A modelling approach to characterise the interaction between behavioral response and epidemics: A study based on COVID-19.

During epidemic outbreaks, human behavior is highly influential on the disease transmission and hence affects the course, duration and outcome of the epidemics. In order to examine the feedback effect between the dynamics of the behavioral response and disease outbreak, a simple SIR- type model is established by introducing the independent variable of effective contact rate, characterizing how human behavior interacts with disease transmission dynamics and allowing for the feedback changing over time along the progress of epidemic and population's perception of risk. By a particle swarm optimization algorithm in the solution procedures and time series of COVID-19 data with different shapes of infection peaks, we show that the proposed model, together with such behavioral change mechanism, is capable of capturing the trend of the selected data and can give rise to oscillatory prevalence of different magnitude over time, revealing how different levels of behavioral response affect the waves of infection as well as the evolution of the disease.