scholarly journals Changes in the incidence of seasonal influenza in response to COVID-19 social distancing measures: an observational study based on Canada’s national influenza surveillance system

2020 ◽  
Author(s):  
Andrew Pierce ◽  
Margaret Haworth-Brockman ◽  
Diana Marin ◽  
Zulma V Rueda ◽  
Yoav Keynan
Keyword(s):  
Surveillance System ◽  
Seasonal Influenza ◽  
Influenza Season ◽  
Influenza Surveillance ◽  
Social Distancing ◽  
Canadian Health Care System ◽  
Canadian Health ◽  
Primary Focus ◽  
Using Data ◽  
Novel Coronavirus

Abstract Objectives: Seasonal influenza is an acute respiratory infection that presents a significant annual burden to Canadians and the Canadian health care system. Social distancing measures that were implemented to control the novel coronavirus outbreak were also investigated for their ability to lessen the incidence of seasonal influenza.Methods: We conducted an ecological study using data from Canada’s national influenza surveillance system to investigate whether social distancing measures to control COVID-19 reduced the incidence of seasonal influenza. Data taken from three separate time frames facilitated analysis of the 2019-20 influenza season prior to, during, and following the implementation of COVID-19 related measures and enabled comparisons to the same time periods during three preceding flu seasons. The incidence of specific influenza strains was of primary focus. Further analysis was performed to determine the number of new laboratory-confirmed influenza or influenza like illness outbreaks.Results: Our results indicate a premature end to the 2019-20 influenza season, with a significantly fewer number of cases and outbreaks being recorded following the enactment of many COVID-19 social distancing polices. The incidence of influenza strains A (H3N2), A (unsubtyped), and B were all significantly lower at the tail-end of the 2019-20 influenza season, compared with preceding seasons.Conclusion: Specific social distancing measures and behaviours may serve as effective tools to limit the spread of influenza transmission moving forward, as they become more familiar.

scholarly journals Changes in the incidence of seasonal influenza in response to COVID-19 social distancing measures: an observational study based on Canada’s national influenza surveillance system

2021 ◽  
Author(s):  
Andrew Pierce ◽  
Margaret Haworth-Brockman ◽  
Diana Marin ◽  
Zulma V. Rueda ◽  
Yoav Keynan
Keyword(s):  
Surveillance System ◽  
Seasonal Influenza ◽  
Influenza Season ◽  
Influenza Surveillance ◽  
Social Distancing ◽  
Primary Focus ◽  
Using Data ◽  
Novel Coronavirus ◽  
Time Frames ◽  
Incident Cases

Abstract Objectives Seasonal influenza is an acute respiratory infection that presents a significant annual burden to Canadians and the Canadian healthcare system. Social distancing measures that were implemented to control the 2019–2020 novel coronavirus outbreak were investigated for their ability to lessen the incident cases of seasonal influenza. Methods We conducted an ecological study using data from Canada’s national influenza surveillance system to investigate whether social distancing measures to control COVID-19 reduced the incident cases of seasonal influenza. Data taken from three separate time frames facilitated analysis of the 2019–2020 influenza season prior to, during, and following the implementation of COVID-19-related measures and enabled comparisons with the same time periods during three preceding flu seasons. The incidence, which referred to the number of laboratory-confirmed cases of specific influenza strains, was of primary focus. Further analysis determined the number of new laboratory-confirmed influenza or influenza-like illness outbreaks. Results Our results indicate a premature end to the 2019–2020 influenza season, with significantly fewer cases and outbreaks being recorded following the enactment of many COVID-19 social distancing policies. The incidence of influenza strains A (H3N2), A (unsubtyped), and B were all significantly lower at the tail end of the 2019–2020 influenza season as compared with preceding seasons (p = 0.0003, p = 0.0007, p = 0.0019). Conclusion Specific social distancing measures and behaviours may serve as effective tools to limit the spread of influenza transmission moving forward, as they become more familiar.

scholarly journals What will the next influenza season bring about: seasonal influenza or the new A(H1N1)v? An analysis of German influenza surveillance data

