Mapping the Characteristics of Respiratory Infectious Disease Epidemics in China Based on the Baidu Index from November 2022 to January 2023

Dazhu Huo^1*; Ting Zhang^2*; Xuan Han²; Liuyang Yang²; Lei Wang³; Ziliang Fan⁴; Xiaoli Wang⁵; Jiao Yang²; Qiangru Huang²; Ge Zhang⁶; Ye Wang²; Jie Qian²; Yanxia Sun²; Yimin Qu²; Yugang Li²; Chuchu Ye⁷; Luzhao Feng²; Zhongjie Li²; Weizhong Yang^2#; Chen Wang^1#

1School of Health Policy and Management, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China;

2School of Population Medicine and Public Health, Chinese Academy of Medical Sciences (CAMS) & Peking Union Medical College, Beijing, China;

3Yichang Center for Disease Prevention and Control, Yichang City, Hubei Province, China;

4Weifang Center for Disease Prevention and Control, Weifang City, Shandong Province, China;

5Beijing Center for Disease Prevention and Control, Beijing, China;

6School of Public Health, Dali University, Dali City, Yunnan Province, China;

7Shanghai Pudong New Area Center for Disease Control and Prevention, Shanghai, China.

^*Joint first authors.

^#Corresponding authors: Chen Wang, cyh-birm@263.net; Weizhong Yang, yangweizhong@cams.cn.

Infectious diseases pose a significant global health and economic burden, underscoring the critical need for precise predictive models. The Baidu index provides enhanced real-time surveillance capabilities that augment traditional systems. This study aimed to develop a surveillance model leveraging the Baidu index for accurate detection of disease propagation trends, facilitating timely and effective public health responses. Data on the keyword "fever" were extracted from the Baidu search engine across 255 cities in China from November 2022 to January 2023. Various onset and peak detection criteria combining thresholds and consecutive days were tested to identify the start and peak of influenza epidemics. The most effective scenario for indicating epidemic commencement involved a 90th percentile threshold exceeded for seven consecutive days, minimizing false starts. Peak detection was optimized using a 7-day moving average for its balance of stability and precision. The use of internet search data, such as the Baidu index, significantly improves the timeliness and accuracy of disease surveillance models. This innovative approach supports quicker public health interventions, demonstrating its potential in enhancing epidemic monitoring and response efforts.

基于百度指数绘制呼吸道传染病疫情流行特征 — 中国，2022年11月–2023年1月

霍大柱^1*；张婷^2*；韩萱²；杨柳飏²；王蕾³；范子亮⁴；王小莉⁵；杨娇²；黄蔷如²；张戈⁶；王也²；钱捷²；孙艳霞²；曲翌敏²；李玉刚²；叶楚楚⁷；冯录召²；李中杰²；杨维中^2#；王辰^1#

1 中国医学科学院北京协和医学院卫生健康管理政策学院，北京，中国；

2 中国医学科学院北京协和医学院群医学及公共卫生学院，北京，中国；

3 湖北省宜昌市疾病预防控制中心，宜昌市，湖北省，中国；

4 山东省潍坊市疾病预防控制中心，潍坊市，山东省，中国；

5 北京市疾病预防控制中心，北京，中国；

6 大理大学公共卫生学院，大理市，云南省，中国；

7 上海市浦东新区疾病预防控制中心，上海市，中国。

*共同第一作者

#通讯作者：王辰，cyh-birm@263.net；杨维中，yangweizhong@cams.cn。

传染病不仅严重威胁全球公共卫生，还对经济造成重大负担。现有的传染病监测系统亟需扩大监测渠道并通过引入新技术进行提升其监测能力。本研究旨在开发一种利用百度指数进行传染病传播趋势精确判断的监测模型，以实现快速而有效的公共卫生应对。从2022年11月至2023年1月，本研究提取了中国255个城市中关于“发热”关键词的百度搜索引擎数据。测试了结合阈值和连续天数的多种起始和高峰检测标准，以识别流行的开始和高峰。研究表明流行开始的最有效场景是连续七天超过90百分位的阈值，最大程度地减少了误报。流行高峰检测则通过使用7天移动平均值来优化，以平衡稳定性和精确性。使用如百度指数的互联网搜索数据进行传染病监测，可在一定程度上提升传统传染病监测系统的及时性和准确性。这种创新方法支持更快的公共卫生干预，展示了其在增强传染病监控和响应方面的潜力。

