National Monitoring and Analysis ofInternal Exposure of Nuclear Medicine Workers in ¹³¹I Treatment— China, 2021–2023

Xiaoliang Li¹;Jianxiang Liu¹; Fei Tuo¹; Weihong Chen²; JianfengZhang¹; Shuo Wang¹; Quanfu Sun^1,#

1.Key Laboratory of Radiological Protection and Nuclear Emergency, NationalInstitute for Radiological Protection, Chinese Center for Disease Control andPrevention, Beijing, China;

2.Department of Occupational and Environmental Health, School of Public Health, TongjiMedical College, Huazhong University of Science and Technology, Wuhan City,Hubei Province, China.

^# Corresponding authors: Quanfu Sun, sunquanfu@nirp.chinacdc.cn.

The effective dose caused by the externalexposure of medical radiation workers has dramatically declined in China. Bycontrast, less attention has been given to internal exposure to radiation. Thisstudy aimed to describe the national monitoring of the internal exposure of Chinesenuclear medicine (NM) workers from 2021 to 2023. These findings provideessential baseline data for enhancing radiation protection protocols in NMfacilities and optimizing national internal exposure monitoring. All thenon-military hospitals in China with an NM program were investigated. Portable gammaspectrometers were used to measure the ¹³¹Iactivities of the thyroidof staff members at ¹³¹Itreatment sites. A total of 998 hospitals inChina had an NM program in 2023. Detectable rate (measurements above minimumdetectable activity) decreased from 26.2% in 2021 to 20.1% in 2023. Theproportion of measurements exceeding 1.0×102 Bq decreased from 12.8% in 2021 to10.0% in 2023. Detectable rate varied with job categories (P=0.001),with the detectable rate of cleaners being the highest. In China, ¹³¹Iwas detected in the thyroid of about one-fifth of the subjects working at radioiodinetreatment sites. Detectable rate exhibited as low downward trend in recentyears.

¹³¹I工作人员内照射监测与分析 — 中国，2021–2023年

李小亮¹；刘建香¹；拓飞¹；陈卫红²；张建峰¹；王硕¹；孙全富^1,#

1. 辐射防护与核应急重点实验室，辐射防护与核安全医学所，中国疾病预防控制中心，北京，中国；

2. 劳动卫生与环境卫生学系，公共卫生学院，同济医学院，华中科技大学，武汉市，湖北省，中国。

^# 通信作者：孙全富, sunquanfu@nirp.chinacdc.cn。

中国从事医学应用的放射工作人员外照射所致的有效剂量显著下降。相比之下，我们对其内照射关注较少。本研究旨在描述2021-2023年中国核医学工作人员内照射的全国监测情况。这些结果为加强我国核医学应用的辐射防护和优化全国职业照射监测方案提供了必要的基线数据。本研究调查了中国所有开展核医学项目的非军队医院。用便携式γ谱仪测量了¹³¹I治疗场所工作人员甲状腺的¹³¹I活度。截至2023年，全国共有998家医院开展核医学项目。¹³¹I检出（测量值高于最低探测活度）率从2021年的26.2%下降到2023年的20.1%。测量值超过1.0×10² Bq的比例从2021年的12.8%下降到2023年的10.0%。¹³¹I检出率随工作岗位的不同而不同（P=0.001），其中保洁人员的检出率最高。我国放射性碘治疗场所工作人员中，约有五分之一可在甲状腺中检测到¹³¹I，检出率近年来呈缓慢下降趋势。

For more information: https://weekly.chinacdc.cn/en/article/doi/10.46234/ccdcw2025.057

Two-Year Surveillance of Dengue, Zika,and Chikungunya Viruses Among Chinese Blood Donors — Guangxi and Yunnan PLADs,China, 2022–2023

Ying Yan^1,&;Xinru Liu^2,&; Shaofang Lu^3,&; Le Chang^1,2;Jing Dong³; Huimin Ji¹; Huizhen Sun¹; LunanWang^1,2,#

1. National Center for Clinical Laboratories, Institute of Geriatric Medicine,Chinese Academy of Medical Sciences, Beijing Hospital/National Center ofGerontology, Beijing, China;

2. National Center for Clinical Laboratories, Chinese Academy ofMedical Sciences& Peking Union Medical College, Beijing, China;

3. Xishuangbanna Central Blood Station, Xishuangbanna City, YunnanProvince, China.

^& Joint first authors.

