Development of a behavioural modulation strategy for disease control based on network interventions

The impact of human behaviour evolution poses a major challenge in the control of COVID-19. The key to overcoming this problem is incorporating behavioural factors into disease interventions. This paper proposes a novel behavioural modulation means based on network intervention strategies, aiming to achieve disease prevention at the population level through behavioural modulation of seed nodes. Taking individual decision-making behaviour as a representative example, we explore the efficacy of the proposed behavioural modulation method within a coupled behaviour-disease model. Using epidemic threshold and infection density as indicators, the results demonstrate that behavioural modulation under various network intervention strategies can effectively control disease transmission within populations. On this basis, the intervention costs incurred by implementing behavioural modulation are also noteworthy. Further analysis reveals an optimal interval of intervention proportions capable of simultaneously achieving epidemic control and cost savings, which can guide the practical implementation of such control measures. The above conclusions are validated through simulation with representative real-world contact network data. Our work has led to new advances in realizing disease control from a behavioural perspective, which is of great value as a guide for the public health sector in the development of epidemic prevention policies.