If you want to go very basic and you feel the assumptions hold true - you could do a Fisher's exact test. An example how this would look in the SAS program is as follows.
Obviously you would not have to do this in SAS, but using it as an example. From this basic analyses using your sample you would get p-value < 0.0001, and an odds ratio 2.9360 (CI: 2.7678, 3.1145), meaning red segments are approximately 3 times more likely to be infected than blue segments.
However, with this much data you could probably do more advance analyses (multivariable logistic regression, controlling for other potential predictors). I also wondered if positioning of the segments affected infection. But it is all up to you.