Abstract
Lyme disease is the most commonly reported vector-borne disease in the United States, and the number of cases reported each year continues to rise. The complex nature of the relationships between the pathogen (Borrelia burgdorferi sensu stricto), the tick vector (Ixodes scapularis Say), multiple vertebrate hosts, and numerous environmental factors creates challenges for understanding and predicting tick population and pathogen transmission dynamics. LYMESIM is a mechanistic model developed in the late 1990s to simulate the life-history of I. scapularis and transmission dynamics of B. burgdorferi s.s. Here we present LYMESIM 2.0, a modernized version of LYMESIM, that includes several modifications to enhance the biological realism of the model and to generate outcomes that are more readily measured under field conditions. The model is tested for three geographically distinct locations in New York, Minnesota, and Virginia. Model-simulated timing and densities of questing nymphs, infected nymphs, and abundances of nymphs feeding on hosts are consistent with field observations and reports for these locations. Sensitivity analysis highlighted the importance of temperature in host finding for the density of nymphs, the importance of transmission from small mammals to ticks on the density of infected nymphs, and temperature-related tick survival for both density of nymphs and infected nymphs. A key challenge for accurate modeling of these metrics is the need for regionally representative inputs for host populations and their fluctuations. LYMESIM 2.0 is a useful public health tool that downstream can be used to evaluate tick control interventions and can be adapted for other ticks and pathogens.
Trends in tick population dynamics and pathogen transmission in emerging tick-borne pathogens in Europe: an introduction