2009 ◽  
Vol 14 (32) ◽  
Author(s):  
H Uphoff ◽  
S Geis ◽  
A Grüber ◽  
A M Hauri
Keyword(s):  
Influenza A ◽  
Seasonal Influenza ◽  
Influenza Season ◽  
Influenza Viruses ◽  
Moderate Intensity ◽  
Influenza Surveillance ◽  
Influenza B ◽  
Low Intensity ◽  
Influenza A H1n1 ◽  
Using Data

For the next influenza season (winter 2009-10) the relative contributions to virus circulation and influenza-associated morbidity of the seasonal influenza viruses A(H3N2), A(H1N1) and B, and the new influenza A(H1N1)v are still unknown. We estimated the chances of seasonal influenza to circulate during the upcoming season using data of the German influenza sentinel scheme from 1992 to 2009. We calculated type and subtype-specific indices for past exposure and the corresponding morbidity indices for each season. For the upcoming season 2009-10 our model suggests that it is unlikely that influenza A(H3N2) will circulate with more than a low intensity, seasonal A(H1N1) with more than a low to moderate intensity, and influenza B with more than a low to median intensity. The probability of a competitive circulation of seasonal influenza A with the new A(H1N1)v is low, increasing the chance for the latter to dominate the next influenza season in Germany.

scholarly journals Estimating the undetected burden of influenza hospitalizations in children

2006 ◽  
Vol 135 (6) ◽  
pp. 951-958 ◽  
Author(s):  
C. G. GRIJALVA ◽  
G. A. WEINBERG ◽  
N. M. BENNETT ◽  
M. A. STAAT ◽  
A. S. CRAIG ◽  
...  
Keyword(s):  
Surveillance System ◽  
Clinical Laboratory ◽  
Influenza Season ◽  
Influenza Surveillance ◽  
Surveillance Systems ◽  
Emerging Infections ◽  
Surveillance Network ◽  
Laboratory Surveillance ◽  
Capture Recapture ◽  
Using Data

SUMMARYDuring the 2004–2005 influenza season two independent influenza surveillance systems operated simultaneously in three United States counties. The New Vaccine Surveillance Network (NVSN) prospectively enrolled children hospitalized for respiratory symptoms/fever and tested them using culture and RT–PCR. The Emerging Infections Program (EIP) and a similar clinical-laboratory surveillance system identified hospitalized children who had positive influenza tests obtained as part of their usual medical care. Using data from these systems, we applied capture–recapture analyses to estimate the burden of influenza related-hospitalizations in children aged <5 years. During the 2004–2005 influenza season the influenza-related hospitalization rate estimated by capture–recapture analysis was 8·6/10 000 children aged <5 years. When compared to this estimate, the sensitivity of the prospective surveillance system was 69% and the sensitivity of the clinical-laboratory based system was 39%. In the face of limited resources and an increasing need for influenza surveillance, capture–recapture analysis provides better estimates than either system alone.

scholarly journals Using a Bayesian Method to Assess Google, Twitter, and Wikipedia for ILI Surveillance

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Danielle Sharpe ◽  
Richard Hopkins ◽  
Robert L. Cook ◽  
Catherine W. Striley
Keyword(s):  
Seasonal Changes ◽  
Bayesian Method ◽  
Influenza Season ◽  
Healthcare Providers ◽  
High Sensitivity ◽  
Change Points ◽  
Influenza Surveillance ◽  
Web Based ◽  
Using Data ◽  
The Web

