Department of Preventive Medicine, School of Medicine, Shihezi University, Shihezi, 832003, PR China.
Key Laboratory for Prevention and Control of Emerging Infectious Diseases and Public Health Security, The Xinjiang Production and Construction Corps, Urumqi, China.
BMC Infect Dis. 2024 Feb 14;24(1):200. doi: 10.1186/s12879-024-09007-7.
A lack of health resources is a common problem after the outbreak of infectious diseases, and resource optimization is an important means to solve the lack of prevention and control capacity caused by resource constraints. This study systematically evaluated the similarities and differences in the application of coronavirus disease (COVID-19) resource allocation models and analyzed the effects of different optimal resource allocations on epidemic control.
A systematic literature search was conducted of CNKI, WanFang, VIP, CBD, PubMed, Web of Science, Scopus and Embase for articles published from January 1, 2019, through November 23, 2023. Two reviewers independently evaluated the quality of the included studies, extracted and cross-checked the data. Moreover, publication bias and sensitivity analysis were evaluated.
A total of 22 articles were included for systematic review; in the application of optimal allocation models, 59.09% of the studies used propagation dynamics models to simulate the allocation of various resources, and some scholars also used mathematical optimization functions (36.36%) and machine learning algorithms (31.82%) to solve the problem of resource allocation; the results of the systematic review show that differential equation modeling was more considered when testing resources optimization, the optimization function or machine learning algorithm were mostly used to optimize the bed resources; the meta-analysis results showed that the epidemic trend was obviously effectively controlled through the optimal allocation of resources, and the average control efficiency was 0.38(95%CI 0.25-0.51); Subgroup analysis revealed that the average control efficiency from high to low was health specialists 0.48(95%CI 0.37-0.59), vaccines 0.47(95%CI 0.11-0.82), testing 0.38(95%CI 0.19-0.57), personal protective equipment (PPE) 0.38(95%CI 0.06-0.70), beds 0.34(95%CI 0.14-0.53), medicines and equipment for treatment 0.32(95%CI 0.12-0.51); Funnel plots and Egger's test showed no publication bias, and sensitivity analysis suggested robust results.
When the data are insufficient and the simulation time is short, the researchers mostly use the constructor for research; When the data are relatively sufficient and the simulation time is long, researchers choose differential equations or machine learning algorithms for research. In addition, our study showed that control efficiency is an important indicator to evaluate the effectiveness of epidemic prevention and control. Through the optimization of medical staff and vaccine allocation, greater prevention and control effects can be achieved.
传染病爆发后，卫生资源不足是一个常见问题，资源优化是解决资源约束导致的防控能力不足的重要手段。本研究系统评估了冠状病毒病（COVID-19）资源分配模型的应用异同，并分析了不同最优资源分配对疫情控制的影响。
对中国知网、万方、维普、中国生物医学文献服务系统、PubMed、Web of Science、Scopus 和 Embase 从 2019 年 1 月 1 日至 2023 年 11 月 23 日期间发表的文章进行了系统检索。两位审查员独立评估纳入研究的质量，提取和交叉核对数据。此外，还评估了发表偏倚和敏感性分析。
共纳入 22 篇文章进行系统综述；在最优分配模型的应用中，59.09%的研究使用传播动力学模型模拟各种资源的分配，一些学者还使用数学优化函数（36.36%）和机器学习算法（31.82%）来解决资源分配问题；系统评价结果表明，在检验资源优化时更考虑微分方程建模，优化函数或机器学习算法主要用于优化床位资源；Meta 分析结果表明，通过资源最优分配，疫情趋势得到明显有效控制，平均控制效率为 0.38（95%CI 0.25-0.51）；亚组分析表明，从高到低的平均控制效率分别为卫生专家 0.48（95%CI 0.37-0.59）、疫苗 0.47（95%CI 0.11-0.82）、检测 0.38（95%CI 0.19-0.57）、个人防护装备（PPE）0.38（95%CI 0.06-0.70）、床位 0.34（95%CI 0.14-0.53）、治疗药物和设备 0.32（95%CI 0.12-0.51）；漏斗图和 Egger 检验表明无发表偏倚，敏感性分析提示结果稳健。
数据不足且模拟时间较短时，研究人员多采用构造器进行研究；数据较充足且模拟时间较长时，研究人员选择微分方程或机器学习算法进行研究。此外，本研究表明控制效率是评估防控效果的重要指标，通过医护人员和疫苗的优化配置，可以取得更大的防控效果。
