Influenza-like Illness Cases Research Articles

1615. Influenza Outbreaks in Long-Term Care Facilities, 2017–2018 Influenza Season, Dallas, Texas

BackgroundHigh rates of influenza-related hospitalizations and deaths occurred in the United States during the 2017–2018 influenza season. A record number of influenza outbreaks were reported in long-term care facilities (LTCF) in Dallas County. Public health surveillance of influenza-related intensive care unit (ICU) admissions and deaths in acute care hospitals improved early identification of outbreaks in LTCFs.MethodsA confirmed LTCF influenza outbreak was defined as at least 1 lab-confirmed influenza case plus at least 1 case of influenza-like illness among residents or staff within 72 hours. Outbreaks were self-reported by facilities or identified by the health department during investigations of ICU hospitalizations and deaths. CDC guidance for influenza outbreak management was provided and daily active surveillance was continued for at least 1 week after the last case was identified. Data collected included: numbers of ill residents and staff, vaccination rates, dates of illness and chemoprophylaxis initiation, hospitalizations and deaths. Fisher exact tests and Chi-square were performed using SAS 9.4.ResultsDuring this influenza season, 32 confirmed influenza outbreaks were identified in Dallas County LTCFs: 17 in skilled nursing facilities (SNF), 13 in assisted-living facilities (ALF) and 2 in hybrid SNF/ALF. The average attack rate in residents was 9.8% (range: 1–35%). Influenza hospitalization rates were higher in ALF compared with SNF outbreaks (OR: 2.2). Influenza-associated mortality rates were higher in ALF compared with SNF (OR: 3.1). Of the 32 outbreaks, 20 (63%) were self-reported by facilities to public health and 12 (38%) were identified through health department review of influenza-associated ICU hospitalizations. Facilities where outbreak cases were identified through public health surveillance of ICU admissions had significantly lower overall attack rates (5.9% vs. 12.1%, P = 0.01) and shorter time to initiation of facility-wide chemoprophylaxis (0.4 vs. 2.4 days, P = 0.05).ConclusionActive surveillance of influenza-associated ICU admissions in acute-care hospitals facilitated the early identification of influenza outbreaks in LTCFs, which was associated with lower overall attack rates and shorter time to initiation of facility-wide chemoprophylaxis.Disclosures All authors: No reported disclosures.

The Fever Coach Mobile App for Participatory Influenza Surveillance in Children: Usability Study.

BackgroundEffective surveillance of influenza requires a broad network of health care providers actively reporting cases of influenza-like illnesses and positive laboratory results. Not only is this traditional surveillance system costly to establish and maintain but there is also a time lag between a change in influenza activity and its detection. A new surveillance system that is both reliable and timely will help public health officials to effectively control an epidemic and mitigate the burden of the disease.ObjectiveThis study aimed to evaluate the use of parent-reported data of febrile illnesses in children submitted through the Fever Coach app in real-time surveillance of influenza activities.MethodsFever Coach is a mobile app designed to help parents and caregivers manage fever in young children, currently mainly serviced in South Korea. The app analyzes data entered by a caregiver and provides tailored information for care of the child based on the child’s age, sex, body weight, body temperature, and accompanying symptoms. Using the data submitted to the app during the 2016-2017 influenza season, we built a regression model that monitors influenza incidence for the 2017-2018 season and validated the model by comparing the predictions with the public influenza surveillance data from the Korea Centers for Disease Control and Prevention (KCDC).ResultsDuring the 2-year study period, 70,203 diagnosis data, including 7702 influenza reports, were submitted. There was a significant correlation between the influenza activity predicted by Fever Coach and that reported by KCDC (Spearman ρ=0.878; P<.001). Using this model, the influenza epidemic in the 2017-2018 season was detected 10 days before the epidemic alert announced by KCDC.ConclusionsThe Fever Coach app successfully collected data from 7.73% (207,699/2,686,580) of the target population by providing care instruction for febrile children. These data were used to develop a model that accurately estimated influenza activity measured by the central government agency using reports from sentinel facilities in the national surveillance network.

