, Volume 2, Issue 1, pp 38–46

Effect of Climate Change on Lyme Disease Risk in North America

  • John S. Brownstein
  • Theodore R. Holford
  • Durland Fish
Original Contributions


An understanding of the influence of climate change on Ixodes scapularis, the main vector of Lyme disease in North America, is a fundamental component in assessing changes in the spatial distribution of human risk for the disease. We used a climate suitability model of I. scapularis to examine the potential effects of global climate change on future Lyme disease risk in North America. A climate-based logistic model was first used to explain the current distribution of I. scapularis in North America. Climate-change scenarios were then applied to extrapolate the model in time and to forecast vector establishment. The spatially modeled relationship between I. scapularis presence and large-scale environmental data generated the current pattern of I. scapularis across North America with an accuracy of 89% (P < 0.0001). Extrapolation of the model revealed a significant expansion of I. scapularis north into Canada with an increase in suitable habitat of 213% by the 2080s. Climate change will also result in a retraction of the vector from the southern U.S. and movement into the central U.S. This report predicts the effect of climate change on Lyme disease risk and specifically forecasts the emergence of a tickborne infectious disease in Canada. Our modeling approach could thus be used to outline where future control strategies and prevention efforts need to be applied.

Key words

autologistic model global climate model geographic information systems Ixodes scapularis greenhouse effect spatial model 


Global climate change has been implicated in having a potentially serious effect on the future spatial and temporal distribution of vector-borne diseases. Although the local abundance of vectors may be guided by density-dependent factors such as competition, predation, and parasitism, the geographic range of arthropod species’ habitat is controlled by large-scale density-independent factors. In particular, climate factors can be used in the prediction of habitat type (Holdridge, 1971) and, thus, suitability for arthropods (Rogers et al., 1996; Duchateau et al., 1997; Robinson et al., 1997; Cumming, 2002). Because climate has a well-documented role in the maintenance of vectors and pathogens in nature, some studies have predicted that the present warming phase of the Earth will result in the redistribution of many vector-borne diseases (Reeves et al., 1994; Patz et al., 1996). For instance, warming temperatures have been predicted to both enhance transmission intensity and extend the distribution of diseases such as malaria and dengue (Martens et al., 1995; Lindsay and Martens, 1998; Patz et al., 1998; Hales et al., 2002; Tanser et al., 2003). In particular, climate change may open up previously uninhabitable territory for arthropod vectors, increase reproductive and biting rates, and shorten the pathogen incubation period (Shope, 1991; Patz et al., 1996). There is therefore an imperative need for further studies on the effect of climate change on a broad range of vector-borne disease systems.

The tick Ixodes scapularis, the primary vector of Lyme disease in North America (Keirans et al., 1996; Dennis et al., 1998), is highly dependent on climate patterns (Stafford, 1994; Lindsay et al., 1995; Bertrand and Wilson, 1996). The abiotic environment plays a vital role in the survival of I. scapularis; both water stress and temperature regulate off-host mortality (Needham and Teel, 1991; Bertrand and Wilson, 1996). Because 98% of the 2-year life cycle occurs off the host, climate should act as an essential determinant of distribution of established tick populations across North America (Fish, 1993). The effect of climate on I. scapularis population maintenance suggests the potential for climate change to alter the current vector distribution. Although recent emergence of Lyme disease throughout the northeastern and mid-Atlantic states has been linked to reforestation (Barbour and Fish, 1993), additional influence of environmental change can be expected considering the anticipated shifts in climate.

A shift in the distribution is a public health concern in the U.S., where Lyme disease is the most prevalent vectorborne disease, with more than 100,000 cases reported since 1982 (Orloski et al., 2000). In Canada, established populations of I. scapularis are currently limited to a small number of foci in southern Ontario, and current climate conditions are likely to preclude the expansion of the tick into wider distribution in eastern Canada (Lindsay et al., 1998; Barker and Lindsay, 2000).

The ideal climate-driven model of a disease system would involve a biological approach in which the dynamics of both vector and pathogen were modeled explicitly. The successful application of such a model depends on an accurate estimation of the relationships of climatic factors and disease cycle parameters (Rogers and Randolph, 2000). However, the complex relationship between the tick vector and the environment hinders a detailed understanding of the ecologic constraints to its maintenance, thus precluding the current use of biological models in climate-change projections. Statistical climate-matching approaches have, therefore, been proposed as a substitute for quantitative biological models (Randolph and Rogers, 2000; Brownstein et al., 2003).

