Seasonal distribution and upsurge of respiratory viruses among indigenous tribes with ILI and SARI in a far-flung Car Nicobar Island.

BACKGROUND: Respiratory viral illnesses among children are a prominent cause of morbidity and mortality in the developing world. The aim of this study is to understand the seasonal pattern and surge of respiratory viruses among the Nicobarese tribe. METHODS: Respiratory specimens were collected from both ARI and SARI cases attended the BJR district hospital in Car Nicobar Island, India, between 2021 and 2022. Respiratory viruses were identified from the specimens by using the qRT-PCR assay. Meteorological parameters were collected and evaluated using Microsoft Excel and SPSS 21. The significant association between the surge of respiratory viruses and each climatic parameter was evaluated. RESULTS: In this hospital-based cross-sectional study, 471 ILI cases were enrolled, and 209 of these were positive for respiratory viral infections. Of these respiratory virus infections, 201 (96.2%) were infected with a single respiratory virus infection, and 8 (3.8%) had mixed viral infections. Fever, cough, and chills were the most common symptoms of respiratory illness among this indigenous population. There was a significant link between respiratory viruses and influenza-like illness in children (below 5 years and 6 to 15 years). CONCLUSION: This prevalence study revealed that viral respiratory infections were more common in children than adults. Among these respiratory viruses, respiratory syncytial virus A (RSV) and influenza B virus were predominantly reported among tribal children up to age five years. In the year 2021, these viruses were recorded frequently during the winter season. Climate factors such as high humidity, high precipitation, moderate temperature, and moderate rainfall are found to be correlated with respiratory viral infections. This study implicates important information for preventing a further outbreak of respiratory viral infections in Car Nicobar Island.

Seasonality of influenza-like illness and short-term forecasting model in Chongqing from 2010 to 2022.

BACKGROUND: Influenza-like illness (ILI) imposes a significant burden on patients, employers and society. However, there is no analysis and prediction at the hospital level in Chongqing. We aimed to characterize the seasonality of ILI, examine age heterogeneity in visits, and predict ILI peaks and assess whether they affect hospital operations. METHODS: The multiplicative decomposition model was employed to decompose the trend and seasonality of ILI, and the Seasonal Auto-Regressive Integrated Moving Average with exogenous factors (SARIMAX) model was used for the trend and short-term prediction of ILI. We used Grid Search and Akaike information criterion (AIC) to calibrate and verify the optimal hyperparameters, and verified the residuals of the multiplicative decomposition and SARIMAX model, which are both white noise. RESULTS: During the 12-year study period, ILI showed a continuous upward trend, peaking in winter (Dec. - Jan.) and a small spike in May-June in the 2-4-year-old high-risk group for severe disease. The mean length of stay (LOS) in ILI peaked around summer (about Aug.), and the LOS in the 0-1 and ≥ 65 years old severely high-risk group was more irregular than the others. We found some anomalies in the predictive analysis of the test set, which were basically consistent with the dynamic zero-COVID policy at the time. CONCLUSION: The ILI patient visits showed a clear cyclical and seasonal pattern. ILI prevention and control activities can be conducted seasonally on an annual basis, and age heterogeneity should be considered in the health resource planning. Targeted immunization policies are essential to mitigate potential pandemic threats. The SARIMAX model has good short-term forecasting ability and accuracy. It can help explore the epidemiological characteristics of ILI and provide an early warning and decision-making basis for the allocation of medical resources related to ILI visits.

Application of the Time Derivative (TD) Method for Early Alert of Influenza Epidemics.