Detecting influenza and emerging avian influenza virus by influenza and pneumonia surveillance systems in a large city in China, 2005 to 2016

BackgroundDetecting avian influenza virus has become an important public health strategy for controlling the emerging infectious disease.MethodsThe HIS (hospital information system) modified influenza surveillance system (ISS) and a newly built pneumonia surveillance system (PSS) were used to monitor the influenza viruses in Changsha City, China. The ISS was used to monitor outpatients in two sentinel hospitals and to detect mild influenza and avian influenza cases, and PSS was used to monitor inpatients in 49 hospitals and to detect severe and death influenza cases.ResultsFrom 2005 to 2016, there were 3,551,917 outpatients monitored by the ISS system, among whom 126,076 were influenza-like illness (ILI) cases, with the ILI proportion (ILI%) of 3.55%. After the HIS was used, the reported incident cases of ILI and ILI% were increased significantly. From March, 2009 to September, 2016, there were 5,491,560 inpatient cases monitored by the PSS system, among which 362,743 were pneumonia cases, with a proportion of 6.61%. Among pneumonia cases, about 10.55% (38,260/362,743) of cases were severe or death cases. The pneumonia incidence increased each year in the city. Among 15 avian influenza cases reported from January, 2005 to September, 2016, there were 26.7% (4/15) mild cases detected by the HIS-modified ISS system, while 60.0% (9/15) were severe or death cases detected by the PSS system. Two H5N1 severe cases were missed by the ISS system in January, 2009 when the PSS system was not available.ConclusionsThe HIS was able to improve the efficiency of the ISS for monitoring ILI and emerging avian influenza virus. However, the efficiency of the system needs to be verified in a wider area for a longer time span in China.

Evaluation of the diagnostic utility of case definitions to detect influenza virus infection in Vojvodina, Serbia

Introduction/Objective. A case definition recommended by the World Health Organization is commonly used for influenza surveillance worldwide. The aim of this study was to evaluate prognostic values of proposed case definitions of Influenza Like Illness (ILI), Severe Acute Respiratory Illness (SARI) and Acute Respiratory Distress Syndrome (ARDS) for laboratory confirmed-influenza and to compare the age distribution of influenza patients across virus types and subtypes in Vojvodina. Methods. We conducted a descriptive epidemiological study using surveillance reports and laboratory data from October 1, 2010 to May 20, 2017 (seven surveillance seasons). Results. We included 2,937 participants, 48.6% of whom were laboratory-confirmed influenza cases, and most of the confirmed cases (30.1%) were detected in February. In the 15?29 years age group, the type A influenza (H3N2) was more frequent among patients with ILI (54.9% vs. 34.2%, p = 0.040), and less frequent in patients with SARI (39.4% vs. 65.8%, p = 0.009) compared with influenza type B. In patients aged 30?64 years with ARDS, influenza type B was more common than influenza type A (H3N2) (13.4% vs. 6.2%, p = 0.032), but less common in compared to influenza type A (H1N1) pdm09 (13.4% vs. 25.7%, p = 0.017). The SARI case definition of influenza was associated with an increased likelihood of laboratoryconfirmed influenza for all age groups (p &lt; 0.05). During the epidemic period, it was observed that the ILI case definition had the highest diagnostic value for influenza in the age group 5?14 (AUC = 0.733; 95% CI: 0.704?0.764), while the SARI and ARDS case definitions were the best predictors of influenza for patients 15?29 years of age (AUC = 0.565; 95% CI: 0.504?0.615 and AUC = 0.708; 95% CI: 0.489?0.708, respectively). The case definition of ARDS had the maximum sensitivity (100%) among patients 15?29 years of age. Conclusion. The proposed case definitions of influenza appeared to be good predictors of influenza and therefore can be useful for influenza surveillance, especially in the countries with limited laboratory capacities.

Molecular detection of enterovirus D68 among children with acute respiratory tract infection in Ghana.