^# Corresponding author: LunanWang, lnwang@nccl.org.cn.

Emerging and re-emergingtransfusion-transmitted arboviruses remain a persistent public health challengeto global blood safety. This study aims to establish a comprehensive nucleicacid testing (NAT) quality control system for Dengue virus (DENV), Zika virus(ZIKV), and Chikungunya virus (CHIKV) screening in blood donations, to evaluatethe performance of domestic screening reagents, and to assess the prevalence ofthese arboviruses in border regions. Pseudovirus quality control materialsbased on the Moloney murine leukemia virus (MMLV) vector were constructed toevaluate the limit of detection (LoD) and precision of six blood screeningreagents. An external quality assessment (EQA) was conducted across eightcentral blood stations in Guangxi and Yunnan provinces. These blood stationsemployed either reagent A or E for DENV/ZIKV/CHIKV triplex-assay screening ofblood donor samples collected during epidemic seasons (June–August) in 2022 and2023. The six reagents exhibited varied limits of detection (LoD). Allevaluated reagents exhibited excellent precision with coefficient of variation(CV) values <5%. All eight central blood stations achieved EQA scores above80. In 2022, a total of 45,383 blood samples were screened, with no positivecases detected. In 2023, 44,972 blood donors were screened, and nine samplestested positive at the Xishuangbanna central blood station. Confirmatorytesting verified six Dengue virus serotype 1 (DENV-1) infections among thesecases. This study successfully established a robust quality assurance systemfor NAT-based arbovirus screening in China. The detection of DENV-positivesamples underscores the persisting risk of transfusion-transmitted infectionsin endemic regions. Continued surveillance and enhanced screening strategiesare essential to safeguard blood safety, particularly in arboviral hotspotregions with tropical/subtropical climates prone to recurrent outbreaks.

登革、寨卡和基孔肯雅病毒在中国献血者中的检测结果— 广西壮族自治区和云南省，中国，2022–2023年

闫颖^1,&；刘新茹^1,2,&；鲁绍芳^3,&；常乐¹；董静³；姬慧敏¹；孙慧珍¹；王露楠^1,2,#

1.国家卫生健康委临床检验中心，老年医学研究院，中国医学科学院，北京医院，北京，中国；

2.国家卫生健康委临床实验室中心，北京协和医学院&中国医学科学院，北京，中国；

3.西双版纳州中心血站，西双版纳市，云南省，中国。

^& 共同第一作者

^# 通信作者:王露楠，lnwang@nccl.org.cn。

本研究旨在建立针对登革病毒（DENV）、寨卡病毒（ZIKV）和基孔肯雅病毒（CHIKV）的血液筛查核酸检测质量控制体系，并在边境地区血站开展血液筛查以掌握蚊媒病毒的流行情况。采用基于莫洛尼鼠白血病病毒（MMLV）载体构建的假病毒制备质控品评估六种血液筛查试剂的检出限和精密度，并在云南和广西的八家血站开展室间质量评价（EQA）。八家边境地区血站使用供应商A或E的DENV/ZIKV/CHIKV三联检试剂，对2022年和2023年的6月至8月期间的所有献血者样本进行了筛查。六种试剂的最低检出限不一，精密度良好。八家血站的EQA得分均超过80分。2022年共筛查45,383份血液样本，未发现阳性样本。2023年，对44,972名献血者进行了筛查，在西双版纳中心血站中，有9份样本筛查阳性，其中6份确认阳性，为登革热病毒1型（DENV-1）。本研究成功建立了蚊媒病毒的血液筛查质量控制体系。鉴于2023年发现的DENV阳性献血情况和2024年的登革病毒流行，建议建立相应的监测和预警机制，以保障血液安全。

For more information: https://weekly.chinacdc.cn/en/article/doi/10.46234/ccdcw2025.058

Developing Machine Learning Models Basedon Clinical Manifestations to Predict Influenza — Chongqing Municipality, China,2022–2023

QianqianZeng^1,&; Hongyu Zhou^2,&; Jiang Long³; YiJian⁴; Li Feng⁵; Liangbo Hu⁶; Hongyu Zhou⁶;Weimin Zhu¹; Zhe Yuan¹; Yajuan Chen¹; GuangzhaoYi^1,#

1. TheFirst Affiliated Hospital of Chongqing Medical University, Chongqing, China;

2.Chongqing Medical University, Chongqing, China;

3.Chongqing Center for Disease Control and Prevention, Chongqing, China;

4.Cloudwalk Technology, Chongqing, China;

5.People’s Hospital of Chongqing Banan District, Chongqing, China;

6. The AffiliatedYongchuan Hospital of Chongqing Medical University, Chongqing, China.

^&Joint firstauthors.

