Mortality in Catalonia during the summer of 2022 and its relation with high temperatures and COVID-19 cases.

Purpose: To analyse the association between the mortality during the summer 2022 and either high temperatures or the COVID-19 wave with data from the Catalan Health Care System (7.8 million people). Methods: We performed a retrospective study using publicly available data of meteorological variables, influenza-like illness (ILI) cases (including COVID-19) and deaths. The study comprises the summer months of the years 2021 and 2022. To compare the curves of mortality, ILI and temperature we calculated the z-score of each series. We assessed the observed lag between curves using the cross-correlation function. Finally, we calculated the correlation between the z-scores using the Pearson correlation coefficient (R2). Results: During the study period, 33,967 deaths were reported in Catalonia (16,416 in the summer of 2021 and 17,551 in the summer of 2022). In 2022, the observed lag and the correlation between the z-scores of temperature and all-cause deaths was 3 days and R2 = 0.86, while between ILI and all-cause deaths was 22 days and R2 = 0.21. This high correlation between temperature and deaths increased up to 0.91 when we excluded those deaths reported as COVID-19 deaths, while the correlation between ILI and non-COVID-19 deaths decreased to -0.19. No correlation was observed between non-COVID deaths and temperature or ILI cases in 2021. Conclusion: Our study suggests that the main cause of the increase in deaths during summer 2022 in Catalonia was the high temperatures and its duration. The contribution of the COVID-19 seems to be limited.

Evaluation Of Random Forest Model on Influenza Like Illness (ILI) Surveillance Based on Patients Attending Fever OPD During COVID-19 Pandemic at Tertiary Care Hospital, In Raigad District, Maharashtra, India

Introduction: The machine learning and artificial intelligence tools, party and random forest can be used to evaluate surveillance data for better outcomes. The primary objective of the study was to evaluate the utility and reliability of machine learning and artificial intelligence model primary data for the Influenzas Like illness (ILI) Surveillance of patients attending fever OPD in a tertiary care hospital during covid 19 pandemic. The secondary objective was to estimate model statistics to measure the effect of parameters. Methodology: This is a secondary data analysis study based on surveillance data in the tertiary care hospital attached to medical college. The data of 3723 cases was collected by Surveillance team for Influenzas Like Illness (ILI) under Department of Community Medicine in Fever OPD during covid pandemic from 23 March 2020 to 30 June 2020. Data consisted (11) variables. Data was analysed using R Software (4.2.2). Machine learning (ML) and Artificial Intelligence tool party and random forest were applied. Result(s): The random forest model performed better than Party model with model accuracy of 0.9557, AUC of random forest model were 87.4% (sensitivity 0.9533, specificity 0.9685), 89.7% (sensitivity 0.9059, specificity 0.9957) and 88.3% (Sensitivity 0.965, Specificity 0.9527) for confirmed, probable and suspected with different cut-offs. The model found Severity of Patient (Mild, Moderate, Severe), the day of Fever OPD Visit, nature of illness (is asymptomatic?) and age of patient as the most significant factors in decreasing order by mean decrease in Accuracy while the Severity of Patient (Mild, Moderate, Severe), the day of Fever OPD Visit, age of patient and number of symptomatic Complaint (NOC) were found the most significant factors in decreasing order by mean decrease in Gini to predict Covid-19 Test Results. Conclusion(s): The party algorithm was consistent for train and test dataset while for the random forest results were good on train dataset while same model had seen difficulty in prediction class for the test dataset. Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.

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.

Trends in Influenza Infections in Three States of India from 2015-2021: Has There Been a Change during COVID-19 Pandemic?

