Table 2.

Summary of the characteristics of the three models used to estimate the number of averted cases of Lyme disease due to type specific immunity

Model	Key assumptionsa	Benefits	Limitations
Deterministic probability	Lyme disease patients’ probability of exposure to infectious bite similar to general population.	Extremely simple and flexible. Allows a separate analysis focusing on infections caused by invasive strains of B. burgdorferi.	May over estimate the impact of immunity on averted cases.
	Immunity is permanent.
		Provides the upper limit of averted cases.
Equilibrium dynamic	Lyme disease patients’ probability of exposure to infectious bite similar to general population.	Simple.	May under estimate the impact of immunity on averted cases.
		Provides the lower limit of averted cases.
	Immunity lasts 5 to 30 years.
	Lyme disease patients are at risk for tick bites for 30 years.
Individual-based stochastic	Lyme disease patients’ probability of exposure to infectious bite higher than in general population.	Most complex, allows manipulation of many parameters.	Simulations are time-demanding.
		May provide the most realistic estimate of the number of averted cases.
	Immunity lasts 5 to 30 years.
	Patients are at risk for tick bites for 30 years.

^aall models share the key assumptions that immunity provides 100 % protection to a particular OspC type of B. burgdorferi, that there is no cross-immunity across different OspC types, and that in the absence of immunity the likelihood of developing infection with a particular OspC type follows the strain frequencies presented in Table 1