PDF(Pubmed)
Abstract：
BACKGROUND: Application of accumulated experience and management measures in the prevention and control of coronavirus disease 2019 (COVID-19) has generally depended on the subjective judgment of epidemic intensity, with the quality of prevention and control management being uneven. The present study was designed to develop a novel risk management system for COVID-19 infection in outpatients, with the ability to provide accurate and hierarchical control based on estimated risk of infection.
METHODS: Infection risk was estimated using an auto regressive integrated moving average model (ARIMA). Weekly surveillance data on influenza-like-illness (ILI) among outpatients at Xuanwu Hospital Capital Medical University and Baidu search data downloaded from the Baidu Index in 2021 and 22 were used to fit the ARIMA model. The ability of this model to estimate infection risk was evaluated by determining the mean absolute percentage error (MAPE), with a Delphi process used to build consensus on hierarchical infection control measures. COVID-19 control measures were selected by reviewing published regulations, papers and guidelines. Recommendations for surface sterilization and personal protection were determined for low and high risk periods, with these recommendations implemented based on predicted results.
RESULTS: The ARIMA model produced exact estimates for both the ILI and search engine data. The MAPEs of 20-week rolling forecasts for these datasets were 13.65% and 8.04%, respectively. Based on these two risk levels, the hierarchical infection prevention methods provided guidelines for personal protection and disinfection. Criteria were also established for upgrading or downgrading infection prevention strategies based on ARIMA results.
CONCLUSIONS: These innovative methods, along with the ARIMA model, showed efficient infection protection for healthcare workers in close contact with COVID-19 infected patients, saving nearly 41% of the cost of maintaining high-level infection prevention measures and enhancing control of respiratory infections.
METHODS: Infection risk was estimated using an auto regressive integrated moving average model (ARIMA). Weekly surveillance data on influenza-like-illness (ILI) among outpatients at Xuanwu Hospital Capital Medical University and Baidu search data downloaded from the Baidu Index in 2021 and 22 were used to fit the ARIMA model. The ability of this model to estimate infection risk was evaluated by determining the mean absolute percentage error (MAPE), with a Delphi process used to build consensus on hierarchical infection control measures. COVID-19 control measures were selected by reviewing published regulations, papers and guidelines. Recommendations for surface sterilization and personal protection were determined for low and high risk periods, with these recommendations implemented based on predicted results.
RESULTS: The ARIMA model produced exact estimates for both the ILI and search engine data. The MAPEs of 20-week rolling forecasts for these datasets were 13.65% and 8.04%, respectively. Based on these two risk levels, the hierarchical infection prevention methods provided guidelines for personal protection and disinfection. Criteria were also established for upgrading or downgrading infection prevention strategies based on ARIMA results.
CONCLUSIONS: These innovative methods, along with the ARIMA model, showed efficient infection protection for healthcare workers in close contact with COVID-19 infected patients, saving nearly 41% of the cost of maintaining high-level infection prevention measures and enhancing control of respiratory infections.
摘要：
背景：在2019年冠状病毒病（COVID-19）防控中积累的经验和管理措施的应用通常取决于对流行强度的主观判断，防控管理质量参差不齐。本研究旨在开发门诊患者COVID-19感染的新型风险管理系统，能够根据估计的感染风险提供准确和分层的控制。
方法：使用自回归综合移动平均模型（ARIMA）估计感染风险。首都医科大学宣武医院门诊患者流感样疾病（ILI）的每周监测数据以及2021年和22年从百度指数下载的百度搜索数据用于拟合ARIMA模型。通过确定平均绝对百分比误差（MAPE）来评估该模型估计感染风险的能力，使用Delphi过程就分层感染控制措施达成共识。COVID-19控制措施是通过审查公布的法规来选择的，文件和指南。确定了低风险期和高风险期的表面消毒和个人防护建议，这些建议是根据预测结果实施的。
结果：ARIMA模型为ILI和搜索引擎数据提供了精确的估计。这些数据集的20周滚动预测的MAPE分别为13.65%和8.04%，分别。基于这两个风险水平，分级感染预防方法为个人防护和消毒提供了指导。还根据ARIMA结果建立了升级或降低感染预防策略的标准。
结论：这些创新方法，以及ARIMA模型，对与COVID-19感染患者密切接触的医护人员表现出有效的感染保护，节省了维持高水平感染预防措施和加强呼吸道感染控制的近41％的成本。
方法：使用自回归综合移动平均模型（ARIMA）估计感染风险。首都医科大学宣武医院门诊患者流感样疾病（ILI）的每周监测数据以及2021年和22年从百度指数下载的百度搜索数据用于拟合ARIMA模型。通过确定平均绝对百分比误差（MAPE）来评估该模型估计感染风险的能力，使用Delphi过程就分层感染控制措施达成共识。COVID-19控制措施是通过审查公布的法规来选择的，文件和指南。确定了低风险期和高风险期的表面消毒和个人防护建议，这些建议是根据预测结果实施的。
结果：ARIMA模型为ILI和搜索引擎数据提供了精确的估计。这些数据集的20周滚动预测的MAPE分别为13.65%和8.04%，分别。基于这两个风险水平，分级感染预防方法为个人防护和消毒提供了指导。还根据ARIMA结果建立了升级或降低感染预防策略的标准。
结论：这些创新方法，以及ARIMA模型，对与COVID-19感染患者密切接触的医护人员表现出有效的感染保护，节省了维持高水平感染预防措施和加强呼吸道感染控制的近41％的成本。
