Epidemiological Characteristics of Dengue Fever — China, 2005–2023

Zhuowei Li¹; Xiaoxia Huang¹; Aqian Li¹; Shanshan Du¹; Guangxue He¹; Jiandong Li^1#

1. National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, NHC Key Laboratory of Biosafety, NHC Key Laboratory of Medical Virology and Viral Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Diseases Control and Prevention, Beijing, China.

^# Corresponding author: Jiandong Li, lijd@ivdc.chinacdc.cn.

The global incidence of dengue fever has increased significantly over the past two decades, and China faces a significant upward trend in dengue control challenges. Data were obtained from China's NNDRS from 2005 to 2023. Joinpoint regression software was used to analyze temporal trends, while SaTScan software was used to analyze spatial, seasonal, and spatiotemporal distributions. ArcGIS software was used to visualize clusters. A total of 117,892 dengue cases were reported from 2005 to 2023, with significant fluctuation in annual reported cases. Dengue was not endemic in mainland China. Autochthonous outbreaks most likely occurred in the southwest, southeast coastal, and inland areas of southern mainland China. These outbreaks have occurred between June and November, generally peaking in September or October, around EW 40. Dengue challenges in Chinese mainland are increasing. Timely case monitoring, proactive control interventions, and staff mobilization should be implemented before June to ensure a timely response to autochthonous outbreaks.

登革热报告病例流行病学特征分析 — 中国，2005–2023年

李卓威¹；黄晓霞¹；李阿茜¹；杜珊珊¹；何广学^1,2；李建东^1#

1.传染病溯源预警与智能决策全国重点实验室，国家卫生健康委员会生物安全重点实验室，医学病毒和病毒病重点实验室，中国疾病预防控制中心病毒病预防控制所，北京，中国；

2.滨州医学院公共卫生学院，烟台市，山东省，中国。

^# 通信作者：李建东，lijd@ivdc.chinacdc.cn。

在过去二十年里，全球登革热发病率显著上升，中国登革热的防控也面临着明显挑战。数据来源于2005-2023年中国疾病预防控制信息系统（NNDRS）。利用Joinpoint软件分析时间趋势，利用SaTScan软件分析季节和时空分布。ArcGIS软件用于可视化聚类地图。从2005年到2023年，共报告了117892例登革热病例，每年报告的病例有明显的波动。登革热在中国尚未形成稳定的本地化传播，西南地区、东南沿海和华南内陆地区发生本地传播疫情风险高。高峰期集中在6月至11月间，一般在9月或10月（流行病学周EW40）前后到达顶峰。中国登革热疫情面临的挑战与日俱增，为及时应对中国本地传播疫情，应在6月前开展及时的病例监测、积极的控制干预和人员动员。

For more information: https://weekly.chinacdc.cn/en/article/doi/10.46234/ccdcw2024.217

Trends and Spatial Pattern Analysis of Typhoid and Paratyphoid Fever Incidence — Yunnan Province, China, 1989–2022

Xiulian Shen^1*; Liqiong Zhang^2*; Lining Guo³; Jibo He^1#; Weijun Yu^4#

1. Epidemic Surveillance/Public Health Emergency Response Center, Yunnan Provincial Center for Disease Control and Prevention, Kunming City, Yunnan Province, China;

2. Department of Intervention Research, Yunnan Institute for Drug Abuse, Kunming City, Yunnan Province, China;

3. Hunan District Center for Disease Control and Prevention, Shenyang City, Liaoning Province, China;

4. Institute for Prevention and Control of Infection and Infectious Diseases, Liaoning Provincial Center for Disease Control and Prevention, Shenyang City, Liaoning Province, China.

^* Joint first authors.

^# Corresponding authors: Jibo He, 5706343@qq.com; Weijun Yu, lncdcywj@163.com.

