Influenza-like Illness Rates Research Articles

Can telephone data contribute to a national influenza surveillance system in Australia? A retrospective analysis

Introduction: Data collected from telephone helplines that provide health advice can improve the timeliness and accuracy of disease surveillance, contributing to an appropriate and rapid public health response. We show how these data can forewarn health professionals of increased rates of influenza-like illness (ILI) in the community and discuss implications for COVID-19 syndromic surveillance. Methods: The healthdirect helpline (HH) captures demographic details and characteristics of symptoms from users in 6 Australian states and territories. We compare ILI activity in the HH with ILI activity in emergency department (ED), laboratory, Flutracking and general practice (GP) data using cross-correlation functions. Results: Helpline data correlated strongly with ED data (range in yearly correlations from 0.82-0.99), GP data (0.66 – 0.95) and Flutracking data (0.62-0.89), but yearly correlations with laboratory data varied (0.49-0.95). The highest correlation with laboratory and GP data occurred when HH activity was 1-2 weeks in advance of these data, while correlations with ED and Flutracking data were strongest with no time lag. Discussion: Our analysis demonstrates that the number of ILI-related calls to the HH is a reliable indicator of ILI incidence in Australia. An increase in calls is likely to occur simultaneously with an increase in visits to EDs and prior to an increase in positive laboratory influenza tests and visits to GPs. A surveillance system including these data would assist health practitioners to receive timely and accurate estimates of the level of ILI in the community to better respond to and prepare for seasonal and epidemic influenza.

Decreased Seasonal Influenza Rates Detected in a Crowdsourced Influenza-Like Illness Surveillance System During the COVID-19 Pandemic: Prospective Cohort Study.

Seasonal respiratory viruses had lower incidence during their 2019-2020 and 2020-2021 seasons, which overlapped with the COVID-19 pandemic. The widespread implementation of precautionary measures to prevent transmission of SARS-CoV-2 has been seen to also mitigate transmission of seasonal influenza. The COVID-19 pandemic also led to changes in care seeking and access. Participatory surveillance systems have historically captured mild illnesses that are often missed by surveillance systems that rely on encounters with a health care provider for detection. This study aimed to assess if a crowdsourced syndromic surveillance system capable of detecting mild influenza-like illness (ILI) also captured the globally observed decrease in ILI in the 2019-2020 and 2020-2021 influenza seasons, concurrent with the COVID-19 pandemic. Flu Near You (FNY) is a web-based participatory syndromic surveillance system that allows participants in the United States to report their health information using a brief weekly survey. Reminder emails are sent to registered FNY participants to report on their symptoms and the symptoms of household members. Guest participants may also report. ILI was defined as fever and sore throat or fever and cough. ILI rates were determined as the number of ILI reports over the total number of reports and assessed for the 2016-2017, 2017-2018, 2018-2019, 2019-2020, and 2020-2021 influenza seasons. Baseline season (2016-2017, 2017-2018, and 2018-2019) rates were compared to the 2019-2020 and 2020-2021 influenza seasons. Self-reported influenza diagnosis and vaccination status were captured and assessed as the total number of reported events over the total number of reports submitted. CIs for all proportions were calculated via a 1-sample test of proportions. ILI was detected in 3.8% (32,239/848,878) of participants in the baseline seasons (2016-2019), 2.58% (7418/287,909) in the 2019-2020 season, and 0.27% (546/201,079) in the 2020-2021 season. Both influenza seasons that overlapped with the COVID-19 pandemic had lower ILI rates than the baseline seasons. ILI decline was observed during the months with widespread implementation of COVID-19 precautions, starting in February 2020. Self-reported influenza diagnoses decreased from early 2020 through the influenza season. Self-reported influenza positivity among ILI cases varied over the observed time period. Self-reported influenza vaccination rates in FNY were high across all observed seasons. A decrease in ILI was detected in the crowdsourced FNY surveillance system during the 2019-2020 and 2020-2021 influenza seasons, mirroring trends observed in other influenza surveillance systems. Specifically, the months within seasons that overlapped with widespread pandemic precautions showed decreases in ILI and confirmed influenza. Concerns persist regarding respiratory pathogens re-emerging with changes to COVID-19 guidelines. Traditional surveillance is subject to changes in health care behaviors. Systems like FNY are uniquely situated to detect disease across disease severity and care seeking, providing key insights during public health emergencies.

