Exploring the Lagged Correlation Between Baidu Index and Influenza-Like Illness - China, 2014-2019.

Introduction: This study investigated the lagged correlation between Baidu Index for influenza-related keywords and influenza-like illness percentage (ILI%) across regions in China. The aim is to establish a scientific foundation for utilizing Baidu Index as an early warning tool for influenza-like illness epidemics. Methods: In this study, data on ILI% and Baidu Index were collected from 30 provincial-level administrative divisions (PLADs) spanning April 2014 to March 2019. The Baidu Index was categorized into Overall Index, Ordinary Index, Prevention Index, Symptom Index, and Treatment Index based on search query themes. The lagged correlation between the Baidu Index and ILI% was examined through the cross-correlation function (CCF) method. Results: Correlating the Baidu Overall Index of 30 PLADs with ILI% revealed CCF values ranging from 0.46 to 0.86, with a median lag of 0.5 days. Subcategory analysis indicated that the Prevention Index and Symptom Index exhibited quicker responses to ILI%, with median lags of -9 and -0.5 days, respectively, compared to 0 and 3 days for the Ordinary and Treatment Indexes. The median lag days between the Baidu Index and the ILI% were earlier in the northern PLADs compared to the southern PLADs. Discussion: The Prevention and Symptom Indexes show promising predictive capabilities for influenza-like illness epidemics.

Laboratory capacity expansion: lessons from establishing molecular testing in regional referral laboratories in Ethiopia.

Respiratory viruses contribute to high morbidity and mortality in Africa. In 2020, the Ohio State University's Global One Health Initiative, in collaboration with the Ethiopian Public Health Institute and the US Centers for Disease Control and Prevention, took action to strengthen Ethiopia's existing respiratory virus surveillance system through decentralization of laboratory testing and scale-up of national and regional capacity for detecting respiratory viruses. In August 2022, four regional laboratories were established, thereby raising the number of reference laboratories conducting respiratory virus surveillance to five. This article highlights lessons learned during implementation and outlines processes undertaken for laboratory scale-up and decentralization.

Outbreak of influenza and SARS-CoV-2 at the Armed Forces of the Philippines Health Service Education and Training Center, September 25-October 10, 2023.

In the last week of September 2023, a surge of influenza-like illness was observed among students of the Armed Forces of the Philippines (AFP) Health Service Education and Training Center, where 48 (27 males and 21 females; age in years: mean 33, range 27-41) of 247 military students at the Center presented with respiratory symptoms. Between September 25 and October 10, 2023, all 48 symptomatic students were evaluated with real-time reverse transcription polymerase chain reaction and sequencing for both influenza and SARS-CoV-2. Thirteen (27%) students were found positive for influenza A/H3 only, 6 (13%) for SARS-CoV-2 only, and 4 (8%) were co-infected with influenza A/H3 and SARS-CoV-2. Seventeen influenza A/ H3N2 viruses belonged to the same clade, 3C.2a1b.2a.2a.3a, and 4 SARSCoV-2 sequences belonged to the JE1.1 lineage, indicating a common source outbreak for both. The influenza A/H3N2 circulating virus belonged to a different clade than the vaccine strain for 2023 (3C.2a1b.2a.2a). Only 4 students had received the influenza vaccine for 2023. In response, the AFP Surgeon General issued a memorandum to all military health institutions on October 19, 2023 that mandated influenza vaccination as a prerequisite for enrollment of students at all education and training centers, along with implementation of non-pharmaceutical interventions and early notification and testing of students exhibiting influenza-like-illness.

Electronic Health Record-Based Algorithm for Monitoring Respiratory Virus-Like Illness.

Viral respiratory illness surveillance has traditionally focused on single pathogens (e.g., influenza) and required fever to identify influenza-like illness (ILI). We developed an automated system applying both laboratory test and syndrome criteria to electronic health records from 3 practice groups in Massachusetts, USA, to monitor trends in respiratory viral-like illness (RAVIOLI) across multiple pathogens. We identified RAVIOLI syndrome using diagnosis codes associated with respiratory viral testing or positive respiratory viral assays or fever. After retrospectively applying RAVIOLI criteria to electronic health records, we observed annual winter peaks during 2015-2019, predominantly caused by influenza, followed by cyclic peaks corresponding to SARS-CoV-2 surges during 2020-2024, spikes in RSV in mid-2021 and late 2022, and recrudescent influenza in late 2022 and 2023. RAVIOLI rates were higher and fluctuations more pronounced compared with traditional ILI surveillance. RAVIOLI broadens the scope, granularity, sensitivity, and specificity of respiratory viral illness surveillance compared with traditional ILI surveillance.