We, therefore, used a previously validated spatially predictive logistic model for I. scapularis to predict current climate-based habitat suitability in North America (Brownstein et al., 2003). This model relies on both seasonal temperature and humidity data to identify the climate constraints on vector distribution in North America. A global circulation model (GCM) was then used to quantify the effect of climate change on the future distribution of I. scapularis.


Climate Model Overview

We used a climate-based habitat suitability model for I. scapularis to forecast the effect of global climate change (Brownstein et al., 2003). This spatially explicit model matched seasonal climate data with the current reported distribution of I. scapularis in the U.S. (Dennis et al., 1998). Climate was shown to successfully predict the current distribution of I. scapularis with an accuracy of 95% (P < 0.0001; Brownstein et al., 2003). Field sampling at locations of varying probability in the northeast U.S. subsequently confirmed the validity of the environmental model.

The suitability model was extrapolated to North America by using seasonal climate information. The climate data were derived from a 0.5 × 0.5° global dataset of 30-year average monthly climate surfaces derived from interpolation of station data from 1961 to 1990 (New et al., 1999). Climate variables selected for analysis included monthly vapor pressure and minimum, maximum, and mean monthly temperature. Suitable I. scapularis land cover was also included in the model as the proportion of deciduous forest per 0.5° pixel. Deciduous forest cover was selected from the Global Land Cover Characteristics database for North America derived from classification of 1-km resolution advanced very-high-resolution radiometer imagery from 1992 and 1993. We chose the International Geosphere Programme Land Cover Classification for extraction of deciduous forest cover (Loveland et al., 2000). The analysis involved extrapolating the original logistic model for the relationship between environment and known established I. scapularis populations in the U.S. As for the national scale model, spatial autocorrelation was accounted for in the model by applying an autologistic approach (Augustin et al., 1996). This method incorporates a smoothing filter, called the autologistic term, as an additional covariate in the logistic model (Augustin et al., 1996; Osborne et al., 2001). Because vector population status in many locations in the U.S. is unknown and because we did not include any surveillance data from Canada or Mexico, the model incorporated the modified Gibbs sampler to estimate the distribution in unknown areas (Augustin et al., 1996, 1998). This Monte Carlo–type method involves iterating the procedure of fitting the autologistic model, deriving the probability surface for all locations, and then recalculating the autologistic term until stability is achieved. This procedure was implemented with a program written with Microsoft (Redmond, WA) Visual C++ and produced a final probability surface for the current suitability of I. scapularis in North America. We selected a probability cutoff point for suitability by determining the best combination of sensitivity and specificity. This threshold value was used to decide whether a given cell could support an established vector population.

Climate Model Forecast

The derived relationship between climate and I. scapularis establishment was then used to predict climate-based habitat suitability in future years by applying global climate-change forecasting. Because many climate-change experiments have been completed, with varying results, the modeling center of choice was selected according to a set of reliability criteria determined by the Intergovernmental Panel on Climate Change (IPCC), Task Group on Scenarios for Climate Impact Assessment (Carter et al., 1999). On the basis of the criteria of vintage, resolution, and validity, we selected the Canadian Global Coupled Model (CGCM1) produced by the Canadian Centre for Climate Modelling and Analysis (Flato et al., 2000).

The CGCM1 provided change data for each of the four climatic variables used in the suitability model. Two historically forced integrations were considered. The first projects an increase in greenhouse gas emissions at a rate of 1% per year. The second combines both greenhouse gas and sulfate aerosol changes. These integrations forced with and without sulfate produce global mean temperature increases of 3.85 and 4.91°C, respectively, by 2080. The reliability of these climate projections is reflected in the model’s reproduction of present-day climate and historical variation. The model accurately predicted the observed 0.6°C increase in global mean temperature over the past century (Flato et al., 2000). Climate-change data for each variable and integration were obtained for three time points—the 2020s (2010–2039), the 2050s (2040–2069), and the 2080s (2070–2099)—from the IPCC Data Distribution Centre. The data were imported, processed, and geocoded by a program written in Microsoft (Redmond, WA) Visual C++. For each simulation output, a gridded surface, which contains the monthly climate information, was produced.

