Some drugs can be used to treat multiple diseases, suggesting potential patterns in drug treatment. Determination of drug treatment patterns can improve our understanding of the mechanisms of drug action, enabling drug repurposing. A drug can be associated with a multilayer tissue-specific protein–protein interaction (TSPPI) network for the diseases it is used to treat. Proteins usually interact with other proteins to achieve functions that cause diseases. Hence, studying drug treatment patterns is similar to studying common module structures in multilayer TSPPI networks. Therefore, we propose a network-based model to study the treatment patterns of drugs. The method was designated SDTP (studying drug treatment pattern) and was based on drug effects and a multilayer network model. To demonstrate the application of the SDTP method, we focused on analysis of trichostatin A (TSA) in leukemia, breast cancer, and prostate cancer. We constructed a TSPPI multilayer network and obtained candidate drug-target modules from the network. Gene ontology analysis provided insights into the significance of the drug-target modules and co-expression networks. Finally, two modules were obtained as potential treatment patterns for TSA. Through analysis of the significance, composition, and functions of the selected drug-target modules, we validated the feasibility and rationality of our proposed SDTP method for identifying drug treatment patterns. In summary, our novel approach used a multilayer network model to overcome the shortcomings of single-layer networks and combined the network with information on drug activity. Based on the discovered drug treatment patterns, we can predict the potential diseases that the drug can treat. That is, if a disease-related protein module has a similar structure, then the drug is likely to be a potential drug for the treatment of the disease.

In order to understand the influencing factors affecting the COVID-19 propagation, and analyze the development trend of the epidemic situation in the world, COVID-19 propagation model to simulate the COVID-19 propagation in the population is proposed in this paper. First of all, this paper analyzes the economic factors and interventions affecting the COVID-19 propagation in various different countries. Then, the touch number for COVID-19 High-risk Population Dynamic Network in this paper was redefined, and it predicts and analyzes the development trend of the epidemic situation in different countries. The simulation data and the published confirmed data by the world health organization could fit well, which also verified the reliability of the model. Finally, this paper also analyzes the impact of public awareness of prevention on the control of the epidemic. The analysis shows that increasing the awareness of prevention, timely and early adoption of protective measures such as wearing masks, and reducing travel can greatly reduce the risk of infection and the outbreak scale.

In this paper, the epidemic spreading was investigated from the point of view of fractal. Firstly, the real network was abstracted as a fully connected fractal network. Based on the fractal network, the fractal spreading process was studied. The fractal spreading process was simulated to analyze the change of infection density during transmission. The results showed that the infection density presented an upward trend of the ladder-shaped, and a jumping change in infection density occurred during a certain time. As an illustration, the pandemic of COVID-19 was analyzed, the results indicated that the proposed method was valid. Experiments and analyses have shown that intervention at key jump points in virus propagation can effectively control the spread of the virus.

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Recently, the study about the disease transmission has received widespread attention. In the dynamics process of infectious disease, individual’s cognition about disease-related knowledge is an important factor that controls disease transmission. The disease-related information includes the cause, symptoms, transmission route and so on. Disease-related knowledge would influence the individual’s attitude toward disease, and influence the transmission rate and scale of the infectious disease. In order to study the impact of individual cognition on the transmission of disease, the disease transmission model based on individual cognition is proposed in this paper. Based on this model, we numerically simulate the transmission of disease in the small-world network and the BA scale-free network, respectively, and analyze the transmission dynamics behavior of the infectious disease. The simulation experiment verifies the validity of the theoretical result, which shows that this model is closer to the reality than traditional models.