安庆市2017—2018年流行性感冒流行病学分析

目的　分析安庆市2017—2018年流行性感冒（流感）监测结果，掌握本地流感流行特征，为制定防控策略提供科学依据。方法　登录中国流感监测信息系统，对安庆市2017—2018年流感样病例（influenza like illness, ILI）数据、病原学数据进行统计学分析。结果　安庆市哨点监测ILI百分比为3.46%，流感监测网络实验室检测流感病毒阳性率为13.84%，主要型别为新H1N1，占42.18%，流感病毒阳性率呈现“一前一后，一小一大”的夏季和冬季高峰，＜15岁组和≥15岁组流感病毒阳性率的差异有统计学意义（χ2=4.648，P=0.031）。安庆市共报告聚集性疫情23起，其中16起发生在城市，7起发生在农村，城市和农村的流感病毒阳性率的差异无统计学意义（χ2=3.181，P=0.074）。结论　安庆市2017—2018年哨点监测流感活动呈双峰分布，流感病毒以新H1N1和乙型Yamagata系为主，聚集性疫情上升明显，建议做好学校聚集性疫情的防控工作。     

2011—2015年北京市儿童流行性感冒流行特征分析

目的 了解北京市儿童2011—2015年流行性感冒(流感)的流行特征和流感病毒优势株的变化,为儿童流感的防控提供参考.方法 利用北京市儿童医院2011—2015年监测到的流感样病例(influenza like illness,ILI)及流感病原学监测数据,分析流感流行趋势和流感病毒的构成情况.结果 在7331597例门、急诊病例中监测到ILI 638623例,ILI占门、急诊就诊病例的8.71％,0～岁组所占比例最高,为69.56％;采集门诊ILI咽拭子标本5351例,其中流感病毒核酸阳性515例.各年份优势毒株构成不同,夏季也出现了ILI就诊的高峰.结论 2011—2015年各年份流感流行高峰出现在冬春季,小于5岁儿童是主要易感人群,建议夏季就诊高峰对儿童ILI开展多病原检测,从而可采取有针对性的预防及治疗措施.     

2017-2019年贵阳市流行性感冒病原学监测分析

目的 了解2017—2019年贵阳市流行性感冒（简称流感）病原学特征和变化趋势,为制定防控措施提供资料依据。方法 从中国疾病预防控制信息系统子系统中国流感监测信息系统中,收集并分析2017—2019年贵阳市流感监测哨点医院流感样病例以及暴发和聚集性疫情流感样病例标本的病原学检测结果。结果 2017年1月—2019年12月间共收集流感样病例标本13 028份,检出流感病毒阳性标本1 568份,阳性率为12.04%;2019年阳性率最高18.33%,各年度流感病毒阳性率差异有统计学意义（χ²=226.816,P<0.05）。阳性标本中,甲型H1N1占37.76%（592份）、甲型H3N2占30.55%（479份）、BV型占19.07%（299份）、BY型占10.84%（170份）、混合型流感病毒占1.78%（28份）;不同年度各型流感病毒阳性率差异除混合型（χ²=1.195,P>0.05）外,均有统计学意义（P<0.05）。结论 贵阳市不同年度流感亚型优势株不同,学校和托幼机构极易发生聚集性流感疫情,应加强相关场所的管理和健康宣...     

2013-2021年海南省流感病原学检测及流行特征分析

目的 了解海南省流行性感冒(简称流感)流行特征及其相关影响因素,为预防和控制流感提供科学依据。方法 对2013-2021年海南省6家国家级哨点医院流感样病例(influenza-like illness, ILI)的病原学监测数据进行统计分析。结果 2013-2021共检测ILI标本50 415份,其中流感病毒阳性标本5 581份,阳性率为11.07%;优势流行株为乙型、A(H1N1)pdm09型和H3N2型;5～14岁年龄组阳性检出率最高(17.56%),0～4岁年龄组流感病毒阳性率最低(7.32%)。2014-2016年、2017-2018年和2019-2020年4个监测年度,流感呈现双峰流行,夏季流行期集中在4-9月,最高峰值53.64%,冬季流行期集中在11月-次年3月,最高峰值47.30%;2013-2014年、2016-2017年和2018-2019年3个监测年度,流感流行无明显夏季流行高峰,冬春季流行期集中在10月-次年3月,最高峰值54.17%;2020-2021年流感病毒阳性检出率呈现断崖式下降,阳性检出率仅为0.25%,无明显流行期。结论 海南省不同监测年度流感流行...     

