemic influenza. Nature 432(7019):904–906.
Mordecai EA, Paaijmans KP, Johnson LR, Balzer C, Ben-Horin T,
de Moor E, et al. 2013. Optimal temperature for malaria
transmission is dramatically lower than previously
predicted. Ecol Lett 16(1):22–30.
Morecroft MD, Taylor ME, Oliver HR. 1998. Air and soil
microclimates of deciduous woodland compared to an
open site. Agric For Meteorol 90(2):141–156.
Morin X, Chuine I. 2006. Niche breadth, competitive strength and
range size of tree species: a trade-off based framework to
understand species distribution. Ecol Lett 9(2):185–195.
Morshed MG, Scott JD, Fernando K, Mann RB, Durden LA. 2003.
Lyme disease spirochete, Borrelia burgdorferi endemic
at epicenter in Rondeau Provincial Park, Ontario. J Med
Entomol. 40(1):91–94.
Mossong J, Muller CP. 2000. Estimation of the basic reproduction
number of measles during an outbreak in a partially
vaccinated population. Epidemiol Infect 124(2):273–278.
Mukandavire Z, Liao S, Wang J, Gaff H, Smith DL, Morris JG Jr.
2011. Estimating the reproductive numbers for the
2008–2009 cholera outbreaks in Zimbabwe. Proc Natl Acad
Sci USA 108(21):8767–8772.
Music B, Caya D. 2007. Evaluation of the hydrological cycle over
the Mississippi river basin as simulated by the Canadian
Regional Climate Model (CRCM). J  Hydrometeorol
8(5):969–988.
Nakicenovic N, Swart R. 2000. Special Report on Emissions
Scenarios: A Special Report of Working Group III of the
Intergovernmental Panel on Climate Change. Cambridge
UK:Cambridge University Press.
Natural Resources Canada. 2013. Additions and Deletions
of Forest Area. Available: http://www.nrcan.gc.ca/
forests/canada/sustainable-forest-management/criteriaindicators/13233 [accessed 6 December 2013].
Nishiura H. 2010a. Correcting the actual reproduction number:
a simple method to estimate R 0 from early epidemic
growth data. Int J Environ Res Public Health 7(1):291–302.
Nishiura H. 2010b. Time variations in the generation time of
an infectious disease: implications for sampling to
appropriately quantify transmission potential. Math Biosci
Eng 7(4):851–869.
Ogden NH, Barker IK, Beauchamp G, Brazeau S, Charron DF,
Maarouf A, et  al. 2006a. Investigation of ground level
and remote-sensed data for habitat classification and
prediction of survival of Ixodes scapularis in habitats of
southeastern Canada. J Med Entomol 43(2):403–414.
Ogden NH, Bigras-Poulin M, O’Callaghan CJ, Barker IK,
Kurtenbach K, Lindsay LR, et al. 2007. Vector seasonality,
host infection dynamics and fitness of pathogens
transmitted by the tick Ixodes scapularis. Parasitology
134(2):209–227.
Ogden NH, Bigras-Poulin M, O’Callaghan CJ, Barker IK,
Lindsay LR, Maarouf A, et al. 2005. A dynamic population
model to investigate effects of climate on geographic
range and seasonality of the tick Ixodes scapularis. Int J
Parasitol 35(4):375–389.
Ogden NH, Bouchard C, Kurtenbach K, Margos G, Lindsay LR,
Trudel L, et al. 2010. Active and passive surveillance and
phylogenetic analysis of Borrelia burgdorferi elucidate
the process of Lyme disease risk emergence in Canada.
Environ Health Perspect 118:909–914; doi:10.1289/
ehp.0901766.
Ogden NH, Lindsay LR, Beauchamp G, Charron D, Maarouf A,
O’Callaghan CJ, et al. 2004. Investigation of relationships
between temperature and developmental rates of tick
Ixodes scapularis (Acari: Ixodidae) in the laboratory and
field. J Med Entomol 41(4):622–633.
Ogden NH, Lindsay LR, Leighton P. 2013. Predicting the rate of
invasion of the agent of Lyme disease, Borrelia burgdorferi
in North America. J Appl Ecol 50(2):510–518.
Ogden NH, Lindsay LR, Morshed M, Sockett PN, Artsob H.
2009. The emergence of Lyme disease in Canada. CMAJ
180(12):1221–1224.

637

 Ogden et al.

Ogden NH, Maarouf A, Barker IK, Bigras-Poulin M, Lindsay LR,
Morshed MG, et  al. 2006b. Climate change and the
potential for range expansion of the Lyme disease vector
Ixodes scapularis in Canada. Int J Parasitol 36(1):63–70.
Ogden NH, St-Onge L, Barker IK, Brazeau S, Bigras-Poulin M,
Charron DF, et al. 2008. Risk maps for range expansion of
the Lyme disease vector, Ixodes scapularis, in Canada
now and with climate change. Int J Health Geogr 7:24;
doi:10.1186/1476-072X-7-24.
Padmanabha H, Durham D, Correa F, Diuk-Wasser M,
Galvani A. 2012. The interactive roles of Aedes aegypti
super-production and human density in dengue trans­
mission. PLoS Negl Trop Dis 6(8):e1799; doi:10.1371/journal.
pntd.0001799.
Patz JA, Martens WJ, Focks DA, Jetten TH. 1998. Dengue fever
epidemic potential as projected by general circulation
models of global climate change. Environ Health Perspect
106:147–153.
Pinho ST, Ferreira CP, Esteva L, Barreto FR, Morato e
Silva VC, et al. 2010. Modelling the dynamics of dengue
real epidemics. Philos Transact A Math Phys Eng Sci
368(1933):5679–56793.
Plans Rubio P. 2012. Is the basic reproductive number (R0) for
measles viruses observed in recent outbreaks lower than
in the pre-vaccination era? [Letter]. Euro Surveill 17(31):22.
Poletti P, Messeri G, Ajelli M, Vallorani R, Rizzo C, Merler S.
2011. Transmission potential of Chikungunya virus and
control measures: the case of Italy. PLoS One 6(5):e18860;
doi:10.1371/journal.pone.0018860.
Pourbohloul B, Ahued A, Davoudi B, Meza R, Meyers LA,
Skowronski DM, et al. 2009. Initial human transmission
dynamics of the pandemic (H1N1) 2009 virus in North
America. Influenza Other Respir Viruses 3(5):215–222.
Quinnell RJ, Courtenay O, Garcez L, Dye C. 1997. The
epidemiology of canine Leishmaniasis: transmission
rates estimated from a cohort study in Amazonian Brazil.
Parasitology 115(pt 2):143–156.
Reiter P. 2001. Climate change and mosquito-borne disease.
Environ Health Perspect 109(suppl 1):141–161.

