Control Of Influenza Epidemics Research Articles

Analysis of the effectiveness of non-pharmaceutical interventions on influenza during the Coronavirus disease 2019 pandemic by time-series forecasting

BackgroundCoronavirus disease 2019 (COVID-19) was first identified in South Korea during the 2019–2020 seasonal influenza epidemic. The social distancing measures, as effective non-pharmaceutical interventions (NPIs), adopted to mitigate the spread of COVID-19 might have influenced influenza activity. We evaluated IFV(influenza virus) activity during the COVID-19 pandemic and the effect of NPI intensity on influenza transmission.MethodsIFV activity and epidemic duration during COVID-19 pandemic were predicted under a counterfactual scenario with no NPIs against COVID-19. The Seasonal Autoregressive Integrated Moving Average Model was used to quantify the effects of NPIs on the transmission of influenza virus. Influenza-like illness/1000 outpatients and IFV positivity rate from the 2011–2012 to 2021–2022 seasons were used in this study.ResultsComparison of the 2020–2021 and 2021–2022 seasonal influenza activities with those in 2013–2019 showed that COVID-19 outbreaks and associated NPIs such as face mask use, school closures, and travel restrictions reduced the influenza incidence by 91%. Without NPIs against COVID-19, the rates of influenza-like illness and IFV positivity would have been high during the influenza epidemic season, as in previous seasons. NPI intensity decreased the transmission of influenza; the magnitude of the reduction increased as the intensity of social-distancing measures increased (weak social distancing; step-by-step daily recovery: 58.10%, strong social distancing; special quarantine measures: 95.12%).ConclusionsOur results suggest that NPIs and personal hygiene can be used to suppress influenza transmission. NPIs against COVID-19 may be useful strategies for the prevention and control of influenza epidemics.

Modeling and analysis of a H1N1 model with relapse and effect of Twitter

Twitter can play an important role in the control of influenza epidemics. We introduce a quantitative approach to evaluate the effects of Twitter on the modeling of the spread of influenza epidemics in this paper. Statistically significant correlations between the number of the percentage of tweets that are self-reporting flu and data of influenza-like illness reported cases are found from Pearson correlation and cross-correlation analyses, during the 2009 H1N1 flu outbreak in England. A new H1N1 model with relapse which involves impact of Twitter are also proposed. Stability of all the equilibria of our model are obtained. The occurrence of backward and forward bifurcation are also established. The best-fit parameter values in our model are identified by gray wolf optimizer and nonlinear least square method from the above data. For determining key parameters during the outbreak of the disease with Twitter impact, the uncertainty and sensitivity analyses are explored by using a Latin hypercube sampling (LHS) method and evaluating the partial rank correlation coefficients (PRCCs). Our results show that Twitter reports have important implications for the control of infectious diseases and Twitter can serve as a good indicator of influenza epidemics.

Integration of Multiple Regression Model in an Epidemiological Decision Support System

Prevention and control of influenza epidemics are major challenges for public health care services. Early identification of flu outbreak is an important step towards implementing effective disease interventions for reducing mortality and morbidity in human populations. Indeed, health officials need a real geo-making tool for monitoring and prediction. The aim of the current study is to discuss a novel spatiotemporal tool for monitoring and predicting the phenomenon of the seasonal influenza epidemic spread in the human population using multiple regression analysis. The suggested tool is mainly based on three sub-systems. It allows generating simulation data by the use of a simulation system, integrating data sources in a data warehouse (DW) system and performing a specific online analysis Spatial On-Line Analytical Processing (SOLAP). Our proposal enables also to illustrate evolution of disease through visualizations in time and space. It can examine social network effects to better understand the topological structure of social contact and the impact of its properties. A regression analysis is performed on the influenza epidemic to examine the main factors influencing flu incidence number and therefore to predict and track influenza epidemic.

Relative timing of influenza disease by age group.

A detailed understanding of influenza movement in communities during yearly epidemics is needed to inform improved influenza control programs. We sought to determine the relative timing of influenza presentation and symptom onset by age group and influenza strain. Prospective, laboratory-confirmed surveillance was performed over three moderate influenza seasons in emergency departments and inpatient settings of both medical centers in Winston-Salem, NC. Influenza disease presented first in school age children through community epidemics of influenza A(H1N1)pdm09 and influenza B, and first in persons 5-49 years old for influenza A(H3N2). This finding indicates that influenza prevention in persons 5-49 years of age may be particularly important in influenza epidemic control.