2015—2020年铜陵市流行性感冒监测结果分析

目的 了解铜陵市流行性感冒(简称流感)流行特征,为辖区流感防控提供科学依据。方法 通过中国流感监测信息系统收集2015—2020年铜陵市流感样病例(influenza-like illnesses,ILI)、病原学监测和ILI暴发疫情等监测数据,分析ILI的分布特征以及病毒阳性标本的人群、时间分布和分型等流行特征。结果 2015—2020年,铜陵市2家哨点医院报告ILI共计26 862例,其中5岁以下人群占比最高,为46.04%(12 367/26 862);流感样病例百分比(ILI%)为3.11%(26 862/862 671),各年度ILI%差异有统计学意义(χ²=941.478,P<0.05);监测年度内共出现8次就诊高峰,主要发生在冬春季。共采集ILI标本8 490份,总阳性率为12.60%(1 082/8 490),男性和女性阳性率分别为14.44%(586/4 059)和11.19%(496/4 431);各年龄组中,5～14岁组阳性率较高,为22.41%(322/1 437);各年度中,以2017—2018年度阳性率较高,为20.63%(255...     

2016-2019年新疆流感监测分析

目的了解2016—2019年新疆流感样病例(influenza-like illness,ILI)的流行病学特征和病原学监测数据,为流感防控工作提供科学依据。方法通过中国流感监测信息系统收集2016—2019年新疆流感哨点医院报告的ILI、病原学监测数据和流感聚集性疫情报告资料,分析流行病学特征。结果 2016年第14 w—2019年第13 w,新疆共报告门诊病例9 820 366例,其中流感样病例405 394例,ILI%为4.1%;病例集中在15岁以下人群;检测样本38 975份,检出流感病毒阳性6 584份、阳性率16.9%,分离病毒株为新甲型H1N1型848株、H3型603株、BV型47株和BY型606株,聚集性疫情中病毒型别与当年流行的病毒型别一致。结论新疆流感疫情集中在每年的第40 w至次年第20 w,新甲型H1N1,B型和H3型交替流行;流感主要发生在冬春季,以学龄儿童为主;应加强学龄儿童的流感防控工作。     

十堰市2005～2007年流行性感冒病原学监测结果分析

2005～2007年6月对湖北十堰市进行流行性感冒(简称流感)的病原学监测,共监测流感样病例(ILI)鼻咽拭子标本1125份,分离出流感病毒248株,阳性分离率为22.04%;经分离鉴定H1N1亚型69株,H3N2亚型66株,B型Victoria系109株,4株未能分型。未监测到B型Yamagatata系毒株,未发现流感变异株。     

2010～2011年马鞍山市流感疫情监测分析

目的了解马鞍山市流感流行动态,探索流行规律,为流感的防制提供科学依据。方法收集监测医院流感样病例(ILI)数据,采集部分ILI咽拭子标本,采用实时荧光定量RT-PCR技术检测流感病毒核酸。结果马鞍山市2010～2011年共报告ILI 13 151例,占门诊就诊人数的4.79%。采咽拭子3 185份,核酸检测阳性486份,阳性率15.26%,其中A型H1N1亚型47份、A型H3N2亚型119份、B型320份。结论 2010～2011年马鞍山市流感呈现冬春季及夏秋季两个发病高峰,流感病毒以B型为主;流感的监测应进一步加强。     

