Prudent behaviour accelerates disease transmission

TL;DR

This paper introduces a model where the replacement of ill essential workers accelerates disease spread, revealing that behavioral processes like relational exchange significantly impact transmission dynamics, especially in networked populations.

Contribution

It demonstrates that relational exchange can accelerate disease transmission in network models, a phenomenon not captured by traditional mass-action models, supported by empirical influenza and dengue data.

Findings

Relational exchange accelerates disease spread in network models.

Mass-action models underestimate the impact of behavioral processes.

Empirical data supports the model's explanation of disease patterns.

Abstract

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.