Infectious disease prevention strategies for social health: a reinforcement learning-based assessment of the effectiveness of social capital investments

Disease prevention has always had an important impact on the development of human life and health.The integration of complex network theory and disease has become one of the major trends in epidemiologic research.However, aspects such as individual vaccination behavior and vaccination costs are affected by social capital investment.Based on this, the article investigates a reinforcement learning model of social capital investment on disease prevention.Based on the mechanism of infectious disease dynamics on complex networks, the article investigates the Markov decision process and composition of the reinforcement learning model, and utilizes the theory related to infectious disease dynamics and reinforcement learning to study the mechanism of voluntary vaccination based on epidemic perception.It was found that when the ratio of two kinds of investment (partial investment and full investment) reaches the set maximum value, the full investment policy of targeting selection is more effective in reducing the scale of disease infection in the whole social network and reducing the total social cost, followed by partial investment, and the full investment policy of random selection brings the smallest effect.However, the results may differ for different population investment ratios and partial investment ratios, and both the full investment policy and partial investment policy can effectively control disease prevention, which is conducive to the healthy and prosperous development of the whole society.