BACKGROUND: In order to minimize the spread of seasonal influenza epidemic to communities worldwide, the Korea Disease Control and Prevention Agency has issued an influenza epidemic alert using the influenza epidemic threshold formula based on the results of the influenza-like illness (ILI) rate. However, unusual changes have occurred in the pattern of respiratory infectious diseases, including seasonal influenza, after the coronavirus disease 2019 (COVID-19) pandemic. As a result, the importance of detecting the onset of an epidemic earlier than the existing epidemic alert system is increasing. Accordingly, in this study, the Time Derivative (TD) method was suggested as a supplementary approach to the existing influenza alert system for the early detection of seasonal influenza epidemics. METHODS: The usefulness of the TD method as an early epidemic alert system was evaluated by applying the ILI rate for each week during past seasons when seasonal influenza epidemics occurred, ranging from the 2013-2014 season to the 2022-2023 season to compare it with the issued time of the actual influenza epidemic alert. RESULTS: As a result of applying the TD method, except for the two seasons (2020-2021 season and 2021-2022 season) that had no influenza epidemic, an influenza early epidemic alert was suggested during the remaining seasons, excluding the 2017-2018 and 2022-2023 seasons. CONCLUSION: The TD method is a time series analysis that enables early epidemic alert in real-time without relying on past epidemic information. It can be considered as an alternative approach when it is challenging to set an epidemic threshold based on past period information. This situation may arise when there has been a change in the typical seasonal epidemic pattern of various respiratory viruses, including influenza, following the COVID-19 pandemic.

Real-time investigation of an influenza A(H3N2) virus outbreak in a refugee community, November 2022.

OBJECTIVES: To report epidemiological and virological results of an outbreak investigation of influenza-like illness (ILI) among refugees in Northern Italy. STUDY DESIGN: Outbreak investigation of ILI cases observed among nearly 100 refugees in Northern Italy unvaccinated for influenza. METHODS: An epidemiological investigation matched with a differential diagnosis was carried out for each sample collected from ILI cases to identify 10 viral pathogens (SARS-CoV-2, influenza virus type A and B, respiratory syncytial virus, metapneumovirus, parainfluenza viruses, rhinovirus, enterovirus, parechovirus, and adenovirus) by using specific real-time PCR assays according to the Centers for Disease Control and Prevention (CDC) protocols. In cases where the influenza virus type was identified, complete hemagglutinin (HA) gene sequencing and the related phylogenetic analysis were conducted. RESULTS: The outbreak was caused by influenza A(H3N2): the attack rate was 83.3% in children aged 9-14 years, 84.6% in those aged 15-24 years, and 28.6% in adults ≥25 years. Phylogenetic analyses uncovered that A(H3N2) strains were closely related since they segregated in the same cluster, showing both a high mean nucleotide identity (100%), all belonging to the genetic sub-group 3C.2a1b.2a.2, as those mainly circulating into the general population in the same period. CONCLUSIONS: The fact that influenza outbreak strains as well as the community strains were genetically related to the seasonal vaccine strain suggests that if an influenza prevention by vaccination strategy had been implemented, a lower attack rate of A(H3N2) and ILI cases might have been achieved.

Identifying and warning against spatial conflicts of land use from an ecological environment perspective: A case study of the Ili River Valley, China.

Spatial conflicts of land use (SCLU) arise during land-use change, which causes an imbalance of land-use spatial patterns and negatively affects society, the economy, and ecology. Previous research has focused on identifying and measuring SCLU, with less attention on the negative effects. The incorporation of risk assessment methods to evaluate potential conflict risks has been limited. The current study presents methods for measuring SCLU and assessing potential conflict risks from the ecological environment perspective. The spatial comprehensive conflicts index and potential conflict risk index were used to identify and measure the SCLU and to assess and warn against potential conflict risks, respectively, based on a case study in the Ili River Valley in China. The impacts of terrain restriction and land-use change on the SCLU were explored. Results indicate that (1) the SCLU area in the Ili River Valley decreased by 2,608 km2 from 2010 to 2020, compared to the previous decade, the degree of conflict weakened, and the main body of the SCLU gradually shifted northwest. (2) The potential risk areas cover 20,268 km2 in 2020-2030 and are mainly distributed in the "Khorgas City-Huocheng County-Yining City-Yining County" group of towns, as well as along the Ili-Kunes rivers and in the ecological protection zone in the south of the Ili River Valley, which shows the clustering along the city and distribution along the river in the spatial pattern. (3) Topography had a significant impact on the SCLU, and the main types of land-use change in the severe-conflict zone were the reduction of grassland and the expansion of arable and built-up land. For future conflict mitigation in the Ili River Valley, cautioning against urban sprawl and safeguarding land ecological security is critical. This study systematically investigates and analyzes SCLU across three dimensions: theory, methodology, and application to produce a theoretical and practical framework to identify SCLU and assess potential conflict risks.