BackgroundAcute respiratory tract infections of viral origin remain a leading cause of morbidity, mortality and economic loss regardless of age or gender. A small number of acute respiratory tract infection cases caused by enterovirus D68 (EV-D68) have been reported regularly to Centers for Disease Control and Prevention since 1987 by countries in North America, Europe and Asia. However, in 2014 and 2015, the number of reported confirmed EV-D68 infections was much greater than in previous years. The National Influenza Centre (NIC), Ghana carries out surveillance of respiratory infections, focusing on those caused by influenza virus; however, there is inadequate information on other viruses causing respiratory infections in Ghana, including EV-D68.ObjectivesTo investigate the association of EV-D68 with Severe Acute Respiratory Infections (SARI) and Influenza-Like Illness (ILI) in Ghana.MethodsThis was a retrospective cross-sectional study which involved archived human respiratory specimens stored at –80 °C at the NIC from 2014 to 2015. Using a random sampling method, oropharyngeal and nasopharyngeal swabs from patients with SARI and ILI that were negative by real-time PCR for human influenza viruses were screened for EV-D68 using real-time reverse transcription-polymerase chain reaction (rRT-PCR).ResultsEnterovirus D68 was detected in 4 (2.2%) out of 182 SARI samples tested. EV-D68 was detected in children younger than 5 years (4 – 100% of positives) and was not detected in children older than 5 years. Enterovirus D68 was detected more frequently in SARI cases (3%) than in ILI cases (1.2%).ConclusionThis study has shown for the first time the presence of EV-D68 in acute respiratory infections in Ghana. The results confirmed minimal EV-D68 circulation in the Ghanaian population.

Epidemiology of respiratory infections among adults in Qatar (2012-2017).

BackgroundLimited data is available about the etiology of influenza like illnesses (ILIs) in Qatar.ObjectivesThis study aimed at providing preliminary estimates of influenza and other respiratory infections circulating among adults in Qatar.MethodsWe retrospectively collected data of about 44,000 patients who visited Hamad General Hospital clinics, sentinel sites, and all primary healthcare centers in Qatar between 2012 and 2017. All samples were tested for influenza viruses, whereas about 38,000 samples were tested for influenza and a panel of respiratory viruses using Fast Track Diagnostics (FTD) RT-PCR kit.ResultsAmong all ILIs cases, 20,278 (46.5%) tested positive for at least one respiratory pathogen. Influenza virus was predominating (22.6%), followed by human rhinoviruses (HRVs) (9.5%), and human coronaviruses (HCoVs) (5%). A detection rate of 2–3% was recorded for mycoplasma pneumonia, adenoviruses, human parainfluenza viruses (HPIVs), respiratory syncytial virus (RSV), and human metapneumovirus (HMPV). ILIs cases were reported throughout the year, however, influenza, RSV, and HMPV exhibited strong seasonal peaks in the winter, while HRVs circulated more during fall and spring. Elderly (>50 years) had the lowest rates of influenza A (13.9%) and B (4.2%), while presenting the highest rates of RSV (3.4%) and HMPV (3.3%). While males had higher rates of HRVs (11.9%), enteroviruses (1.1%) and MERS CoV (0.2%), females had higher proportions of influenza (26.3%), HPIVs (3.2%) and RSV (3.6%) infections.ConclusionThis report provides a comprehensive insight about the epidemiology of ILIs among adults in the Qatar, as a representative of Gulf States. These results would help in improvement and optimization of diagnostic procedures, as well as control and prevention of the respiratory infections.

Evaluation of the influenza-like illness surveillance system in Tunisia, 2012\u20132015

BackgroundThis study was initiated to evaluate, for the first time, the performance and quality of the influenza-like illness (ILI) surveillance system in Tunisia.MethodsThe evaluation covered the period of 2012–2015 and used different data sources to measure indicators related to data quality and completeness, representativeness, timeliness, simplicity, acceptability, flexibility, stability and utility.ResultsDuring the evaluation period, 485.221 ILI cases were reported among 6.386.621 outpatients at 268 ILI sentinel sites. To conserve resources, cases were only enrolled and tested for influenza during times when the number of patients meeting the ILI case definition exceeded 7% (10% after 2014) of the total number of outpatients for the week. When this benchmark was met, five to 10 patients were enrolled and sampled by nasopharyngeal swabs the following week. In total, The National Influenza Center (NIC) received 2476 samples, of which 683 (27.6%) were positive for influenza. The greatest strength of the system was its representativeness and flexibility. The timeliness of the data and the acceptability of the surveillance system performed moderately well; however, the utility of the data and the stability and simplicity of the surveillance system need improvement. Overall, the performance of the Tunisian influenza surveillance system was evaluated as performing moderately well for situational awareness in the country and for collecting representative influenza virologic samples.ConclusionsThe influenza surveillance system in Tunisia provided pertinent evidence for public health interventions related to influenza situational awareness. To better monitor influenza, we propose that ILI surveillance should be limited to sites that are currently performing well and the quality of data collected should be closely monitored and improved.