For more information: https://weekly.chinacdc.cn/en/article/doi/10.46234/ccdcw2024.195

Establishment of a Lateral Flow Dipstick Detection Method for Influenza A Virus Based on CRISPR/Cas12a System

Xiaoyan Zhao¹; Ximing Zheng¹; Xiyong Yang¹; Qi Guo³; Yi Zhang^2#; Jun Lou^1#

1 Department of Clinical Laboratory, Zhumadian Central Hospital, Zhumadian City, Henan Province, China;

2 National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China;

3 Laboratory of Virology, Beijing Key Laboratory of Etiology of Viral Diseases in Children, Capital Institute of Pediatrics, Beijing, China

^#Corresponding author: Yi Zhang, zhangyicdc@126.com; Jun Lou, 13783378825@163.com.

This study aimed to develop a rapid, visual PCR-CRISPR/Cas12-LFD method for detecting influenza A by utilizing the conserved region of the matrix protein gene. We crafted universal degradation primers and clustered regularly interspaced short palindromic repeats RNA (CRISPR RNA, crRNA) targeting the conserved matrix protein gene of the influenza virus (IFV), integrated with lateral flow dipstick (LFD) technology. This new PCR-CRISPR/Cas12-LFD approach was designed to determine its sensitivity and specificity through the analysis of various clinical samples collected in 2023. The developed nucleic acid assay for influenza A viruses (IAV) demonstrated a sensitivity of 10 copies/mL without cross-reactivity with other respiratory pathogens. Evaluation of 82 clinical samples showed high concordance with results from fluorescent Polymerase Chain Reaction (PCR), achieving a kappa value of 0.95. A highly sensitive and specific PCR-CRISPR/Cas12-LFD method has been successfully established for the detection of influenza A, offering a robust tool for its diagnosis and aiding in the prevention and control of this virus.

基于CRISPR/Cas12a系统的甲型流感病毒试纸条检测方法的建立

赵小燕¹；郑锡铭¹；杨喜永¹；郭琪³；张益^2#；娄峻^1#

1 驻马店市中心医院医学检验科，驻马店市，河南省，中国；

2 中国疾病预防控制中心病毒病预防控制所，北京，中国；

3 首都儿科研究所病毒学研究室，儿童病毒病原学北京重点实验室，北京,中国。

#通讯作者：张益，Email: zhangyicdc@126.com；娄峻，Email: 13783378825@163.com。

本研究基于基质蛋白（M, Matrix）基因的保守区域，建立一种快速检测甲型流感的PCR-CRISPR/Cas12-LFD可视化检测方法。以流感病毒基质蛋白（M, Matrix）基因的保守区域为靶标，设计合成通用型的简并引物和成簇规律间隔的短回文重复序列 RNA（Clustered Regularly Interspaced Short Palindromic Repeats RNA，CRISPR RNA，crRNA），结合侧流层析试纸条（Lateral flow dipstick, LFD）技术，建立针对甲型流感病毒的PCR-CRISPR/Cas12-LFD方法，分析其灵敏度和特异性，并通过检测2023年流行的不同亚型临床样本进行验证。本研究建立了针对甲型流感病毒的核酸检测方法，灵敏度达到10copies/mL，和其他呼吸道病原体不存在交叉反应；验证了82份临床样本，检测结果和荧光PCR一致性较高，两种方法的kappa系数为0.95。建立一种针对甲型流感病毒的高灵敏度且特异性的检测方法，为甲型流感病毒的诊断提供了有力的工具，有助于甲型流感病毒的预防和控制。

For more information: https://weekly.chinacdc.cn/en/article/doi/10.46234/ccdcw2024.198

Development and Diagnosis Performance of IgM-Based Rapid Antigen Test for Early Detection of SARS-CoV-2 Infection in a Large Cohort of Suspected COVID-19 Cases — USA, Poland, and Sweden, 2021–2022

Yihua Huang^1*; Yiyi Pu^2*; Youhong Weng^3,4*; Yahan Wu³; Qing He³; Sofia Litchev⁵; Longyou Zhao¹; Haojie Ding³; Yunru Lai¹; Jie Li¹; Xiaojun Zheng⁶; Jinshu Chen⁶; XianqinXiong⁶; Shaohong Lu³; Fei Gao^6#; Meng Gao^3#; Qingming Kong^2#

