The Effects of Mask Wearing, Mobility Change and SARS-CoV-2 Interference on Seasonal Influenza in Northern China, Southern China, England, and the United States

2021 ◽  
Author(s):  
Shasha Han ◽  
Ting Zhang ◽  
Yan Lyu ◽  
Shengjie Lai ◽  
Peixi Dai ◽  
...  
Keyword(s):  
United States ◽  
Seasonal Influenza ◽  
Southern China ◽  
Northern China ◽  
The United States

scholarly journals Impact of COVID-19 outbreaks and interventions on influenza in China and the United States

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Luzhao Feng ◽  
Ting Zhang ◽  
Qing Wang ◽  
Yiran Xie ◽  
Zhibin Peng ◽  
...  
Keyword(s):  
United States ◽  
Respiratory Diseases ◽  
Seasonal Influenza ◽  
Influenza Virus Infection ◽  
Northern China ◽  
The United States ◽  
Behavioral Changes ◽  
Influenza Epidemic ◽  
Lower And Upper Bounds ◽  
Influenza Activity

AbstractCoronavirus disease 2019 (COVID-19) was detected in China during the 2019–2020 seasonal influenza epidemic. Non-pharmaceutical interventions (NPIs) and behavioral changes to mitigate COVID-19 could have affected transmission dynamics of influenza and other respiratory diseases. By comparing 2019–2020 seasonal influenza activity through March 29, 2020 with the 2011–2019 seasons, we found that COVID-19 outbreaks and related NPIs may have reduced influenza in Southern and Northern China and the United States by 79.2% (lower and upper bounds: 48.8%–87.2%), 79.4% (44.9%–87.4%) and 67.2% (11.5%–80.5%). Decreases in influenza virus infection were also associated with the timing of NPIs. Without COVID-19 NPIs, influenza activity in China and the United States would likely have remained high during the 2019–2020 season. Our findings provide evidence that NPIs can partially mitigate seasonal and, potentially, pandemic influenza.

scholarly journals Air Pollution Increases Influenza Hospitalizations

2020 ◽  
Author(s):  
Gregor Singer ◽  
Joshua Graff Zivin ◽  
Matthew Neidell ◽  
Nicholas Sanders
Keyword(s):  
United States ◽  
Air Pollution ◽  
Air Quality ◽  
Seasonal Influenza ◽  
Hospital Admissions ◽  
The United States ◽  
Inpatient Hospitalization ◽  
Vaccination Rates ◽  
Health Burden ◽  
Severe Influenza

AbstractSeasonal influenza is a recurring health burden shared widely across the globe. We study whether air quality affects the occurrence of severe influenza cases that require inpatient hospitalization. Using longitudinal information on local air quality and hospital admissions across the United States, we find that poor air quality increases the incidence of significant influenza hospital admissions. Effects diminish in years with greater influenza vaccine effectiveness. Apart from increasing vaccination rates, improving air quality may help reduce the spread and severity of influenza.

Deaths From Seasonal Influenza Among Pregnant Women in the United States, 1998–2005

2010 ◽  
Vol 115 (5) ◽  
pp. 919-923 ◽  
Author(s):  
William M. Callaghan ◽  
Susan Y. Chu ◽  
Denise J. Jamieson
Keyword(s):  
United States ◽  
Pregnant Women ◽  
Seasonal Influenza ◽  
The United States

scholarly journals The Geographic Synchrony of Seasonal Influenza: A Waves across Canada and the United States

PLoS ONE ◽  
2011 ◽  
Vol 6 (6) ◽  
pp. e21471 ◽  
Author(s):  
Dena L. Schanzer ◽  
Joanne M. Langley ◽  
Trevor Dummer ◽  
Samina Aziz
Keyword(s):  
United States ◽  
Influenza A ◽  
Seasonal Influenza ◽  
The United States

scholarly journals Lessons from the Past: Comparison of the Disease Burden of the Influenza A (H1N1) Pandemic 2009-10 and Seasonal Influenza 2010-2019 in the United States