Impact of air pollutants on influenza-like illness outpatient visits under COVID-19 pandemic in the subcenter of Beijing, China.

This study aimed to explore the association between air pollutants and outpatient visits for influenza-like illnesses (ILI) under the coronavirus disease 2019 (COVID-19) stage in the subcenter of Beijing. The data on ILI in the subcenter of Beijing from January 1, 2018 to December 31, 2020 were obtained from the Beijing Influenza Surveillance Network. A generalized additive Poisson model was applied to examine the associations between the concentrations of air pollutants and daily outpatient visits for ILI when controlling meteorological factors and temporal trend. A total of 171 943 ILI patients were included. In the pre-coronavirus disease 2019 (COVID-19) stage, an increased risk of ILI outpatient visits was associated to a high air quality index (AQI) and the high concentrations of particulate matter less than 2.5 (PM2.5 ), particulate matter 10 (PM10 ), sulphur dioxide (SO2 ), nitrogen dioxide (NO2 ), and carbon monoxide (CO), and a low concentration of ozone (O3 ) on lag0 day and lag1 day, while a higher increased risk of ILI outpatient visits was observed by the air pollutants in the COVID-19 stage on lag0 day. Except for PM10 , the concentrations of other air pollutants on lag1 day were not significantly associated with an increased risk of ILI outpatient visits during the COVID-19 stage. The findings that air pollutants had enhanced immediate effects and diminished lag-effects on the risk of ILI outpatient visits during the COVID-19 pandemic, which is important for the development of public health and environmental governance strategies.

Coupling an individual adaptive-decision model with a SIRV model of influenza vaccination reveals new insights for epidemic control.

Past seasonal influenza epidemics and vaccination experience may affect individuals' decisions on whether to be vaccinated or not, decisions that may be constantly reassessed in relation to recent influenza related experience. To understand the potentially complex interaction between experience and decisions and whether the vaccination rate is likely to reach a critical coverage level or not, we construct an adaptive-decision model. This model is then coupled with an influenza vaccination dynamics (SIRV) model to explore the interaction between individuals' decision-making and an influenza epidemic. Nonlinear least squares estimation is used to obtain the best-fit parameter values in the SIRV model based on data on new influenza-like illness (ILI) cases in Texas. Uncertainty and sensitivity analyses are then carried out to determine the impact of key parameters of the adaptive decision-making model on the ILI epidemic. The results showed that the necessary critical coverage rate of ILI vaccination could not be reached by voluntary vaccination. However, it could be reached in the fourth year if mass media reports improved individuals' memory of past vaccination experience. Individuals' memory of past vaccination experience, the proportion with histories of past vaccinations and the perceived cost of vaccination are important factors determining whether an ILI epidemic can be effectively controlled or not. Therefore, health authorities should guide people to improve their memory of past vaccination experience through media reports, publish timely data on annual vaccination proportions and adjust relevant measures to appropriately reduce vaccination perceived cost, in order to effectively control an ILI epidemic.

Evaluation of pharmacist guided intervention using procalcitonin and respiratory virus testing.