On estimation the relative risk of small area and visualization spatio-temporal map

ObjectiveThe purpose is to propose a serial of approach for estimation for disease risk for ILI in "small area" and present the risk values by spatio-temporal disease mapping or an interactive visualization with HTML format.IntroductionDisease mapping is a method used to descript the geographical variation in risk (heterogeneity of risk) and to provide the potential reason (factors or confounders) to explain the distribution. Possibly the most famous uses of disease mapping in epidemiology were the studies by John Snow of the cholera epidemics in London. Accurate estimation relative risk of small areas such as mortality and morbidity, by different age, ethnic group, interval and regions, is important for government agencies to identify hazards and mitigate disease burden. Recently, as the innovative algorithms and the available software, more and more disease risk index has been pouring out. This abstract will provide several estimation risk index, from raw incidence to model-based relative risks, and use visual approach to display them.MethodsAll the data are from a syndromic surveillance and real-time early warning system in the Yunnan province in the China. For brief introduction aim, we are using the ILI (Influenza-like illness) data in December 2017 in one county. The relative risks of disease in small area are including: raw incidence, a standardized morbidity ratio (SMR), Empirical Bayes smoothing estimation relative risk (EB-RR) and the Besag-York-Mollio model (BYM). The incidence in each small area is common used for descriptive the risk but fail to comparable directly since the different population at risk in each area. SMR is a good way to deal with this incomparability. But SMR can give rise to imprecisely estimate in areas with small populations. Empirical Bayes estimation approach has been used for smoothing purpose and can be seen as a compromise between relative risks and P-values. However, all above approaches are inept to have spatial or spatio-temporal structure in mind. BYM based the Bayesian inference can handle both the area-specific spatial structured component (such as intrinsic conditional autoregressive component) and the exchangeable random effect (unstructured component). All the analyses are implemented in the R software with INLA package (http://www.r-inla.org). The outcome of relative risk estimation with visual way and interactive maps showing are using ggplot2 and leaflet packages.Results1, the spatio-temporal raw cases of ILI from 2017/12/01 to 2017/12/31 is Fig.12. the SMR and EB-RR estimation RR of ILI are in Fig.2 and Fig.33. the most excited is the interactive visualization with HTML format for all the risk indexes is visited http://rpubs.com/ynsxx/424814 in detail. And the screenshot is Fig.4ConclusionsSmall area disease risk estimation is important for disease prevention and control. The faster function of computer with power R software can lead to advance in disease mapping, allowing for complex spatio-temporal models and communicate the results with visualization way.

Evaluating tools to define influenza baseline and threshold values using surveillance data, Egypt, season 2016/17

BackgroundEstablishing influenza thresholds and transmission intensity can help evaluate seasonal changes in influenza severity and potential pandemics. We aimed to evaluate the moving epidemic method (MEM) for calculating influenza thresholds for season 2016/17 in Egypt using four parameters, to identify the most useful parameter. Also to measure the agreement between both the country-specific statistical empirical method and World Health Organization method to MEM for determining the length and intensity level of activity of the influenza season. MethodsRoutinely epidemiological and laboratory data from sentinel surveillance sites for Severe Acute Respiratory Infection (SARI) and influenza-like illness (ILI) were used for calculating thresholds for seasons between 2010/11 and 2015/16 to test 2016/17 season. The parameters calculated were: screened ILI consultation rate × 1000, screened ILI composite parameter, influenza positivity percentage among sampled SARI cases, and influenza positivity percentage among sampled ILI and SARI cases. These parameters assess seasonality and intensity of influenza activity using the three proposed methods (mentioned above). Agreement between the three methods was done using several approaches. ResultsThe intensity of influenza activity by MEM was lower than the other two methods. Agreement between MEM and each of the other two techniques varied appreciably from good to very good for seasonal duration, and poor to fair for intensity level. In addition, parameters including laboratory data showed a pattern of bi-wave activity; the first wave occurred in winter mostly between epidemiological weeks 39 and 52 and the second occurred in spring mostly between weeks 12 and 17. ConclusionParameters including laboratory data were more useful in defining seasonality of influenza. Further exploration of the MEM model in future seasons may help to provide a more comprehensive understanding of its use and application.

