scholarly journals Predicting seasonal influenza epidemics using cross-hemisphere influenza surveillance data and local internet query data

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Yuzhou Zhang ◽  
Laith Yakob ◽  
Michael B. Bonsall ◽  
Wenbiao Hu
Keyword(s):  
Seasonal Influenza ◽  
Surveillance Data ◽  
Influenza Surveillance

scholarly journals Effects of School Holidays on Seasonal Influenza in South Korea, 2014–2016

2020 ◽  
Vol 222 (5) ◽  
pp. 832-835 ◽  
Author(s):  
Sukhyun Ryu ◽  
Sheikh Taslim Ali ◽  
Benjamin J Cowling ◽  
Eric H Y Lau
Keyword(s):  
South Korea ◽  
Seasonal Influenza ◽  
School Closure ◽  
Surveillance Data ◽  
Influenza Surveillance ◽  
School Closures ◽  
School Aged Children ◽  
Severe Influenza ◽  
Influenza Transmission

Abstract School closures are considered as a potential nonpharmaceutical intervention to mitigate severe influenza epidemics and pandemics. In this study, we assessed the effects of scheduled school closure on influenza transmission using influenza surveillance data before, during, and after spring breaks in South Korea, 2014–2016. During the spring breaks, influenza transmission was reduced by 27%–39%, while the overall reduction in transmissibility was estimated to be 6%–23%, with greater effects observed among school-aged children.

scholarly journals Characteristics of seasonal influenza A and B in Latin America: Influenza surveillance data from ten countries

PLoS ONE ◽  
2017 ◽  
Vol 12 (3) ◽  
pp. e0174592 ◽  
Author(s):  
Saverio Caini ◽  
Wladimir J. Alonso ◽  
Angel Balmaseda ◽  
Alfredo Bruno ◽  
Patricia Bustos ◽  
...  
Keyword(s):  
Latin America ◽  
Influenza A ◽  
Seasonal Influenza ◽  
Surveillance Data ◽  
Influenza Surveillance

scholarly journals Increased urbanization reduced the effectiveness of school closures on seasonal influenza epidemics in China

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Hao Lei ◽  
Hangjin Jiang ◽  
Nan Zhang ◽  
Xiaoli Duan ◽  
Tao Chen ◽  
...  
Keyword(s):  
Seasonal Influenza ◽  
Southern China ◽  
Social Contact ◽  
School Closure ◽  
Surveillance Data ◽  
Reproductive Number ◽  
Influenza Surveillance ◽  
Sirs Model ◽  
Influenza Transmission ◽  
The Impact

Abstract Background School closure is a common mitigation strategy during severe influenza epidemics and pandemics. However, the effectiveness of this strategy remains controversial. In this study, we aimed to explore the effectiveness of school closure on seasonal influenza epidemics in provincial-level administrative divisions (PLADs) with varying urbanization rates in China. Methods This study analyzed influenza surveillance data between 2010 and 2019 provided by the Chinese National Influenza Center. Taking into consideration the climate, this study included a region with 3 adjacent PLADs in Northern China and another region with 4 adjacent PLADs in Southern China. The effect of school closure on influenza transmission was evaluated by the reduction of the effective reproductive number of seasonal influenza during school winter breaks compared with that before school winter breaks. An age-structured Susceptible-Infected-Recovered-Susceptible (SIRS) model was built to model influenza transmission in different levels of urbanization. Parameters were determined using the surveillance data via robust Bayesian method. Results Between 2010 and 2019, in the less urbanized provinces: Hebei, Zhejiang, Jiangsu and Anhui, during school winter breaks, the effective reproductive number of seasonal influenza epidemics reduced 14.6% [95% confidential interval (CI): 6.2–22.9%], 9.6% (95% CI: 2.5–16.6%), 7.3% (95% CI: 0.1–14.4%) and 8.2% (95% CI: 1.1–15.3%) respectively. However, in the highly urbanized cities: Beijing, Tianjin and Shanghai, it reduced only 5.2% (95% CI: -0.7–11.2%), 4.1% (95% CI: -0.9–9.1%) and 3.9% (95% CI: -1.6–9.4%) respectively. In China, urbanization is associated with decreased proportion of children and increased social contact. According to the SIRS model, both factors could reduce the impact of school closure on seasonal influenza epidemics, and the proportion of children in the population is thought to be the dominant influencing factor. Conclusions Effectiveness of school closure on the epidemics varies with the age structure in the population and social contact patterns. School closure should be recommended in the low urbanized regions in China in the influenza seasons. Graphical abstract

scholarly journals Forecasting type-specific seasonal influenza after 26 weeks in the United States using influenza activities in other countries

2019 ◽  
Author(s):  
S. B. Choi ◽  
J. Kim ◽  
I. Ahn
Keyword(s):  
United States ◽  
Time Series ◽  
Deep Learning ◽  
Seasonal Influenza ◽  
Prediction Models ◽  
Time Lag ◽  
The United States ◽  
Surveillance Data ◽  
Influenza Surveillance ◽  
The U.S