INTRODUCTION: Acute respiratory infections make up a sizable percentage of emergency department (ED) visits and many result in antibiotics being prescribed. Procalcitonin (PCT) has been found to reduce antibiotic use in both outpatient and critical care settings, yet remains underused in the ED. This study aimed to evaluate whether point of care molecular influenza and Respiratory Syncytial Virus (RSV) testing, PCT, and a pharmacist driven educational intervention in aggregate optimizes antibiotic and antiviral prescribing in the ED setting. METHODS: A randomized trial of the Cobas Liat Flu/RSV Assay, procalcitonin, and the use of pharmacist-led education in patients 0-50 years of age being seen in the ED for Influenza Like Illness (ILI) or acute respiratory illness. The study enrolled 200 ED patients between March 2018 and April 2022. RESULTS: There was little difference in antibiotic or antiviral prescribing between the intervention and control groups in this study (39%-32% = 7.0%, 95% CI: -6.2, 20.2, P = 0.30). However, a post-hoc analysis of the use of procalcitonin showed results were used as indicated in the ED (P = 0.001). CONCLUSION: PCT can be used in both adult and pediatric populations to help guide the decision of whether to treat with antibiotics in the ED setting. Pharmacist guided education may not be a driving factor.

Deep-Learning Model for Influenza Prediction From Multisource Heterogeneous Data in a Megacity: Model Development and Evaluation.

BACKGROUND: In megacities, there is an urgent need to establish more sensitive forecasting and early warning methods for acute respiratory infectious diseases. Existing prediction and early warning models for influenza and other acute respiratory infectious diseases have limitations and therefore there is room for improvement. OBJECTIVE: The aim of this study was to explore a new and better-performing deep-learning model to predict influenza trends from multisource heterogeneous data in a megacity. METHODS: We collected multisource heterogeneous data from the 26th week of 2012 to the 25th week of 2019, including influenza-like illness (ILI) cases and virological surveillance, data of climate and demography, and search engines data. To avoid collinearity, we selected the best predictor according to the weight and correlation of each factor. We established a new multiattention-long short-term memory (LSTM) deep-learning model (MAL model), which was used to predict the percentage of ILI (ILI%) cases and the product of ILI% and the influenza-positive rate (ILI%×positive%), respectively. We also combined the data in different forms and added several machine-learning and deep-learning models commonly used in the past to predict influenza trends for comparison. The R2 value, explained variance scores, mean absolute error, and mean square error were used to evaluate the quality of the models. RESULTS: The highest correlation coefficients were found for the Baidu search data for ILI% and for air quality for ILI%×positive%. We first used the MAL model to calculate the ILI%, and then combined ILI% with climate, demographic, and Baidu data in different forms. The ILI%+climate+demography+Baidu model had the best prediction effect, with the explained variance score reaching 0.78, R2 reaching 0.76, mean absolute error of 0.08, and mean squared error of 0.01. Similarly, we used the MAL model to calculate the ILI%×positive% and combined this prediction with different data forms. The ILI%×positive%+climate+demography+Baidu model had the best prediction effect, with an explained variance score reaching 0.74, R2 reaching 0.70, mean absolute error of 0.02, and mean squared error of 0.02. Comparisons with random forest, extreme gradient boosting, LSTM, and gated current unit models showed that the MAL model had the best prediction effect. CONCLUSIONS: The newly established MAL model outperformed existing models. Natural factors and search engine query data were more helpful in forecasting ILI patterns in megacities. With more timely and effective prediction of influenza and other respiratory infectious diseases and the epidemic intensity, early and better preparedness can be achieved to reduce the health damage to the population.

World Health Organization Influenza-Like Illness Underestimates the Burden of Respiratory Syncytial Virus Infection in Community-Dwelling Older Adults.

