关键词: 呼吸道传染疾病, 慢性病, 排队论, 复杂网络, 离散事件仿真, 二分法, 周期, 优化

Abstract:

The spread of respiratory diseases among people is likely to cause large-scale public health events. During the epidemic prevention and control period, the risk of infection caused by the accumulation of drug purchase can be effectively controlled and reduced by restricting the time cycle of drug purchase. This paper focuses on the drug purchase needs of patients with chronic diseases at the early stage of closure and resumption of work in the epidemic prevention and control period and proposes an optimization method for the time cycle of drug purchase according to the scene in the drugstore. First of all, according to the principle of "no drug purchase across districts" during the epidemic prevention and control period in Wuhan, the possible service relationship between the drugstore and the community was constructed through a complex network model. Then, based on the order data of Shared bikes, the attenuation law of the drug flow along with the distance was extracted, and on this basis, the distribution mechanism of the drug flow in the community was established among the drugstores providing services for it, so as to determine the customer arrival rate at the pharmacy side and realize the coupling of the spatial distribution characteristics of the community and the pharmacy with the queuing theory model. Additionally, the discrete event simulation method was used to simulate the system state under different time cycles of drug purchase. Finally, based on the simulation results, the optimal time cycle was found by using dichotomy to maximize the consideration of patients' demands for drugs and the utilization rate of drugstores. In our study, we took Wuhan central city as an example. First, the number of patients with various chronic diseases was estimated through conditional probability, and then the optimal drug purchase cycle was obtained by using the above method for 28 days. Under the optimal time cycle, the patient's average waiting time was 8.55 min, close to the optimal duration of waiting, and the vast majority of purchasing medicine needs can be met. Also, drugstores can be fully used (More than 87% of drugstores were busy), and the daily stock quantity of drugstores was not more than 100 (bottle or box), which was within the drugstore's capacity. The methods and conclusions of this paper can be used to guide the purchasing decisions of patients with chronic diseases and the preparation of chronic diseases in drugstores.

Key words: respiratory infections, chronic diseases, queuing theory, complex network, discrete event based simulation, dichotomy, cycle, optimization