1 Department of Laboratory Medicine, Lishui Second People's Hospital Affiliated to Wenzhou Medical University, Lishui City, Zhejiang Province, China;

2 Key Laboratory of Biomarkers and In Vitro Diagnosis Translation of Zhejiang province, School of Laboratory Medicine and Bioengineering, Hangzhou Medical College, Hangzhou City, Zhejiang Province, China;

3 School of Basic Medicine and Forensics, Key Laboratory of Bio-tech Vaccine of Zhejiang Province, Engineering Research Center of Novel Vaccine of Zhejiang Province, Hangzhou Medical College, Hangzhou City, Zhejiang Province, China;

4 Department of Chemistry & Biochemistry, University of California, Los Angeles, CA, USA;

5 The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui City, Zhejiang Province, China;

6 Department of Research and Development, Hangzhou AllTest Biotech Co., Ltd, Hangzhou City, Zhejiang Province, China.

^*Joint first authors.

^#Corresponding author: Fei Gao, soar.gao@alltests.com.cn; Meng Gao, yourgm@hotmail.com; Qingming Kong, qmkong_1025@163.com.

Antigen testing has been crucial in effectively managing the coronavirus disease 2019 (COVID-19) pandemic. This study evaluated the clinical performance of a nasopharyngeal swab (NPS)-based antigen rapid diagnostic test (Ag-RDT) compared to the gold standard real-time reverse transcription-polymerase chain reaction (RT-PCR) for early detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We developed an IgM-based rapid antigen test for early detection of SARS-CoV-2 infection. Between July 2021 and January 2022, we analyzed 1,030 nasopharyngeal swab (NPS) samples from participants at three centers in different countries, using both antigen rapid diagnostic tests (Ag-RDT) and RT-PCR. The Ag-RDT demonstrated minimal detection limits as low as 0.1 ng/ml for recombinant N antigen and 100 TCID50/mL for heat-inactivated SARS-CoV-2 virus. Specificity assessments involving four human coronaviruses and 13 other respiratory viruses showed no cross-reactivity. The Ag-RDT assay (ALLtest) exhibited high sensitivity (93.18%–100%) and specificity (99.67%–100%) across all centers. Factors such as cycle threshold (Ct) values and the timing of symptoms since onset were influential, with sensitivity increasing at lower Ct values (<30) and within the first week of symptoms. The ALLtest Ag-RDT demonstrated high reliability and significant potential for diagnosing suspected COVID-19 cases.

基于IgM的严重急性呼吸综合征冠状病毒2抗原快速检测试剂及其在大规模疑似COVID-19病例队列中的早期检测性能研究 — 美国，瑞典，波兰，2021-2022 年

黄益华^1*；蒲依依^2*；翁有红^3，4*；吴亚寒³；何青³；Sofia Litchev⁵；赵龙友¹；丁豪杰³；赖云茹¹；李杰¹；郑孝君⁵；陈金树⁵；熊先勤⁵；陆绍红³；高飞^5#；高孟^3#；孔庆明^2#