Effect of meteorological factors on influenza-like illness from 2012 to 2015 in Huludao, a northeastern city in China.

BackgroundThis study aims to describe the epidemiological patterns of influenza-like illness (ILI) in Huludao, China and seek scientific evidence on the link of ILI activity with weather factors.MethodsSurveillance data of ILI cases between January 2012 and December 2015 was collected in Huludao Central Hospital, meteorological data was obtained from the China Meteorological Data Service Center. Generalized additive model (GAM) was used to seek the relationship between the number of ILI cases and the meteorological factors. Multiple Smoothing parameter estimation was made on the basis of Poisson distribution, where the number of weekly ILI cases was treated as response, and the smoothness of weather was treated as covariates. Lag time was determined by the smallest Akaike information criterion (AIC). Smoothing coefficients were estimated for the prediction of the number of ILI cases.ResultsA total of 29, 622 ILI cases were observed during the study period, with children ILI cases constituted 86.77%. The association between ILI activity and meteorological factors varied across different lag periods. The lag time for average air temperature, maximum air temperature, minimum air temperature, vapor pressure and relative humidity were 2, 2, 1, 1 and 0 weeks, respectively. Average air temperature, maximum air temperature, minimum air temperature, vapor pressure and relative humidity could explain 16.5%, 9.5%, 18.0%, 15.9% and 7.7% of the deviance, respectively. Among the temperature indexes, the minimum temperature played the most important role. The number of ILI cases peaked when minimum temperature was around −13 °C in winter and 18 °C in summer. The number of cases peaked when the relative humidity was equal to 43% and then began to decrease with the increase of relative humidity. When the humidity exceeded 76%, the number of ILI cases began to rise.ConclusionsThe present study first analyzed the relationship between meteorological factors and ILI cases with special consideration of the length of lag period in Huludao, China. Low air temperature and low relative humidity (cold and dry weather condition) played a considerable role in the epidemic pattern of ILI cases. The trend of ILI activity could be possibly predicted by the variation of meteorological factors.

Outbreak detection of influenza-like illness in Prey Veng Province, Cambodia: a community-based surveillance.

ABSTRACTOn June 4, 2016, the Prey Veng Provincial Health Department reported a total of 107 patients with influenza-like illness (ILI) from Chakhlanh village to the Cambodian Ministry of Health. To confirm the outbreak and evaluate its clinical and epidemiological characteristics, the investigation team visited the village and reviewed the case-based surveillance (CBS) data on severe respiratory infection (SRI) and patients’ records in health facilities. The team interviewed all households in the village from May 1 to June 5, 2016 and obtained the following data: age, medical history, date of onset, treatment, symptoms, and history of contact with patients or dead poultry. Nasal swab samples were collected from suspected ILI cases to test for influenza virus by RT-PCR. The investigation detected 498 suspected ILI cases, including 288 females. Although the incidence of suspected ILI cases who visited health centers was 63.0 per 1,000 persons per month, the attack rate was 27.1 per 100 population. The major age group was 5–14 years followed by 0–4 years. Major symptoms were cough, fever, runny nose, and headache. Six of seven nasal swab samples were positive for influenza A/H1N1 pdm09 virus. Most children with flu symptoms had contact with previous cases. This study showed that the ILI outbreak might be caused by seasonal influenza A/H1N1 pdm09 spread from person to person. Poor living conditions and poor hygiene and sanitation practices were environmental factors that caused the outbreak. As the CBS system was unable to identify this epidemic, it needs to be improved.