北京地区某医院2019—2022年儿童流感门诊流行性感冒筛查与监测分析

目的?了解解放军战略支援部队特色医学中心儿童流行性感冒（流感）门诊病例流行病学特点,为该院儿童流感就诊和预防提供指导。方法?收集解放军战略支援部队特色医学中心2019年1月1日—2022年3月31日门诊日志,回顾性分析就诊患儿中流感分型、年龄特征、感染时间分布特征、与新型冠状病毒肺炎（新冠肺炎）流行时间的关系等。结果?2019年1月—2022年3月,该院儿科门诊总计接诊流感阳性病例3672例,其中,甲型流感阳性率最高（2427例,7.94%）,所占比例最高（66.09%）。10～14岁年龄段患儿阳性率最高（629例,17.98%）。发病高峰为冬春季。新冠肺炎疫情期间冬春季病例数减少,主要病原分型由甲型流感转为乙型流感。结论?该院地处北方,儿童流感冬春季高发,患儿发病年龄特征明显。新冠肺炎疫情暴发后,病例数减少,主要病原分型由甲型流感转为乙型流感。     

Fluwatchers A Crowdsourcing Approach: FluWatchers: Evaluation of a crowdsourced influenza-like illness surveillance application for Canadian influenza seasons 2015–2016 to 2018–2019

BackgroundSentinel influenza-like illness (ILI) surveillance is an essential component of a comprehensive influenza surveillance program. Community-based ILI surveillance systems that rely solely on sentinel healthcare practices omit important segments of the population, including those who do not seek medical care. Participatory surveillance, which relies on community participation in surveillance, may address some limitations of traditional ILI systems.ObjectiveWe aimed to evaluate FluWatchers, a crowdsourced ILI application developed to complement and complete ILI surveillance in Canada.MethodsUsing established frameworks for surveillance evaluations, we assessed the acceptability, reliability, accuracy and usefulness of the FluWatchers system 2015–2016, through 2018–2019. Evaluation indicators were compared against national surveillance indicators of ILI and of laboratory confirmed respiratory virus infections.ResultsThe acceptability of FluWatchers was demonstrated by growth of 50%–100% in season-over-season participation, and a consistent season-over-season retention of 80%. Reliability was greater for FluWatchers than for our traditional ILI system, although both systems had week-over-week fluctuations in the number of participants responding. FluWatchers’ ILI rates had moderate correlation with weekly influenza laboratory detection rates and other winter seasonal respiratory virus detections including respiratory syncytial virus and seasonal coronaviruses. Finally, FluWatchers has demonstrated its usefulness as a source of core FluWatch surveillance information and has the potential to fill data gaps in current programs for influenza surveillance and control.ConclusionFluWatchers is an example of an innovative digital participatory surveillance program that was created to address limitations of traditional ILI surveillance in Canada. It fulfills the surveillance system evaluation criteria of acceptability, reliability, accuracy and usefulness.     

Spotlight influenza: Extending influenza surveillance to detect non-influenza respiratory viruses of public health relevance: analysis of surveillance data,Belgium, 2015 to 2019

BackgroundSeasonal influenza-like illness (ILI) affects millions of people yearly. Severe acute respiratory infections (SARI), mainly influenza, are a leading cause of hospitalisation and mortality. Increasing evidence indicates that non-influenza respiratory viruses (NIRV) also contribute to the burden of SARI. In Belgium, SARI surveillance by a network of sentinel hospitals has been ongoing since 2011.AimWe report the results of using in-house multiplex qPCR for the detection of a flexible panel of viruses in respiratory ILI and SARI samples and the estimated incidence rates of SARI associated with each virus.MethodsWe defined ILI as an illness with onset of fever and cough or dyspnoea. SARI was defined as an illness requiring hospitalisation with onset of fever and cough or dyspnoea within the previous 10 days. Samples were collected in four winter seasons and tested by multiplex qPCR for influenza virus and NIRV. Using catchment population estimates, we calculated incidence rates of SARI associated with each virus.ResultsOne third of the SARI cases were positive for NIRV, reaching 49.4% among children younger than 15 years. In children younger than 5 years, incidence rates of NIRV-associated SARI were twice that of influenza (103.5 vs 57.6/100,000 person-months); co-infections with several NIRV, respiratory syncytial viruses, human metapneumoviruses and picornaviruses contributed most (33.1, 13.6, 15.8 and 18.2/100,000 person-months, respectively).ConclusionEarly testing for NIRV could be beneficial to clinical management of SARI patients, especially in children younger than 5 years, for whom the burden of NIRV-associated disease exceeds that of influenza.     