Air Pollution and Influenza: A Systematic Review and Meta-Analysis.

Background: Influenza is the first infectious disease that implements global monitoring and is one of the major public health issues in the world. Air pollutants have become an important global public health issue, in recent years, and much epidemiological and clinical evidence has shown that air pollutants are associated with respiratory diseases. Methods: We comprehensively searched articles published up to 15 November 2022 in PubMed, Web of Science, China National Knowledge Infrastructure (CNKI), Database of Chinese sci-tech periodicals, and Wanfang Database. The search strategies were based on keyword combinations related to influenza and air pollutants. The air pollutants included particulate matter (PM2.5, PM10), nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), and ozone (O3). Meta-analysis was performed using the R programming language (R4.2.1). Results: A total of 2926 records were identified and 1220 duplicates were excluded. Finally, 19 studies were included in the meta-analysis according to inclusion and exclusion criteria. We observed a significant association between partial air pollutants (PM2.5, NO2, PM10 and SO2) and the incidence risk of influenza. The RRs were 1.0221 (95% CI: 1.0093~1.0352), 1.0395 (95% CI: 1.0131~1.0666), 1.007 (95% CI: 1.0009~1.0132), and 1.0352 (95% CI. 1.0076~1.0635), respectively. However, there was no significant relationship between CO and O3 exposure and influenza, and the RRs were 1.2272 (95% CI: 0.9253~1.6275) and 1.0045 (95% CI: 0.9930~1.0160), respectively. Conclusion: Exposure to PM2.5, NO2, PM10, and SO2 was significantly associated with influenza, which may be risk factors for influenza. The association of CO and O3 with influenza needs further investigation.

Clinical profile of children with influenza like illness during pre-monsoon at coastal Karaikal, Puducherry, India.

Influenza infections in developing countries are under reported and WHO estimates that nearly 99% of influenza deaths worldwide occur in children under-five years of age in Asian and African countries. Consequently, this study aims to analyze the use of clinical profile and easily available laboratory parameters to aid identification of the possible viral etiology in the setting of pre-monsoon ILI. A cross-sectional study was carried out for three months among children with ILI attending fever clinic of a tertiary care hospital in Karaikal, South India. In the study population the prevalence of ILI was highest in the age group four to five years followed by school aged children. Adolescents were affected the least. Influenza B was most common virus causing ILI in this region, followed by covid-19 infection. Laboratory parameters depicted a significantly high ESR in COVID-19 infected ILI children. They also exhibited leucopenia and normal platelet counts. Clinical symptoms and laboratory parameters which are easily available and cheaper can be used in resource poor settings of healthcare to identify possible influenza and COVID-19 infected children amongst cases presenting with ILI.

Predicting influenza-like illness trends based on sentinel surveillance data in China from 2011 to 2019: A modelling and comparative study1.

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.

Effectiveness of cocoon strategy vaccination on prevention of influenza-like illness in young infants.

During the initial half-year of their existence, infants cannot receive the influenza vaccine, yet they face the greatest susceptibility to severe influenza complications. In this study, we seek to determine whether influenza vaccination of maternal and household contacts is associated with a reduced risk of influenza-like illness (ILI) and severe acute respiratory infection (SARI) in infants. This work was prospectively conducted during the influenza season. A total of 206 infants were included in this study. The percentage of infants with only the mother vaccinated is 12.6% (n:26), and the percent of infants with all household contacts vaccinated is 16% (n:33). Among the infants with only the mother vaccinated, the effectiveness of influenza vaccine is estimated as 35.3% for ILI and 41.3% for SARI. Among infants with all household contacts vaccinated, the effectiveness is estimated as 48.9% for ILI and 76.9% for SARI. Based on the results of multivariate logistic regression analysis, all-household vaccination is a protective factor against SARI (OR: 0.07 95% CI [0.01-0.56]), household size (OR: 1.75, 95% CI [1.24-2.48]) and presence of secondhand smoke (OR: 2.2, 95% CI [1.12-4.45]) significant risk factors for SARI in infants. The mother alone being vaccinated is not a statistically significant protective factor against ILI (OR: 0.46, 95% CI [0.19-1.18]) or SARI (OR: 0.3, 95% CI [0.11-1.21]). Along with the obtained results and analysis, this study provides clear evidence that influenza vaccination of all household contacts of infants aged 0-6 months is significantly associated with protecting infants from both ILI and SARI.