^# Corresponding author:Guangzhao Yi, 202774@hospital.cqmu.edu.cn.

Clinicalmanifestations are essential for early diagnosis of influenza-like illness(ILI). Machine learning models for influenza prediction were developed and anew ILI definition was introduced.  Aretrospective cohort study was conducted at three hospitals in southwest Chinaduring June 2022 and May 2023. Artificial intelligence was used to extractvariables from medical records and XGBOOST algorithm was used to developprediction models for the total population and three age subgroups. A new ILIdefinition was introduced based on the optimal model and its performance was comparedwith WHO, China CDC and, USA CDC definitions. Totally 200,135 patients wereincluded. 4,249 (36.2%) were confirmed influenza. The predictors of the optimalmodel included epidemiological characteristics, important symptoms and signs,and age for the total population [AUC 0.734 (0.710–0.750), accuracy 0.689(0.669–0.772)]. The new ILI definition was fever (≥37.9 °C) with cough or rhinorrhea, and its AUC, sensitivity, specificityfor diagnosing influenza were 0.618 (0.598–0.639), 0.665and 0.572, outperformed the WHO, China CDC, and USA CDC definitions (P<0.05).Fever, cough, and rhinorrhea maybe the most important indicators for influenza surveillance.

基于临床表现开发预测流感的机器学习模型—重庆直辖市，中国，2022–2023年

曾倩倩^1,&；周泓羽^2,&；龙江³；蹇易⁴；冯丽⁵；胡良波⁶；周宏宇⁶；朱卫民¹；袁喆¹；陈亚娟¹；易光兆^1,#

1.重庆医科大学附属第一医院，重庆，中国；

2.重庆医科大学，重庆，中国；

3.重庆市疾病预防控制中心，重庆，中国；

4.云从科技，重庆，中国；

5.重庆市巴南区人民医院，重庆，中国；

6.重庆医科大学附属永川医院，重庆，中国。

^& 共同第一作者

^# 通信作者：易光兆, 202774@hospital.cqmu.edu.cn。

临床表现对于流感样疾病（influenza-likeillness，ILI）的早期诊断至关重要。我们基于临床表现开发流感的预测模型，并引入新的ILI定义。对中国重庆三家综合医院2022年6月至2023年5月期间的数据开展回顾性队列研究，使用人工智能技术提取变量，并采用XGBOOST算法开发预测模型，基于最优模型建立了一个新的ILI定义，并将其与世界卫生组织（WHO）、中国疾病预防控制中心（CCDC）和美国疾病预防控制中心（USA CDC）的定义进行了性能比较。共纳入200,135名患者，其中4249（36.2%）人确诊为流感。最优模型的预测因子包括流行病学特征、重要症状和体征以及年龄[AUC 0.734 （0.710-0.750），准确率 0.689 （0.669-0.772）]；新的ILI定义为发热（≥37.9°C）伴咳嗽或流涕，其诊断流感的AUC、敏感性和特异性分别为0.618（0.598-0.639）、0.665和0.572，优于WHO、China CDC和USA CDC的定义（P<0.05）。发热、咳嗽和流涕可能是流感监测的最重要的临床症状指标。

For more information:https://weekly.chinacdc.cn/en/article/doi/10.46234/ccdcw2025.059

Infodemic Management and Public HealthEmergency Preparedness Capacities — Khyber Pakhtunkhwa, Pakistan, 2024

MajidAli Tahir^1,2; Ijaz ul Haq^1,3,4,#; Shahbaz Ahmad Zakki¹;Fazli Akbar⁵

1. Department of Public Health & Nutrition,The University of Haripur, Haripur, Khyber Pakhtunkhwa, Pakistan;

2.Center for Disease Control, National Institutes of Health, Islamabad, Pakistan;

3.Department of Clinical Nutrition, King Faisal University, Al-Ahsa, Saudi Arabia;

4. Departmentof Nursing, Children's Hospital of Fudan University, Shanghai, China;

5.Department of Nutrition and Food Hygiene, School of Public Health, SouthernMedical University, Guangzhou City, Guangdong Province, China.

