在全球化高度发展的背景下, 多病毒耦合的传染病传播已成为公共卫生领域的关键问题。传统的传染病传播模型如SEIR(susceptible-exposed-infected-recovered)模型通常仅考虑单一病毒的传播, 难以有效反映多种病毒在同一社会环境下的复杂影响关系。在多病毒耦合的情况下, 不同病毒之间可能存在相互促进或相互抑制等作用, 会影响传染病的传播动态和感染风险。该文对SEIR模型进行扩展与改进, 加入了无症状感染者、隔离者、死亡者等状态, 提出了一种多病毒耦合传播模型, 并考虑了病毒间的相互作用与免疫逃逸对疾病传播的影响。研究发现, 病毒间的相互促进作用能够提升感染规模, 拉长传播周期, 导致感染峰值到达的时间提前, 同时对感染规模大的病毒的传播增强效应比对感染规模小的病毒的更明显; 相互抑制作用可以降低感染规模, 延缓传播速度, 导致感染峰值到达的时间延后, 同时对感染规模大的病毒的传播减弱效应比对感染规模小的病毒的也更明显; 免疫逃逸率的提升可以显著增加感染规模, 延长病毒的传播周期。该文为多病毒耦合环境下的传染病传播提供了新的理论框架, 为疫情风险预测与干预策略研究提供了更为精准、适用的模型支持。

Objective: In the context of advanced globalization, the transmission of infectious diseases, coupled with multiple viruses, has become a key issue in the field of public health. Traditional models for infectious disease transmission, such as susceptible-exposed-infected-covered (SEIR), typically only consider the transmission of a single virus, limiting its capability to capture the complex interactions of multiple viruses within the same social environment. Multi-virus coupling may demonstrate either a mutually promotional effect or a mutually inhibitory effect between different viruses, affecting the dynamics of transmission and the associated infection risks. Methods: This article enhances the SEIR model by incorporating additional states, such as asymptomatic individuals, divided individuals, and mortal individuals, to accurately characterize the complex population state transitions during the transmission of a single virus. On this basis, a multi-virus coupled transmission model is proposed. This model not only considers the impact of various factors, such as mutually promotional effects, mutually inhibitory effects, and immune escape effects, on coupled infections but also integrates the influence of medical resources, changes in personnel behavior, and prevention and control interventions on epidemic transmission. The multi-virus coupled transmission model can quantify the strength of mutual reinforcement or inhibition between viruses by adjusting the interaction coefficient, facilitating the simulation and analysis of the coupling effect between viruses. Results: Results reveal that the mutually promotional effect between viruses can increase the scale of infection, extend the transmission cycle, and lead to an earlier peak infection time. Additionally, this effect is more pronounced for viruses with larger infection scales than for those with smaller infection scales. In contrast, the mutually inhibitory effect can reduce the infection scale, slow down the transmission rate, and delay the peak infection time. The inhibitory effect is more substantial for larger viruses than for smaller ones. The increase in the immune escape rate can significantly increase the scale of infection and prolong the transmission cycle of the virus. When multiple viruses are transmitted together, changes in the transmission rate of viruses with larger infection scales have a considerable impact on the peak infection rate. In addition, comparing the simulation results of the coupled transmission model with real data shows that the trends are consistent, demonstrating a bimodal phenomenon. The simulation results align well with real data in terms of macro indicators, such as peak time and peak duration. However, in terms of the number of infections, the simulated infection rate is relatively lower compared to the detection-positive rate. Conclusions: The research results have effectively explained the infection patterns of social populations under the coexistence of multiple viruses, emphasizing the crucial role of complex interaction mechanisms between different viruses in real-world infectious disease monitoring and prediction. This article presents a new theoretical framework for the transmission of infectious diseases in a multi-virus coupled environment, offering more accurate and applicable model support for epidemic risk analysis and the development of intervention strategies.