Effect of COVID-19 pandemic on influenza; observation of a tertiary level virology laboratory.

The lockdown enforced amid the COVID-19 pandemic has affected the occurrence and trends of various respiratory virus infections, with a particular focus on influenza. Our study seeks to analyze the repercussions of the COVID-19 pandemic on the positivity of the influenza virus throughout a 4-year span, encompassing both the pre-COVID-19 era (2018 and 2019) and the COVID-19 period (2020 and 2021). Data collected from patients clinically diagnosed with Influenza-like Illness and Severe Acute Respiratory Illness (SARI) from January 2018 to December 2021 for influenza virus detection were acquired and analyzed through multiplex RT-qPCR. The statistical analysis was conducted using SPSS (Statistical Package for Social Sciences) Version 21.0 Software. A total of 4464 samples were tested over 4 years (2018-2021), with 3201 samples from the pre-COVID era and 1263 samples from the COVID era. Influenza A positivity dropped from 17.7 to 9.57% and Influenza B positivity decreased from 3.74 to 2.61%. Subtyping revealed changes in prevalence for both viruses. Seasonal variations showed more pronounced peaks in the pre-COVID-19 era with reduced activity during lockdown. Influenza A saw a resurgence in August 2021. Throughout the COVID-19 pandemic (2020-2021) SARI cases did not decrease. The positivity rate for Influenza A slightly rose to 7.79% from 4.23% in the COVID period (2020-2021). This increase correlates with heightened hospitalization rates during the pandemic, sparking concerns of potential coinfection with coronavirus and Influenza A. The notable drop in influenza cases in 2020-2021 is likely due to stringent precautions, lockdowns, drug repurposing, and prioritized testing, indicating no reduction in influenza transmission. Increased influenza positivity in SARI patients during COVID-19 highlights a heightened risk of coinfection. Emphasizing solely on COVID-19 may lead to underreporting of other respiratory pathogens, including influenza viruses.

Infodemiology of Influenza-like Illness: Utilizing Google Trends' Big Data for Epidemic Surveillance.

Background: Influenza-like illness (ILI) encompasses symptoms similar to influenza, affecting population health. Surveillance, including Google Trends (GT), offers insights into epidemic patterns. Methods: This study used multiple regression models to analyze the correlation between ILI incidents, GT keyword searches, and climate variables during influenza outbreaks. It compared the predictive capabilities of time-series and deep learning models against ILI emergency incidents. Results: The GT searches for "fever" and "cough" were significantly associated with ILI cases (p < 0.05). Temperature had a more substantial impact on ILI incidence than humidity. Among the tested models, ARIMA provided the best predictive power. Conclusions: GT and climate data can forecast ILI trends, aiding governmental decision making. Temperature is a crucial predictor, and ARIMA models excel in forecasting ILI incidences.

Disease Burden and Inpatient Management of Children with Acute Respiratory Viral Infections during the Pre-COVID Era in Germany: A Cost-of-Illness Study.