The COVID-19 pandemic and public health response to the pandemic has caused huge setbacks in the management of other infectious diseases. In the present study, we aimed to (i) assess the trends in numbers of samples from patients with influenza-like illness and severe acute respiratory syndrome tested for influenza and the number and proportion of cases detected from 2015-2021 and (ii) examine if there were changes during the COVID-19 period (2020-2021) compared to the pre-COVID-19 period (2015-2019) in three states of India. The median (IQR) number of samples tested per month during the pre-COVID-19 period was 653 (395-1245), compared to 27 (11-98) during the COVID-19 period (p value < 0.001). The median (IQR) number of influenza cases detected per month during the pre-COVID-19 period was 190 (113-372), compared to 29 (27-30) during the COVID-19 period (p value < 0.001). Interrupted time series analysis (adjusting for seasonality and testing charges) confirmed a significant reduction in the total number of samples tested and influenza cases detected during the COVID-19 period. However, there was no change in the influenza positivity rate between pre-COVID-19 (29%) and COVID-19 (30%) period. These findings suggest that COVID-19-related disruptions, poor health-seeking behavior, and overburdened health systems might have led to a reduction in reported influenza cases rather than a true reduction in disease transmission.

AYUSH 64, a polyherbal Ayurvedic formulation in Influenza-like illness - Results of a pilot study.

BACKGROUND: Influenza-like Illness (ILI) refers to a wide range of viral infections with an important cause of morbidity and mortality worldwide. The global incidence of ILI is estimated at 5-10% in adults and 20-30% in children. In India influenza accounts for 20-42% of monthly acute medical illness hospitalizations during the peak rainy season. AYUSH-64, a poly-herbal drug, is in practice for 40 years for various clinical conditions like fevers, microfilaremia, and inflammatory conditions. OBJECTIVE: A pilot study was conducted to evaluate the safety and efficacy of Ayurvedic formulation, AYUSH-64 in clinically diagnosed ILI for accelerating the recovery. MATERIAL AND METHODS: A prospective, open-label, nonrandomized, single group, single-center pilot clinical study with pre-test and post-test design was conducted at Raja Ramdeo Anandilal Podar Central Ayurveda Research Institute for Cancer, Mumbai, an institute of Central Council for Research in Ayurvedic Sciences (CCRAS) between June 2018 and July 2019. A total of 38 participants of clinically diagnosed ILI (18-65 years) were studied with an one-week intervention of 'AYUSH 64' in a dose of 3 gm/day and three weeks post-treatment observation period. Assessment of parameters viz. improvement in the symptoms of ILI, frequency of usage of acetaminophen, antihistaminic and cough syrup, hematology, liver function and kidney function tests along with incidence of secondary complications, and time to return to a normal routine was done. RESULTS: One-week intervention of AYUSH 64 helped to recover from ILI symptoms with reduced frequency of usage of acetaminophen and antihistaminic. The intervention was safe on hematology and biochemical parameters. No serious adverse effects were observed during the study. CONCLUSION: AYUSH 64 along-with standard care in ILI is safe and efficacious and this may be used in other viral infections with pyrexia as add-on to standard care for early recovery and better outcome.

FluWatchers: Evaluation of a crowdsourced influenza-like illness surveillance application for Canadian influenza seasons 2015-2016 to 2018-2019.

BACKGROUND: Sentinel influenza-like illness (ILI) surveillance is an essential component of a comprehensive influenza surveillance program. Community-based ILI surveillance systems that rely solely on sentinel healthcare practices omit important segments of the population, including those who do not seek medical care. Participatory surveillance, which relies on community participation in surveillance, may address some limitations of traditional ILI systems. OBJECTIVE: We aimed to evaluate FluWatchers, a crowdsourced ILI application developed to complement and complete ILI surveillance in Canada. METHODS: Using established frameworks for surveillance evaluations, we assessed the acceptability, reliability, accuracy and usefulness of the FluWatchers system 2015-2016, through 2018-2019. Evaluation indicators were compared against national surveillance indicators of ILI and of laboratory confirmed respiratory virus infections. RESULTS: The acceptability of FluWatchers was demonstrated by growth of 50%-100% in season-over-season participation, and a consistent season-over-season retention of 80%. Reliability was greater for FluWatchers than for our traditional ILI system, although both systems had week-over-week fluctuations in the number of participants responding. FluWatchers' ILI rates had moderate correlation with weekly influenza laboratory detection rates and other winter seasonal respiratory virus detections including respiratory syncytial virus and seasonal coronaviruses. Finally, FluWatchers has demonstrated its usefulness as a source of core FluWatch surveillance information and has the potential to fill data gaps in current programs for influenza surveillance and control. CONCLUSION: FluWatchers is an example of an innovative digital participatory surveillance program that was created to address limitations of traditional ILI surveillance in Canada. It fulfills the surveillance system evaluation criteria of acceptability, reliability, accuracy and usefulness.