Spotlight influenza: Laboratory-confirmed seasonal influenza in people with acute respiratory illness: a literature review and meta-analysis,WHO European Region, 2004 to 2017

BackgroundAcross the World Health Organization European Region, there are few estimates of the proportion of people seeking medical care for influenza-like illness or acute respiratory infections and who have laboratory-confirmed seasonal influenza infection.MethodsWe conducted a meta-analysis of data extracted from studies published between 2004 and 2017 and from sentinel data from the European surveillance system (TESSy) between 2004 and 2018. We pooled within-season estimates by influenza type/subtype, setting (outpatient (OP)/inpatient (IP)) and age group to estimate the proportion of people tested who have laboratory-confirmed and medically-attended seasonal influenza in Europe.ResultsIn the literature review, the pooled proportion for all influenza types was 33% (95% confidence interval (CI): 30–36), higher among OP 36% (95% CI: 33–40) than IP 24% (95% CI: 20–29). Pooled estimates for all influenza types by age group were: 0–17 years, 26% (22–31); 18–64 years, 41% (32–50); ≥ 65 years, 33% (27–40). From TESSy data, 33% (31–34) of OP and 24% (21–27) of IP were positive. The highest proportion of influenza A was in people aged 18–64 years (22%, 16–29). By subtype, A(H1N1)pdm09 was highest in 18–64 year-olds (16%, 11–21%) whereas A(H3N2) was highest in those ≥ 65 years (10%, 2–22). For influenza B, the highest proportion of infections was in those aged 18–64 years (15%, 9–24).ConclusionsLaboratory-confirmed influenza accounted for approximately one third of all acute respiratory infections for which medical care was sought during the influenza season.     

Decline of influenza and respiratory syncytial virus detection in facility-based surveillance during the COVID-19 pandemic,South Africa,January to October 2020

BackgroundIn South Africa, COVID-19 control measures to prevent SARS-CoV-2 spread were initiated on 16 March 2020. Such measures may also impact the spread of other pathogens, including influenza virus and respiratory syncytial virus (RSV) with implications for future annual epidemics and expectations for the subsequent northern hemisphere winter.MethodsWe assessed the detection of influenza and RSV through facility-based syndromic surveillance of adults and children with mild or severe respiratory illness in South Africa from January to October 2020, and compared this with surveillance data from 2013 to 2019.ResultsFacility-based surveillance revealed a decline in influenza virus detection during the regular season compared with previous years. This was observed throughout the implementation of COVID-19 control measures. RSV detection decreased soon after the most stringent COVID-19 control measures commenced; however, an increase in RSV detection was observed after the typical season, following the re-opening of schools and the easing of measures.ConclusionCOVID-19 non-pharmaceutical interventions led to reduced circulation of influenza and RSV in South Africa. This has limited the country’s ability to provide influenza virus strains for the selection of the annual influenza vaccine. Delayed increases in RSV case numbers may reflect the easing of COVID-19 control measures. An increase in influenza virus detection was not observed, suggesting that the measures may have impacted the two pathogens differently. The impact that lowered and/or delayed influenza and RSV circulation in 2020 will have on the intensity and severity of subsequent annual epidemics is unknown and warrants close monitoring.     

Hospital‐based influenza morbidity and mortality surveillance system for influenza‐like illnesses: a comparison with national influenza surveillance systems

The Hospital‐based Influenza Morbidity and Mortality (HIMM) surveillance system is an emergency room (ER)‐based influenza surveillance system in Korea that was established in 2011. The system was established under the assumption that integrated clinical and virologic surveillance could be performed rapidly and easily at seven tertiary hospitals' ER. Here, we assessed the correlation between data generated from the HIMM surveillance system and the Korean national influenza surveillance systems during the 2011–2012 influenza season using cross‐correlation analysis and found strong correlations. Rapid antigen‐test‐based HIMM surveillance would predict the start of influenza epidemic earlier than pre‐existing influenza‐like‐illness‐based surveillance.