Estimating superinfection prevalence

We have published 2 papers in the last year related to superinfection. That is the phenomenon by which a single individual is infected by two strains of the same virus. What is important is that it indicates the immune system is not capable of learning enough from past exposures to protect against a following exposure. This is bad news for vaccine design because if a natural immune memory cannot be developed, it is unlikely an artificial agent can elicit enough memory.

The first paper by Dr Christine Johnston uses a simple computational approach but focuses on the actual prevalence of HSV (herpes simplex virus) dual-infection. `Dual-infection’ was added during the review process because it is more general than superinfection, it allows for simultaneous acquisition and this could not be ruled out in the data. Our method allowed us to guess that even with naive estimates of dual-strain infection prevalence of 3%, the real prevalence was probably more like 7%, not insignificant in the context of vaccine design.

The second is the more rigorous advancement of the methodology which I developed with huge help from Dr Amalia Magaret, a statistician and amazingly thorough thinker. This one was really fun, arising from the almost trivial observation that 3 strains would never be detected by 2 samples, we built a probabilistic sampling formalism inspired by ecology, and blew it out into a expectation maximization inference technique. It shows just how undersampled and how underestimated a prevalence estimate might be in the face of strain unevenness (different relative proportions of each strain in the body).

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