2021 ◽  
Author(s):  
James A Koziol
Keyword(s):  
United States ◽  
Influenza A ◽  
Disease Burden ◽  
Seasonal Influenza ◽  
The United States ◽  
H1n1 Pandemic ◽  
Elderly Adults ◽  
The Past ◽  
Medical Visits ◽  
Influenza A H1n1

Abstract Background Annual influenza outbreaks constitute a major public health concern, both in the United States and worldwide. Comparisons of the health burdens of outbreaks might lead to the identification of specific at-risk populations, for whom public health resources should be marshaled appropriately and equitably. Methods We examined the disease burden of the 2009-10 influenza A (H1N1) pandemic relating to illnesses, medical visits, hospitalizations, and mortality, compared to influenza seasons 2010 to 2019, in the United States, as compiled by the Centers for Disease Control. Results With regard to seasonal influenza, rates of illnesses and medical visits were highest in infants aged 0–4 years, followed by adults aged 50–64 years. Rates of hospitalizations and deaths evinced a starkly different pattern, both dominated by elderly adults aged 65 and over. Youths aged 0 to 17 years were especially adversely affected by the H1N1 pandemic relative to hospitalizations and mortality compared to seasonal influenza; but curiously the opposite pattern was observed in elderly adults (aged 65 and older). Conclusions The disease burden of the 2009-10 influenza A pandemic was strikingly unlike that observed in the subsequent influenza seasons 2010 to 2019, in the United States: the past did not predict the future.

scholarly journals Optimizing antiviral treatment for seasonal influenza in the United States: A Mathematical Modeling Analysis

2020 ◽  
Author(s):  
Matan Yechezkel ◽  
Martial Ndeffo-Mba ◽  
Dan Yamin
Keyword(s):  
United States ◽  
High Risk ◽  
Seasonal Influenza ◽  
Antiviral Treatment ◽  
Social Contact ◽  
Population Level ◽  
The United States ◽  
Transmission Model ◽  
High Risk Patients ◽  
Risk Patients

Seasonal influenza remains a major health burden in the United States. Despite recommendations of early antiviral treatment of high-risk patients, the effective treatment coverage remains very low. We developed an influenza transmission model that incorporates data on infectious viral load, social contact, and healthcare-seeking behavior, to evaluate the population-level impact of increasing antiviral treatment timeliness and coverage among high-risk patients in the US. We found that increasing the rate of early treatment among high-risk patients who received treatment more than 48 hours after symptoms onset, would substantially avert infections and influenza-induced hospitalizations. We found that treatment of the elderly has the highest impact on reducing hospitalizations, whereas treating high-risk individuals aged 5-19 years old has the highest impact on transmission. The population-level impact of increased timeliness and coverage of treatment among high-risk patients was observed regardless of seasonal influenza vaccination coverage and the severity of the influenza season.

scholarly journals Pathogenesis and Transmission of Genetically Diverse Swine-Origin H3N2 Variant Influenza A Viruses from Multiple Lineages Isolated in the United States, 2011–2016

2018 ◽  
Vol 92 (16) ◽  
Author(s):  
Xiangjie Sun ◽  
Joanna A. Pulit-Penaloza ◽  
Jessica A. Belser ◽  
Claudia Pappas ◽  
Melissa B. Pearce ◽  
...  
Keyword(s):  
United States ◽  
Respiratory Tract ◽  
Influenza A ◽  
Seasonal Influenza ◽  
The United States ◽  
Influenza Viruses ◽  
Upper Respiratory Tract ◽  
Pandemic Preparedness

