  Antibodies and humoral memory are key components of the adaptive immune
system. We consider and computationally model mechanisms by which humoral
memory present at baseline might instead increase infection load; we refer to
this effect as EI-HM (enhancement of infection by humoral memory). We first
consider antibody dependent enhancement (ADE) in which antibody enhances the
growth of the pathogen, typically a virus, and typically at intermediate
"Goldilocks" levels of antibody. Our ADE model reproduces ADE in vitro and
enhancement of infection in vivo from passive antibody transfer. But notably
the simplest implementation of our ADE model never results in EI-HM. Adding
complexity, by making the cross-reactive antibody much less neutralizing than
the de novo generated antibody or by including a sufficiently strong
non-antibody immune response, allows for ADE-mediated EI-HM. We next consider
the possibility that cross-reactive memory causes EI-HM by crowding out a
possibly superior de novo immune response. We show that, even without ADE,
EI-HM can occur when the cross-reactive response is both less potent and
"directly" (i.e. independently of infection load) suppressive with regard to
the de novo response. In this case adding a non-antibody immune response to our
computational model greatly reduces or completely eliminates EI-HM, which
suggests that "crowding out" is unlikely to cause substantial EI-HM. Hence, our
results provide examples in which simple models give qualitatively opposite
results compared to models with plausible complexity. Our results may be
helpful in interpreting and reconciling disparate experimental findings,
especially from dengue, and for vaccination.