ObjectiveTo comparatively analyze Google, Twitter, and Wikipedia byevaluating how well change points detected in each web-based sourcecorrespond to change points detected in CDC ILI data.IntroductionTraditional influenza surveillance relies on reports of influenza-like illness (ILI) by healthcare providers, capturing individualswho seek medical care and missing those who may search, post,and tweet about their illnesses instead. Existing research has shownsome promise of using data from Google, Twitter, and Wikipediafor influenza surveillance, but with conflicting findings, studies haveonly evaluated these web-based sources individually or dually withoutcomparing all three of them1-5. A comparative analysis of all threeweb-based sources is needed to know which of the web-based sourcesperforms best in order to be considered to complement traditionalmethods.MethodsWe collected publicly available, de-identified data from the CDCILINet system, Google Flu Trends, HealthTweets.org, and Wikipediafor the 2012-2015 influenza seasons. Bayesian change point analysiswas the method used to detect change points, or seasonal changes,in each of the web-data sources for comparison to change pointsin CDC ILI data. All analyses was conducted using the R package‘bcp’ v4.0.0 in RStudio v0.99.484. Sensitivity and positive predictivevalues (PPV) were then calculated.ResultsDuring the 2012-2015 influenza seasons, a high sensitivity of 92%was found for Google, while the PPV for Google was 85%. A lowsensitivity of 50% was found for Twitter; a low PPV of 43% wasfound for Twitter also. Wikipedia had the lowest sensitivity of 33%and lowest PPV of 40%.ConclusionsGoogle had the best combination of sensitivity and PPV indetecting change points that corresponded with change points found inCDC data. Overall, change points in Google, Twitter, and Wikipediadata occasionally aligned well with change points captured in CDCILI data, yet these sources did not detect all changes in CDC data,which could indicate limitations of the web-based data or signify thatthe Bayesian method is not adequately sensitive. These three web-based sources need to be further studied and compared using otherstatistical methods before being incorporated as surveillance data tocomplement traditional systems.Figure 1. Detection of change points, 2012-2013 influenza seasonFigure 2. Detection of change points, 2013-2014 influenza seasonFigure 3. Detection of change points, 2014-2015 influenza season

scholarly journals National influenza mid-season report, 2020–2021

2021 ◽  
Vol 47 (1) ◽  
pp. 1-4
Author(s):  
Lisa Lee ◽  
Kelly Butt ◽  
Steven Buckrell ◽  
Andrea Nwosu ◽  
Claire Sevenhuysen ◽  
...  
Keyword(s):  
Influenza A ◽  
Seasonal Influenza ◽  
Influenza Season ◽  
Influenza Viruses ◽  
Resistance Testing ◽  
Influenza Surveillance ◽  
Influenza B ◽  
Strain Characterization ◽  
Influenza Activity ◽  
Low Levels

Canada's national influenza season typically starts in the latter half of November (week 47) and is defined as the week when at least 5% of influenza tests are positive and a minimum of 15 positive tests are observed. As of December 12, 2020 (week 50), the 2020-2021 influenza season had not begun. Only 47 laboratory-confirmed influenza detections were reported from August 23 to December 12, 2020; an unprecedentedly low number, despite higher than usual levels of influenza testing. Of this small number of detections, 64% were influenza A and 36% were influenza B. Influenza activity in Canada was at historically low levels compared with the previous five seasons. Provinces and territories reported no influenza-associated adult hospitalizations. Fewer than five hospitalizations were reported by the paediatric sentinel hospitalization network. With little influenza circulating, the National Microbiology Laboratory had not yet received samples of influenza viruses collected during the 2020-2021 season for strain characterization or antiviral resistance testing. The assessment of influenza vaccine effectiveness, typically available in mid-March, is expected to be similarly limited if low seasonal influenza circulation persists. Nevertheless, Canada's influenza surveillance system remains robust and has pivoted its syndromic, virologic and severe outcomes system components to support coronavirus disease 2019 (COVID-19) surveillance. Despite the COVID-19 pandemic, the threat of influenza epidemics and pandemics persists. It is imperative 1) to maintain surveillance of influenza, 2) to remain alert to unusual or unexpected events and 3) to be prepared to mitigate influenza epidemics when they resurge.

scholarly journals Seasonal influenza during the COVID-19 pandemic in Bangladesh

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255646
Author(s):  
Zubair Akhtar ◽  
Fahmida Chowdhury ◽  
Mahmudur Rahman ◽  
Probir Kumar Ghosh ◽  
Md. Kaousar Ahmmed ◽  
...  
Keyword(s):  
Seasonal Influenza ◽  
Web Applications ◽  
Influenza Season ◽  
Tertiary Care ◽  
Age Groups ◽  
Influenza Viruses ◽  
Control Measures ◽  
Mitigation Measures ◽  
Influenza Surveillance ◽  
Severe Acute Respiratory Infection