Real-time prediction of influenza outbreaks in Belgium

Seasonal influenza is a worldwide public health concern. Forecasting its dynamics can improve the management of public health regulations, resources and infrastructure, and eventually reduce mortality and the costs induced by influenza-related absenteism. In Belgium, a network of Sentinel General Practitioners (SGPs) is in place for the early detection of the seasonal influenza epidemic. This surveillance network reports the weekly incidence of influenza-like illness (ILI) cases, which makes it possible to detect the epidemic onset, as well as other characteristics of the epidemic season. In this paper, we present an approach for predicting the weekly ILI incidence in real-time by resorting to a dynamically calibrated compartmental model, which furthermore takes into account the dynamics of other influenza seasons. In order to validate the proposed approach, we used data collected by the Belgian SGPs for the influenza seasons 2010–2016. In spite of the great variability among different epidemic seasons, providing weekly predictions makes it possible to capture variations in the ILI incidence. The confidence region becomes more representative of the epidemic behavior as ILI data from more seasons become available. Since the SIR model is then calibrated dynamically every week, the predicted ILI curve gets rapidly tuned to the dynamics of the ongoing season. The results show that the proposed method can be used to characterize the overall behavior of an epidemic.

Unsupervised extraction of epidemic syndromes from participatory influenza surveillance self-reported symptoms.

Seasonal influenza surveillance is usually carried out by sentinel general practitioners (GPs) who compile weekly reports based on the number of influenza-like illness (ILI) clinical cases observed among visited patients. This traditional practice for surveillance generally presents several issues, such as a delay of one week or more in releasing reports, population biases in the health-seeking behaviour, and the lack of a common definition of ILI case. On the other hand, the availability of novel data streams has recently led to the emergence of non-traditional approaches for disease surveillance that can alleviate these issues. In Europe, a participatory web-based surveillance system called Influenzanet represents a powerful tool for monitoring seasonal influenza epidemics thanks to aid of self-selected volunteers from the general population who monitor and report their health status through Internet-based surveys, thus allowing a real-time estimate of the level of influenza circulating in the population. In this work, we propose an unsupervised probabilistic framework that combines time series analysis of self-reported symptoms collected by the Influenzanet platforms and performs an algorithmic detection of groups of symptoms, called syndromes. The aim of this study is to show that participatory web-based surveillance systems are capable of detecting the temporal trends of influenza-like illness even without relying on a specific case definition. The methodology was applied to data collected by Influenzanet platforms over the course of six influenza seasons, from 2011-2012 to 2016-2017, with an average of 34,000 participants per season. Results show that our framework is capable of selecting temporal trends of syndromes that closely follow the ILI incidence rates reported by the traditional surveillance systems in the various countries (Pearson correlations ranging from 0.69 for Italy to 0.88 for the Netherlands, with the sole exception of Ireland with a correlation of 0.38). The proposed framework was able to forecast quite accurately the ILI trend of the forthcoming influenza season (2016-2017) based only on the available information of the previous years (2011-2016). Furthermore, to broaden the scope of our approach, we applied it both in a forecasting fashion to predict the ILI trend of the 2016-2017 influenza season (Pearson correlations ranging from 0.60 for Ireland and UK, and 0.85 for the Netherlands) and also to detect gastrointestinal syndrome in France (Pearson correlation of 0.66). The final result is a near-real-time flexible surveillance framework not constrained by any specific case definition and capable of capturing the heterogeneity in symptoms circulation during influenza epidemics in the various European countries.

“Epidemiology and aetiology of influenza-like illness among households in metropolitan Vientiane, Lao PDR”: A prospective, community-based cohort study

Respiratory diseases are a major contributor to morbidity and mortality in many tropical countries, including Lao PDR. However, little has been published regarding viral or bacterial pathogens that can contribute to influenza-like illness (ILI) in a community setting. We report on the results of a community-based surveillance that prospectively monitored the incidence of ILI and its causative pathogens in Vientiane capital in Lao PDR. A cohort of 995 households, including 4885 study participants, were followed-up between May 2015 and May 2016. Nasopharyngeal swabs, throat swabs, and sputum specimens were collected from ILI cases identified through active case-finding. Real-Time PCR was used to test nasopharyngeal swabs for 21 respiratory pathogens, while throat and sputum samples were subjected to bacterial culture. Generalized linear mixed models were used to assess potential risk factors for associations with ILI. In total, 548 episodes of ILI were reported among 476 (9.7%) of the study participants and 330 (33.2%) of the study households. The adjusted estimated incidence of ILI within the study area was 10.7 (95%CI: 9.4–11.9) episodes per 100 person-years. ILI was significantly associated with age group (p<0.001), sex (p<0.001), and number of bedrooms (p = 0.04) in multivariate analysis. In 548 nasopharyngeal swabs, the most commonly detected potential pathogens were Streptococcus pneumoniae (17.0%), Staphylococcus aureus (11.3%), influenza A (11.1%; mostly subtype H3N2), rhinovirus (7.5%), and influenza B (8.0%). Streptococci were isolated from 42 (8.6%) of 536 throat swabs, most (27) of which were Lancefield Group G. Co-infections were observed in 132 (24.1%) of the 548 ILI episodes. Our study generated valuable data on respiratory disease burden and patterns of etiologies associated with community-acquired acute respiratory illness Laos. Establishment of a surveillance strategy in Laos to monitor trends in the epidemiology and burden of acute respiratory infections is required to minimize their impact on human health.