Because the CGCM1 dataset has a lower spatial resolution (3.75 × 3.75°) than the observed present climate data (0.5 × 0.5°) used to build the current model, the change data were resampled by interpolation with cubic spline. Spline interpolation is considered the appropriate method for downscaling environmental data when there are a large number of data points and when the surface is expected to vary smoothly. Monthly change surfaces for each climate variable, time step, and GCM scenario were generated (n = 288).

We then applied zonal analysis to associate current monthly climate information for North America with the resampled climate-change layers to yield future climate predictions. Processing was accomplished by a batch script written for ERDAS Imagine (Atlanta, GA). Similar to the current model, future climate data were summarized by calculating the cell statistics for each climate variable, including mean, maximum, minimum, and SD. The autologistic model was then rerun to predict the probability of I. scapularis populations in North America over three time points according to both climate-change scenarios. The six new probability surfaces were classified according to the probability threshold for suitability.

Output Quantification

The classified suitability maps were used to assess the effect of climate change on I. scapularis distribution. The number of suitable pixels for each surface was used to calculate the net percentage change in suitable area for each integration. Integrating the current suitability map with the forecasted maps revealed the expansion and contraction of suitable area. The amount of change in each direction was then quantified. To estimate the effect of climate on human exposure to I. scapularis in the U.S., we converted the habitat suitability surfaces to county maps by zonal analysis. By associating these new suitability maps with U.S. 2000 Census population data (U.S. Census Bureau, 2000), we predicted future changes in human exposure.


The extrapolated autologistic model produced a current probability surface for the distribution of I. scapularis in North America. The regression coefficients converged after five iterations to produce the final probability surface (P < 0.0001) with all four climate variables remaining in the model. The landcover variable did not provide additional fit to the model. The probability threshold was set to 21% and predicted the current distribution of I. scapularis in the U.S. with an overall accuracy of 89%, a sensitivity of 88%, and a specificity of 89%.

The cutoff probability was used to develop a current distribution map for North America (Fig. 1). We classified 10.1% of the pixels (n = 11,779) as suitable for I. scapularis. In the U.S. (n = 3351) and Canada (n = 6804), 28.9 and 3.7% of the area, respectively, was above the critical threshold. Many areas not yet shown to be colonized were classified as having suitable climatic conditions for population maintenance. These areas of suitability were identified in Virginia, North Carolina, Georgia, Minnesota, Iowa, and northern Michigan. In addition, southern Ontario, southern Quebec, New Brunswick, Nova Scotia, Prince Edward Island, and eastern Newfoundland were given a high probability of I. scapularis establishment. A suitable climate was not found in Mexico.
Figure 1

Distribution of climate-based habitat suitability for Ixodes scapularis as predicted by the climate-based autologistic model. Suitable area (yellow) represents 10.1% of North America and predicts the current distribution of I. scapularis (red) with an accuracy of 89%. Nonoverlapped yellow pixels represent suitable areas that have yet to be colonized. The blue line across Ontario represents the northern limit of habitat suitability predicted by Lindsay et al. (1995).

The autologistic model for I. scapularis climate-based suitability was extrapolated to three future time points according to GCM scenarios either forced with greenhouse gases alone or combined with the effects of sulfate aerosols. The probability surfaces were classified according to the sensitivity analysis threshold and subtracted from the current suitability map (Fig. 2). The addition of sulfate aerosols to the climate model yielded an overall slower rate of change, but this effect disappears by the 2080s (Table 1).
Figure 2

Projected distribution of climate-based habitat suitability for Ixodes scapularis during three future time periods: the 2020s, the 2050s, and the 2080s. The simulation is based on climate-change predictions with the Canadian Global Coupled Model integration forced with anticipated increases in both greenhouse gas and sulfate aerosols. Areas of expansion and contraction from the current distribution of I. scapularis are displayed.

Table 1

Future Projected Changesa in Ixodes scapularis Habitat in North America during Three Future Time Periods


Net change (%)


Time period




Expansion (%)

Retraction (%)

Population exposed (%)






















































NA, North America; CAN, Canada; GG, simulations forced with greenhouse gas changes alone; GG+S, simulations forced with greenhouse gas and sulfate aerosol changes.

aPercentage change was calculated by subtracting the current predicted distribution from the future scenarios.