BACKGROUND: Respiratory syncytial virus (RSV) surveillance is heavily dependent on the influenza-like illness (ILI) case definition from the World Health Organization (WHO). Because ILI includes fever in its syndromic case definition, its ability to accurately identify acute respiratory tract infections (ARTI) caused by RSV in older adults is uncertain. METHODS: The accuracy of the WHO ILI and a modified ILI (requiring only self-reported fever) case definitions in identifying patients with PCR-confirmed RSV-ARTI was evaluated in community-dwelling older adults (≥60 years) from the prospective European RESCEU cohort study. RESULTS: Among 1040 participants, 750 ARTI episodes were analyzed including 36 confirmed RSV-ARTI. Due to a general lack of fever, sensitivity for RSV-ARTI was 33% for modified ILI and 11% for ILI. The area under the curve for both ILI definitions was 0.52 indicating poor discrimination for RSV. RSV-ARTI could not be distinguished from all other ARTI based on clinical symptoms. CONCLUSIONS: The use of ILI underestimated the occurrence of RSV-ARTI in community-dwelling older adults up to 9-fold (11% sensitivity). Because worldwide RSV surveillance depends largely on ILI, there is an urgent need for a better approach to measure the occurrence of RSV disease and the impact of future RSV vaccine introduction. Clinical Trials Registration. NCT03621930.

A randomized controlled pilot trial of anakinra for hemodialysis inflammation.

Chronic inflammation is highly prevalent among patients receiving maintenance hemodialysis and is associated with morbidity and mortality. Inhibiting inflammation with anti-cytokine therapy has been proposed but not well studied in this population. Therefore, we conducted the ACTION trial, a pilot, multicenter, randomized, placebo-controlled trial of an IL-1 receptor antagonist, anakinra, to evaluate safety, tolerability, and feasibility, and explore efficacy. Eighty hemodialysis patients with plasma concentrations of high sensitivity C-reactive protein (hsCRP) 2 mg/L and above were randomized 1:1 to placebo or anakinra 100 mg, three times per week via the hemodialysis circuit for 24 weeks, with an additional 24 weeks of post-treatment safety monitoring. Efficacy outcomes included changes in hsCRP (primary), cytokines, and patient-reported outcomes. Rates of serious adverse events and deaths were similar with anakinra and placebo (serious adverse events: 2.71 vs 2.74 events/patient-year; deaths: 0.12 vs 0.22 events/patient-year). The rate of adverse events of interest (including infections and cytopenias) was significantly lower with anakinra than placebo (0.48 vs 1.40 events/patient-year). Feasibility was demonstrated by attaining the enrollment target, a retention rate of 80%, and administration of 72% of doses. The median decrease in hsCRP from baseline to Week 24 was 41% in the anakinra group and 6% in the placebo group, a between-group difference that was not statistically significant. For IL-6, the median decreases were significant: 25% and 0% in the anakinra and placebo groups, respectively. An effect of anakinra on patient-reported outcomes was not evident. Thus, anakinra was well tolerated and did not increase infections or cytopenias. The promising safety data and potential efficacy on CRP and IL-6 provide support for conducting definitive trials of IL-1 inhibition to improve outcomes in hemodialysis patients.

Distinguishing viruses responsible for influenza-like illness.

The many respiratory viruses that cause influenza-like illness (ILI) are reported and tracked as one entity, defined by the CDC as a group of symptoms that include a fever of 100 degrees Fahrenheit, a cough, and/or a sore throat. In the United States alone, ILI impacts 9-49 million people every year. While tracking ILI as a single clinical syndrome is informative in many respects, the underlying viruses differ in parameters and outbreak properties. Most existing models treat either a single respiratory virus or ILI as a whole. However, there is a need for models capable of comparing several individual viruses that cause respiratory illness, including ILI. To address this need, here we present a flexible model and simulations of epidemics for influenza, RSV, rhinovirus, seasonal coronavirus, adenovirus, and SARS/MERS, parameterized by a systematic literature review and accompanied by a global sensitivity analysis. We find that for these biological causes of ILI, their parameter values, timing, prevalence, and proportional contributions differ substantially. These results demonstrate that distinguishing the viruses that cause ILI will be an important aspect of future work on diagnostics, mitigation, modeling, and preparation for future pandemics.

