scholarly journals Seasonal influenza activity for 2005-2006 season seems to be ending in most European countries

2006 ◽  
Vol 11 (15) ◽  
Author(s):  
W J Paget ◽  
A Meijer ◽  
J M Falcão ◽  
J C de Jong ◽  
J Kyncl ◽  
...  
Keyword(s):  
Seasonal Influenza ◽  
European Countries ◽  
Influenza Activity

During the 2005-2006 season, seasonal influenza epidemics started late in countries across Europe

scholarly journals Seasonal influenza beginning in Europe: report from EISS

2007 ◽  
Vol 12 (4) ◽  
Author(s):  
W J Paget ◽  
A Meijer ◽  
T J Meerhoff ◽  
F Ansaldi ◽  
U Buchholz ◽  
...  
Keyword(s):  
United Kingdom ◽  
Northern Ireland ◽  
The Netherlands ◽  
Influenza A ◽  
Seasonal Influenza ◽  
European Countries ◽  
Influenza A Viruses ◽  
Influenza Activity

Increased influenza activity was reported in six countries in the second week of 2007: Greece, the Netherlands, United Kingdom (Northern Ireland), Scotland, Spain and Switzerland. Based on trends of previous years, influenza activity is expected to increase in many more European countries in the weeks ahead. The influenza activity reported so far has mainly been associated with influenza A viruses.

scholarly journals Associations between seasonal influenza and meteorological parameters in Costa Rica, Honduras and Nicaragua

2015 ◽  
Vol 10 (2) ◽  
Author(s):  
Radina P. Soebiyanto ◽  
Wilfrido A. Clara ◽  
Jorge Jara ◽  
Angel Balmaseda ◽  
Jenny Lara ◽  
...  
Keyword(s):  
Costa Rica ◽  
Central America ◽  
Seasonal Influenza ◽  
Meteorological Parameters ◽  
Correlation Coefficients ◽  
Tropical Rainfall Measuring Mission ◽  
Specific Humidity ◽  
Considerable Proportion ◽  
Influenza Activity ◽  
Land Data Assimilation System

Seasonal influenza affects a considerable proportion of the global population each year. We assessed the association between subnational influenza activity and temperature, specific humidity and rainfall in three Central America countries, <em>i.e.</em> Costa Rica, Honduras and Nicaragua. Using virologic data from each country’s national influenza centre, rainfall from the Tropical Rainfall Measuring Mission and air temperature and specific humidity data from the Global Land Data Assimilation System, we applied logistic regression methods for each of the five sub-national locations studied. Influenza activity was represented by the weekly proportion of respiratory specimens that tested positive for influenza. The models were adjusted for the potentially confounding co-circulating respiratory viruses, seasonality and previous weeks’ influenza activity. We found that influenza activity was proportionally associated (P&lt;0.05) with specific humidity in all locations [odds ratio (OR) 1.21-1.56 per g/kg], while associations with temperature (OR 0.69-0.81 per °C) and rainfall (OR 1.01-1.06 per mm/day) were location-dependent. Among the meteorological parameters, specific humidity had the highest contribution (~3-15%) to the model in all but one location. As model validation, we estimated influenza activity for periods, in which the data was not used in training the models. The correlation coefficients between the estimates and the observed were ≤0.1 in 2 locations and between 0.6-0.86 in three others. In conclusion, our study revealed a proportional association between influenza activity and specific humidity in selected areas from the three Central America countries.

scholarly journals Influenza activity remains at high levels in some European countries

2002 ◽  
Vol 6 (13) ◽  
Author(s):  
T Vega ◽  
W J Paget
Keyword(s):  
European Countries ◽  
Influenza Surveillance ◽  
Northern European ◽  
Influenza Activity ◽  
Surveillance Scheme

While most national and subnational networks in Europe reported low clinical morbidity rates to the European Influenza Surveillance Scheme (EISS, http://www.eiss.org) in the week 17 March (week 11), some central and northern European countries continued to report high or increasing levels of influenza activity (1).