First introduction of pandemic influenza A/H1N1 and detection of respiratory viruses in pediatric patients in Central African Republic

BackgroundAcute viral respiratory illnesses in children in sub-Saharan Africa have received relatively little attention, although they are much more frequent causes of morbidity and mortality than in developed countries. Active surveillance is essential to identify the causative agents and to improve clinical management, especially in the context of possible circulation of pandemic viruses.FindingsA prospective study was conducted in the Central African Republic (CAR) between January and December 2010 among infants and children aged 0–15 years attending sentinel sites for influenza-like illness or acute respiratory illness. Nasopharyngeal swabs were collected, and one-step real-time and multiplex reverse transcription-polymerase chain reaction were used to detect respiratory viruses. Respiratory viruses were detected in 49 of the 329 (14.9%) nasopharyngeal samples: 29 (8.8%) contained influenza viruses (5 (1.5%) had pandemic influenza A/H1N1 virus and 24 (7.3%) had influenza B viruses), 11 (3.3%) contained parainfluenza viruses types 1 and 3 and 9 (2.7%) contained human respiratory syncytial virus. Most cases were detected during the rainy season in the CAR. Analysis of the amplicon sequences confirmed the identity of each detected virus.ConclusionsThe influenza surveillance system in the CAR has provided valuable data on the seasonality of influenza and the circulation of other respiratory viruses. Our network could therefore play a valuable role in the prevention and control of influenza epidemics in the CAR.

Evolutionary History and Phylodynamics of Influenza A and B Neuraminidase (NA) Genes Inferred from Large-Scale Sequence Analyses

BackgroundInfluenza neuraminidase (NA) is an important surface glycoprotein and plays a vital role in viral replication and drug development. The NA is found in influenza A and B viruses, with nine subtypes classified in influenza A. The complete knowledge of influenza NA evolutionary history and phylodynamics, although critical for the prevention and control of influenza epidemics and pandemics, remains lacking.Methodology/Principal findingsEvolutionary and phylogenetic analyses of influenza NA sequences using Maximum Likelihood and Bayesian MCMC methods demonstrated that the divergence of influenza viruses into types A and B occurred earlier than the divergence of influenza A NA subtypes. Twenty-three lineages were identified within influenza A, two lineages were classified within influenza B, and most lineages were specific to host, subtype or geographical location. Interestingly, evolutionary rates vary not only among lineages but also among branches within lineages. The estimated tMRCAs of influenza lineages suggest that the viruses of different lineages emerge several months or even years before their initial detection. The d N /d S ratios ranged from 0.062 to 0.313 for influenza A lineages, and 0.257 to 0.259 for influenza B lineages. Structural analyses revealed that all positively selected sites are at the surface of the NA protein, with a number of sites found to be important for host antibody and drug binding.Conclusions/SignificanceThe divergence into influenza type A and B from a putative ancestral NA was followed by the divergence of type A into nine NA subtypes, of which 23 lineages subsequently diverged. This study provides a better understanding of influenza NA lineages and their evolutionary dynamics, which may facilitate early detection of newly emerging influenza viruses and thus improve influenza surveillance.

Single‐dose inhaled laninamivir: registered in Japan and its potential role in control of influenza epidemics

Please cite this paper as: Sunagawa et al. (2012) Single‐dose inhaled laninamivir: registered in Japan and its potential role in control of influenza epidemics. Influenza and Other Respiratory Viruses DOI: 10.1111/j.1750‐2659.2012.00351.x.

Comparative Analysis of the Multiple Test Methods for the Detection of Pandemic Influenza A/H1N1 2009 Virus

Accurate and rapid diagnosis of Pandemic Influenza A/H1N1 2009 virus (H1N1 2009) infection is important for the prevention and control of influenza epidemics and the timely initiation of antiviral treatment. This study was conducted to evaluate the performance of several diagnostic tools for the detection of H1N1 2009. Flocked nasopharyngeal swabs were collected from 254 outpatients of suspected H1N1 2009 during October 2009. This study analyzed the performances of RealTime ready Inf A/H1N1 Detection Set (Roche), Influenza A (H1N1) Real-Time Detection Kit (Bionote), Seeplex Influenza A/B OneStep Typing set (Seeplex reverse transcriptase PCR [RT-PCR]), BinaxNow Influenza A & B test kit (Binax rapid antigen test [RAT]) and SD BIOLINE Influenza Ag kit (SD RAT). Roche and Bionote real-time RT-PCR showed identical results for the H1N1 2009 hemagglutinin gene. Compared with real-time RT-PCR, the sensitivities and specificities were 83.7% and 100% for Seeplex RT-PCR, 64.5% and 94.7% for Binax RAT, and 69.5% and 100% for SD RAT. The sensitivities of Seeplex RT-PCR, Binax RAT and SD RAT in patients aged over 21 years were 73.7%, 47.4% and 57.9%, respectively. The sensitivities of Seeplex RT-PCR, Binax RAT and SD RAT on the day of initial symptoms were mostly lower (68.8%, 56.3% and 31.3%, respectively). In conclusion, multiplex RT-PCR and RAT for the detection of H1N1 2009 were significantly less sensitive than real-time RT-PCR. Also, a negative RAT may require more sensitive confirmatory assays, because it cannot be ruled out from influenza infection.