^# Corresponding author: Ijaz ul Haq, ijazbrt@outlook.com.

Thespread of misinformation and disinformation during infectious disease outbreaksand public health emergencies can significantly impede effective public healthresponses. This infodemic phenomenon creates confusion and erodes public trust,leading to uncertainty in crisis situations. This study aimed to assess thehealth department's capacity and capabilities in infodemic preparedness andmanagement to enhance future emergency preparedness in accordance withInternational Health Regulations (IHR)-2005 guidelines. A mixed-methodscross-sectional study was conducted in the health department of KhyberPakhtunkhwa. Semi-structured interviews with experts were conducted inAugust-September 2024 using a self-administered assessment tool based onIHR-2005 guidelines, supplemented by analysis of departmental documents andsupporting evidence. Data were analyzed using descriptive statistics, withachievement levels scored from 0% to 100% across five categories. The overallpreparedness score was 21.7%, indicating limited capacities and capabilities.The risk communication and community engagement (RCCE) coordination systemshowed some strengths, with 80% of focal persons designated and moderateintersectoral coordination (40%), but lacked emergency spokesperson training.Community engagement activities demonstrated moderate effectiveness (40%). Keyweaknesses included insufficient human resources (20%), inadequatetechnological infrastructure (20%), absence of infodemic analysis (0%), and lackof joint infodemic planning (0%). Significant delays were observed inidentifying and responding to potentially harmful misinformation. Despite someeffective practices in community engagement, substantial gaps exist ininfodemic management preparedness. Critical deficiencies were identified inhuman and technological resources and infodemic response measures. Priorityareas for improvement include human resource development, infodemicsurveillance systems, digital tools implementation, and enhanced collaboration.The adoption of innovative tools and streamlined processes is essential forstrengthening emergency risk communication preparedness.

信息流行病管理和公共卫生应急准备能力—开伯尔-普赫图赫瓦省，巴基斯坦，2024年

MajidAli Tahir^1,2; Ijaz ul Haq^1,3,4,#; Shahbaz Ahmad Zakki¹;Fazli Akbar⁵

1. 公共卫生与营养系，哈里普尔大学，哈利普尔，开伯尔-普赫图赫瓦省，巴基斯坦；

2. 疾病控制中心，国立卫生研究院，伊斯兰堡，巴基斯坦；

3. 临床营养学系，费萨尔国王大学，Al-Ahsa省，沙特阿拉伯；

4. 护理科，复旦大学附属儿科医院，上海，中国；

5.营养与食品卫生，公共卫生学院，南方医科大学，广州市，广东省，中国。

^# 通讯作者: Ijaz ul Haq,ijazbrt@outlook.com。

在传染病暴发和公共卫生突发事件期间，错误信息和虚假信息的传播会严重阻碍有效的公共卫生应对措施。这种信息流行病现象会造成公众认知混乱并削弱社会信任，从而导致危机情境下的不确定性。本研究旨在依据《国际卫生条例（2005）》指南，评估卫生部门在信息流行病准备与管理方面的能力建设，以提升未来突发公共卫生事件的应急准备水平。在开伯尔-普赫图赫瓦省卫生部门开展了本项混合方法的横断面研究，研究者于2024年8月至9月采用基于《国际卫生条例（2005）》指南自主研发的评估工具对专家进行半结构化访谈，辅以部门文件和支持性证据分析。通过描述性统计方法对数据进行分析，将五个维度的实施水平量化为0%-100%的达成度评分。结果显示总体准备度得分为21.7%，表明机构能力建设存在显著不足。风险沟通与社区参与（RCCE）协调机制呈现出部分优势：80%的联络人已指定，部门间协调达成中等水平（40%），但缺乏应急发言人培训相关工作。社区参与活动显示出中等有效性（40%），但主要薄弱环节包括：人力资源配置不足（20%）、技术基础设施薄弱（20%）、信息流行病理学分析缺失（0%）以及联合应对预案缺位（0%），并且在识别与应对潜在有害错误信息方面存在显著滞后。尽管在社区参与方面存在若干有效实践，但信息流行病管理的准备度仍存在重大缺口。研究结果表明人力资源与技术资源配置、信息流行病应对措施存在关键性缺陷。优先改进建议包括：人力资源开发、信息流行病监测系统建设、数字工具应用以及协同机制强化。采用创新工具和优化流程对提升突发公共卫生事件风险沟通准备度具有必要性。

