The Epidemiological Characteristics of Mpox Cases — China, 2023
This study summarized and analyzed the epidemiological characteristics of the domestic mpox cases in China, focused on confirmed mpox cases reported in China from June to December 2023. Demographic and epidemiologic information were collected from both reported data of field epidemiological investigation and the reporting system for infectious diseases. Descriptive epidemiological methods were used to describe the distribution, exposure history, and clustering of the cases. From June to December 2023, a total of 1,712 confirmed cases were reported across 29 provincial-level administrative divisions (PLADs). The number of reported cases from June to December were 106, 491, 501, 305, 127, 80, and 102, respectively. Among the confirmed cases, 1,702 were male and 10 were female. The median age was 31 years, ranging from 15 to 71 years. Among 1,654 male cases with relevant information, about 95% reported being MSM. Through the tracking and investigation of close contacts of confirmed cases, 99 clusters were identified, accounting for 13% (214/1,666) of the total cases. The characteristics of the mpox outbreak in mainland China are consistent with the global outbreak since 2022. The outbreak so far primarily affecting the MSM population and their close contacts. As the transmission of mpox tends to be covert, it is imperative to explore and develop appropriate strategies for the surveillance and communication of high-risk populations, encouraging them to engage in self-health monitoring and seek medical treatment upon experiencing symptoms to achieve early case detection.
猴痘病例疫情流行病学特征分析 — 中国，2023年
本文总结分析我国本土猴痘疫情的流行病学特征。以2023年6-12月我国内地报告的确诊猴痘病例为研究对象。通过中国传染病报告信息管理系统和现场流行病学调查获取确诊病例的相关信息。采用描述性流行病学方法，描述疫情的三间分布、确诊病例暴露史、聚集性等。2023年6-12月，我国29个省份共报告猴痘确诊病例1712例。6-12月报告数分别为106例、491例、501例、305例、127例、80例和102例。确诊病例的中位年龄31岁（范围为15-71岁），其中，男性1702例，女性10例。男性病例中，约95%明确为男男性行为人群（MSM人群）。通过对密切接触者的追踪调查，共发现99起聚集性疫情，涉及214例确诊病例，占13%。我国猴痘疫情特征与国际疫情基本一致，主要影响MSM人群及与其密切接触的关联人群。猴痘的传播具有隐匿性。因此，针对疫情的高风险人群，应探索和制定适当的监测和沟通策略，鼓励其自我健康监测及主动就医，以实现病例的早期发现。
For more information: https://weekly.chinacdc.cn/en/article/doi/10.46234/ccdcw2024.118


Protection of Omicron Sub-Lineage Infection Against Reinfection with Another Omicron Sub-Lineage: Systematic Review, Meta-Analysis, and Meta-Regression — Worldwide, 2022–2023
The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Omicron variant has infected over 90% of the global population at least once. The protection conferred by previous infection is gradually becoming a crucial factor in the pandemic of coronavirus disease 2019 (COVID-19). Our research used systematic reviews and meta-analyses to estimate the degree and longevity of protection against reinfection by another Omicron sub-lineage, relative to uninfected individuals, under a similar vaccination status. Out of 14,105 publications, we selected 10 studies that had either a cohort, test-negative design, or case-control approach, and utilized their data for a statistical analysis. Our findings indicate that the immunity against reinfection tends to vary based on the previous variant encountered and the variant causing reinfection. Moreover, protection offered by Omicron sub-lineage infection against reinfection with another Omicron sub-lineage tends to decrease over time. Protection of BA.1 variant infection against reinfection with BA.2 was 87.5% (47.9–97.0), waning from 89.8% (64.6–97.1) at 1 month to 81.1% (31.9–94.8) at 5 months. Protection of BA.1 infection against reinfection with BA.4/5 (i.e., not distinguishing between BA.4 or BA.5) was 75.2% (42.1–89.4), waning from 77.2% (47.6–90.1) at 5 months to 40.9% (-32.8–73.7) at 12 months. Protection of BA.1 infection against reinfection with XBB waned from 40.0% (32.0–47.0) at 3–8 months to 27.0% (24.0–30.0) after 8 months. Protection of BA.2 infection against reinfection with BA.4/5 was 88.9% (76.6–94.8), waning from 91.6% (80.9–96.3) at 4 months to 80.4% (56.7–91.1) at 8 months. Protection of BA.2 infection against reinfection with XBB waned from 74.0% (72.0–75.0) at 3–6 months to 37.0% (32.0–43.0) after 8 months. Protection of BA.1/2 infection against reinfection with BA.4/5 was 86.2% (73.6–92.8). Protection of BA.1/2 infection against reinfection with BA.2.75 was 49.9% (47.6–52.1). Protection of BA.4/5 infection against reinfection with BA.2.75 was 80.6% (71.2–87.0). The degree of protection from a prior infection increases with more closely related antigenic distance and higher humoral immunity levels. Importantly, our work implies that vaccine updates and booster strategies must consider both the timing of past infections and the antigenic distance between variants to optimize COVID-19 control measures.
