Unravelling the interconnected dynamics of infectious disease spread and health-related beliefs and behaviours
How can we better predict the population behaviour over time in response to an infectious disease and the impact of this behaviour on the spreading of the disease itself?
The COVID-19 pandemic again reminds us of the complex interplay between the occurrence of infectious diseases and health-related beliefs and behaviours. With more successful control, the perceived risk of infection decreases, leading to more high-risk behaviour that may result in resurgence, again increasing perceived risk. This is further complicated by the fact that the rewards of healthy behaviour (e.g. decreased infection risk due to physical distancing) typically arrive later than the satisfaction of more risky behaviours (e.g. intimacy and freedom of movement). Social networks are central to both the spread of infectious diseases and to how individuals’ beliefs and behaviours are influenced by peers, thus contributing to sustained or even widening social inequities in health. Besides the COVID-19 pandemic, similar challenges have plagued efforts to control major infectious diseases like measles, rubella, polio, and malaria. To understand why and how populations behave over time in the context of infectious disease outbreaks, there is a clear need to better integrate mathematical models for the population dynamics for disease and models for individual and group behaviour.
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