Considering the effects of both greenhouse gases and sulfate aerosols, the area with suitable climate decreases by 12% in the 2020s, as retraction (24.7%) overpowers expansion (12.8%). This decrease is solely attributed to a reduction in suitable area in the U.S. (−18.5%) focused in the Midwest. However, expansion does occur in both southern Canada (12.9%) and the southern U.S., including Texas, Oklahoma, and Arkansas. The 2050s reverses the trend of decreasing suitability with an increase in area of 10.3%. In the U.S., suitability returns to the Midwest, and expansion into the central states of Missouri and Kansas occurs. Extension into Canada continues with an additional 49.8% suitable area. The 2080s reveals the most pronounced effect of climate change, with a net increase in suitable area of 68.9%. Contraction of suitable area is confined to the southern states, especially Texas, Mississippi, and Florida. Encroachment continues in the central U.S., filling in previously unsuitable areas and closing the gap between southern and northern populations of I. scapularis. Canada experiences a major expansion of suitability in the 2080s, with a 212.9% increase. In particular, northern Ontario and Manitoba become accessible for I. scapularis colonization.

The potential effect of climate change on human exposure was examined by combining the climate-based suitability surfaces with U.S. 2000 Census population data. Counties suitable for I. scapularis establishment at each time point were revealed (Fig. 3). In the 2020s and 2050s, the model predicts a decrease in human exposure (Table 1). By the 2080s, despite the major burst in suitability, there is very little effect of climate change on net human exposure (−1.9%).
Figure 3

Change in county-based distribution of I. scapularis from the present to the 2080s. The future distribution based on climate-change data, which considers the effects of both greenhouse gas and sulfate aerosols, was overlain on the current predicted distribution. The map reveals future suitable (red) and unsuitable (blue) counties. Counties that remain suitable over time (pink) are also displayed.


Our findings provide a quantitative assessment of the effect of climate change on the future distribution of the most important vector of Lyme disease in North America. The robustness of our model was confirmed through an 89% accurate prediction of the current distribution of I. scapularis populations in the U.S. (Fig. 1). In Canada, the model predicts high probabilities of establishment in southern areas of Ontario, Quebec, and New Brunswick. This finding is validated by Lindsay et al. (1995), who found that a suitable habitat for I. scapularis establishment in Ontario occurs south of a line through North Bay, Ontario (46°2′N; 79°3′W). The limit of establishment according to our model lies within 130 km of North Bay.

Climate change is expected to cause a complicated redistribution of the vector, which reveals two major trends (Fig. 2). First, the redistribution is dominated by expansion. The increase in minimum temperature results in the expansion into higher latitudes, and this is explained by the inverse relationship between tick survival and the degree of subfreezing temperature exposure (Vandyk et al., 1996). This trend is clearly shown by the spreading of suitable area north into Canada. Although I. scapularis has been collected from a variety of locations in Canada (Keirans et al., 1996; Scott et al., 2001), establishment has been shown for only a few locations in southern Ontario (Lindsay et al., 1998; Barker and Lindsay, 2000). Climate change may provide the conditions necessary to yield reproducing populations of I. scapularis either by the systematic advancement from south of the border by movement on mammal hosts or by adventitious introductions from attachment to bird hosts (Klich et al., 1996). Similar expansion has been shown for I. ricinus in Sweden, where the movement north was predicted by an increase in milder daily temperature (Lindgren et al., 2000). Minimum temperature increase also results in the extension of suitability into higher altitudes. Elevation is an important limiting factor for I. scapularis populations because it indirectly affects population establishment through its influence on the complex interaction among climate, physical factors, and biota (Schulze et al., 1984). As a result of increasing temperatures, the model predicts advancement of suitability into the southern Appalachian Mountains.

Second, climate change results in the contraction of suitable area. Because the increase in maximum temperature yields unfavorable conditions for off-host survival of I. scapularis (Needham and Teel, 1991), we predict that this will result in the retraction of the vector from the lower latitudes of the U.S. This effect is exemplified in the 2080s, when major portions of Texas, Mississippi, and Florida become uninhabitable for I. scapularis. A comparable simulation of the effect of climate change on I. ricinus seasonal dynamics predicted that the increase in temperature would clear the risk of tickborne encephalitis from much of its present distribution in Europe (Randolph and Rogers, 2000). Increasing temperatures also produce temporary contraction in the Midwest in the 2020s. However, the combination of covariates in the autologistic model, including the coupling of increases in temperature with increases in relative humidity, will reproduce suitable conditions in the 2050s. It is impossible to assess whether this regional variability will actually lead to a temporary extinction from the area followed by reestablishment of the tick.