Influenza-like illness and antimicrobial prescribing in Australian general practice from 2015 to 2017: a national longitudinal study using the MedicineInsight dataset

ObjectivesTo investigate the epidemiology of influenza-like illness (ILI) by general practice and patient characteristics, and explore whether sociodemographic variables or comorbidities affect antiviral or antibiotic prescribing.DesignOpen cohort study.SettingA representative sample...

Enhanced influenza vaccination among healthcare personnel prevents cases despite community burden

Influenza vaccination for healthcare personnel is not obligatory in Mexico, and compliance relies on promotion and persuasion. The objective of this study was to determine the impact of influenza vaccination compliance on the reduction of influenza and influenza-like illness among healthcare personnel throughout two consecutive influenza seasons. A retrospective study comparing cases of influenza and influenza-like illness among vaccinated and unvaccinated healthcare personnel registered in a Mexican 500-bed University Hospital surveillance and immunization registry during seasons 2015-16 and 2016-17. Total influenza immunization compliance was 21.3% and 42.7%, respectively. Reduction of the number of influenza-like illness (58 in 2015-16 and 15 in 2016-17; P = 0.0001) and confirmed influenza cases (28 in 2015-16 and 13 in 2016-17; P = 0.036) was evident. During 2016-17, influenza activity in the community was higher than 2015-16 (4800 and 1338 cases, respectively). Increased influenza vaccination compliance among healthcare personnel was associated with reduction of the overall number of influenza and influenza-like illness cases, even in the setting of high activity of the disease in the community through 2016-17 and reported suboptimal vaccine effectiveness during both seasons.

A maximum curvature method for estimating epidemic onset of seasonal influenza in Japan

BackgroundDetecting the onset of influenza epidemic is important for epidemiological surveillance and for investigating the factors driving spatiotemporal transmission patterns. Most approaches define the epidemic onset based on thresholds, which use subjective criteria and are specific to individual surveillance systems.MethodsWe applied the empirical threshold method (ETM), together with two non-thresholding methods, including the maximum curvature method (MCM) that we proposed and the segmented regression method (SRM), to determine onsets of influenza epidemics in each prefecture of Japan, using sentinel surveillance data of influenza-like illness (ILI) from 2012/2013 through 2017/2018. Performance of the MCM and SRM was evaluated, in terms of epidemic onset, end, and duration, with those derived from the ETM using the nationwide epidemic onset indicator of 1.0 ILI case per sentinel per week.ResultsThe MCM and SRM yielded complete estimates for each of Japan’s 47 prefectures. In contrast, ETM estimates for Kagoshima during 2012/2013 and for Okinawa during all six influenza seasons, except 2013/2014, were invalid. The MCM showed better agreement in all estimates with the ETM than the SRM (R2 = 0.82, p < 0.001 vs. R2 = 0.34, p < 0.001 for epidemic onset; R2 = 0.18, p < 0.001 vs. R2 = 0.05, p < 0.001 for epidemic end; R2 = 0.28, p < 0.001 vs. R2 < 0.01, p = 0.35 for epidemic duration). Prefecture-specific thresholds for epidemic onset and end were established using the MCM.ConclusionsThe Japanese national epidemic onset threshold is not applicable to all prefectures, particularly Okinawa. The MCM could be used to establish prefecture-specific epidemic thresholds that faithfully characterize influenza activity, serving as useful complements to the influenza surveillance system in Japan.

Detection and characterization of type B influenza virus from influenza-like illness cases during the 2017-2018 winter influenza season in Beijing, China.