This study explored the incidence trends and spatial clustering of typhoid and paratyphoid fever (TPF) in Yunnan Province to provide scientific evidence for developing and improving prevention and control strategies. Temporal trends were investigated by calculating the annual percent change (APC) and average annual percent change (AAPC), along with their 95% CIs. Spatial clustering of TPF across Yunnan Province was examined using global Moran's I and local indicators of spatial association (LISA) statistics. A total of 206,066 TPF cases were reported in Yunnan Province from 1989 to 2022, with an average annual incidence of 13.98 per 100,000 population and a case fatality rate of 2.5 per 1,000. The greatest number of cases was reported during July and August. The 25–34-year age group had the highest incidence, and farmers were prominently represented. TPF incidence in Yunnan Province showed a significant decrease and spatial clustering. From 2005 to 2022, 13 county-level cities/counties/municipal districts in 5 prefectures (cities) in Yunnan Province were identified as statistically significant H-H spatial clusters of TPF incidence. A total of 24 TPF outbreaks were reported in Yunnan Province from 2005 to 2022. The incidence of TPF in Yunnan Province showed a significant decrease and spatial clustering. Control strategies should focus on high-incidence areas, seasons, and populations to reduce the incidence of TPF.

伤寒、副伤寒发病趋势及空间模式分析 — 中国云南省，1989–2022年

沈秀莲^1*；张丽琼^2*；郭力宁³；何继波^1#；于维君^4#

1. 云南省疾病预防控制中心疾病监测/突发公共卫生事件处置中心；

2. 云南省药物依赖防治研究所干预研究部；

3. 沈阳市浑南区疾病预防控制中心；

4. 辽宁省疾病预防控制中心感染与传染性疾病预防控制所。

^* 共同第一作者。

^# 通信作者：何继波，5706343@qq.com；于维君，lncdcywj@163.com。

本研究探索云南省伤寒、副伤寒发病趋势及空间聚集性，为制定防治规划、策略和措施提供科学依据。通过计算年度变化百分比和平均年度变化百分比及其95%置信区间来研究云南省伤寒、副伤寒发病时间趋势。使用全局莫兰值统计量和局部莫兰值统计量探索云南省伤寒、副伤寒发病空间聚集性。1989—2022年，云南省共报告206,066例伤寒、副伤寒病例，年平均发病率为13.98/10万，病死率为2.5‰。夏季的7月和8月病例数最多。发病率最高的年龄组为25-34岁，农民占比最高。云南省伤寒、副伤寒发病呈显著下降趋势及空间聚集性。2005—2022年，云南省5个州（市）的13个县（市）区被确定为伤寒、副伤寒显著高发空间聚集区。2005—2022年，云南省共报告24起伤寒、副伤寒暴发疫情。云南省伤寒、副伤寒发病呈显著下降趋势及显著空间聚集性。防控策略应重点关注高发地区、高发季节和高发人群，以减少伤寒、副伤寒发病。

For more information: https://weekly.chinacdc.cn/en/article/doi/10.46234/ccdcw2024.216.

Monitoring the Status of Multi-Wave Omicron Variant Outbreaks — 71 Countries, 2021–2023

Chuanqing Xu^1#; Lianjiao Dai¹; Songbai Guo¹; Xiaoyu Zhao²; Xiaoling Liu³

1. School of Science, Beijing University of Civil Engineering and Architecture, Beijing, China

2. School of Mathematics, Shandong University, Jinan City, Shandong Province, China

3. Hanshan Normal University, Chaozhou City, Guangdong Province, China

^# Corresponding author: Chuanqing Xu, xuchuanqing@bucea.edu.cn.

Since the emergence of the novel coronavirus strain Omicron in November 2021, countries around the world have experienced one to four waves of the COVID-19 pandemic. Analyzing the characteristics of epidemic development after the emergence of the Omicron strain and the impact of income inequality on COVID-19 mortality helps us better understand the spread of novel coronavirus infections. This paper retrieved data on novel coronavirus epidemics in 71 countries from November 14, 2021, to June 11, 2023, and categorized the data according to the number of epidemic waves, the income level of the country, and geographic location. Data were analyzed using descriptive statistical analysis, Fisher’s exact test, and Kruskal–Wallis one-way ANOVA. The median time interval between the first and second waves was 70 days (interquartile range: 43.75–91), and the median time interval between the second and third waves was 87.5 days (interquartile range: 49–119); the time intervals were log-normally distributed. There was no correlation between the number of epidemic waves and the income level of the country or the geographic location. The mortality rate of the first wave was significantly higher than that of the second and third waves. During the initial Omicron epidemic period, there was no significant difference in the mortality rate of the first wave between different geographic locations, while the mortality rate in high-income countries was significantly lower than that in countries with other income levels. We still need to pay attention to the novel coronavirus epidemic. Income inequality impacts outbreak mortality in the early stages of Omicron epidemics, and in most countries, virus strains are likely to move from low to high population prevalence after 2–4 months.

