Etiological characteristics of influenza-like illness in Jiangsu province from 2012 to 2016

Influenza-like illness(ILI)is an acute respiratory infection caused by various pathogens.However,the epidemiologic characteristics of ILI pathogens in Jiangsu province are unclear.To better understand the ILI etiology,the characteristics of the pathogens from nasopharyngeal swab samples of patients with ILI collected from 2012 to 2016 in 6 hospitals in Jiangsu province were studied.The pathogens,including influenza virus,respiratory syncytial virus(RSV),rhinovirus(HRV),adenovirus(ADV),herpes simplex virus(HSV),human coronavirus(hCoV),Streptococcus pneumoniae and Haemophilus influenzae,were detected by real-time PCR.At least one pathogen was identified in 1334 of the patients(40.23%).Among viruses,HRV,influenza A virus(Flu A),ADV and RSV were the most frequently detected.ADV was the only pathogen that was distributed evenly in different years and regions(P>0.05).The etiological distribution varied in different age groups.Streptococcus pneumoniae was the most common pathogen in co-infections with a co-detection rate of 64.57%(319/494).The spectrum of etiologies could help to estimate disease burden and provide guidance for vaccination.

Epidemiology of influenza viruses from 2009-2013-A sentinel surveillance report from Union territory of Puducherry, India

Objective: To report the i ndings of inl uenza surveillance programme from Union territory of Puducherry and to document the clinical and epidemiological data of inl uenza viruses over a i ve year period from 2009-2013. Methods: Respiratory samples were collected from patients with influenza-like illness from 2009-2013 as part of routine diagnostic and surveillance activity. Detection of pandemic inl uenza A(H1N1) 2009, inl uenza A(H3N2) and inl uenza B was done using Real-time PCR. Results: Of the total 2 247 samples collected from patients with inl uenza-like illness during the study period 287(12.7%) and 92(4.0%) were positive for inl uenza A(H1N1) 2009 and inl uenza A(H3N2) respectively. A subset of 557 of these samples were also tested for inl uenza B and 24(4.3%) were positive. Signii cantly higher positivity rate for both viruses was observed in adults when compared with children. The peak positivity of influenza A(H1N1) 2009 was observed in 2009 followed by 2012, while that of inl uenza A(H3N2) was more uniformly distributed with the exception of 2012. Overall mortality rate due to influenza A(H1N1) 2009 was 7.6% while it was 1% for influenza A(H3N2). Each year influenza-like illness and influenza virus activity coincided with period of high rainfall and low temperature except in the first half of 2012. Conclusions: As the sole referral laboratory in this region, the data provides a comprehensive picture of inl uenza activity. This information will be useful in future planning of the vaccine schedule and inl uenza pandemic preparedness.

Effect of human rhinovirus infection in pediatric patients with influenza-like illness on the 2009 pandemic influenza A（H1N1） virus

Background Some research groups have hypothesized that human rhinoviruses （HRVs） delayed the circulation of the 2009 pandemic influenza A（H1N1） virus （A（H1N1）pdm09） at the beginning of Autumn 2009 in France.This study aimed to evaluate the relationship between HRV and A（H1N1）pdm09 in pediatric patients with influenza-like illness in Beijing,China.Methods A systematic analysis to detect A（H1N1）pdm09 and seasonal influenza A virus （FLU A） was performed on 4 349 clinical samples from pediatric patients with influenza-like illness during the period June 1,2009 to February 28,2010,while a one-step real-time RT-PCR （rRT-PCR） assay was used to detect HRV in 1 146 clinical specimens selected from those 4 349 specimens.Results During the survey period,only one wave of A（H1N1）pdm09 was observed.The percentage of positive cases for A（H1N1）pdm09 increased sharply in September with a peak in November 2009 and then declined in February 2010.Data on the monthly distribution of HRVs indicated that more HRV-positive samples were detected in September （2.2％） and October （3.3％）,revealing that the peak of HRV infection in 2009 was similar to that of other years.Among the 1 146 specimens examined for HRVs,21 （1.8％） were HRV-positive,which was significantly lower than that reported previously in Beijing （15.4％ to 19.2％） （P ＜0.01）.Overall,6 samples were positive for both A（H1N1）pdm09 and HRV,which represented a positive relative frequency of 1.60％ and 2.08％ HRV,considering the A（H1N1）pdm09-positive and-negative specimens,respectively.The odds ratio was 0.87 （95％ CI 0.32; 2.44,P=0.80）.Conclusions HRVs and A （H1N1）pdm09 co-circulated in this Chinese population during September and October 2009,and the HRV epidemic in 2009 did not affect A（H1N1）pdm09 infection rates in Beijing,China as suggested by other studies.However,the presence of A（H1N1）pdm09 might explain the unexpected reduction in the percentage of HRV positive cases during the period studied.