AbstractTo identify countries that have seasonal patterns similar to the time series of influenza surveillance data in the United States and other countries, and to forecast the 2018–2019 seasonal influenza outbreak in the U.S. using linear regression, auto regressive integrated moving average, and deep learning. We collected the surveillance data of 164 countries from 2010 to 2018 using the FluNet database. Data for influenza-like illness (ILI) in the U.S. were collected from the Fluview database. This cross-correlation study identified the time lag between the two time-series. Deep learning was performed to forecast ILI, total influenza, A, and B viruses after 26 weeks in the U.S. The seasonal influenza patterns in Australia and Chile showed a high correlation with those of the U.S. 22 weeks and 28 weeks earlier, respectively. The R2 score of DNN models for ILI for validation set in 2015–2019 was 0.722 despite how hard it is to forecast 26 weeks ahead. Our prediction models forecast that the ILI for the U.S. in 2018–2019 may be later and less severe than those in 2017–2018, judging from the influenza activity for Australia and Chile in 2018. It allows to estimate peak timing, peak intensity, and type-specific influenza activities for next season at 40th week. The correlation for seasonal influenza among Australia, Chile, and the U.S. could be used to decide on influenza vaccine strategy six months ahead in the U.S.

FluHMM: A simple and flexible Bayesian algorithm for sentinel influenza surveillance and outbreak detection

2018 ◽  
Vol 28 (6) ◽  
pp. 1826-1840 ◽  
Author(s):  
Theodore Lytras ◽  
Kassiani Gkolfinopoulou ◽  
Stefanos Bonovas ◽  
Baltazar Nunes
Keyword(s):  
Public Health ◽  
Public Health Practice ◽  
R Package ◽  
Sentinel Surveillance ◽  
Surveillance Data ◽  
Outbreak Detection ◽  
Influenza Surveillance ◽  
Bayesian Algorithm ◽  
Public Health Action ◽  
Health Action

Timely detection of the seasonal influenza epidemic is important for public health action. We introduce FluHMM, a simple but flexible Bayesian algorithm to detect and monitor the seasonal epidemic on sentinel surveillance data. No comparable historical data are required for its use. FluHMM segments a typical influenza surveillance season into five distinct phases with clear interpretation (pre-epidemic, epidemic growth, epidemic plateau, epidemic decline and post-epidemic) and provides the posterior probability of being at each phase for every week in the period under surveillance, given the available data. An alert can be raised when the probability that the epidemic has started exceeds a given threshold. An accompanying R package facilitates the application of this method in public health practice. We apply FluHMM on 12 seasons of sentinel surveillance data from Greece, and show that it achieves very good sensitivity, timeliness and perfect specificity, thereby demonstrating its usefulness. We further discuss advantages and limitations of the method, providing suggestions on how to apply it and highlighting potential future extensions such as with integrating multiple surveillance data streams.

scholarly journals Real-time estimation of the influenza-associated excess mortality in Hong Kong

2019 ◽  
Vol 147 ◽  
Author(s):  
Jessica Y. Wong ◽  
Edward Goldstein ◽  
Vicky J. Fang ◽  
Benjamin J. Cowling ◽  
Peng Wu
Keyword(s):  
Hong Kong ◽  
Real Time ◽  
Excess Mortality ◽  
Time Estimation ◽  
Surveillance Data ◽  
Control Measures ◽  
Influenza Surveillance ◽  
Mortality Data ◽  
Real Time Estimation ◽  
The Impact

Abstract Statistical models are commonly employed in the estimation of influenza-associated excess mortality that, due to various reasons, is often underestimated by laboratory-confirmed influenza deaths reported by healthcare facilities. However, methodology for timely and reliable estimation of that impact remains limited because of the delay in mortality data reporting. We explored real-time estimation of influenza-associated excess mortality by types/subtypes in each year between 2012 and 2018 in Hong Kong using linear regression models fitted to historical mortality and influenza surveillance data. We could predict that during the winter of 2017/2018, there were ~634 (95% confidence interval (CI): (190, 1033)) influenza-associated excess all-cause deaths in Hong Kong in population ⩾18 years, compared to 259 reported laboratory-confirmed deaths. We estimated that influenza was associated with substantial excess deaths in older adults, suggesting the implementation of control measures, such as administration of antivirals and vaccination, in that age group. The approach that we developed appears to provide robust real-time estimates of the impact of influenza circulation and complement surveillance data on laboratory-confirmed deaths. These results improve our understanding of the impact of influenza epidemics and provide a practical approach for a timely estimation of the mortality burden of influenza circulation during an ongoing epidemic.

scholarly journals Association Between Seasonal Influenza and Absolute Humidity: Time-Series Analysis with Daily Surveillance Data in Japan

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Keita Shimmei ◽  
Takahiro Nakamura ◽  
Chris Fook Sheng Ng ◽  
Masahiro Hashizume ◽  
Yoshitaka Murakami ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Seasonal Influenza ◽  
Absolute Humidity ◽  
Surveillance Data ◽  
Series Analysis

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.

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