1. A kit comprising a diagnostic test panel for diagnosing Lyme disease and a non-transitory computer program product comprising software which comprises code for interpreting test results from the diagnostic test panel, wherein the code is configured to cause a computer to perform steps comprising: (a) receiving results from performing the diagnostic test panel on a test population comprising a statistically significant sample of individuals with at least one objective sign of Lyme disease and a statistically significant control sample of healthy individuals or persons with cross-reacting medical conditions; (b) generating, by the computer, a score function from a linear combination of said diagnostic test panel results, said linear combination expressed as β T Y, wherein D is Lyme disease; Y 1 ,. .. , Y k is a set of K diagnostic tests for D; Y is a vector of diagnostic test panel results {Y 1 ,. .. , Y k }; D′=not D; β is a vector of coefficients {β 1 ,. .. , β k } for Y; and β T is the transpose of β; (c) performing, by the computer, a receiver operating characteristic (ROC) regression or alternative regression technique of the score function, wherein β coefficients are calculated to maximize the area under the curve (AUC) of the empiric ROC as approximated by: AUC ⁡ ( β ) = 1 n D · n H ⁢ ∑ i ∈ D , j ∈ D ⁢ I ⁡ ( β T ⁢ Y i > β T ⁢ Y j ) , wherein I is an indicator function, N=the number of study subjects, n D is the number of patients with Lyme disease D, n H is the number of healthy controls, n D +n H =N ; i=1,. .. , n D , i ε D are patients with Lyme disease; j=1,. .. , n H ,j ε H are healthy controls; (d) calculating, by the computer, for each individual the pre-test odds of Lyme disease; (e) generating a diagnostic likelihood ratio of Lyme disease by determining the frequency of each individual's test score in said diseased population relative to said control population, (f) multiplying said pretest odds by said likelihood ratio to determine the post-test odds of Lyme disease for each individual; (g) converting, by the computer, a set of posttest odds into posttest probabilities for each methodology and creating an ROC curve for each methodology by altering its respective post-test probability cutoff value; (h) comparing, by the computer, the ROC areas generated by one or more regression techniques to determine an optimal methodology, comprising tests included in the diagnostic test panel and the weight to be assigned each test score alone or in combination; (i) dichotomizing, by the computer, the optimal methodology by finding that point on the final ROC graph tangent to a line with a slope of (1−p)C/p·B, where p is the population prevalence of Lyme disease, B is the regret associated with failing to treat patients with Lyme disease and C is the regret associated with treating a patient without Lyme disease, thereby generating a posttest probability cutoff value; and (j) displaying, by the computer, an optimized diagnostic test panel for Lyme disease diagnosis, wherein the weight each individual test score is to be assigned alone or in combination, and the cutoff value against which positive or negative diagnoses are to be made.