1 温州医科大学附属丽水市第二人民医院检验科，丽水市，浙江省，中国；

2 浙江省生物标志物与体外诊断转化重点实验室，杭州医学院检验医学院、生物工程学院，杭州市，浙江省，中国；

3 浙江省生物技术疫苗重点实验室，新型疫苗浙江省工程研究中心，杭州医学院基础医学与法医学院，杭州市，浙江省，中国；

4 温州医科大学附属第五医院，丽水市，浙江省，中国；

5 杭州奥泰生物科技股份有限公司研发部，杭州市，浙江省，中国。

*共同第一作者。

#通讯作者：高飞，soar.gao@alltests.com.cn；高孟，yourgm@hotmail.com；孔庆明，qmkong_1025@163.com。

抗原检测在有效应对2019冠状病毒病（COVID-19）大流行中扮演了关键角色。本研究旨在评估基于鼻咽拭子（NPS）的抗原快速诊断检测（Ag-RDT）与金标准实时逆转录聚合酶链反应（RT-PCR）在早期检测新型冠状病毒（SARS-CoV-2）中的临床性能。我们开发了一种基于IgM的快速抗原检测方法，用于早期检测SARS-CoV-2感染。在2021年7月至2022年1月期间，我们使用Ag-RDT和RT-PCR方法对来自不同国家3个中心的1,030份鼻咽拭子（NPS）样本进行了分析，这些样本来自2021年7月至2022年1月的参与者。Ag-RDT显示了极低的检测限，对于重组N抗原为0.1 ng/ml，对于热灭活的SARS-CoV-2病毒为100 TCID50/mL。对4种人类冠状病毒和13种其他呼吸道病毒的特异性评估未发现交叉反应。Ag-RDT检测（ALLtest）在所有中心表现出高敏感性（93.18%–100%）和高特异性（99.67%–100%）。检测敏感性受Ct值和症状出现时间等因素影响，在较低的Ct值（<30）和症状出现的第一周内检测敏感性较高。ALLtest Ag-RDT在诊断疑似COVID-19病例方面展现出卓越的可靠性和显著的潜力。

For more information: https://weekly.chinacdc.cn/en/article/doi/10.46234/ccdcw2024.199

Development and Comparison of Time Series Models in Predicting Severe Fever with Thrombocytopenia Syndrome Cases — Hubei Province, China, 2013–2020

Zixu Wang^{1, 2*}; Jinwei Zhang^3*; Wenyi Zhang^4*; Nianhong Lu¹; Qiong Chen¹; Junhu Wang¹; Yingqing Mao¹; Haiming Yi¹; Yixin Ge¹; Hongming Wang¹; Chao Chen¹; Wei Guo¹; Xin Qi⁵; Yuexi Li^7#; Ming Yue^6#; Yong Qi^1#

1 Huadong Research Institute for Medicine and Biotechniques, Nanjing City, Jiangsu Province, China

2 Bengbu Medical College, Bengbu City, Anhui Province, China;

3 Department of Anesthesiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing City, Jiangsu Province, China;

4 Chinese PLA Center for Disease Control and Prevention, Beijing, China;

5 The Second People's Hospital of Yiyuan County, Zibo City, Shandong Province, China;

6 Department of Infectious Diseases, The First Affiliated Hospital of Nanjing Medical University, Nanjing City, Jiangsu Province, China;

7 School of Public Health, Nanjing Medical University, Nanjing City, Jiangsu Province, China.

† Joint first authors.

* Corresponding author: Yuexi Li, liyxi2007@126.com; Ming Yue, njym08@163.com; Yong Qi, qslark@126.com.

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease caused by the SFTS virus, which has a high mortality rate. Predicting the number of SFTS cases is essential for early outbreak warning and can offer valuable insights for establishing prevention and control measures. In this study, data on monthly SFTS cases in Hubei Province, China, from 2013 to 2020 were collected. Various time series models based on seasonal auto-regressive integrated moving average (SARIMA), Prophet, eXtreme Gradient Boosting (XGBoost), and long short-term memory (LSTM) were developed using these historical data to predict SFTS cases. The established models were evaluated and compared using mean absolute error (MAE) and root mean squared error (RMSE). Four models were developed and performed well in predicting the trend of SFTS cases. The XGBoost model outperformed the others, yielding the closest fit to the actual case numbers and exhibiting the smallest MAE (2.54) and RMSE (2.89) in capturing the seasonal trend and predicting the monthly number of SFTS cases in Hubei Province. The developed XGBoost model represents a promising and valuable tool for SFTS prediction and early warning in Hubei Province, China.

发热伴血小板减少综合征时间序列预测模型的建立与比较 — 湖北省，2013–2020年

王子旭^{1, 2*}；张津玮^3*；张文义^4*；陆年宏¹；陈琼¹；王俊虎¹；毛颖清¹；易海鸣¹；葛艺欣¹；王洪铭¹；陈超¹；郭伟¹；齐新⁵；李越希^7#；岳明^6#；齐永^1#

