  Infectious diseases often spread faster near their peak than would be
predicted given early data on transmission. Despite the commonality of this
phenomena, there are no known general mechanisms able to cause an exponentially
spreading dis- ease to begin spreading faster. Indeed most features of real
world social networks, e.g. clustering1,2 and community structure3, and of
human behaviour, e.g. social distancing4 and increased hygiene5, will slow
disease spread. Here, we consider a model where individuals with essential
societal roles-e.g. teachers, first responders, health-care workers, etc.- who
fall ill are replaced with healthy individuals. We refer to this process as
relational exchange. Relational exchange is also a behavioural process, but one
whose effect on disease transmission is less obvious. By incorporating this
behaviour into a dynamic network model, we demonstrate that replacing
individuals can accelerate disease transmission. Furthermore, we find that the
effects of this process are trivial when considering a standard mass-action
model, but dramatic when considering network structure. This result highlights
another critical shortcoming in mass-action models, namely their inability to
account for behavioural processes. Lastly, using empirical data, we find that
this mechanism parsimoniously explains observed patterns across more than
seventeen years of influenza and dengue virus data. We anticipate that our
findings will advance the emerging field of disease forecasting and will better
inform public health decision making during outbreaks.