Introduction During the 2019 novel coronavirus infectious disease (COVID-19) pandemic in 2020, limited data from several countries suggested reduced seasonal influenza viruses’ circulation. This was due to community mitigation measures implemented to control the pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We used sentinel surveillance data to identify changes in the 2020 influenza season compared with previous seasons in Bangladesh. Methods We used hospital-based influenza surveillance (HBIS) data of Bangladesh that are generated year-round and are population-representative severe acute respiratory infection (SARI) data for all age groups from seven public and two private tertiary care level hospitals data from 2016 to 2019. We applied the moving epidemic method (MEM) by using R language (v4.0.3), and MEM web applications (v2.14) on influenza-positive rates of SARI cases collected weekly to estimate an average seasonal influenza curve and establish epidemic thresholds. Results The 2016–2019 average season started on epi week 18 (95% CI: 15–25) and lasted 12.5 weeks (95% CI: 12–14 weeks) until week 30.5. The 2020 influenza season started on epi week 36 and ended at epi week 41, lasting for only five weeks. Therefore, influenza epidemic started 18 weeks later, was 7.5 weeks shorter, and was less intense than the average epidemic of the four previous years. The 2020 influenza season started on the same week when COVID-19 control measures were halted, and 13 weeks after the measures were relaxed. Conclusion Our findings suggest that seasonal influenza circulation in Bangladesh was delayed and less intense in 2020 than in previous years. Community mitigation measures may have contributed to this reduction of seasonal influenza transmission. These findings contribute to a limited but growing body of evidence that influenza seasons were altered globally in 2020.

scholarly journals Impact of nonpharmaceutical interventions on laboratory detections of influenza A and B in Canada

2021 ◽  
Vol 47 (3) ◽  
pp. 142-148
Author(s):  
Philippe Lagacé-Wiens ◽  
Claire Sevenhuysen ◽  
Liza Lee ◽  
Andrea Nwosu ◽  
Tiffany Smith
Keyword(s):  
Influenza A ◽  
Influenza Season ◽  
National Laboratory ◽  
Influenza Surveillance ◽  
Social Distancing ◽  
Health Authorities ◽  
Laboratory Surveillance ◽  
Rate Of Decline ◽  
Nonpharmaceutical Interventions ◽  
The Impact

Background: The first coronavirus disease 2019 (COVID-19) case was reported in Canada on January 25, 2020. In response to the imminent outbreak, many provincial and territorial health authorities implemented nonpharmaceutical public health measures to curb the spread of disease. “Social distancing” measures included restrictions on group gatherings; cancellation of sports, cultural and religious events and gatherings; recommended physical distancing between people; school and daycare closures; reductions in non-essential services; and closures of businesses. Objectives: To evaluate the impact of the combined nonpharmaceutical interventions imposed in March 2020 on influenza A and B epidemiology by comparing national laboratory surveillance data from the intervention period with 9-year historical influenza season control data. Methods: We obtained epidemiologic data on laboratory influenza A and B detections and test volumes from the Canadian national influenza surveillance system for the epidemiologic period December 29, 2019 (epidemiologic week 1) through May 2, 2020 (epidemiologic week 18). COVID-19-related social distancing measures were implemented in Canada from epidemiologic week 10 of this period. We compared influenza A and B laboratory detections and test volumes and trends in detection during the 2019–20 influenza season with those of the previous nine influenza seasons for evidence of changes in epidemiologic trends. Results: While influenza detections the week prior to the implementation of social distancing measures did not differ statistically from the previous nine seasons, a steep decline in positivity occurred between epidemiologic weeks 10 and 14 (March 8–April 4, 2020). Both the percent positive on week 14 (p≤0.001) and rate of decline between weeks 10 and 14 (p=0.003) were significantly different from mean historical data. Conclusion: The data show a dramatic decrease in influenza A and B laboratory detections concurrent with social distancing measures and nonpharmaceutical interventions in Canada. The impact of these measures on influenza transmission may be generalizable to other respiratory viral illnesses during the study period, including COVID-19.

scholarly journals Front-Line Emergency Department Clinician Acceptability and Use of a Prototype Real-Time Cloud-Based Influenza Surveillance System

2021 ◽  
Vol 9 ◽  
Author(s):  
Richard E. Rothman ◽  
Yu-Hsiang Hsieh ◽  
Anna DuVal ◽  
David A. Talan ◽  
Gregory J. Moran ◽  
...  
Keyword(s):  
Emergency Department ◽  
Real Time ◽  
Surveillance System ◽  
Influenza Season ◽  
Respiratory Illness ◽  
Surveillance Data ◽  
Influenza Surveillance ◽  
Additional Information ◽  
Related Information ◽  
System Data