Seasonal influenza activity based on laboratory surveillance in Malaysia, 2011-2016

2018 ◽  
Vol 91 (3) ◽  
pp. 498-502 ◽  
Author(s):  
I-Ching Sam ◽  
Wan Noraini ◽  
Sukhvinder Singh Sandhu ◽  
Ismail Norizah ◽  
Sengol Selvanesan ◽  
...  
Keyword(s):  
Seasonal Influenza ◽  
Influenza Activity ◽  
Laboratory Surveillance

scholarly journals Patterns of seasonal influenza activity in U.S. core-based statistical areas, described using prescriptions of oseltamivir in Medicare claims data

Epidemics ◽  
2019 ◽  
Vol 26 ◽  
pp. 23-31 ◽  
Author(s):  
F. Scott Dahlgren ◽  
David K. Shay ◽  
Hector S. Izurieta ◽  
Richard A. Forshee ◽  
Michael Wernecke ◽  
...  
Keyword(s):  
Seasonal Influenza ◽  
Claims Data ◽  
Medicare Claims ◽  
Influenza Activity ◽  
Medicare Claims Data

scholarly journals Evaluation of mechanistic and statistical methods in forecasting influenza-like illness

2018 ◽  
Vol 15 (144) ◽  
pp. 20180174 ◽  
Author(s):  
Sasikiran Kandula ◽  
Teresa Yamana ◽  
Sen Pei ◽  
Wan Yang ◽  
Haruka Morita ◽  
...  
Keyword(s):  
Statistical Methods ◽  
Seasonal Influenza ◽  
Ad Hoc ◽  
Weighted Average ◽  
Surveillance Systems ◽  
Domain Experts ◽  
Influenza Activity ◽  
The Usa ◽  
The Individual

A variety of mechanistic and statistical methods to forecast seasonal influenza have been proposed and are in use; however, the effects of various data issues and design choices (statistical versus mechanistic methods, for example) on the accuracy of these approaches have not been thoroughly assessed. Here, we compare the accuracy of three forecasting approaches—a mechanistic method, a weighted average of two statistical methods and a super-ensemble of eight statistical and mechanistic models—in predicting seven outbreak characteristics of seasonal influenza during the 2016–2017 season at the national and 10 regional levels in the USA. For each of these approaches, we report the effects of real time under- and over-reporting in surveillance systems, use of non-surveillance proxies of influenza activity and manual override of model predictions on forecast quality. Our results suggest that a meta-ensemble of statistical and mechanistic methods has better overall accuracy than the individual methods. Supplementing surveillance data with proxy estimates generally improves the quality of forecasts and transient reporting errors degrade the performance of all three approaches considerably. The improvement in quality from ad hoc and post-forecast changes suggests that domain experts continue to possess information that is not being sufficiently captured by current forecasting approaches.

scholarly journals Impact of Public Health Interventions on Seasonal Influenza Activity During the COVID-19 Outbreak in Korea

2020 ◽  
Author(s):  
Hyunju Lee ◽  
Heeyoung Lee ◽  
Kyoung-Ho Song ◽  
Eu Suk Kim ◽  
Jeong Su Park ◽  
...  
Keyword(s):  
Public Health ◽  
Seasonal Influenza ◽  
Health Interventions ◽  
Influenza Surveillance ◽  
Influenza B ◽  
Public Health Interventions ◽  
National Response ◽  
Influenza Activity ◽  
High Level ◽  
The Impact

