Pathogen Access and Benefit-Sharing: Can the WHO Pandemic Agreement Bridge the Equity Divide?

Long Chen^1,2,#

1. Guanghua Law School, Zhejiang University, Hangzhou City, Zhejiang Province, China;

2. Institute of Digital Jurisprudence, Zhejiang University, Hangzhou City, Zhejiang Province, China.

^# Corresponding author: Long Chen, 0025131@zju.edu.cn.

The adoption of the WHO Pandemic Agreement in May 2025 marks a pivotal shift toward institutionalizing global pandemic governance. Anchored in principles of equity, solidarity, and human rights, the agreement establishes a Pathogen Access and Benefit-Sharing (PABS) System, which aims to ensure equitable access to pandemic-related health products (PRHPs). However, operational ambiguities — particularly in defining pathogen scope, integrating traditional knowledge, enforcing manufacturer obligations, and coordinating with multilateral frameworks like the Convention on Biological Diversity and Nagoya Protocol — pose significant implementation risks. Crucially, the agreement’s effectiveness is intertwined with broader health system resilience. However, specific provisions for PABS integration within a strengthened health system architecture remain underdeveloped. Moreover, critical gaps persist regarding financing, compliance, One Health integration, digital governance, community engagement, and alignment with broader health systems. The success of the agreement hinges on resolving these gaps through subsequent protocols and sustained political commitment.

病原体获取与惠益共享：世界卫生组织《大流行协定》能否弥合公平鸿沟？

陈龙^1,2,#
1. 光华法学院，浙江大学，杭州市，浙江省，中国；

2. 数字法治研究院，浙江大学，杭州市，浙江省，中国。

^# 通信作者: 陈龙，0025131@zju.edu.cn。

世界卫生组织《大流行协定》于2025年5月通过，标志着全球大流行治理迈入制度化构建的关键阶段。该协定以公平、团结和人权原则为基石，设立了“病原体获取与惠益共享”（PABS）系统，旨在确保大流行相关卫生产品（PRHPs）的公平获取。然而，其在操作层面存在的诸多模糊性——特别是涉及病原体范围界定、传统知识纳入、制造商义务履行，以及与《生物多样性公约》及《名古屋议定书》等多边框架的协调——构成了显著的实施风险。尤为关键的是，该协定的有效性不仅取决于其自身设计，更与更广泛的卫生系统韧性深度关联；然而，关于如何将PABS系统有机整合至强化后的卫生系统架构之中，具体条款仍有待细化。此外，在资金机制、合规保障、全健康（One Health）整合、数字治理、社区参与以及与整体卫生系统的协同等领域，仍存在亟待填补的关键缺口。协定的最终成功，有赖于通过后续议定书的磋商与持续的政治承诺来系统性地解决这些难题。

For more information: https://weekly.chinacdc.cn/en/article/doi/10.46234/ccdcw2026.009

Analysis of Rabies Epidemiological Characteristics and Failed Post-Exposure Prophylaxis Cases — Hunan Province, China, 2019–2024

Shengbao Chen¹, Hao Yang¹, Zhihong Deng¹, Zhifei Zhan¹, Zhihui Dai¹, Fangling He¹, Juan Wang¹, Rongjiao Liu¹, Ziqi Yang¹, Kaiwei Luo^1,#

1. Center for Disease Control and Prevention of Human Province (Hunan Academy of Preventive Medicine), Changsha City, Hunan Province, China.

^# Corresponding author: Kaiwei Luo, cfk@hncdc.com.

