Infodemic as a Nonlinear Process in the Public Health System

The article considers infodemic as a nonlinear process in the public health system, capable of generating wave dynamics of information activity and indirectly influencing behavioral determinants of health. A compartmental SIRPH model is proposed, which formalizes transitions between the states of ignorance (S), involvement (I), active dissemination (P), doubt/skepticism (H), and “exit” from the information process (R). The empirical basis is an agent-oriented simulation of information dissemination in a random graph of 2000 agents; the parameters of the system of ordinary differential equations are estimated by minimizing the loss function with enhanced attention to peak values, and numerical integration is performed using the 4th order Runge-Kutta method. The results show that the model adequately reproduces the integral macrodynamics of key states and accurately reproduces their peak levels, which is methodologically important for analyzing critical phases of infodemics as periods of maximum influence on health-related behaviors. The parametric structure of the model reveals a negative feedback loop, implemented through the interaction of individuals involved in the information process and a subgroup of individuals who have doubts about the reliability of messages. This mechanism is interpreted as a “deactivation” of informational involvement and a reduction in the effective pool of individuals capable of sustaining the infodemic wave. This justifies the feasibility of communication interventions aimed at improving critical thinking, medical and digital literacy as tools for reducing infodemic pressure and minimizing behavioral risks to public health. The practical significance of the results lies in the possibility of applying the proposed approach to monitor and predict peak phases of infodemics for the purpose of early warning and optimal timing of risk communication, fact-checking, and preventive refutation of typical disinformation narratives; comparative assessment of the effectiveness of communication strategies; further integration of the infodemic process with epidemic models (through transmission parameters, vaccination coverage, or timeliness of seeking medical care) for quantitative analysis of the potential consequences of the infodemic for morbidity, hospitalizations, and the burden on the healthcare system.