应急状态下新冠流行曲线预测的方法学研究——基于北京市百度搜索和传统流感样病例监测

Surveillance is an essential work on infectious diseases prevention and control.When the pandemic occurred,the inadequacy of traditional surveillance was exposed,but it also provided a valuable opportunity to explore new surveillance methods.This study aimed to estimate the transmission dynamics and epidemic curve of severe acute respiratory syndrome coronavirus 2(SARS-Co V-2)Omicron BF.7 in Beijing under the emergent situation using Baidu index and influenza-like illness(ILI)surveillance.A novel hybrid model(multiattention bidirectional gated recurrent unit(MABG)-susceptible-exposed-infected-removed(SEIR))was developed,which leveraged a deep learning algorithm(MABG)to scrutinize the past records of ILI occurrences and the Baidu index of diverse symptoms such as fever,pyrexia,cough,sore throat,anti-fever medicine,and runny nose.By considering the current Baidu index and the correlation between ILI cases and coronavirus disease 2019(COVID-19)cases,a transmission dynamics model(SEIR)was formulated to estimate the transmission dynamics and epidemic curve of SARS-Co V-2.During the COVID-19 pandemic,when conventional surveillance measures have been suspended temporarily,cases of ILI can serve as a useful indicator for estimating the epidemiological trends of COVID-19.In the specific case of Beijing,it has been ascertained that cumulative infection attack rate surpass 80.25%(95%confidence interval(95%CI):77.51%-82.99%)since December 17,2022,with the apex of the outbreak projected to transpire on December 12.The culmination of existing patients is expected to occur three days subsequent to this peak.Effective reproduction number(Rt)represents the average number of secondary infections generated from a single infected individual at a specific point in time during an epidemic,remained below 1 since December 17,2022.The traditional disease surveillance systems should be complemented with information from modern surveillance data such as online data sources with advanced technical support.Modern surveillance channels should be used primarily in emerging infectious and disease outbreaks.Syndrome surveillance on COVID-19 should be established to following on the epidemic,clinical severity,and medical resource demand.

Predicting influenza-like illness trends based on sentinel surveillance data in China from 2011 to 2019:A modelling and comparative study

Background Influenza is an acute respiratory infectious disease with a significant global disease burden.Additionally,the coronavirus disease 2019 pandemic and its related non-pharmaceutical interventions(NPIs)have introduced uncertainty to the spread of influenza.However,comparative studies on the performance of innovative models and approaches used for influenza prediction are limited.Therefore,this study aimed to predict the trend of influenza-like illness(ILI)in settings with diverse climate characteristics in China based on sentinel surveillance data using three approaches and evaluate and compare their predictive performance.Methods The generalized additive model(GAM),deep learning hybrid model based on Gate Recurrent Unit(GRU),and autoregressive moving average-generalized autoregressive conditional heteroscedasticity(ARMA—GARCH)model were established to predict the trends of ILI 1-,2-,3-,and 4-week-ahead in Beijing,Tianjin,Shanxi,Hubei,Chongqing,Guangdong,Hainan,and the Hong Kong Special Administrative Region in China,based on sentinel surveillance data from 2011 to 2019.Three relevant metrics,namely,Mean Absolute Percentage Error(MAPE),Root Mean Squared Error(RMSE),and R squared,were calculated to evaluate and compare the goodness of fit and robustness of the three models.Results Considering the MAPE,RMSE,and R squared values,the ARMA—GARCH model performed best,while the GRU-based deep learning hybrid model exhibited moderate performance and GAM made predictions with the least accuracy in the eight settings in China.Additionally,the models’predictive performance declined as the weeks ahead increased.Furthermore,blocked cross-validation indicated that all models were robust to changes in data and had low risks of overfitting.Conclusions Our study suggested that the ARMA—GARCH model exhibited the best accuracy in predicting ILI trends in China compared to the GAM and GRU-based deep learning hybrid model.Therefore,in the future,the ARMA—GARCH model may be used to predict ILI trends in public health practice across diverse climatic zones,thereby contributing to influenza control and prevention efforts.

Viral respiratory infections among Hajj pilgrims in 2013

Middle East respiratory syndrome coronavirus(MERS-Co V) has emerged in the Arabian Gulf region, with its epicentre in Saudi Arabia, the host of the ‘Hajj' which is the world's the largest mass gathering. Transmission of MERS-Co V at such an event could lead to its rapid worldwide dissemination. Therefore, we studied the frequency of viruses causing influenza-like illnesses(ILI) among participants in a randomised controlled trial at the Hajj 2013. We recruited 1038 pilgrims from Saudi Arabia, Australia and Qatar during the first day of Hajj and followed them closely for four days. A nasal swab was collected from each pilgrim who developed ILI. Respiratory viruses were detected using multiplex RT-PCR. ILI occurred in 112/1038(11%) pilgrims. Their mean age was 35 years, 49(44%) were male and 35(31%) had received the influenza vaccine pre-Hajj. Forty two(38%) pilgrims had laboratory-confirmed viral infections; 28(25%) rhinovirus, 5(4%) influenza A, 2(2%) adenovirus, 2(2%) human coronavirus OC43/229 E, 2(2%) parainfluenza virus 3, 1(1%) parainfluenza virus 1, and 2(2%) dual infections. No MERS-Co V was detected in any sample. Rhinovirus was the commonest cause of ILI among Hajj pilgrims in 2013. Infection control and appropriate vaccination are necessary to prevent transmission of respiratory viruses at Hajj and other mass gatherings.

Regional and temporal patterns of influenza: Application of functional data analysis

Background:The accurate estimation of temporal patterns of influenza may help in utilizing hospital resources and guiding influenza surveillance.This paper proposes functional data analysis(FDA)to improve the prediction of temporal patterns of influenza.Methods:We illustrate FDA methods using the weekly Influenza-like Illness(ILI)activity level data from the U.S.We propose to use the Fourier basis function for transforming discrete weekly data to the smoothed functional ILI activities.Functional analysis of variance(FANOVA)is used to examine the regional differences in temporal patterns and the impact of state's political orientation.Results:The ILI activity has a very distinct peak at the beginning and end of the year.There are significant differences in average level of ILI activities among geographic regions.However,the temporal patterns in terms of the peak and flat time are quite consistent across regions.The geographic and temporal patterns of ILI activities also depend on the political make-up of states.The states affiliated with Republicans had higher ILI activities than those affiliated with Democrats across the whole year.The influence of political party affiliation on temporal pattern is quite different among geographic regions.Conclusions:Functional data analysis can help us to reveal the temporal variability in average ILI levels,rate of change in ILI levels,and the effect of geographical regions.Consideration should be given to wider application of FDA to generate more accurate estimates in public health and biomedical research.