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引用本文:杨谧,王世超.区域性应对新冠肺炎疫情防控的药械保障能力评价体系研究[J].中国现代应用药学,2020,37(24):3045-3050.
YANG Mi,WANG Shichao.Research on the Evaluation System of Drugs and Medical Devices Supporting Capability for Regional Response to the Prevention and Control of COVID-19[J].Chin J Mod Appl Pharm(中国现代应用药学),2020,37(24):3045-3050.
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区域性应对新冠肺炎疫情防控的药械保障能力评价体系研究
杨谧, 王世超
浙江英特药业有限责任公司, 杭州 310053
摘要:
目的 研究区域性应对新冠肺炎疫情防控的药械保障能力评价体系。方法 本研究选取了省级区域2020年2月4日—2020年2月21日的药械生产能力、社会募集能力、运输能力、供应能力、需求满足能力和疫情防控效果等相关数据,构建VAR向量自回归模型。结果 该区域的社会募集能力增速、供应能力增速和需求满足能力对疫情防控效果有正向积极作用。结论 需求满足能力对疫情防控的贡献度最大、起效速度最快。本研究建立的评价体系为区域性应对突发疫情的药械保障供应能力建设提供参考。
关键词:  新冠肺炎  药械保障能力  VAR向量自回归
DOI:10.13748/j.cnki.issn1007-7693.2020.24.019
分类号:R955
基金项目:
Research on the Evaluation System of Drugs and Medical Devices Supporting Capability for Regional Response to the Prevention and Control of COVID-19
YANG Mi, WANG Shichao
Zhejiang Int'l Medicine Co., Ltd., Hangzhou 310053, China
Abstract:
OBJECTIVE To research evaluation system of drugs and medical devices supporting capability for regional response to the prevention and control of COVID-19. METHODS The research collected provincial area correlated data of production capability, social fundraising capability, transport capability, supply capability,demand satisfying capability and effects of prevention and control of epidemic situation from February 4th to February 21th in 2020, constructed VAR autoregression model. RESULTS The growth rate of social fundraising capability, the growth rate of supply capability and demand satisfying capability were positive for the effects of prevention and control in this provincial area. CONCLUSION The demand satisfying capability not only makes the most important contribution but also affects fastest on COVID-19 prevention and control in this provincial area. The evaluation system established in this article provides a reference for the construction of reginal drugs and medical devices supporting capability in response to sudden epidemics.
Key words:  COVID-19  drugs and medical devices supporting capability  VAR autoregression
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