文章摘要
范君言,沈佳莹,胡明,赵岳,林剑生,曹广文.上海市新型冠状病毒肺炎流行与疫情时空变化分析[J].中华流行病学杂志,2022,43(11):1699-1704
上海市新型冠状病毒肺炎流行与疫情时空变化分析
Spatiotemporal changes of COVID-19 outbreak in Shanghai
收稿日期:2022-06-08  出版日期:2022-11-22
DOI:10.3760/cma.j.cn112338-20220608-00511
中文关键词: 新型冠状病毒肺炎  流行特征  空间自相关
英文关键词: COVID-19  Epidemiological characteristics  Spatial autocorrelation
基金项目:国家自然科学基金(82041022);上海科学技术委员会(20JC1410200,20431900404)
作者单位E-mail
范君言 海军军医大学海医系流行病学教研室, 上海 200433  
沈佳莹 同济大学医学院, 上海 200331  
胡明 海军军医大学海医系流行病学教研室, 上海 200433  
赵岳 海军军医大学海医系流行病学教研室, 上海 200433  
林剑生 暨南大学基础医学院, 广州 510632  
曹广文 海军军医大学海医系流行病学教研室, 上海 200433 gcao@smmu.edu.cn 
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中文摘要:
      目的 阐明2022年上海市新型冠状病毒肺炎(新冠肺炎)的流行特征和空间聚集性演变规律。方法 收集2022年3月1日至5月31日上海市各行政区卫生健康委员会官方网站公布的新冠肺炎疫情数据,应用GeoDa软件进行空间自相关分析;利用logistic增长模型进行拟合预测并与实际感染病例进行对比。结果 上海市各行政区中,浦东新区确诊病例数和无症状感染者人数最多,占总病例数的29.30%和35.58%,各区累计罹患率和感染率差异有统计学意义(P<0.001),其中黄浦区显著高于其他区域。2022年3月1日至5月31日新冠肺炎罹患率具有全局空间正相关性(P<0.05),不同时段新冠肺炎罹患率空间分布不同,其中3月16-29日、4月6-12日和5月18-24日3个时段内Moran's I值差异无统计学意义(P>0.05)。局部自相关分析结果表明,8个时段共探测到22个高-高聚集区,高风险流行的热点区域经历一个“少-多-少”的变化过程。logistic增长模型拟合与实际感染者情况基本吻合。结论 本轮上海市新冠肺炎疫情整体分布具有明显的空间聚集性,防控措施有效阻止了疫情的增长,尤其在空间上遏制了高风险传播区域的扩散,减少了向其他省份的传播风险。
英文摘要:
      Objective To clarify the epidemiological characteristics and spatiotemporal clustering dynamics of COVID-19 in Shanghai in 2022. Methods The COVID-19 data presented on the official websites of Municipal Health Commissions of Shanghai during March 1, 2022 and May 31, 2022 were collected for a spatial autocorrelation analysis by GeoDa software. A logistic growth model was used to fit the epidemic situation and make a comparison with the actual infection situation. Results Pudong district had the highest number of symptomatic and asymptomatic infectants, accounting for 29.30% and 35.58% of the total infectants. Differences in cumulative attack rates and infection rates among 16 districts (P<0.001) were significant. The rates were significantly higher in Huangpu district than in other districts. The attack rate of COVID-19 from March 1, 2022 to May 31, 2022 had a global spatial positive correlation (P<0.05). Spatial distribution of COVID-19 attack rate was different at different periods. The global autocorrelation coefficient from March 16 to March 29, April 6 to April 12 and May 18 to May 24 had no statistical significance (P>0.05). Our local autocorrelation analysis showed that 22 high-high clustering areas were detected in eight periods.The high-risk hot-spot areas have experienced a "less-more-less" change process. The growth model fitting results were consistent with the actual infection situation. Conclusion There was a clear spatiotemporal correlation in the distribution of COVID-19 in Shanghai. The comprehensive prevention and control measures of COVID-19 epidemic in Shanghai have effectively prohibited the growth of the epidemic, not only curbing the spatially spread of high-risk epidemic areas, but also reducing the risk of transmission to other cities.
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