Abstract Background Coronavirus disease 2019 (COVID-19) was introduced in Korea early with a large outbreak in mid-February. We reviewed the public health interventions used during the COVID-19 outbreak and describe the impact on seasonal influenza activity in Korea. Methods National response strategies, public health interventions and daily COVID-19–confirmed cases in Korea were reviewed during the pandemic. National influenza surveillance data were compared between 7 sequential seasons. Characteristics of each season, including rate of influenza-like illness (ILI), duration of epidemic, date of termination of epidemic, distribution of influenza virus strain, and hospitalization, were analyzed. Results After various public health interventions including enforced public education on hand hygiene, cough etiquette, staying at home with respiratory symptoms, universal mask use in public places, refrain from nonessential social activities, and school closures the duration of the influenza epidemic in 2019/2020 decreased by 6–12 weeks and the influenza activity peak rated 49.8 ILIs/1000 visits compared to 71.9–86.2 ILIs/1000 visits in previous seasons. During the period of enforced social distancing from weeks 9–17 of 2020, influenza hospitalization cases were 11.9–26.9-fold lower compared with previous seasons. During the 2019/2020 season, influenza B accounted for only 4%, in contrast to previous seasons in which influenza B accounted for 26.6–54.9% of all cases. Conclusions Efforts to activate a high-level national response not only led to a decrease in COVID-19 but also a substantial decrease in seasonal influenza activity. Interventions applied to control COVID-19 may serve as useful strategies for prevention and control of influenza in upcoming seasons.

Emergency department and ‘Google flu trends’ data as syndromic surveillance indicators for seasonal influenza

2014 ◽  
Vol 142 (11) ◽  
pp. 2397-2405 ◽  
Author(s):  
L. H. THOMPSON ◽  
M. T. MALIK ◽  
A. GUMEL ◽  
T. STROME ◽  
S. M. MAHMUD
Keyword(s):  
Emergency Department ◽  
Linear Regression ◽  
Syndromic Surveillance ◽  
Regression Models ◽  
Seasonal Influenza ◽  
Linear Regression Models ◽  
Physician Visits ◽  
Influenza Activity ◽  
Ed Visits ◽  
Highly Correlated

SUMMARYWe evaluated syndromic indicators of influenza disease activity developed using emergency department (ED) data – total ED visits attributed to influenza-like illness (ILI) (‘ED ILI volume’) and percentage of visits attributed to ILI (‘ED ILI percent’) – and Google flu trends (GFT) data (ILI cases/100 000 physician visits). Congruity and correlation among these indicators and between these indicators and weekly count of laboratory-confirmed influenza in Manitoba was assessed graphically using linear regression models. Both ED and GFT data performed well as syndromic indicators of influenza activity, and were highly correlated with each other in real time. The strongest correlations between virological data and ED ILI volume and ED ILI percent, respectively, were 0·77 and 0·71. The strongest correlation of GFT was 0·74. Seasonal influenza activity may be effectively monitored using ED and GFT data.

scholarly journals Epidemiology of seasonal influenza in Bangkok between 2009 and 2012

2013 ◽  
Vol 7 (10) ◽  
pp. 734-740 ◽  
Author(s):  
Slinporn Prachayangprecha ◽  
Jarika Makkoch ◽  
Kamol Suwannakarn ◽  
Preeyaporn Vichaiwattana ◽  
Sumeth Korkong ◽  
...  
Keyword(s):  
Rainy Season ◽  
Influenza A ◽  
Seasonal Influenza ◽  
Seasonal Pattern ◽  
Laboratory Data ◽  
Influenza Virus Infection ◽  
Influenza Viruses ◽  
Influenza B ◽  
Rainy Period ◽  
Influenza Activity

Introduction: This study investigated influenza activity in Bangkok, Thailand between June 2009 and July 2012. Methodology: Real-time reverse transcription polymerase chain reaction (RT-PCR) was performed to detect influenza viruses among patients with influenza-like illnesses. Results: Of the 6417 patients tested, influenza virus infection was detected in 42% (n = 2697) of the specimens. Influenza A pH1N1 viruses comprised the predominant strain between 2009 and 2010, and seasonal influenza (H3) had a high prevalence in 2011. Laboratory data showed a prevalence and seasonal pattern of influenza viruses. In 2009, influenza activity peaked in July, the rainy season. In 2010, influenza activity happened in two phases, with the initial one at the beginning of the year and another peak between June and August 2010, which again corresponded to the rainy period. Influenza activity was low for several consecutive weeks at the beginning of 2011, and high H3N2 activity was recorded during the rainy season between July and September 2011. However, from the beginning of 2012 through July 2012, pH1N1, influenza H3N2, and influenza B viruses continuously circulated at a very low level. Conclusion: The seasonal pattern of influenza activity in Thailand tended to peak during rainy season between July and September.

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