1 华东医学生物技术研究所，南京市，江苏省，中国；

2 蚌埠医科大学，蚌埠市，安徽省，中国；

3 南京鼓楼医院，南京大学附属医院，南京市，江苏省，中国；

4 中国人民解放军疾病预防控制中心，北京，中国；

5 沂源县第二人民医院，淄博市，山东省，中国 ；

6 南京医科大学第一附属医院，南京市，江苏省，中国；

7 南京医科大学公共卫生学院，南京市，江苏省，中国。

*共同第一作者。

#通讯作者: 李越希, liyxi2007@126.com; 岳明, njym08@163.com; 齐永, qslark@126.com。

发热伴血小板减少综合征(Severe fever with thrombocytopia syndrome， SFTS)是由发热伴血小板减少综合征病毒引起的一种新发传染病，具有很高的死亡率。预测SFTS病例数对于早期暴发的预警至关重要，并可为制定预防和控制措施提供有价值的见解。收集2013-2020年湖北省每月SFTS病例资料。利用这些历史数据，建立基于季节性差分自回归滑动平均模型(seasonal auto-regressive integrated moving average, SARIMA)、Prophet、极端梯度提升算法（eXtreme Gradient Boosting, XGBoost)和长短时记忆网络(long short-term memory, LSTM)等的各种时间序列模型来预测SFTS病例。采用平均绝对误差(mean absolute error, MAE)和均方根误差(root mean squared error, RMSE)对建立的模型进行评价和比较。建立的4种模型，均能较好地预测SFTS病例趋势。XGBoost模型与实际病例数拟合最接近，MAE(2.54)和RMSE(2.89)最小，在预测病例随时间的变化趋势和湖北省月度SFTS病例数方面优于其他模型。本研究建立的XGBoost模型为湖北省SFTS疫情预测和预警提供了一个有前景和价值的工具。。

For more information: https://weekly.chinacdc.cn/en/article/doi/10.46234/ccdcw2024.200

A Norovirus-Related Gastroenteritis Outbreak Stemming from a Potential Source of Infection — Pudong New Area, Shanghai Municipality, China, April 2024

Zou Chen^1,2; Hong Zhang^1,2; Yifeng Shen^1,2; Chuchu Ye^1,2#

1 Shanghai   Pudong New Area Center for Disease Control and Prevention, Shanghai, China;

2 Fudan University Pudong Institute of Preventive Medicine, Shanghai, China.

^# Corresponding Authors: Chuchu Ye, ccye@pdcdc.sh.cn.

On April 27, 2024, an outbreak of norovirus-related gastroenteritis occurred in Pudong New Area, Shanghai. Through investigation of the outbreak and laboratory testing, the objectives were to identify pathogens, characterize outbreaks and implement effective control strategies. There were 11 cases in the outbreak, the attack rate was 5.02% (11/219), the main symptoms were vomiting (100%), abdominal pain (82%), all cases were mild. This outbreak was precipitated by a potential source of infection in a child resuming class after a 72-hour quarantine post-symptom resolution, leading to a cluster of cases within the class. Testing revealed that three case samples were positive for Norovirus GII nucleic acid, while all environmental samples tested negative. Through the implementation of public health response, including strengthening morning checks, case isolation, disinfection, and suspending collective activities. All cases recovered and resumed classes on May 6, with no new cases, and the outbreak was closed. The investigation showed that although a 72-hour quarantine post-symptom resolution of norovirus infection, the detoxification period of the virus may exceed 72 hours, and further investigation into the detoxification period of asymptomatic patients is recommended to improve outbreak control strategies.

一起源自潜在感染源的诺如病毒相关胃肠炎暴发疫情调查 — 中国上海市浦东新区，2024年4月

陈诹^1,2；张鸿^1,2；沈奕峰^1,2；叶楚楚^1,2#

1 上海市浦东新区疾病预防控制中心，上海，中国；

2 复旦大学浦东预防医学研究院，上海，中国。

#通讯作者：叶楚楚，ccye@pdcdc.sh.cn。

2024年4月27日，上海市浦东新区发生了一起诺如病毒相关胃肠炎暴发疫情。通过暴发调查和实验室检测，旨在识别病原体，描绘疫情特征和实施有效控制策略。该疫情共报告11例病例，罹患率为5.02%（11/219），主要症状为呕吐（100%）和腹痛（82%），所有病例症状均较轻。调查显示，疫情可能起源于一个潜在感染源，一名儿童在症状缓解后72小时隔离后返回教室，随后同班的儿童陆续出现症状。实验室检测显示三份病例样本诺如病毒GII核酸阳性，而环境样本均阴性。通过实施公共卫生响应措施包括加强晨检、病例隔离、消毒、暂停集体活动等，5月6日，所有病例均康复复课，无新病例，疫情得到控制。调查显示，尽管诺如病毒感染的隔离期为症状缓解后72小时，但病毒排毒时间可能超过72小时，仍有传播风险，建议对无症状感染者的排毒期进行更深入的研究，以便改进疫情控制策略。

For more information: https://weekly.chinacdc.cn/en/article/doi/10.46234/ccdcw2024.197