多波奥密克戎疫情的监测研究 — 71国家，2021–2023年

许传青^1#；代莲娇¹；郭松柏¹；赵晓宇²；刘晓玲³

1. 北京建筑大学理学院，北京，中国

2. 山东大学数学院，济南市，山东省，中国

3. 韩山师范学院数学院，潮州，中国。

^# 通信作者：许传青，xuchuanqing@bucea.edu.cn。

自2021年11月新型冠状病毒毒株Omicron出现至今，世界各国已经历1至4波新冠大流行. 分析Omicron病毒株出现后的疫情发展特征，以及收入水平的不平等现象对COVID-19死亡率的影响，更好的了解新型冠状病毒感染的传播情况。本文检索了2021年11月14日至2023年6月11日71个国家的新型冠状病毒流行数据，并对数据按照疫情发生波数、国家收入水平、国家所在地理位置进行分类整理，通过描述性统计分析、Fisher精确性检验、Kruskal-Wallis单因素方差分析进行分析。第一波疫情与第二波疫情时间间隔中位数为70天（四分位数间距为：43.75-91），第二波疫情与第三波疫情的时间间隔中位数为87.5天（四分位数间距为：49-119），且时间间隔都服从对数正态分布；疫情发生波数与国家收入水平、所在地理位置均无相关关系；第一波疫情的死亡率显著高于第二波、第三波疫情；在Omicron变异株初次流行时期，不同地理位置的第一波疫情死亡率无显著性差异，而高收入水平国家的疫情死亡率显著低于其他收入水平的国家。我们仍需重视新型冠状病毒的流行，收入水平的不平等会对Omicron流行初期的疫情死亡率产生影响，大多数国家都面临着病毒株在2-4个月后从低位流行变为高位流行的问题。

For more information: https://weekly.chinacdc.cn/en/article/doi/10.46234/ccdcw2024.218

Impact of COVID-19 Interventions on Respiratory and Intestinal Infectious Disease Notifications — Jiangsu Province, China, 2020–2023

Ziying Chen^1*; Xin Liu^2*; Jinxing Guan¹; Yingying Shi²; Wendong Liu²; Zhihang Peng^3#; Jianli Hu^1,2,4#

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

2. Department of Acute infectious Diseases Control and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing City, Jiangsu Province, China

3. National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China

4. Jiangsu Province Engineering Research Center of Health Emergency, Nanjing City, Jiangsu Province, China

^* Joint first authors.

^# Corresponding authors: Jianli Hu, jshjl@jscdc.cn; Zhihang Peng, zhihangpeng@njmu.edu.cn.

Many measures have been implemented to control the COVID-19 pandemic, reshaping the epidemic patterns of other infectious diseases. We estimated the impact of the COVID-19 pandemic on respiratory and intestinal infectious diseases and potential changes following “reopening”. The best intervention and counterfactual models were selected from among the SARMA, neural network and hybrid models according to the minimum MAPE in the test set, and the relative change rate between the actual notification rate and that predicted by the best model was calculated for the entire COVID-19 epidemic prevention period and during the “reopening” period. Compared with the predicted notification rate based on the counterfactual model, the total relative change rates for the 9 infectious diseases were -44.24%, respiratory infections (-55.41%) and intestinal infections (-26.59%) during 2020-2022. Compared with the predicted notification rate based on the intervention model, the total relative change rates were +247.98%, respiratory infections (+389.59%) and intestinal infections (+50.46%) in 2023. Among them, the relative increase in influenza (+499.98%) and HFMD(+70.97%) is significant. Measures taken by Jiangsu Province in response to COVID-19 have effectively constrained the spread of respiratory and intestinal infectious diseases. Influenza and HFMD rebounded significantly after lifting COVID-19 intervention restrictions.