In the winter of 2017-2018, there was significant influenza activity in China, resulting in unprecedented usage of influenza rapid antigen tests (IRAT) and neuraminidase inhibitors (NAIs). The aim of this study was to characterize the most prevalent influenza virus type in a clinical setting with respect to diagnosis and concomitant NAI treatment. From Dec 2017 to Jan 2018, 3257 patients with influenza-like illness (ILI) were screened using IRAT. We summarized and compared the results with the last influenza season. Subtyping of influenza B viruses and identification of NAI drug resistance mutations were carried out by sequencing the HA and NA genes and aligning these with genetic isotypes. The performance of IRAT and RT-PCR was compared. Screening results indicated that influenza B virus was the leading cause of this influenza epidemic, with children being more susceptible to infection than adults. Phylogenetic analysis revealed that the prevailing influenza B virus belonged to the Yamagata lineage and were genetically similar to strains isolated from North America in the same influenza season. Cross-continental spread of influenza/B/Yamagata occurred. NAI resistance mutations were not identified in the 18 samples analyzed. The current antiviral protocol was still effective for influenza B control. RT-PCR positivity was significantly higher than that of IRAT (P = 0.004). IRAT and RT-PCR had a consistency rate of 86.9%, with the consistency rates of the positive and negative cases being 54.3% and 97.3%, respectively. Clinicians should be alert to the possibility of obtaining false negative results when using IRAT, and RT-PCR is recommended to improve the accuracy of pathogen detection.

Optimizing influenza vaccine policies for controlling 2009-like pandemics and regular outbreaks.

BackgroundThis study examined the effectiveness of various vaccine policies against influenza. The transmission rate was calculated by use of the time-series influenza-like illness case during the year of 2009 and recent epidemics in Taiwan.MethodsWe developed a stochastic compartmental model to analyze the transmission of influenza, where the population was stratified by location and age group, and the vaccine distribution was considered using the current policy. The simulation study compared the previous vaccine policy and a new policy with expanded coverage and various lengths of the vaccination campaign. The sensitivity analysis investigated different levels of vaccine efficacy to confirm the robustness of the recommended policies.ResultsDoubling vaccine coverage can decrease the number of infections effectively in the regular epidemic scenario. However, a peak of infections occurs if the duration of implementing vaccination is too long. In the 2009-like pandemic scenario, both increasing vaccine doses and reducing the program’s duration can mitigate infections, although the early outbreak restricts the effectiveness of vaccination programs.ConclusionsThe finding indicates that only increasing vaccine coverage can reduce influenza infections. To avoid the peak of infections, it is also necessary to execute the vaccination activity immediately. Vaccine efficacy significantly impacts the vaccination policy’s performance. When vaccine efficacy is low, neither increasing vaccination doses nor reducing vaccination timeframe prevents infections. Therefore, the variation in vaccine efficacy should be taken into account when making immunization policies against influenza.

Demographic Determinants of Influenza Vaccination and Infection, Brazos County, Texas, 2017.

Background:Seasonal influenza constitutes an enormous public health burden. The 2017-2018 influenza season was the most severe since the 2009 novel Influenza A (H1N1) pandemic. The State of Texas, like other states, experienced unusually high and persistent influenza activity.Methods:Data on confirmed cases of influenza and influenza-like illness (ILI) in Brazos County during December 2017 were collected by the Brazos County Health Department (BCHD) from medical records. Records of vaccine administered between January 1 and December 31, 2017 were collected from vaccination providers. The total number of influenza cases for 2017 was compiled from regular weekly influenza counts reported to BCHD by healthcare providers.Results:A total of 1651 cases of laboratory-confirmed influenza were reported in Brazos County residents in December 2017, 10 times more than that reported in December 2016. The highest rates of infection were in 2 ZIP codes where vaccination rates were also high. A total of 16 027 influenza vaccinations were given to individuals reporting a residential address in Brazos County in 2017. Vaccination coverage was lowest among adults aged 18 to 49, whereas those aged 65 or older and Hispanics were most likely to be hospitalized.Discussion:Overall, vaccination coverage in Brazos County, Texas is low, less than half of the Healthy People 2020 target. The development of health education materials and an increased use of social media, local television and radio, and communication methods that can reach parents, younger adults, and Hispanic residents are needed.