Respiratory viral infections (RVIs) are common reasons for healthcare consultations. The inpatient management of RVIs consumes significant resources. From 2009 to 2014, we assessed the costs of RVI management in 4776 hospitalized children aged 0-18 years participating in a quality improvement program, where all ILI patients underwent virologic testing at the National Reference Centre followed by detailed recording of their clinical course. The direct (medical or non-medical) and indirect costs of inpatient management outside the ICU ('non-ICU') versus management requiring ICU care ('ICU') added up to EUR 2767.14 (non-ICU) vs. EUR 29,941.71 (ICU) for influenza, EUR 2713.14 (non-ICU) vs. EUR 16,951.06 (ICU) for RSV infections, and EUR 2767.33 (non-ICU) vs. EUR 14,394.02 (ICU) for human rhinovirus (hRV) infections, respectively. Non-ICU inpatient costs were similar for all eight RVIs studied: influenza, RSV, hRV, adenovirus (hAdV), metapneumovirus (hMPV), parainfluenza virus (hPIV), bocavirus (hBoV), and seasonal coronavirus (hCoV) infections. ICU costs for influenza, however, exceeded all other RVIs. At the time of the study, influenza was the only RVI with antiviral treatment options available for children, but only 9.8% of influenza patients (non-ICU) and 1.5% of ICU patients with influenza received antivirals; only 2.9% were vaccinated. Future studies should investigate the economic impact of treatment and prevention of influenza, COVID-19, and RSV post vaccine introduction.

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.

SARS-CoV-2, influenza A/B and respiratory syncytial virus positivity and association with influenza-like illness and self-reported symptoms, over the 2022/23 winter season in the UK: a longitudinal surveillance cohort.

BACKGROUND: Syndromic surveillance often relies on patients presenting to healthcare. Community cohorts, although more challenging to recruit, could provide additional population-wide insights, particularly with SARS-CoV-2 co-circulating with other respiratory viruses. METHODS: We estimated the positivity and incidence of SARS-CoV-2, influenza A/B, and RSV, and trends in self-reported symptoms including influenza-like illness (ILI), over the 2022/23 winter season in a broadly representative UK community cohort (COVID-19 Infection Survey), using negative-binomial generalised additive models. We estimated associations between test positivity and each of the symptoms and influenza vaccination, using adjusted logistic and multinomial models. RESULTS: Swabs taken at 32,937/1,352,979 (2.4%) assessments tested positive for SARS-CoV-2, 181/14,939 (1.2%) for RSV and 130/14,939 (0.9%) for influenza A/B, varying by age over time. Positivity and incidence peaks were earliest for RSV, then influenza A/B, then SARS-CoV-2, and were highest for RSV in the youngest and for SARS-CoV-2 in the oldest age groups. Many test positives did not report key symptoms: middle-aged participants were generally more symptomatic than older or younger participants, but still, only ~ 25% reported ILI-WHO and ~ 60% ILI-ECDC. Most symptomatic participants did not test positive for any of the three viruses. Influenza A/B-positivity was lower in participants reporting influenza vaccination in the current and previous seasons (odds ratio = 0.55 (95% CI 0.32, 0.95)) versus neither season. CONCLUSIONS: Symptom profiles varied little by aetiology, making distinguishing SARS-CoV-2, influenza and RSV using symptoms challenging. Most symptoms were not explained by these viruses, indicating the importance of other pathogens in syndromic surveillance. Influenza vaccination was associated with lower rates of community influenza test positivity.

A cost-consequence analysis of the Xpert Xpress CoV-2/Flu/RSV plus test strategy for the diagnosis of influenza-like illnesses.

AIMS: Influenza-like illnesses (ILI) affect millions each year in the United States (US). Determining definitively the cause of symptoms is important for patient management. Xpert Xpress CoV-2/Flu/RSV plus (Xpert Xpress) is a rapid, point-of-care (POC), multiplex real-time polymerase chain reaction (RT-PCR) test intended for the simultaneous qualitative detection and differentiation of SARS-CoV-2, influenza A/B, and respiratory syncytial virus (RSV). The objective of our analysis was to develop a cost-consequence model (CCM) demonstrating the clinico-economic impacts of implementing PCR testing with Xpert Xpress compared to current testing strategies. METHODS: A decision tree model, with a 1-year time horizon, was used to compare testing with Xpert Xpress alone to antigen POC testing and send-out PCR strategies in the US outpatient setting from a payer perspective. A hypothetical cohort of 1,000,000 members was modeled, a portion of whom develop symptomatic ILIs and present to an outpatient care facility. Our main outcome measure is cost per correct treatment course. RESULTS: The total cost per correct treatment course was $1,131 for the Xpert Xpress strategy compared with a range of $3,560 to $5,449 in comparators. POC antigen testing strategies cost more, on average, than PCR strategies. LIMITATIONS: Simplifying model assumptions were used to allow for modeling ease. In clinical practice, treatment options, costs, and diagnostic test sensitivity and specificity may differ from what is included in the model. Additionally, the most recent incidence and prevalence data was used within the model, which is not reflective of historical averages due to the SARS-CoV-2 pandemic. CONCLUSION: The Xpert Xpress CoV-2/Flu/RSV plus test allows for rapid and accurate diagnostic results, leading to reductions in testing costs and downstream healthcare resource utilization compared to other testing strategies. Compared to POC antigen testing strategies, PCR strategies were more efficient due to improved diagnostic accuracy and reduced use of confirmatory testing.