For more information:https://weekly.chinacdc.cn/en/article/doi/10.46234/ccdcw2025.060

A Prediction Model and Method for IndoorRadon Concentration by A Radon Simulation Chamber

Shuyuan Liu¹; Li Zhang²;Wei Cheng³; Yongzhong Ma⁴; Kuke Ding^1,2,#

1. National Institute for RadiologicalProtection, Chinese Center for Disease Control and Prevention, Beijing, China;

2. Office for Public Health Management,Chinese Center for Disease Control and Prevention, Beijing, China;

3. Key Laboratory of Beam Technology ofMinistry of Education, College of Nuclear Science and Technology, BeijingNormal University, Beijing, China;

4. Institute of Radiation HealthProtection, Beijing Center for Disease Prevention and Control, Beijing, China.

^# Correspondingauthors: Ding Kuke, Dingkk@chinacdc.cn.

This study aimed to establish a newpredictive model for indoor radon concentrations. We constructed a radonexperimental model using prefabricated block walls and measured surface radonexhalation rates across multi-layer walls. The geometric parameters of variousbuilding envelopes (walls, floors, and roofs) were incorporated to calculateindoor radon concentrations from each source. Natural ventilation rates werealso considered in developing the indoor radon concentration prediction model. Usingclosed-loop measurements, we determined the surface radon exhalation rates ofprefabricated block walls and established fitting functions for multiple wallsunder varying temperatures and thicknesses. Based on indoor geometricparameters and natural ventilation rates, we developed a comprehensiveprediction model for indoor radon concentrations. The model accuratelypredicted indoor equilibrium radon concentrations from prefabricated walls(thickness 0.155–0.268 m) at 23℃, with deviations less than 10% from measuredvalues within ventilation rates of 0.115±0.015 /h. This scientifically rigorousand practical approach to predicting radon concentration, based on buildingcomposition and measurements of radon exhalation rates, enables proactiveassessment of indoor radon concentrations and facilitates evidence-based healthrisk prevention strategies.

利用氡模拟室的氡浓度预测模型及方法

刘淑媛¹；张荔²；程伟³；马永忠⁴；丁库克^1,2,#

1.辐射防护与核安全医学所, 中国疾病预防控制中心，北京 ，中国；

2.公共卫生管理处, 中国疾病预防控制中心，北京 ，中国；

3.射线术技术教育部重点实验室，核科学与技术学院，北京师范大学，北京，中国；

4.放射卫生防护所，北京市疾病预防控制中心，北京，中国。

^# 通信作者：丁库克，Dingkk@chinacdc.cn。

本研究旨在建立一种新的室内氡浓度预测模型。利用预制砌块墙体构建氡实验模型并检测多层墙体表面的氡析出率，结合各围护结构 (墙体、地面、顶板)的几何参数，求证出房间各"源项"氡析出而产生的室内氡浓度，同时考虑房间的自然换气率，在此基础上形成室内氡浓度预测模型。基于闭环式氡析出率测量方法求证出各预制砌块墙体表面的氡析出率，建立多个墙体在不同温度与不同厚度下的氡析出率拟合函数，根据室内几何参数和换气率等条件得到"源项"氡析出率与室内氡浓度的函数关系，并建立室内氡浓度预测模型。该模型准确地预测了室内温度为23℃、换气率为0.115±0.015/h时0.155–0.268m厚的预制墙体氡析出而累积的室内平衡氡浓度，与实测氡浓度相比的偏差小于10%。基于建筑物的构成及氡析出率测量所形成的氡浓度预测模型具有科学性和实用性，并与实际测量结果有很好的一致性，有助于提前做好室内氡浓度的评估工作及健康风险防控。

For more information:https://weekly.chinacdc.cn/en/article/doi/10.46234/ccdcw2025.061