This study analyzed the epidemiological characteristics of rabies and the causes of post-exposure management failure in Hunan Province from 2019 to 2024, providing evidence for rabies prevention and control strategies in China. Data on reported human rabies cases, exposures, and post-exposure prophylaxis (PEP) were analyzed using descriptive epidemiological methods. 240 rabies cases were reported in Hunan Province (2019–2024) with an average annual incidence rate of 0.0592 per 100,000 people. A significant decreasing trend was observed (x²_trend=32.72, P<0.05). Five factors showed statistically significant differences in their effects on the incubation period: site of exposure, wound management, vaccination after exposure, passive immunization preparations, and sources of animals causing exposure (all P<0.05).  In the last six years, there was no increasing trend in the proportion of failed PEP as a percentage of all rabies cases in that year (x²_trend=1.809, P=0.86). The median incubation period was 16.0 (IQR14.0-22.0) days for failed PEP cases with exposed areas, including to the head and/or face, compared to 31.0 (IQR 24.0-50.0) days for those without such exposure. The difference was statistically significant (U=20.50, P=0.025). The current situation of rabies prevention and control in Hunan Province remains dire. Therefore, comprehensive measures should be implemented to help reduce the incidence of rabies. These include adopting standardized dog management practices, strengthening control measures in high-risk areas, and improving public awareness of PEP.

狂犬病流行特征及暴露处置失败病例分析 — 湖南省，中国，2019–2024年

陈生宝¹，杨浩¹，邓志红¹，湛志飞¹，戴志辉¹，何方玲¹，王娟¹，刘荣娇¹，杨子琪¹，罗垲炜^1,#

1. 湖南省疾病预防控制中心（湖南省预防医学科学院），长沙市，湖南省，中国。

^# 通信作者：罗垲炜，cfk@hncdc.com。

本文旨在通过分析湖南省2019–2024年狂犬病流行病学特征及暴露处置失败原因，为我国狂犬病防控工作提供参考依据。采用描述流行病学方法对狂犬病病例、狂犬病暴露及暴露后预防相关数据进行分析。2019–2024年湖南省共报告狂犬病240例，年均发病率为0.0592/10万，发病情况整体呈下降趋势（c²_趋势=32.72，P＜0.05）。暴露部位、伤口处理情况、暴露后是否注射疫苗以及是否接种被动免疫制剂、致伤动物来源5个因素对潜伏期的影响差异有统计学意义（P＜0.05）。近6年暴露处置失败病例占当年全部狂犬病例的比例无增加趋势（c²_趋势=1.809，P=0.86）。暴露部位含头面部的暴露处置失败病例潜伏期中位数为16.0（14.0, 22.0）天，不含头面部的为31.0（24.0, 50.0），两者之间有统计学差异（U=20.50，P=0.025）。目前湖南省的狂犬病防控形势依然严峻，因此，应采取一些综合措施如通过规范全省犬只管理，加强狂犬病高发地区的防控措施，提升群众在暴露后的正确处置意识，以帮助降低狂犬病发病率。

For more information: https://weekly.chinacdc.cn/en/article/doi/10.46234/ccdcw2026.010

Validation of the Rapid Fluorescent Focus Inhibition Test for Rabies Virus Neutralizing Antibodies — China, 2025

Zixin Fang¹, Xiaoyan Tao¹, Shuqing Liu¹, Qian Liu¹, Minghui Zhang¹, Nuo Yang¹, Zeheng Hu², Tom Jin², Eric Tsao², Pengcheng Yu^1,#, Wuyang Zhu¹

1. NHC Key Laboratory of Medical Virology and Viral Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention & Chinese Academy of Preventive Medicine, Beijing, China;

2. Synermore Biologics (Suzhou) Co., Ltd., Suzhou City, Jiangsu Province, China.

^# Corresponding author: Pengcheng Yu, yupc@ivdc.chinacdc.cn.