Intelligent Forest Hospital as a New Management System for Hospital-Acquired Infection Control

Yingxin Liu¹; Zhousheng Lin²; Guanwen Lin³; Wanmin Lian⁴; Junzhang Tian⁵; Guowei Li^1,6,7#; Hongying Qu^1,5#

1 Center for Clinical Epidemiology and Methodology (CCEM), The Affiliated Guangdong Second Provincial General Hospital of Jinan University, Guangzhou City, Guangdong Province, China;

2 Medical Department, The Affiliated Guangdong Second Provincial General Hospital of Jinan University, Guangzhou City, Guangdong Province, China;

3 Hospital-Acquired Infection Control Department, The Affiliated Guangdong Second Provincial General Hospital of Jinan University, Guangzhou City, Guangdong Province, China;

4 Information Department, The Affiliated Guangdong Second Provincial General Hospital of Jinan University, Guangzhou City, Guangdong Province, China;

5 Institute for Healthcare Artificial Intelligence Application, The Affiliated Guangdong Second Provincial General Hospital of Jinan University, Guangzhou City, Guangdong Province, China;

6 Father Sean O’Sullivan Research Centre, St. Joseph’s Healthcare Hamilton, Hamilton, ON, Canada;

7 Department of Health Research Methods, Evidence, and Impact (HEI), McMaster University, Hamilton, ON, Canada.

*Corresponding author: Guowei Li, ligw@gd2h.org.cn; Hongying Qu, tggd2h@163.com.

A new system called Intelligent Forest Hospital (IFH) is being implemented at Guangdong Second Provincial General Hospital to enhance hospital-acquired infection (HAI) control, especially in the aftermath of the COVID-19 pandemic. IFH leverages advancements in artificial intelligence, 5G, and cloud networking to implement customized indoor air quality control strategies across various medical settings. The system utilizes intelligent disinfection devices and air purification systems to maintain safe indoor environments similar to forest sanatoriums. A dynamic 3D hospital model provides real-time monitoring of critical air quality parameters, enabling prompt risk alerts and automated disinfection when necessary. While challenges remain in areas like costs, privacy, and general applicability, the integration of IFH with cutting-edge technologies shows promise in enhancing HAI prevention and management. The aim is to create a safe and pleasant environment for patients, staff, and visitors, particularly in the context of COVID-19 and potential future pandemics. Further research is needed to optimize the system and address implementation barriers, but IFH represents an innovative approach to infection control in healthcare settings.

智慧森林医院作为医院感染防控的新型体系

刘颖欣¹；林周胜²；林冠文³；连万民⁴；田军章⁵；黎国威^1,6,7#；瞿红鹰^1,5*

1 暨南大学附属广东省第二人民医院临床流行病与方法学中心，广州市，广东省，中国；

2 暨南大学附属广东省第二人民医院医务部，广州市，广东省，中国；

3 暨南大学附属广东省第二人民医院感染管理科，广州市，广东省，中国；

4 暨南大学附属广东省第二人民医院信息科，广州市，广东省，中国；

5 暨南大学附属广东省第二人民医院人工智能医疗应用研究所，广州市，广东省，中国；

6 圣约瑟夫医疗中心, 汉密尔顿市, 安大略省, 加拿大；

7 麦克马斯特大学, 汉密尔顿市, 安大略省, 加拿大。

*通讯作者: 黎国威, ligw@gd2h.org.cn; 瞿红鹰, tggd2h@163.com。

近期，受新冠疫情院感的提示，广东省第二人民医院上线了智慧森林医院系统，以便更好地有效防控医院感染的风险。智慧森林医院系统通过融合人工智能、5G和云网络等数字化科技，在众多场景中灵活采用空气质量管理模式，结合多种智能消毒设备和空气质量管理系统，达到有效杀灭有害物质、增加空气负离子浓度、打造医院“森林氧吧”的目的。此外，智慧森林医院通过整合安保感控全方位感控系统、建立动态3D院区模型、实时监测医院的环境指标值，以实现精准的风险预警，并在必要时进行自动空气消毒净化。尽管当前还存在多方面的挑战（如成本控制、隐私保护和通用推广性等），该系统可为患者、医务人员和家属创造一个洁净、安全、舒适的医疗环境，是医院空气质量管理理念策略的一次成功创新。

For more information: https://weekly.chinacdc.cn/en/article/doi/10.46234/ccdcw2024.201