Modeling the Sensitivity of Blacklegged Ticks (Ixodes scapularis) to Temperature and Land Cover in the Northeastern United States.

The prevalence of Lyme disease and other tick-borne diseases is dramatically increasing across the United States. While the rapid rise in Lyme disease is clear, the causes of it are not. Modeling Ixodes scapularis Say (Acari: Ixodidae), the primary Lyme disease vector in the eastern United States, presents an opportunity to disentangle the drivers of increasing Lyme disease, including climate, land cover, and host populations. We improved upon a recently developed compartment model of ordinary differential equations that simulates I. scapularis growth, abundance, and infection with Borrelia burgdorferi (Spirochaetales: Spirochaetaceae) by adding land cover effects on host populations, refining the representation of growth stages, and evaluating output against observed data. We then applied this model to analyze the sensitivity of simulated I. scapularis dynamics across temperature and land cover in the northeastern United States. Specifically, we ran an ensemble of 232 simulations with temperature from Hanover, New Hampshire and Storrs, Connecticut, and land cover from Hanover and Cardigan in New Hampshire, and Windsor and Danielson in Connecticut. Consistent with observations, simulations of I. scapularis abundance are sensitive to temperature, with the warmer Storrs climate significantly increasing the number of questing I. scapularis at all growth stages. While there is some variation in modeled populations of I. scapularis infected with B. burgdorferi among land cover distributions, our analysis of I. scapularis response to land cover is limited by a lack of observations describing host populations, the proportion of hosts competent to serve as B. burgdorferi reservoirs, and I. scapularis abundance.

Effect of Rising Temperature on Lyme Disease: Ixodes scapularis Population Dynamics and Borrelia burgdorferi Transmission and Prevalence.

Warmer temperatures are expected to increase the incidence of Lyme disease through enhanced tick maturation rates and a longer season of transmission. In addition, there could be an increased risk of disease export because of infected mobile hosts, usually birds. A temperature-driven seasonal model of Borrelia burgdorferi (Lyme disease) transmission among four host types is constructed as a system of nonlinear ordinary differential equations. The model is developed and parametrized based on a collection of lab and field studies. The model is shown to produce biologically reasonable results for both the tick vector (Ixodes scapularis) and the hosts when compared to a different set of studies. The model is used to predict the response of Lyme disease risk to a mean annual temperature increase, based on current temperature cycles in Hanover, NH. Many of the risk measures suggested by the literature are shown to change with increased mean annual temperature. The most straightforward measure of disease risk is the abundance of infected questing ticks, averaged over a year. Compared to this measure, which is difficult and resource-intensive to track in the field, all other risk measures considered underestimate the rise of risk with rise in mean annual temperature. The measure coming closest was "degree days above zero." Disease prevalence in ticks and hosts showed less increase with rising temperature. Single field measurements at the height of transmission season did not show much change at all with rising temperature.