The rapid fluorescent focus inhibition test (RFFIT) is a cell-based virus neutralization assay and the gold standard for quantifying rabies virus neutralizing antibodies (RVNA) in the serum. It is used to assess the biological efficacy of rabies vaccines and evaluate protective immunity in both humans and animals. Despite its broad application, RFFIT requires thorough validation to ensure reliability. RFFIT was validated in this study using the third World Health Organization international standard for anti-rabies immunoglobulin (WHO-3 SRIG) and negative human sera. The validation followed the guidelines outlined by the Food and Drug Administration Guidance for Industry and International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) Q2 (R1) guidelines and included the assessment of intra-assay and intermediate precision, dilutability, linearity, range, accuracy, specificity, robustness, and stability. The RFFIT method demonstrated good precision, with intra-assay and intermediate-precision geometric coefficient of variation (GCV) <30%. Dilutability was confirmed, with 95% of positive samples showing geometric mean concentration (GMC) differences within ±30% compared to undiluted controls. The standard and detection values were described by y=1.0091x - 0.1128 (R²⁼0.9948); 95.56% of the samples showed 70–130% recovery. Specificity was verified using homologous and heterologous antigen competition and a matrix with no significant cross-reactivity. The assay was robust to variations in cells, reagents, and time, with titer differences within ±30%. Stability of samples and reagents under freeze–thaw and different short-term storage conditions was confirmed. The assay was successfully validated for quantifying RVNA content in serum samples.

狂犬病病毒中和抗体快速荧光灶抑制试验验证 — 中国，2025年

方梓莘¹，陶晓燕¹，刘淑清¹，刘茜¹，张明慧¹，杨诺¹，胡泽衡²，靳志刚²，曹一孚²，于鹏程^1,#，朱武洋¹

1. 国家卫生健康委员会生物安全重点实验室，病毒病预防控制所，中国疾病预防控制中心（中国预防医学科学院），北京，中国；

2. 兴盟生物医药有限公司，苏州市，江苏省，中国。

^# 通信作者：于鹏程，yupc@ivdc.chinacdc.cn。

快速荧光灶抑制试验（RFFIT）是一种基于细胞的病毒中和试验，是定量检测血清中狂犬病病毒中和抗体（RVNA）的金标准。它常用于评估人和动物狂犬病疫苗免疫后血清的有效水平。尽管RFFIT应用广泛，但仍需经过全面验证以确保其可靠性。我们使用第三版WHO狂犬病免疫球蛋白国际标准品（SRIG）和阴性人血清对RFFIT进行了验证。该验证遵循美国FDA工业指南和ICH Q2(R1)指南，内容包括评估批内精密度、中间精密度、稀释度、线性、线性范围、准确度、特异性、耐用性及稳定性。RFFIT方法显示出了良好的精密度，批内和中间精密度的GCV%均小于30%。95%的阳性样本的几何平均浓度（GMC）与未稀释对照相比差异在±30%以内，证明其良好的稀释性。理论值与实测值的线性方程为 y=1.0091x-0.1128 (R²=0.9948)；95.56%的样本回收率在70%-130%之间。通过同源和异源抗原竞争及基质试验验证了特异性，且未发现显著的交叉反应。该方法对细胞、试剂和时间等变量的变化均表现出耐用性，滴度差异在±30%以内。稳定性研究也证实了样品和试剂在冻融及不同短期储存条件下的稳定性。该试验在所有验证参数中均表现出可接受性，适用于血清样本中RVNA的定量检测。

For more information: https://weekly.chinacdc.cn/en/article/doi/10.46234/ccdcw2026.011

Machine Learning Models for Predicting Latent Tuberculosis Infection Risk in Close Contacts of Patients with Pulmonary Tuberculosis — Henan Province, China, 2024

Dingyong Sun^1,&, Xuan Wu^2,&, Yanqiu Zhang¹, Weidong Wang¹, Mengya He¹, Linqi Diao^1,#

1. Department of Tuberculosis Prevention and Control Center, Henan Center for Disease Control and Prevention, Zhengzhou City, Henan Province, China.

2. Department of Epidemiology, School of Public Health, Zhengzhou University, Zhengzhou City, Henan Province, China.

^& Joint first authors.

^# Corresponding authors: Linqi Diao, lqdiao@163.com.