Despite the predicted redistribution, most of the current I. scapularis habitat remains suitable. This stability is especially reflected in the northeast U.S., the main focus of Lyme disease on the continent, where the vector, given a static landscape, will remain established over the next 80 years. The level of human exposure to I. scapularis will also remain approximately constant even though some changes may occur in populations at risk (Table 1). In fact, future population growth in the U.S. will be most evident in the South, including Texas and Florida (Campbell, 1996). With the projected net population change concentrated in areas of future unsuitability, climate change may actually contribute to a decrease in the proportion of the population exposed to I. scapularis in the U.S., even though its distribution will expand.

Other factors besides climate shifts will likely influence vector distribution and abundance, particularly on a local level. Although incorporating landcover at the continental scale did not increase model fit, our model of suitability is still contingent on the presence of a suitable physical landscape. Previously, landscape features such as deciduous forest and sandy soils that are correlated with I. scapularis presence (Kitron et al., 1991; Glass et al., 1994; Bertrand and Wilson, 1996) were used to develop a habitat suitability model for I. scapularis (Guerra et al., 2002). For instance, although our model predicts large areas of climate suitability, the distribution of I. scapularis in these areas is discontinuous as a result of landscape variability (e.g., agricultural and residential patchiness; Glass et al., 1994; Nicholson and Mather, 1996; Walker et al., 1996; Dister et al., 1997). Therefore, the application of this model at a higher resolution should be accompanied by landcover data. Because of the importance of landscape in the habitat suitability of I. scapularis, landcover change resulting directly through landscape modification and indirectly through climate change should also be examined for its effect on the future distribution of I. scapularis.

Landscape structure may also play an indirect role in the presence of I. scapularis through its influence on the abundance of the white-tailed deer, its main reproductive host. Although the current range of the white-tailed deer contains the entire expected distribution of I. scapularis with the exception of Newfoundland (Wilson and Ruff, 1999), it is host population density that will determine whether an introduction of I. scapularis can result in population maintenance (Spielman et al., 1985). Subsequently, white-tailed deer are more likely influenced by shifts in vegetation distribution than by thermal conditions because of their physiological tolerance to heat load (Johnston and Schmitz, 1997). Changes in landscape structure may therefore play an additional role in dictating future tick distribution.

Furthermore, environmental factors may also be responsible for controlling the enzootic maintenance of the Lyme disease agent, Borrelia burgdorferi. Climate change may exert an indirect effect on infection prevalence via its relationship with host species composition. Increases in temperature may result in the northward expansion of the southern hosts of I. scapularis. In the South, host composition is believed to be dominated by lizard species (Oliver et al., 1993) that are either inefficient or incompetent reservoirs of infection for immature ticks, thus resulting in overall low infection rates (Spielman et al., 1985). The movement of these hosts northward could result in the disruption of the enzootic cycle of B. burgdorferi in the North and reduce public health impact of Lyme disease.


Our model provides a climate-based prediction for the future distribution of Lyme disease in North America and highlights the probable public health effects of climate change in Canada. Projected changes in tick distribution could be used to form health policy and guide intervention measures for Lyme disease (Hayes and Piesman, 2003). Spatially explicit environmental models that predict disease emergence can form valuable tools for strengthening public health preparedness.



The authors thank Brandon Brei, Nita Madhav, and David Skelly for their helpful input. J.S.B. was supported by NASA Headquarters under Earth Science Fellowship grant NGT5-01-0000-0205 and the National Science and Engineering Research Council of Canada. This work was also supported by The Harold G. and Leila Y. Mathers Charitable Foundation (D.F.) and U.S. Department of Agriculture/Agricultural Research Service Cooperative Agreement 58-0790-2-072 (D.F.).


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Copyright information

© EcoHealth Journal Consortium 2005

Authors and Affiliations

  • John S. Brownstein
    • 1
    • 2
    • 3
  • Theodore R. Holford
    • 1
  • Durland Fish
    • 1
  1. 1.Department of Epidemiology and Public HealthSchool of Medicine, Yale UniversityNew Haven
  2. 2.Division of Emergency MedicineChildren’s Hospital BostonBoston
  3. 3.Department of PediatricsHarvard Medical SchoolBoston

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