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SEIHRS_gv模型——基于短期数据预测流行性感冒样病例疫情流行趋势

金鑫 于静波 崔爽爽 王岩 于浩

金鑫, 于静波, 崔爽爽, 王岩, 于浩. SEIHRS_gv模型——基于短期数据预测流行性感冒样病例疫情流行趋势[J]. 中华疾病控制杂志, 2024, 28(9): 1075-1082. doi: 10.16462/j.cnki.zhjbkz.2024.09.013
引用本文: 金鑫, 于静波, 崔爽爽, 王岩, 于浩. SEIHRS_gv模型——基于短期数据预测流行性感冒样病例疫情流行趋势[J]. 中华疾病控制杂志, 2024, 28(9): 1075-1082. doi: 10.16462/j.cnki.zhjbkz.2024.09.013
JIN Xin, YU Jingbo, CUI Shuangshuang, WANG Yan, YU Hao. SEIHRS_gv model——predicting the influenza-like illness epidemic trend based on short term data[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2024, 28(9): 1075-1082. doi: 10.16462/j.cnki.zhjbkz.2024.09.013
Citation: JIN Xin, YU Jingbo, CUI Shuangshuang, WANG Yan, YU Hao. SEIHRS_gv model——predicting the influenza-like illness epidemic trend based on short term data[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2024, 28(9): 1075-1082. doi: 10.16462/j.cnki.zhjbkz.2024.09.013

SEIHRS_gv模型——基于短期数据预测流行性感冒样病例疫情流行趋势

doi: 10.16462/j.cnki.zhjbkz.2024.09.013
基金项目: 

天津市卫生健康科技项目 TJWI2022MS046

详细信息
    通讯作者:

    于静波,E-mail: yujingbo2333@163.com

    于浩,E-mail: tjcdc_yuhao@163.com

  • 中图分类号: R181.8;R183

SEIHRS_gv model——predicting the influenza-like illness epidemic trend based on short term data

Funds: 

The Scientific and Technological Project of Tianjin Health TJWI2022MS046

More Information
  • 摘要:   目的  利用天津市流行性感冒(简称流感)样病例(influenza-like illness, ILI)监测数据,开发ILI疫情流行趋势预测模型;量化评估疫情防控措施对ILI产生的医疗负担影响。  方法  选取2023年11月6日―2023年11月15日天津市ILI数据进行SEIHRS_gv模型拟合,以2023年11月15日―2024年3月31日数据进行模型验证。选择均方根误差(root mean square error, RMSE)、平均绝对误差(mean absolute error, MAE)及决定系数(r-square, R2)评价模型预测能力。  结果  SEIHRS_gv模型可预测ILI疫情流行趋势、峰值及拐点,使用10 d数据进行预测,当R2达到0.85,RMSE为949.5,提高疫情防控措施强度可减少就诊人群数量。  结论  SEIHRS_gv模型在本轮ILI疫情预测中仅需几天数据,就可获得高精度预测结果,可作为评估医院就诊压力、指导疫情控制措施的高效预测模型。
  • 图  1  每日新增确诊病例数影响因素的相关性分析表

    Figure  1.  Correlation analysis of factors affecting the daily number of newly confirmed cases

    图  2  SEIHRS_gv模型流程图

    Figure  2.  The flowchart of the SEIHRS_gv model

    图  3  每日主动就诊人数预测曲线和实际曲线对比

    Figure  3.  Comparison between the predicted curve and the actual curve of the daily visits

    图  4  每日主动就诊人数预测曲线和实际曲线对比(7、8、9 d)

    Figure  4.  Comparison between the predicted curve and the actual curve of the daily visits(7, 8, 9 d)

    图  5  不同政策收缩指数取值的每日就诊人数曲线

    Figure  5.  Curves of daily visits with different values of the policy contraction index

    图  6  不同α取值下的每日就诊人数预测曲线

    Figure  6.  Prediction curves of daily visits in different alpha values

    表  1  SEIHRS_gv模型参数取值表

    Table  1.   Parameter values of the SEIHRS_gv model

    参数 Parameter 定义 Definition 取值 Value 来源 Source
    β 传播系数 Infectious rate 0.045 MCMC
    α 单位时间内,潜伏期人群以一定的速率α转化为感染人群
    Within a unit of time, latent individuals transform into infected individuals at a rate of α
    1、1/10、1/20、1/50 实际疫情推算
    Actual Epidemic
    c 感染人群每天接触人数
    Number of daily contacts of infected individuals
    10 文献
    Literature[9-10]
    pH 感染人群以一定比例pH需要去医院就诊
    Infected individuals need to seek medical attention at a rate of pH
    0.001 MCMC
    pHR 主动就诊人群以一定比例pHR恢复健康
    Proactively seeking medical treatment leads to a recovery rate of pHR for the population
    0.999 实际疫情推算
    Actual epidemic
    γH 假设感染人群从感染到需要去医院就诊的时间为tγHt的倒数
    The reciprocal of t, which t is the time from infection to the need for medical treatment for infected individuals
    1/2 文献
    Literature[9, 11-12]
    γI 单位时间内从患者群体中移除的比率为γI,设置为发病到康复的时间的倒数
    γI is the removal rate from the patient population per unit time, it was set up as the reciprocal of the time from onset to recovery
    1/5 文献
    Literature[13-14]
    γHR 假设感染人群从去医院就诊到康复的时间为tγHRt的倒数
    Assuming that t is the time that an infected person needs to go to the hospital for treatment, γHR is the reciprocal of t
    1/5 文献
    Literature[15-16]
    ρ 已康复人群由于体内抗体水平下降或免疫逃逸失去部分免疫力并再次变得易感的比率
    The rate that people with immunity from ILI became susceptible again due to a decrease in antibody levels or immune evasion
    1/90 实际疫情推算
    Actual epidemic
    gv 政策收缩指数[8] Government stringency index 0~1 文献 Literature[8]
    注:MCMC,马尔科夫链蒙特卡洛; ILI, 流行性感冒样病例。
    Note: MCMC, Markov chain Monte Carlo; ILI, influenza-like illness.
    下载: 导出CSV

    表  2  不同政策收缩指数取值对应措施

    Table  2.   Corresponding measures for different policy contraction index values

    收缩指数
    Stringency index
    疫情防控措施
    Anti-pandemic measures
    0 无政策 No policy
    0.25 提倡个人防护、增大宣传活动、倡导减缓流动,有症状人自愿检测
    Promote personal protection, increase publicity activities, advocate slowing down mobility, and encourage symptomatic individuals to voluntarily undergo testing
    0.50 限制公众集会、公共场所关闭、取消公共活动,有症状的人进行检测,在特定公开场合佩戴口罩
    Restricting public gatherings, closing public places, canceling public activities, testing symptomatic individuals, and wearing masks in specific public places
    0.75 学校关闭、工作场所关闭,区域筛查,在公共场所佩戴口罩
    School and workplace closure, regional screening and wearing masks in public places
    1.00 居家要求,全员筛查,严格口罩佩戴
    Home requirements, full staff screening and strict mask wearing
    下载: 导出CSV
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出版历程
  • 收稿日期:  2024-06-18
  • 修回日期:  2024-08-21
  • 网络出版日期:  2024-10-24
  • 刊出日期:  2024-09-10

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