新冠肺炎干预措施对呼吸道和肠道传染病报告的影响 — 中国江苏省，2020–2023年

陈紫颖^1*；刘鑫^2*；官锦兴¹；时影影²；刘文东²；彭志行^3#；胡建利^1,2,4#

1.公共卫生学院, 南京医科大学, 南京市，江苏省, 中国；

2.急性传染病防制所, 江苏省疾病预防控制中心, 南京市，江苏省, 中国；

3.传染病溯源预警与智能决策全国重点实验室, 中国疾病预防控制中心, 北京, 中国；

4. 江苏省卫生应急工程研究中心，南京市，江苏省, 中国。

^* 共同第一作者

^# 通信作者：胡建利，jshjl@jscdc.cn；彭志行，zhihangpeng@njmu.edu.cn。

新冠期间采取的诸多措施重塑了其他传染病的流行模式，因此我们评估了新冠肺炎大流行对江苏省呼吸道和肠道传染病的影响，以及“重新开放”后的潜在变化。根据测试集中的最小MAPE，从SARMA、神经网络和组合模型中选择最佳干预模型和反事实模型，并计算整个新冠肺炎防疫期间和“重新开放”后实际报告发病率与最佳模型预测的发病率之间的相对变化率。与基于反事实模型的预测发病率相比，2020~2022年期间，9种传染病的总相对变化率为-44.24%，呼吸道传染病为-55.41%，肠道传染病为-26.59%。与基于干预模型预测的发病率相比，2023年的总相对变化率为+247.98%，呼吸道传染病为+389.59%，肠道传染病为+50.46%，其中，流感（+499.98%）和手足口病（+70.97%）的相对变化率比较显著。江苏省为应对新冠肺炎而采取的措施有效地遏制了呼吸道和肠道传染病的传播。在解除新冠肺炎干预限制后，流感和手足口病显著反弹。

For more information: https://weekly.chinacdc.cn/en/article/doi/10.46234/ccdcw2024.219

Drawing on the Development Experiences of Infectious Disease Surveillance Systems Around the World

Huimin Sun¹; Weihua Hu¹; Yongyue Wei¹; Yuantao Hao^1,2,3#

1. Department of Epidemiology and Health Statistics, School of Public Health, Peking University, Beijing, China

2. Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China

3. Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China.

^# Corresponding author: Yuantao Hao, haoyt@bjmu.edu.cn.

High-quality infectious disease surveillance systems are foundational to infectious disease prevention and control. Current major infectious disease surveillance systems globally can be categorized as either indicator-based, which are more specific, or event-based, which are more timely. Modern surveillance systems commonly utilize multi-source data, strengthened information sharing, advanced technology, and improved early warning accuracy and sensitivity. International experience may provide valuable insights for China. China’s existing infectious disease surveillance systems require urgent enhancements to monitor emerging infectious diseases and improve the integration and learning capabilities of early warning models. Methods such as establishing multi-stage surveillance systems, promoting cross-sectoral and cross-provincial data sharing, applying advanced technologies like artificial intelligence, and cultivating professional talent should be adopted to enhance the development of intelligent and multipoint-triggered infectious disease surveillance systems in China.

借鉴全球传染病监测系统的发展经验

孙慧敏¹；胡伟华¹；魏永越¹；郝元涛^1,2,3#

1. 北京大学公共卫生学院流行病与卫生统计学系，北京，中国；

2. 北京大学公众健康与重大疫情防控战略研究中心，北京，中国；

3. 重大疾病流行病学教育部重点实验室（北京大学），北京，中国。

# 通信作者: 郝元涛，haoyt@bjmu.edu.cn。

高质量的传染病监测系统是传染病防控的基石。目前，全球主要的传染病监测系统可分为基于指标的监测和基于事件的监测，前者更具体，后者更及时。现代监测系统的共性在于利用多源数据、强化信息共享、整合先进技术并提高预警的准确性和灵敏度。国际经验为我国提供了宝贵的启示，我国现有的传染病监测系统亟待加强，预警模型的整合和学习能力需要提升，以更好地监测新发和突发传染病。可以通过建立多阶段监测系统、促进跨部门和跨省数据共享、应用人工智能等技术、培养专业人才等措施，推动中国智慧化多点触发传染病监测系统的发展。

For more information: https://weekly.chinacdc.cn/en/article/doi/10.46234/ccdcw2024.220.