奥密克戎变异株感染对预防奥密克戎再感染的保护效果：系统综述、Meta分析和Meta回归 — 世界范围，2022–2023
全世界超过90%的人口至少感染过一次新冠病毒奥密克戎（Omicron）变异株。既往感染所诱导的保护效果正逐渐成为新冠病毒大流行的关键因素。我们使用系统评价和Meta分析来估计在相同的新冠病毒疫苗接种状态下，相对于未感染新冠病毒人群，既往Omicron变异株感染对Omicron变异株重复感染的保护效果和持续时间。在14105篇文献中，我们选择了10项队列研究，检测阴性设计研究或病例对照研究进行分析。我们发现，对重复感染的保护效果根据不同既往感染和重复感染的变异株而不同。既往Omicron感染对另一个Omicron亚分支重复感染的保护效果会随着时间的推移而下降。BA.1感染对BA.2重复感染的保护效果为87.5%（47.9-97.0），从1个月的89.8%（64.6-97.1）下降到5个月的81.1%（31.9-94.8）。而BA.1感染对BA.4/5重复感染的保护效果为75.2%（42.1-89.4），从5个月的77.2%（47.6-90.1）下降到12个月的40.9%（-32.8-73.7）。BA.1感染对XBB重复感染的保护效果从3-8个月的40.0%（32.0-47.0）下降到8个月后的27.0%（24.0-30.0）。BA.2感染对BA.4/5重复感染的保护效果为88.9%（76.6-94.8），从4个月的91.6%（80.9-96.3）下降至8个月的80.4%（56.7-91.1）。BA.2感染对XBB重复感染的保护效果从3-6个月的74.0%（72.0-75.0）下降到8个月后的37.0%（32.0-43.0）。BA.1/2感染对BA.4/5重复感染的保护效果为86.2%（73.6-92.8）。BA.1/2感染对BA.2.75重复感染的保护效果为49.9%（47.6-52.1）。BA.4/5感染对BA.2.75重复感染的保护效果为80.6%（71.2-87.0）。对既往感染的保护效果会因更近的抗原距离和更高的体液免疫水平而增加。结果表明，新冠疫苗成分更新和接种策略必须考虑既往感染的时间和变异株之间的抗原距离，以优化新冠疫情防控措施。
For more information: https://weekly.chinacdc.cn/en/article/doi/10.46234/ccdcw2024.103


Exploring the Lagged Correlation Between Baidu Index and Influenza-Like Illness — China, 2014–2019
This study investigated the lagged correlation between Baidu Index for influenza-related keywords and influenza-like illness percentage (ILI%) across regions in China. The aim is to establish a scientific foundation for utilizing Baidu Index as an early warning tool for influenza-like illness epidemics. Data on ILI% and Baidu Index were collected from 30 provincial-level administrative divisions (PLADs) spanning April 2014 to March 2019. The Baidu Index was categorized into Overall Index, Ordinary Index, Prevention Index, Symptom Index, and Treatment Index based on search query themes. The lagged correlation between the Baidu Index and ILI% was examined through the cross-correlation function (CCF) method. Correlating the Baidu Overall Index of 30 PLADs with ILI% revealed CCF values ranging from 0.46 to 0.86, with a median lag of 0.5 days. Subcategory analysis indicated that the Prevention Index and Symptom Index exhibited quicker responses to ILI%, with median lags of -9 and -0.5 days, respectively, compared to 0 and 3 days for the Ordinary and Treatment Indexes. The median lag days between the Baidu Index and the ILI% were earlier in the northern PLADs compared to the southern PLADs. The Prevention and Symptom Indexes show promising predictive capabilities for influenza-like illness epidemics.
百度指数与流感样疾病的滞后相关性研究 — 中国，2014–2019
本研究调查中国各地区流感相关关键词的百度指数与流感样疾病百分比（ILI%）之间的滞后相关性。为利用百度指数进行流感样疾病流行的早期预警提供科学基础。本研究以2014年4月至2019年3月的30个省级行政区为研究对象，收集各省份ILI%和百度指数数据。百度指数根据搜索查询主题分类为综合指数、一般指数、预防指数、症状指数和治疗指数。采用互相关函数（CCF）方法检验百度指数与ILI%之间的滞后相关性。分析30省的综合指数与ILI%之间的滞后相关性，结果显示CCF值在0.46-0.86之间，中位滞后天数为0.5天。亚分类百度指数与ILI%的分析结果表明，预防指数和症状指数对 ILI% 的反应更快，中位滞后天数分别为-9天和-0.5天，而一般指数和治疗指数分别为0天和3天。与南方省份相比，北方省份的百度指数和ILI%之间的中位滞后天数更早。预防指数和症状指数显示出了对流感样疾病流行的潜在预测能力。
For more information:https://weekly.chinacdc.cn/en/article/doi/10.46234/ccdcw2024.084


Comparison Between Threshold Method and Artificial Intelligence Approaches for Early Warning of Respiratory Infectious Diseases— Weifang City, Shandong Province, China, 2020–2023 
Respiratory infectious diseases, such as influenza and COVID-19, present significant global public health challenges. The emergence of artificial intelligence (AI) and big data offers opportunities to improve traditional disease surveillance and early warning systems. The study analyzed data from January 2020 to May 2023, comprising influenza-like illness (ILI) statistics, Baidu index, and clinical data from Weifang. Three methodologies were evaluated: the adaptive dynamic threshold method (ADTM) for dynamic threshold adjustments, machine learning supervised method (MLSM), and machine learning unsupervised method (MLUM) utilizing anomaly detection. The comparison focused on sensitivity, specificity, timeliness, and warning consistency. ADTM issued 37 warnings with a sensitivity of 71% and specificity of 85%. MLSM generated 35 warnings, with a sensitivity of 82% and specificity of 87%. MLUM produced 63 warnings with a sensitivity of 100% and specificity of 80%. The initial warnings from ADTM and MLUM preceded those from MLSM by five days. The Kappa coefficient indicated moderate agreement between the methods, with values ranging from 0.52 to 0.62 (P < 0.05). The study explores the comparison between traditional methods and two machine learning approaches for early warning systems. It emphasizes the validation of machine learning's reliability and underscores the unique advantages of each method. Furthermore, it stresses the significance of integrating machine learning models with various data sources to enhance public health preparedness and response, alongside acknowledging limitations and the need for broader validation.