Distinct Composition and Assembly Processes of Bacterial Communities in a River from the Arid Area: Ecotypes or Habitat Types?

The composition, function, and assembly mechanism of the bacterial community are the focus of microbial ecology. Unsupervised machine learning may be a better way to understand the characteristics of bacterial metacommunities compared to the empirical habitat types. In this study, the composition, potential function, and assembly mechanism of the bacterial community in the arid river were analysed. The Dirichlet multinomial mixture method recognised four ecotypes across the three habitats (biofilm, water, and sediment). The bacterial communities in water are more sensitive to human activities. Bacterial diversity and richness in water decreased as the intensity of human activities increased from the region of water II to water I. Significant differences in the composition and potential function profile of bacterial communities between water ecotypes were also observed, such as higher relative abundance in the taxonomic composition of Firmicutes and potential function of plastic degradation in water I than those in water II. Habitat filtering may play a more critical role in the assembly of bacterial communities in the river biofilm, while stochastic processes dominate the assembly process of bacterial communities in water and sediment. In water I, salinity and mean annual precipitation were the main drivers shaping the biogeography of taxonomic structure, while mean annual temperature, total organic carbon, and ammonium nitrogen were the main environmental factors influencing the taxonomic structure in water II. These results would provide conceptual frameworks about choosing habitat types or ecotypes for the research of microbial communities among different niches in the aquatic environment.

Sentinel surveillance for influenza A viruses in Lahore District Pakistan in flu season 2015-2016.

BACKGROUND: Influenza A virus (IAV) remains an important global public health threat with limited epidemiological information available from low-and-middle-income countries. The major objective of this study was to describe the proportions, temporal and spatial distribution, and demographic and clinical characteristics of IAV positive patients with influenza like illness (ILI) and severe acute respiratory illness (SARI) in Lahore, Pakistan. METHODS: Prospective surveillance was established in a sentinel hospital from October 2015 to May 2016. All eligible outpatients and inpatients with ILI or SARI were enrolled in the study. Nasal and/or throat swabs were collected along with clinico-epidemiological data. Samples were tested by real-time RT-PCR (rRT-PCR) to identify IAV and subtype. The descriptive analysis of data was done in R software. RESULTS: Out of 311 enrolled patients, 284 (91.3%) were ILI and 27 (8.7%) were SARI cases. A distinct peak of ILI and SARI activity was observed in February. Fifty individuals (16%) were positive for IAV with peak positivity observed in December. Of 50 IAV, 15 were seasonal H3N2, 14 were H1N1pdm09 and 21 were unable to be typed. The majority of IAV positive cases (98%) presented with current or history of fever, 88% reported cough and 82% reported sore throat. The most common comorbidities in IAV positive cases were hepatitis C (4%), obesity (4%) and tuberculosis (6%). The highest incidence of patients reporting to the hospital was seen three days post symptoms onset (66/311) with 14 of these (14/66) positive for IAV. CONCLUSION: Distinct trends of ILI, SARI and IAV positive cases were observed which can be used to inform public health interventions (vaccinations, hand and respiratory hygiene) at appropriate times among high-risk groups. We suggest sampling from both ILI and SARI patients in routine surveillance as recommended by WHO.

Metagenomic binning and assembled genome analysis revealed the distinct composition of resistome and mobilome in the Ili River.