Competing Bioaerosols May Influence the Seasonality of Influenza-Like Illnesses, including COVID-19. The Chicago Experience.

Data from Chicago confirm the end of flu season coincides with the beginning of pollen season. More importantly, the end of flu season also coincides with onset of seasonal aerosolization of mold spores. Overall, the data suggest bioaerosols, especially mold spores, compete with viruses for a shared receptor, with the periodicity of influenza-like illnesses, including COVID-19, a consequence of seasonal factors that influence aerosolization of competing species.

Relationship between temporal anomalies in PM2.5 concentrations and reported influenza/influenza-like illness activity.

A small number of studies suggest atmospheric particulate matter with diameters 2.5 micron and smaller (PM2.5) may possibly play a role in the transmission of influenza and influenza-like illness (ILI) symptoms. Those studies were predominantly conducted under moderately to highly polluted outdoor atmospheres. The purpose of this study was to extend the data set to include a less polluted atmospheric environment. A relationship between PM2.5 and ILI activity extended to include lightly to moderately polluted atmospheres could imply a more complicated mechanism than that suggested by existing studies. We obtained concurrent PM2.5 mass concentration data, meteorological data and reported Influenza and influenza-like illness (ILI) activity for the light to moderately polluted atmospheres over the Tucson, AZ region. We found no relation between PM2.5 mass concentration and ILI activity. There was an expected relation between ILI, activity, temperature, and relative humidity. There was a possible relation between PM2.5 mass concentration anomalies and ILI activity. These results might be due to the small dataset size and to the technological limitations of the PM measurements. Further study is recommended since it would improve the understanding of ILI transmission and thereby improve ILI activity/outbreak forecasts and transmission model accuracies.

SARS-CoV-2-Specific Antibody Detection for Seroepidemiology: A Multiplex Analysis Approach Accounting for Accurate Seroprevalence.

BACKGROUND: The COVID-19 pandemic necessitates better understanding of the kinetics of antibody production induced by infection with SARS-CoV-2. We aimed to develop a high-throughput multiplex assay to detect antibodies to SARS-CoV-2 to assess immunity to the virus in the general population. METHODS: Spike protein subunits S1 and receptor binding domain, and nucleoprotein were coupled to microspheres. Sera collected before emergence of SARS-CoV-2 (n = 224) and of non-SARS-CoV-2 influenza-like illness (n = 184), and laboratory-confirmed cases of SARS-CoV-2 infection (n = 115) with various severities of COVID-19 were tested for SARS-CoV-2-specific IgG concentrations. RESULTS: Our assay discriminated SARS-CoV-2-induced antibodies and those induced by other viruses. The assay specificity was 95.1%-99.0% with sensitivity 83.6%-95.7%. By merging the test results for all 3 antigens a specificity of 100% was achieved with a sensitivity of at least 90%. Hospitalized COVID-19 patients developed higher IgG concentrations and the rate of IgG production increased faster compared to nonhospitalized cases. CONCLUSIONS: The bead-based serological assay for quantitation of SARS-CoV-2-specific antibodies proved to be robust and can be conducted in many laboratories. We demonstrated that testing of antibodies against multiple antigens increases sensitivity and specificity compared to single-antigen-specific IgG determination.