Research on Dynamic Outpatient Respiratory Nosocomial Infection Control Methods Through Multi-Data Prediction
Yuncong Wang, Wenhui Ma, Yang Yang, Huijie Zhao, Zhongjing Zhao, Xia Zhao Hospital Infection Management Division, Xuanwu Hospital Capital Medical University, Beijing, People’s Republic of ChinaCorrespondence: Yuncong Wang, Hospital Infection Management Division, Xuanwu Hospital Capital Medical Unive...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Dove Medical Press
2025-04-01
|
| Series: | Risk Management and Healthcare Policy |
| Subjects: | |
| Online Access: | https://www.dovepress.com/research-on-dynamic-outpatient-respiratory-nosocomial-infection-contro-peer-reviewed-fulltext-article-RMHP |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850151105238925312 |
|---|---|
| author | Wang Y Ma W Yang Y Zhao H Zhao Z Zhao X |
| author_facet | Wang Y Ma W Yang Y Zhao H Zhao Z Zhao X |
| author_sort | Wang Y |
| collection | DOAJ |
| description | Yuncong Wang, Wenhui Ma, Yang Yang, Huijie Zhao, Zhongjing Zhao, Xia Zhao Hospital Infection Management Division, Xuanwu Hospital Capital Medical University, Beijing, People’s Republic of ChinaCorrespondence: Yuncong Wang, Hospital Infection Management Division, Xuanwu Hospital Capital Medical University, No. 45 ChangChun Street, Xicheng District, Beijing, 100053, People’s Republic of China, Tel +86 10 83198692, Email 18618247182@163.comObjective: This study aimed to develop a dynamic prevention and control method for fluctuating respiratory nosocomial infections in outpatients.Methods: Six sets of surveillance data such as influenza-like case counts and their predicted results were used in the autoregressive integrated moving average model (ARIMA) to forecast the onset and end time points of the epidemic peak. A Delphi process was then used to build consensus on hierarchical infection control measures for epidemic peaks and plateaus. The data, predicted results, and hierarchical infection control measures can assist dynamic prevention and control of respiratory nosocomial infections with changes in the infection risk.Results: The ARIMA model produced exact estimates. The mean absolute percentage errors (MAPE) of the data selected to estimate the time range of the high-risk and low-risk periods were 15.8%, 9.2%, 15.4%, 16.8%, 25.6%. The hierarchical infection control measures included three categories and nine key points. A risk-period judgment matrix was also designed to connect the surveillance data and the hierarchical infection control measures.Conclusion: Through a mathematical model, dynamic prevention and control of respiratory tract infections in outpatients was constructed based on the daily medical service monitoring data of hospitals. It is foreseeable that when applied in medical institutions, this method will provide accurate and low-cost infection prevention and control outcomes.Keywords: respiratory nosocomial infection, ARIMA, outpatient, dynamic infection control |
| format | Article |
| id | doaj-art-b6dbfa269a4e4e21ba34eb39e3009488 |
| institution | OA Journals |
| issn | 1179-1594 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Dove Medical Press |
| record_format | Article |
| series | Risk Management and Healthcare Policy |
| spelling | doaj-art-b6dbfa269a4e4e21ba34eb39e30094882025-08-20T02:26:22ZengDove Medical PressRisk Management and Healthcare Policy1179-15942025-04-01Volume 1813231332102091Research on Dynamic Outpatient Respiratory Nosocomial Infection Control Methods Through Multi-Data PredictionWang YMa WYang YZhao HZhao ZZhao XYuncong Wang, Wenhui Ma, Yang Yang, Huijie Zhao, Zhongjing Zhao, Xia Zhao Hospital Infection Management Division, Xuanwu Hospital Capital Medical University, Beijing, People’s Republic of ChinaCorrespondence: Yuncong Wang, Hospital Infection Management Division, Xuanwu Hospital Capital Medical University, No. 45 ChangChun Street, Xicheng District, Beijing, 100053, People’s Republic of China, Tel +86 10 83198692, Email 18618247182@163.comObjective: This study aimed to develop a dynamic prevention and control method for fluctuating respiratory nosocomial infections in outpatients.Methods: Six sets of surveillance data such as influenza-like case counts and their predicted results were used in the autoregressive integrated moving average model (ARIMA) to forecast the onset and end time points of the epidemic peak. A Delphi process was then used to build consensus on hierarchical infection control measures for epidemic peaks and plateaus. The data, predicted results, and hierarchical infection control measures can assist dynamic prevention and control of respiratory nosocomial infections with changes in the infection risk.Results: The ARIMA model produced exact estimates. The mean absolute percentage errors (MAPE) of the data selected to estimate the time range of the high-risk and low-risk periods were 15.8%, 9.2%, 15.4%, 16.8%, 25.6%. The hierarchical infection control measures included three categories and nine key points. A risk-period judgment matrix was also designed to connect the surveillance data and the hierarchical infection control measures.Conclusion: Through a mathematical model, dynamic prevention and control of respiratory tract infections in outpatients was constructed based on the daily medical service monitoring data of hospitals. It is foreseeable that when applied in medical institutions, this method will provide accurate and low-cost infection prevention and control outcomes.Keywords: respiratory nosocomial infection, ARIMA, outpatient, dynamic infection controlhttps://www.dovepress.com/research-on-dynamic-outpatient-respiratory-nosocomial-infection-contro-peer-reviewed-fulltext-article-RMHPrespiratory nosocomial infectionarimaoutpatientdynamic infection control |
| spellingShingle | Wang Y Ma W Yang Y Zhao H Zhao Z Zhao X Research on Dynamic Outpatient Respiratory Nosocomial Infection Control Methods Through Multi-Data Prediction Risk Management and Healthcare Policy respiratory nosocomial infection arima outpatient dynamic infection control |
| title | Research on Dynamic Outpatient Respiratory Nosocomial Infection Control Methods Through Multi-Data Prediction |
| title_full | Research on Dynamic Outpatient Respiratory Nosocomial Infection Control Methods Through Multi-Data Prediction |
| title_fullStr | Research on Dynamic Outpatient Respiratory Nosocomial Infection Control Methods Through Multi-Data Prediction |
| title_full_unstemmed | Research on Dynamic Outpatient Respiratory Nosocomial Infection Control Methods Through Multi-Data Prediction |
| title_short | Research on Dynamic Outpatient Respiratory Nosocomial Infection Control Methods Through Multi-Data Prediction |
| title_sort | research on dynamic outpatient respiratory nosocomial infection control methods through multi data prediction |
| topic | respiratory nosocomial infection arima outpatient dynamic infection control |
| url | https://www.dovepress.com/research-on-dynamic-outpatient-respiratory-nosocomial-infection-contro-peer-reviewed-fulltext-article-RMHP |
| work_keys_str_mv | AT wangy researchondynamicoutpatientrespiratorynosocomialinfectioncontrolmethodsthroughmultidataprediction AT maw researchondynamicoutpatientrespiratorynosocomialinfectioncontrolmethodsthroughmultidataprediction AT yangy researchondynamicoutpatientrespiratorynosocomialinfectioncontrolmethodsthroughmultidataprediction AT zhaoh researchondynamicoutpatientrespiratorynosocomialinfectioncontrolmethodsthroughmultidataprediction AT zhaoz researchondynamicoutpatientrespiratorynosocomialinfectioncontrolmethodsthroughmultidataprediction AT zhaox researchondynamicoutpatientrespiratorynosocomialinfectioncontrolmethodsthroughmultidataprediction |