638 

Reiter P, Thomas CJ, Atkinson PM, Hay SI, Randolph SE,
Rogers DJ, et al. 2004. Global warming and malaria: a call
for accuracy. Lancet Infect Dis 4(6):323–324.
Reithinger R, Espinoza JC, Davies CR. 2003. The transmission
dynamics of canine American cutaneous leishmaniasis in
Huánuco, Peru. Am J Trop Med Hyg 69(5):473–480.
Rogers DJ, Randolph SE. 2000. The global spread of malaria in
a future, warmer world. Science 289(5485):1763–1766.
Santman-Berends IM, Stegeman JA, Vellema P, van Schaik G.
2013. Estimation of the reproduction ratio (R0) of bluetongue
based on serological field data and comparison with other
BTV transmission models. Prev Vet Med 108(4):276–284.
Scinocca JF, McFarlane NA, Lazare M, Li J, Plummer D. 2008.
The CCCma third generation AGCM and its extension
into the middle atmosphere. Atmos Chem Phys Discuss
8(2):7883–7930.
Scott JD, Fernando K, Durden LA, Morshed MG. 2004. Lyme
disease spirochete, Borrelia burgdorferi, endemic in
epicenter at Turkey Point, Ontario. J Med Entomol
41(2):226–230.
Service MW. 1997. Mosquito (Diptera: Culicidae) dispersal-the
long and short of it. J Med Entomol 34(3):579–588.
Smith DL, McKenzie FE, Snow RW, Hay SI. 2007. Revisiting the
basic reproductive number for malaria and its implications
for malaria control. PLoS Bio. 5(3):e42; doi:10.1371/journal.
pbio.0050042.
Stadler T, Kouyos R, von Wyl V, Yerly S, Böni J, Bürgisser P,
et al. 2012. Estimating the basic reproductive number from
viral sequence data. Mol Biol Evol 29(1):347–357.
Stegeman A, Bouma A, Elbers AR, de Jong MC, Nodelijk G,
de Klerk F, et  al. 2004. Avian influenza A virus (H7N7)
epidemic in the Netherlands in 2003: course of the epidemic
and effectiveness of control measures. J Infect Dis
190(12):2088–2095.
Tanser FC, Sharp B, le Sueur D. 2003. Potential effect of
climate change on malaria transmission in Africa. Lancet
362(9398):1792–1798.
Truscott J, Fraser C, Cauchemez S, Meeyai A, Hinsley W,
Donnelly CA, et  al. 2012. Essential epidemiological

volume

mechanisms underpinning the transmission dynamics of
seasonal influenza. J R Soc Interface 9(67):304–312.
Tuite AR, Greer AL, Whelan M, Winter AL, Lee B, Yan P, et al.
2010. Estimated epidemiologic parameters and morbidity
associated with pandemic H1N1 influenza. CMAJ
182(2):131–136.
Vynnycky E, Trindall A, Mangtani P. 2007. Estimates of the
reproduction numbers of Spanish influenza using
morbidity data. Int J Epidemiol 36(4):881–889.
Watson TG, Anderson RC. 1976. Ixodes scapularis Say on
white-tailed deer (Odocoileus virginianus) from Long
Point, Ontario. J Wildl Dis 12(1):66–71.
White L, Wallinga J, Finelli L, Reed C, Riley S, Lipsitch M, et al.
2009. Estimation of the reproductive number and the serial
interval in early phase of the influenza A/H1N1 pandemic
in the USA. Influenza Other Respir Viruses 3(6):267–276.
Wood CL, Lafferty KD. 2013. Biodiversity and disease: a
synthesis of ecological perspectives on Lyme disease
transmission. Trends Ecol Evol 28(4):239–247.
Wu X, Duvvuri VR, Lou Y, Ogden NH, Pelcat Y, Wu J. 2013.
Developing a temperature-driven map of the basic
reproductive number of the emerging tick vector of
Lyme disease Ixodes scapularis in Canada. J Theor Biol
319:50–61.
Xiao Y, Tang S, Zhou Y, Smith RJ, Wu J, Wang N. 2013.
Predicting the HIV/AIDS epidemic and measuring the
effect of mobility in mainland China. J Theor Biol
317:2712–85.
Yang Y, Sugimoto JD, Halloran ME, Basta NE, Chao DL,
Matrajt L, Potter G, et al. 2009. The transmissibility and
control of pandemic influenza A (H1N1) virus. Science
326(5953):729–733.
Zhang Z, Chen D, Ward MP, Jiang Q. 2012. Transmissibility of
the highly pathogenic avian influenza virus, subtype H5N1
in domestic poultry: a spatio-temporal estimation at the
global scale. Geospat Health 7(1):135–143.

122   number 6   June 2014  •  Environmental Health Perspectives