Objectives: To assess emergency department (ED) clinicians' perceptions of a novel real-time influenza surveillance system using a pre- and post-implementation structured survey.Methods: We created and implemented a laboratory-based real-time influenza surveillance system at two EDs at the beginning of the 2013-2014 influenza season. Patients with acute respiratory illness were tested for influenza using rapid PCR-based Cepheid Xpert Flu assay. Results were instantaneously uploaded to a cloud-based data aggregation system made available to clinicians via a web-based dashboard. Clinicians received bimonthly email updates summating year-to-date results. Clinicians were surveyed prior to, and after the influenza season, to assess their views regarding acceptability and utility of the surveillance system data which were shared via dashboard and email updates.Results: The pre-implementation survey revealed that the majority (82%) of the 151 ED clinicians responded that they “sporadically” or “don't,” actively seek influenza-related information during the season. However, most (75%) reported that they would find additional information regarding influenza prevalence useful. Following implementation, there was an overall increase in the frequency of clinician self-reporting increased access to surveillance information from 50 to 63%, with the majority (75%) indicating that the surveillance emails impacted their general awareness of influenza. Clinicians reported that the additional real-time surveillance data impacted their testing (65%) and treatment (51%) practices.Conclusions: The majority of ED clinicians found surveillance data useful and indicated the additional information impacted their clinical practice. Accurate and timely surveillance information, distributed in a provider-friendly format could impact ED clinician management of patients with suspected influenza.

Influenza surveillance system of Japan and acute encephalitis and encephalopathy in the influenza season

2000 ◽  
Vol 42 (2) ◽  
pp. 187-191 ◽  
Author(s):  
Nobuhiko Okabe ◽  
Kazuyo Yamashita ◽  
Kiyosu Taniguchi and Sakae Inouye
Keyword(s):  
Surveillance System ◽  
Influenza Season ◽  
Influenza Surveillance ◽  
Acute Encephalitis

scholarly journals Epidemiology of influenza: findings from surveillance system of 40 hospital in Thailand, 2010-2019

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
T Eamchotchawalit ◽  
P Piyaraj ◽  
P Narongdej ◽  
S Charoensakulchai ◽  
C Chanthowong
Keyword(s):  
Real Time ◽  
Surveillance System ◽  
Influenza A ◽  
Seasonal Influenza ◽  
Influenza Viruses ◽  
Cold Chain ◽  
Influenza Surveillance ◽  
Influenza B ◽  
Private Hospitals ◽  
Influenza A H1n1

Abstract Background Although recent efforts from some Asian countries to describe burden of influenza disease and seasonality, these data are missing for the vast majority, including the private section of Thailand. A near real-time laboratory-based influenza surveillance system, in a network of 40 hospitals was implemented aiming to determine influenza strains circulating in the private hospitals of Thailand and know characteristics, trend and burden of influenza viruses. Methods We obtained the data by monitoring patients with influenza-like illness (ILI) at a network of 40 private hospitals across Thailand. Throat-swab specimens in viral transport media were collected and transported within 24 h of collection using a cold-chain system. The respiratory samples were tested by rapid influenza diagnostic tests and real-time reverse transcription polymerase chain reaction. Results From January 2010 to November 2019, a total of 1,300,594 subjects were tested and 320,499 cases of influenza were identified. Of those positive cases, 116,317(36.3%) were influenza type B, 185,512(57.9%) were influenza A unspecified subtype, 8,833(2.7%) were influenza A(H1N1)pdm2009 and 6,371(1.9%) were seasonal influenza A(H3N2). Positive rate were 50.5 and 49.5 in female and male. Positivity rate was 41.4% in persons 15-49 years followed by 29.1% in 15-14 years, 17.6% in under five children and 11.7% in &gt; 49 years. In 2018-2019 season, the highest positivity rate observed in February and March (39.3%) followed by April (34.2%) and January (32.3%) while the lowest positivity rate was in May (18.1%). Conclusions In Thailand, seasonal Influenza A(H3N2), Influenza A(H1N1)pdm2009 and Influenza B viruses were circulating during 2010-2019. In last season, positivity rate and number of cases peaked in February and March. Key messages Influenza is one of public health problems in Thailand. The need to introduce influenza vaccine and antivirus is important to prevent and treat the disease in future.

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