Coevolution of non-pharmaceutical interventions and infectious disease spreading in age-structured populations

In the face of emerging major infectious diseases, non-pharmaceutical interventions (NPIs) such as mask-wearing are the primary means of disease control in the early stages due to the lack of effective pharmaceutical interventions. Yet the current understanding of the dynamic feedback loop between NPIs and infectious disease spreading remains limited. In this study, we proposed an asymmetrically coupled dynamic model of the coevolution of mask-wearing behaviour and infectious disease spreading in an age-structured population to describe the bidirectional relationship between NPIs such as mask-wearing and infectious disease spreading. A theoretical analysis framework was developed by extending the microscopic Markov chain method. To reflect the spreading process more realistically, we conducted numerical simulations from a mesoscopic perspective through an age-contact data-driven approach. Critical transition, multi-wave epidemics, multi-transitions, and mixed-phase transitions resulting from the coevolution of mask and disease are observed. The results highlight the critical role of NPIs in controlling disease spreading. The mask-wearing can effectively reduce the daily peak infection density and prevent large-scale outbreaks within a short period. However, it will also prolong the duration of the epidemic and cause the disease to exist in the population for a long time. There is a trade-off between the two. For certain diseases with specific spreading capabilities, pharmaceutical interventions such as vaccination, which can directly reduce the disease’s infectiousness, may result in more people being infected if NPIs are not implemented as early as possible. In addition, results from Anhui indicate that people aged 42 years are at high risk of being infected because of their large population base, which suggests that priority should be given to the prevention and control of this group.