Rivers play an important role in receiving and transporting the resistome among different environmental compartments. However, the difference in resistome and mobilome between the water and sediment and their underlying mechanisms were still poorly understood. In this study, the Ili River, an important water source in the arid area of Central Asia, was selected as the studied target. The comprehensive profile of resistome and mobilome and their host in water and sediment were studied based on metagenomic binning and assembled genome (MAG) analysis. The relative abundance of resistome and mobilome in sediment were 28.0 - 67.8 × /Gb and 46.5 - 121.1 × /Gb, respectively, which were significantly higher than those in water (23.1 - 52.8 ×/Gb and 25.3 - 67.7 ×/Gb). Multidrug and macrolides-lincosamides-streptogramin (MLS) resistance genes were the main ARG types in both water and sediment from relative abundance. Transposases dominated the relative abundance of mobilome, followed by insert elements and integrases. Strong correlations were found between the relative abundance of resistome and mobilome (r > 0.6 and p < 0.01) in both water and sediment, indicating the mobilome played an important role in the propagation of resistome in the Ili River. The main hosts for multidrug resistance genes via MAG analysis differed in water (Alphaproteobacteria and Gammaproteobacteria) and sediment (Gammaproteobacteria). Distinct compositions of resistome and mobilome existed between water and sediment in the Ili River. Specificity-occupancy analysis of the differential resistome and mobilome showed that occurrence frequencies and habitat selections of the differential ARGs shaped the resistome of water and sediment. In contrast, habitat was the main driver that shaped the mobilome in the Ili River.

Rapidly adapting primary care sentinel surveillance across seven countries in Europe for COVID-19 in the first half of 2020: strengths, challenges, and lessons learned.

As the COVID-19 pandemic began in early 2020, primary care influenza sentinel surveillance networks within the Influenza - Monitoring Vaccine Effectiveness in Europe (I-MOVE) consortium rapidly adapted to COVID-19 surveillance. This study maps system adaptations and lessons learned about aligning influenza and COVID-19 surveillance following ECDC / WHO/Europe recommendations and preparing for other diseases possibly emerging in the future. Using a qualitative approach, we describe the adaptations of seven sentinel sites in five European Union countries and the United Kingdom during the first pandemic phase (March-September 2020). Adaptations to sentinel systems were substantial (2/7 sites), moderate (2/7) or minor (3/7 sites). Most adaptations encompassed patient referral and sample collection pathways, laboratory testing and data collection. Strengths included established networks of primary care providers, highly qualified testing laboratories and stakeholder commitments. One challenge was the decreasing number of samples due to altered patient pathways. Lessons learned included flexibility establishing new routines and new laboratory testing. To enable simultaneous sentinel surveillance of influenza and COVID-19, experiences of the sentinel sites and testing infrastructure should be considered. The contradicting aims of rapid case finding and contact tracing, which are needed for control during a pandemic and regular surveillance, should be carefully balanced.

Hydrochemical and stable isotopic spatiotemporal variation characteristics and their environmental significance in the Kashi River Mountain Area of Ili, Xinjiang, China.

The supply sources of a water resource in arid area can be determined through analysis of the hydrochemical and stable isotopic characteristics of runoff in an alpine glacier river basin. Using mathematical statistical analyses, Piper diagrams, and Gibbs diagrams, this study analyzed the spatiotemporal variations of the hydrochemistry and the stable hydrogen and oxygen isotopes of the Kashi River in Ili (Xinjiang, China) to investigate their characteristics and environmental importance. Runoff samples were collected in the mountainous area of the Kashi River from December 2017 to November 2018. Results showed that the runoff water type of the Kashi River is Ca2+-HCO3- and the spring water type is Ca2+-HCO3--SO42-. The main factors controlling the hydrochemical composition of runoff were rock weathering. Under the influence of supply sources and water conservancy facilities, the hydrochemistry and stable isotopic characteristics of the runoff showed evident spatiotemporal variation. The hydrochemical and stable isotopic values of runoff in winter and spring were lower and more consistent in comparison with those in summer and autumn. The characteristics of the spatial variations of hydrochemical and stable isotopic values in runoff differed with elevation. The supply sources in the Kashi River in different seasons were determined by analyzing the temporal variation characteristics of the hydrochemistry and stable isotopes of runoff.