人工智能与预警界值法应用于呼吸道传染病预警的比较研究 — 中国山东省潍坊市，2020年–2023年
当前，如流感和新冠病毒感染的呼吸道传染病对全球公共卫生构成了巨大的挑战。随着人工智能（AI）和大数据的出现，为优化呼吸道传染病的监测及预警体系提供了机遇。本研究基于2020年1月至2023年5月山东省潍坊市的流感样病例（ILI）统计数据、百度指数以及临床医疗数据开展了传染病预警分析。对比评估了三种预警方法：自适应动态预警界值法（ADTM）、基于全监督策略的机器学习法（MLSM）及基于无监督策略的机器学习法（MLUM）。通过对比敏感性、特异性、时效性和预警一致性评估三种预警方法。结果表明，在测试阶段，ADTM产生了37次预警，敏感性为71%，特异性为85%；MLSM发出了35次预警，敏感性为82%，特异性为87%；MLUM发出了63次预警，敏感性为100%，特异性为80%。其中，ADTM和MLUM的初始预警比MLSM早五天。与此同时，三者的Kappa系数在0.52至0.62区间内（P < 0.05），表明三种方法间具有中等一致性。本研究主要探索了传统预警界值方法和两种机器学习方法在传染病预警实践中运用效果。通过比较研究，突出了每种方法的优势。此外，本研究还强调机器学习模型与多源数据融合的传染病预警方法，对增强公共卫生准备和响应具有重要意义。同时，本研究的局限性主要在于验证不足，期待在下一步开展更广泛的验证。
For more information: https://weekly.chinacdc.cn/en/article/doi/10.46234/ccdcw2024.119


The COVID-19 Vaccines Evaluation Program: Implementation, Management, and Experiences, 2021–2023
China CDC launched the COVID-19 Vaccine Evaluation Program (COVEP) in 2021 to assess the real-world performance of China-produced COVID-19 vaccines. This initiative was crucial due to the limited domestic infections in China at the time, necessitating overseas studies. COVEP established ten cooperative agreements with health institutes across five World Health Organization regions, focusing on critical vaccine performance questions. The program’s findings provided both domestic and international confidence in China-produced vaccines, often corroborated by subsequent domestic studies. COVEP not only addressed immediate vaccine efficacy concerns but also established a new mechanism for cross-national collaborative research on public health issues. This approach aligns with China CDC’s strategic planning and development of the national disease control and prevention system, offering a model for future international collaborations. By facilitating these partnerships, COVEP demonstrated the importance of global cooperation in addressing urgent public health challenges and evaluating medical interventions in diverse real-world settings.
COVID-19疫苗评估项目: 执行，管理和经验, 2021–2023
2021年，随着中国生产的COVID-19疫苗获得国际监管部门批准并在多个国家使用，由于当时国内感染病例较少，需要开展海外研究以评估COVID-19疫苗的真实世界保护效果。中国疾病预防控制中心（中国CDC）实施了 COVID-19 疫苗评估项目（COVEP），建立并支持开展与海外卫生机构的合作研究，以进行对中国生产的 COVID-19 疫苗真实世界的保护效果评估和影响评价。项目共在世界卫生组织的五个地区建立十项合作协议，以解决至关重要的疫苗保护效果问题。来自 COVEP 项目的证据增强了国内外对中国生产的 COVID-19 疫苗的信心。在随后国内有关的研究也可以看到类似的疫苗保护效果结果。通过 COVEP项目的实施，中国疾病预防控制中心建立起支持跨国合作研究项目的新机制，以解决重要的公共卫生问题，并为国际合作提供参考模式，这与中国疾控中心全面战略规划和加强国家疾病预防控制体系高质量发展的目标相一致。
For more information: https://weekly.chinacdc.cn/en/article/doi/10.46234/ccdcw2024.120