ABSTRACTWhile several swine-origin influenza A H3N2 variant (H3N2v) viruses isolated from humans prior to 2011 have been previously characterized for their virulence and transmissibility in ferrets, the recent genetic and antigenic divergence of H3N2v viruses warrants an updated assessment of their pandemic potential. Here, four contemporary H3N2v viruses isolated during 2011 to 2016 were evaluated for their replicative ability in bothin vitroandin vivoin mammalian models as well as their transmissibility among ferrets. We found that all four H3N2v viruses possessed similar or enhanced replication capacities in a human bronchial epithelium cell line (Calu-3) compared to a human seasonal influenza virus, suggestive of strong fitness in human respiratory tract cells. The majority of H3N2v viruses examined in our study were mildly virulent in mice and capable of replicating in mouse lungs with different degrees of efficiency. In ferrets, all four H3N2v viruses caused moderate morbidity and exhibited comparable titers in the upper respiratory tract, but only 2 of the 4 viruses replicated in the lower respiratory tract in this model. Furthermore, despite efficient transmission among cohoused ferrets, recently isolated H3N2v viruses displayed considerable variance in their ability to transmit by respiratory droplets. The lack of a full understanding of the molecular correlates of virulence and transmission underscores the need for close genotypic and phenotypic monitoring of H3N2v viruses and the importance of continued surveillance to improve pandemic preparedness.IMPORTANCESwine-origin influenza viruses of the H3N2 subtype, with the hemagglutinin (HA) and neuraminidase (NA) derived from historic human seasonal influenza viruses, continue to cross species barriers and cause human infections, posing an indelible threat to public health. To help us better understand the potential risk associated with swine-origin H3N2v viruses that emerged in the United States during the 2011-2016 influenza seasons, we use bothin vitroandin vivomodels to characterize the abilities of these viruses to replicate, cause disease, and transmit in mammalian hosts. The efficient respiratory droplet transmission exhibited by some of the H3N2v viruses in the ferret model combined with the existing evidence of low immunity against such viruses in young children and older adults highlight their pandemic potential. Extensive surveillance and risk assessment of H3N2v viruses should continue to be an essential component of our pandemic preparedness strategy.

scholarly journals An Automated Approach for Finding Spatio-Temporal Patterns of Seasonal Influenza in the United States: Algorithm Validation Study (Preprint)

2018 ◽  
Author(s):  
Prathyush Sambaturu ◽  
Parantapa Bhattacharya ◽  
Jiangzhuo Chen ◽  
Bryan Lewis ◽  
Madhav Marathe ◽  
...  
Keyword(s):  
United States ◽  
Compression Ratio ◽  
Seasonal Influenza ◽  
Minimum Description Length ◽  
Temporal Patterns ◽  
The United States ◽  
Disease Spread ◽  
Influenza Like Illness ◽  
Incidence Data ◽  
Spatio Temporal

BACKGROUND Agencies such as the Centers for Disease Control and Prevention (CDC) currently release influenza-like illness incidence data, along with descriptive summaries of simple spatio-temporal patterns and trends. However, public health researchers, government agencies, as well as the general public, are often interested in deeper patterns and insights into how the disease is spreading, with additional context. Analysis by domain experts is needed for deriving such insights from incidence data. OBJECTIVE Our goal was to develop an automated approach for finding interesting spatio-temporal patterns in the spread of a disease over a large region, such as regions which have specific characteristics (eg, high incidence in a particular week, those which showed a sudden change in incidence) or regions which have significantly different incidence compared to earlier seasons. METHODS We developed techniques from the area of transactional data mining for characterizing and finding interesting spatio-temporal patterns in disease spread in an automated manner. A key part of our approach involved using the principle of minimum description length for representing a given target set in terms of combinations of attributes (referred to as clauses); we considered both positive and negative clauses, relaxed descriptions which approximately represent the set, and used integer programming to find such descriptions. Finally, we designed an automated approach, which examines a large space of sets corresponding to different spatio-temporal patterns, and ranks them based on the ratio of their size to their description length (referred to as the compression ratio). RESULTS We applied our methods using minimum description length to find spatio-temporal patterns in the spread of seasonal influenza in the United States using state level influenza-like illness activity indicator data from the CDC. We observed that the compression ratios were over 2.5 for 50% of the chosen sets, when approximate descriptions and negative clauses were allowed. Sets with high compression ratios (eg, over 2.5) corresponded to interesting patterns in the spatio-temporal dynamics of influenza-like illness. Our approach also outperformed description by solution in terms of the compression ratio. CONCLUSIONS Our approach, which is an unsupervised machine learning method, can provide new insights into patterns and trends in the disease spread in an automated manner. Our results show that the description complexity is an effective approach for characterizing sets of interest, which can be easily extended to other diseases and regions beyond influenza in the US. Our approach can also be easily adapted for automated generation of narratives.

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.

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