Intrest in influenza and influenza like illnesses in Poland 2016-2021 based on Google Trend's data analysis.

INTRODUCTION: Influenza and Influenza Like Illness (ILI) are viral diseases that show seasonality in incidence. Influenza can be effectively prevented with immunization. Vaccination is recommended for risk groups, especially for the elderly, immunocompromised population, with chronic diseases and pregnant women. AIM OF THE STUDY: The purpose of the study was to assess interest in influenza and influenza vaccine in Poland by users of the Google search engine over the last five years using the Google Trends (GT) tool. MATERIAL AND METHODS: Data collected from Google Trends (GT) over a five-year period beginning on 11/9/2016 was analyzed. The time trend analysis focused at four phrases in Polish "grypa", "grypa objawy", "szczepionka przeciw grypie" and "szczepionka na grype", which can be translated into English, as; "flu", "flu symptoms", "flu vaccine", and a colloquial phrase meaning "against the flu vaccine". RESULTS: Before the outbreak of the COVID-19 pandemic, a seasonal increase in inquiries is evident, mimicking the increase in influenza and ILI cases or an increase in interest in the topic during the season in which prophylaxis is carried out using available vaccines. During the COVID-19 pandemic, the seasonal interest in the above topic was disrupted. CONCLUSIONS: During the COVID-19 pandemic, there were changes in the search terms in Polish. This included the timing and amount of queries related to influenza-themed expressions. During the COVID-19 pandemic, there was an increase in influenza and ILI queries during the period of increased incidence of COVID-19.

Human parvovirus 4: An emerging etiological agent in cases presenting with influenza like illness.

This study was planned to study the association of parvovirus 4 (PARV4) with Influenza-like illness (ILI). A total of 1111 patients with a clinical diagnosis of ILI and 220 healthy controls were tested for Influenza A/HINI/and H3N2, Influenza B, and PARV4. Further sequencing was done to analyze the genotype distribution of parvovirus 4. Influenza A/HINI, A/H3N2, and B were detected in 334 (30.06%), 9 (0.81%), and 10 (0.9%) cases respectively. PARV4 was detected in 135 (12.15%) cases and one healthy control. Parvovirus 4 was significantly higher in cases as compared to controls (relative risk, 30.77%; p < .0006). Sequencing of 20 isolates suggests the dominance of genotype 2 in our region.

Estimation of influenza-attributable burden in primary care from season 2014/2015 to 2018/2019, France.

Influenza viruses cause seasonal epidemics whose intensity varies according to the circulating virus type and subtype. We aim to estimate influenza-like illness (ILI) incidence attributable to influenza viruses in France from October 2014 to May 2019. Physicians participating in the French Sentinelles network reported the number of patients with ILI seen in consultation and performed nasopharyngeal swabs in a sample of these patients. The swabs were tested by RT-PCR for the presence of influenza viruses. These clinical and virological data were combined to estimate ILI incidence attributable to influenza viruses by subtypes and age groups. Influenza incidence rates over seasons ranged from 1.9 (95% CI, 1.9; 2.0) to 3.4% (95% CI, 3.2; 3.6) of the population. Each season, more than half of ILI cases were attributable to influenza. Children under 15 years were the most affected, with influenza incidence rates ranging from 3.0 (95% CI, 2.8;3.3) to 5.7% (95% CI, 5.3;6.1). Co-circulation of several (sub)types of influenza viruses was observed each year, except in 2016/2017 where A(H3N2) viruses accounted for 98.0% of the influenza cases. Weekly ILI incidences attributable to each influenza virus (sub)type were mostly synchronized with ILI incidence, except in 2014/2015 and 2017/2018, where incidence attributable to type B viruses peaked few weeks later. The burden of medically attended influenza among patients with ILI is significant in France, varying considerably across years and age groups. These results show the importance of influenza surveillance in primary care combining clinical and virological data.