Influenza-like illness symptoms due to endemic human coronavirus reinfections are not influenced by the length of the interval separating reinfections.

After 3 years of its introduction to humans, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been declared as endemic. Little is known about the severity of the disease manifestation that future infections may cause, especially when reinfections occur after humoral immunity from a previous infection or vaccination has waned. Such knowledge could inform policymakers regarding the frequency of vaccination. Reinfections by endemic human coronaviruses (HCoVs) can serve as a model system for SARS-CoV-2 endemicity. We monitored 44 immunocompetent male adults with blood sampling every 6 months (for 17 years), for the frequency of HCoV (re-)infections, using rises in N-antibodies of HCoV-NL63, HCoV-29E, HCoV-OC43, and HCoV-HKU1 as markers of infection. Disease associations during (re-)infections were examined by comparison of self-reporting records of influenza-like illness (ILI) symptoms, every 6 months, by all participants. During 8,549 follow-up months, we found 364 infections by any HCoV with a median of eight infections per person. Symptoms more frequently reported during HCoV infection were cough, sore throat, and myalgia. Two hundred fifty-one of the 364 infections were species-specific HCoV-reinfections, with a median interval of 3.58 (interquartile range 1.92-5.67) years. The length of the interval between reinfections-being either short or long-had no influence on the frequency of reporting ILI symptoms. All HCoV-NL63, HCoV-229E, HCoV-OC43, and HCoV-HKU1 (re-)infections are associated with the reporting of ILIs. Importantly, in immunocompetent males, these symptoms are not influenced by the length of the interval between reinfections. IMPORTANCE: Little is known about the disease following human coronavirus (HCoV) reinfection occurring years after the previous infection, once humoral immunity has waned. We monitored endemic HCoV reinfection in immunocompetent male adults for up to 17 years. We found no influence of reinfection interval length in the disease manifestation, suggesting that immunocompetent male adults are adequately protected against future HCoV infections.

Influenza-like illness surveillance may underestimate the incidence of respiratory syncytial virus in adult outpatients.

OBJECTIVES: Although respiratory syncytial virus (RSV) is a leading cause of acute respiratory infections (ARIs), it is unclear which of the case definitions that prompt swab collection predicts RSV best. We aimed to profile RSV-positive adults and to identify possible RSV case definitions. METHODS: This individual-based pooled analysis was based on influenza-like illness (ILI) surveillance conducted among Italian outpatient adults. All samples were tested for influenza, RSV and other respiratory viruses. RESULTS: RSV was detected in 5.2% of the 1240 ILI adults tested. The prevalence of fever/feverishness was significantly lower (83.3%) in individuals positive for RSV and those negative for both viruses (79.4%) than in influenza-positive subjects (96.2%). Conversely, 98.3% of RSV-positive adults reported cough. Compared with subjects who tested negative, the adjusted relative risk ratio of cough in RSV-positive subjects was much higher than in influenza-positive subjects (6.89 vs 2.79). Using ARI with cough as the RSV case definition increased specificity. CONCLUSION: As fever/feverishness is more common among influenza than RSV cases, ILI-based surveillance may underestimate RSV incidence in adult outpatients. While broad ARI definitions are useful for routine RSV surveillance, their low specificity may hamper vaccine effectiveness studies. The use of further ARI qualifiers like cough increases specificity.

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.

Monitoring COVID-19 in Belgian general practice: A tool for syndromic surveillance based on electronic health records.

