Information feedback provokes multi-peak dynamics in the modern pandemic spreading

We propose a simple agent-based mathematical model of epidemic development capable of generating various multi-peak dynamic patterns typical of the COVID-19 pandemic. Each agent is assigned a very simple kind of behavior—moving in a homogeneous interaction space with a constant speed. Infection transmission becomes probable when the agents meet at a certain distance. Next, we assume that all our agents have three features of intelligence: (i) information-induced feedback, (ii) delay reaction to danger, and (iii) danger adaptation. All these features are accounted for in the model by the infection probability which becomes a dynamic variable driven by the additional differential equation. The information feedback means that this probability decreases as the number of infection cases increases. It reflects the fact that in the modern world, mass media monitor and report on the pandemic situation, and as the spread of infection progresses people start to protect themselves from the infection more actively, which finally leads to a decrease in the number of infection cases. Characteristic timings accounted for in our model by the delayed reaction to the information feedback and danger adaptation time are also important for the probability dynamics. Surprisingly, within these simple assumptions that did not account for any molecular specificity of COVID-19, except quite a long exposure time, we immediately get a multi-peak dynamic of the pandemic development. Here we show different conceptual cases of the such rhythmicity evolving under different parametric conditions.