We explored risk factors for latent tuberculosis infection (LTBI) and developed a risk prediction model using machine learning algorithms. Patients with active pulmonary TB in months 3 to 6 of anti-TB treatment in Henan Province, China, July–September 2024 were selected as index cases. Close contacts identified through epidemiological investigation underwent tuberculin-purified protein derivative testing to determine LTBI status. Face-to-face questionnaires were conducted to collect epidemiological data. The dataset was divided into training and testing sets (6:4), using a fixed random seed. Five models — logistic regression (LR), decision tree (DT), random forest (RF), support vector machines (SVM), and multilayer perceptron (MLP) — were trained and evaluated using the mean squared error (MSE) and coefficient of determination. The test set was subjected to external validation. Receiver operating characteristic curve analysis, area under the curve (AUC), and F1-scores were used to quantify predictive performance. Among 795 close contacts, LTBI prevalence was 401 (50.5%). By MSE, models ranked: SVM (0.121), RF (0.165), DT (0.197), LR (0.229), and MLP (0.233). SVM identified five key predictors: contact typeofindex case, key population classification, residential area, frequency of participation in group activities, and etiological results. Internal validation showed strong performance (AUC=0.921, F1=0.858), whereas external validation showed moderate performance (AUC=0.752, F1=0.694). The SVM model incorporating contact type, index case, key population classification, residential area, frequency of group activity participation, and etiological results demonstrated robust predictive value for LTBI risk. This model shows promise for the targeted screening and management of high-risk populations.

基于机器学习模型下肺结核密切接触者结核潜伏感染风险预测研究 — 河南省，中国，2024年

孙定勇^1,& 吴璇^2,& 张艳秋¹ 王伟东¹ 何梦雅¹ 刁琳琪^1,#

1. 结核病预防控制所，河南省疾病预防控制中心，郑州市，河南省，中国；

2. 流行病学系，公共卫生学院，郑州大学，郑州市，河南省，中国。

^& 共同第一作者。

^# 通信作者：刁琳琪，lqdiao@163.com。

本研究探讨肺结核密切接触者发生结核分枝杆菌潜伏感染的相关危险因素，并基于机器学习算法构建其风险预测模型。从2024年6至12月，我们在河南省21个市县选取治疗处于第3-6个月的活动性肺结核患者。使用 PPD 检测确定、访谈和测试这些患者的密切接触者。非潜伏感染者最多纳入一个，潜伏感染者全部纳入。面对面收集调查问卷，将调查问卷和“系统”中指示病例相关因素纳入密切接触者发生结核分枝杆菌潜伏感染（LTBI）可能的影响因素进行分析。采用SPSS modeler 18.0和Python 3.12软件进行数据分析，以单因素分析中具有统计学意义的指标，根据随机种子法将数据集按照6：4分为训练集和测试集，通过使用训练集，我们建立了logistics回归、决策树、随机森林、支持向量机和BP神经网络风险预测模型，并进行模型性能比较以平均绝对值误差（MSE）和相关系数平方（R²）进行模型效能分析。通过验证集，对各模型进行外部验证。以受试者工作特征曲线(ROC) 和F1分数综合评估风险预测模型的预测价值。本研究共调查795例肺结核患者密切接触者，其中，LTBI占比为50.5%（401/795）；模型预测精度从大到小依次为支持向量机（0.121）、随机森林（0.165）、决策树（C5.0，0.197）、逻辑回归（0.229）和多层感知器（0.233）；支持向量机模型结果显示影响LTBI发生重要性的前5位因素依次为指示病例的类型、指示病例的重点人群分类、居住地、参加集体活动的频率和指示病例的病原学结果；预测模型曲线下面积（AUC）为 0.921，F1分数为0.858，对应的敏感度与特异性分别为0.888和0.831。外部验证AUC为0.752，F1分数为0.694，其敏感度与特异性分别为 0.659和0.711。基于机器学习算法构建的以密接者的接触类型、指示病例的重点人群分类、密接者的居住地、参加集体活动的频率和指示病例的病原学结果为预测特征的支持向量机模型对发生LTBI风险有较好的预测价值，可以把该模型应用于此类高风险人群的管理识别。

For more information: https://weekly.chinacdc.cn/en/article/doi/10.46234/ccdcw2026.012