BACKGROUND: COVID-19 may initially manifest as flu-like symptoms. As such, general practitioners (GPs) will likely to play an important role in monitoring the pandemic through syndromic surveillance. OBJECTIVES: To present a COVID-19 syndromic surveillance tool in Belgian general practices. METHODS: We performed a nationwide observational prospective study in Belgian general practices. The surveillance tool extracted the daily entries of diagnostic codes for COVID-19 and associated conditions (suspected or confirmed COVID-19, acute respiratory infection and influenza-like illness) from electronic medical records. We calculated the 7-day rolling average for these diagnoses and compared them with data from two other Belgian population-based sources (laboratory-confirmed new COVID-19 cases and hospital admissions for COVID-19), using time series analysis. We also collected data from users and stakeholders about the syndromic surveillance tool and performed a thematic analysis. RESULTS: 4773 out of 11,935 practising GPs in Belgium participated in the study. The curve of contacts for suspected COVID-19 followed a similar trend compared with the curves of the official data sources: laboratory-confirmed COVID-19 cases and hospital admissions but with a 10-day delay for the latter. Data were quickly available and useful for decision making, but some technical and methodological components can be improved, such as a greater standardisation between EMR software developers. CONCLUSION: The syndromic surveillance tool for COVID-19 in primary care provides rapidly available data useful in all phases of the COVID-19 pandemic to support data-driven decision-making. Potential enhancements were identified for a prospective surveillance tool.

Data extracted daily from electronic medical records can be used to monitor the COVID-19 pandemic in general practice.The Barometer provided rapidly available data to support data-driven decision-making.Improvements such as a greater standardisation were identified for a potential future tool using the same technology.

A systematic review of tools for predicting complications in patients with influenza-like illness.

Objective: To identify tools that predict the risk of complications for patients presenting to an outpatient clinic or an emergency department (ED) with influenza-like illness. Methods: We searched Medline, Embase, Cochrane Library and CINAHL from inception to July 2023. We included articles reporting on the derivation or validation of a score or algorithm used to stratify the risk of hospitalization or mortality among patients with influenza-like illness in the ED or outpatient clinic. Results: Twelve articles reporting on eight scores and six predictive models were identified. For predicting the need for hospitalization, the area under the curve (AUC) of the PMEWS and the CURB-65 ranged respectively from 0.76 to 0.94, and 0.65 to 0.88. The Community Assessment Tool had an AUC of 0.62. For predicting inpatient mortality, AUC was 0.66 for PMEWS and 0.79 for CURB-65, 0.79 for the SIRS criteria and 0.86 for the qSOFA score. Two scores were developed without external validation during the Covid-19 pandemic. The CovHos score and the Canadian Covid discharge score had an AUC ranged from 0.70 to 0.91. The predictive models performed adequately (AUC from 0.76 to 0.92) but will require external validation for clinical use. Tool diversity and study population heterogeneity precluded meta-analysis. Conclusion: Although the CURB, PMEWS and qSOFA scores appear to predict accurately the risk of complications of influenza-like illness, none were reliable enough to justify their widespread ED use. Refinement of an existing tool or development of a new tool to optimize the management of these patients is needed.

Identify future epidemic threshold and intensity for influenza-like illness in Iraq by using the moving epidemic method.

Objectives: Influenza-like illness (ILI) entered the Iraq surveillance system in 2021. The alert threshold was determined using the cumulative sum 2 method, which did not provide other characteristics. This study uses the moving epidemic method (MEM) to describe duration and estimate alert thresholds for ILI in Iraq for 2023-2024. Methods: MEM default package was used to estimate influenza 2023-2024 epidemic thresholds. Analysis was repeated using optimum parameter of epidemic timing for fixed criteria method, which is 3.3. Arithmetic means and 95% confidence interval upper limit were used to estimate threshold. Geometric mean and 40%, 90%, and 97.3% confidence interval upper limits were used to estimate intensity levels. Aggregated Centers for Disease Control and Prevention surveillance data were used to detect epidemic thresholds, length, sensitivity, and predictive values. Results: ILI activity starts at week 30 and lasts 7 weeks. Optimized epidemic threshold is 4513 cases, lower than default (4540 cases). Optimized medium-intensity level was higher than default, and high and very high-intensity levels were lower. Conclusions: MEM is essential to determine an influenza epidemic's threshold and intensity levels. Despite requiring 3-5 years of data, using it on data for 2.5 years has resulted in an epidemic threshold slightly higher than the threshold calculated using the cumulative sum 2 method.