The coupling process of awareness and disease under the influence of synergistic-hedging effect of information resources
Liang’an Huo, Jinglin Zhao
University of Shanghai for Science and Technology Shanghai Pudong New Area Gongli Hospital
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摘要与影响
Information serves as a crucial yet invisible resources and can be categorized into two types: positive information resources and negative information resources. Different types of information resources interact to produce synergistic and hedging effects, which in turn influence individuals' adoption of targeted protective behaviors. These effects shape the scope and effectiveness of disease control measures and ultimately the dynamics of disease transmission. Additionally, the availability of medical resources enhances both individuals' overall resilience and the efficiency of disease response. To address these complexities, we propose a coupled model of awareness diffusion and disease transmission that incorporates the synergistic-hedging effect of information resources and influence of medical resources on disease containment. Using the microscopic Markov chain approach (MMCA), we derive the disease transmission threshold. Monte Carlo simulations reveal that the hedging effect of negative information resources on positive information resources contributes to an increased scale of infection, especially in the early stages of an outbreak. Conversely, the synergistic effects of positive information resources become more effective in inhibiting disease transmission as the sensitivity of information resources increases. Although this sensitivity does not affect the outbreak threshold, it significantly reduces the scale of infection. Notably, while increasing sensitivity to information resources gradually reduces the infection scale, enhanced the utilization of medical resources exerts a significantly stronger effect, rapidly and substantially lowering infection scale. Moreover, a combined strategy involving both information and medical resources proves to be the most effective in controlling disease outbreaks.
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生物医学Mathematical and Theoretical Epidemiology and Ecology Models
Complex Network Analysis Techniques · COVID-19 epidemiological studies
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