Multi-step influenza forecasting through singular value decomposition and kernel ridge regression with MARCOS-guided gradient-based optimization

This research delves into the significance of influenza outbreaks in public health, particularly the importance of accurate forecasts using weekly Influenza-like illness (ILI) rates. The present work develops a novel hybrid machine-learning model by combining singular value decomposition with kernel ridge regression (SKRR). In this context, a novel hybrid model known as H-SKRR is developed by combining two robust forecasting approaches, SKRR and ridge regression, which aims to improve multi-step-ahead predictions for weekly ILI rates in Southern and Northern China. The study begins with feature selection via XGBoost in the preprocessing phase, identifying optimal precursor information guided by importance factors. It decomposes the original signal using multivariate variational mode decomposition (MVMD) to address non-stationarity and complexity. H-SKRR is implemented by incorporating significant lagged-time components across sub-components. The aggregated forecasted values from these sub-components generate ILI values for two horizons (i.e., 4-and 7-weekly ahead). Employing the gradient-based optimization (GBO) algorithm fine-tunes model parameters. Furthermore, the deep random vector functional link (dRVFL), Ridge regression, and gated recurrent unit neural network (GRU) models were employed to validate the MVMD-H-SKRR-GBO paradigm's effectiveness. The outcomes, assessed using the MARCOS (Measurement of alternatives and ranking according to compromise solution) method as a multi-criteria decision-making method, highlight the superior accuracy of the MVMD-H-SKRR-GBO model in predicting ILI rates. The results clearly highlight the exceptional performance of the MVMD-H-SKRR-GBO model, with outstanding precision demonstrated by impressive R, RMSE, IA, and U95 % values of 0.946, 0.388, 0.970, and 1.075, respectively, at t + 7.

Analysis of the effectiveness of non-pharmaceutical interventions on influenza during the Coronavirus disease 2019 pandemic by time-series forecasting

BackgroundCoronavirus disease 2019 (COVID-19) was first identified in South Korea during the 2019–2020 seasonal influenza epidemic. The social distancing measures, as effective non-pharmaceutical interventions (NPIs), adopted to mitigate the spread of COVID-19 might have influenced influenza activity. We evaluated IFV(influenza virus) activity during the COVID-19 pandemic and the effect of NPI intensity on influenza transmission.MethodsIFV activity and epidemic duration during COVID-19 pandemic were predicted under a counterfactual scenario with no NPIs against COVID-19. The Seasonal Autoregressive Integrated Moving Average Model was used to quantify the effects of NPIs on the transmission of influenza virus. Influenza-like illness/1000 outpatients and IFV positivity rate from the 2011–2012 to 2021–2022 seasons were used in this study.ResultsComparison of the 2020–2021 and 2021–2022 seasonal influenza activities with those in 2013–2019 showed that COVID-19 outbreaks and associated NPIs such as face mask use, school closures, and travel restrictions reduced the influenza incidence by 91%. Without NPIs against COVID-19, the rates of influenza-like illness and IFV positivity would have been high during the influenza epidemic season, as in previous seasons. NPI intensity decreased the transmission of influenza; the magnitude of the reduction increased as the intensity of social-distancing measures increased (weak social distancing; step-by-step daily recovery: 58.10%, strong social distancing; special quarantine measures: 95.12%).ConclusionsOur results suggest that NPIs and personal hygiene can be used to suppress influenza transmission. NPIs against COVID-19 may be useful strategies for the prevention and control of influenza epidemics.

Impact of non-pharmaceutical interventions during COVID-19 on future influenza trends in Mainland China

BackgroundInfluenza is a common illness for its high rates of morbidity and transmission. The implementation of non-pharmaceutical interventions (NPIs) during the COVID-19 pandemic to manage its dissemination could affect the transmission of influenza.MethodsA retrospective analysis, between 2018 and 2023, was conducted to examine the incidence of influenza virus types A and B among patients in sentinel cities located in North or South China as well as in Wuhan City. For validations, data on the total count of influenza patients from 2018 to 2023 were collected at the Central Hospital of Wuhan, which is not included in the sentinel hospital network. Time series methods were utilized to examine seasonal patterns and to forecast future influenza trends.ResultsNorthern and southern cities in China had earlier outbreaks during the NPIs period by about 8 weeks compared to the 2018–2019. The implementation of NPIs significantly reduced the influenza-like illness (ILI) rate and infection durations. Influenza B Victoria and H3N2 were the first circulating strains detected after the relaxation of NPIs, followed by H1N1 across mainland China. The SARIMA model predicted synchronized H1N1 outbreak cycles in North and South China, with H3N2 expected to occur in the summer in southern cities and in the winter in northern cities over the next 3 years. The ILI burden is expected to rise in both North and South China over the next 3 years, with higher ILI% levels in southern cities throughout the year, especially in winter, and in northern cities mainly during winter. In Wuhan City and the Central Hospital of Wuhan, influenza levels are projected to peak in the winter of 2024, with 2 smaller peaks expected during the summer of 2023.ConclusionsIn this study, we report the impact of NPIs on future influenza trends in mainland China. We recommend that local governments encourage vaccination during the transition period between summer and winter to mitigate economic losses and mortality associated with influenza.

Temporal Association Among Influenza-Like Illness, Cardiovascular Events, and Vaccine Dose in Patients With High-Risk Cardiovascular Disease

Influenza-like illness (ILI) activity has been associated with increased risk of cardiopulmonary (CP) events during the influenza season. High-dose trivalent influenza vaccine was not superior to standard-dose quadrivalent vaccine for reducing these events in patients with high-risk cardiovascular (CV) disease in the Influenza Vaccine to Effectively Stop Cardio Thoracic Events and Decompensated Heart Failure (INVESTED) trial. To evaluate whether high-dose trivalent influenza vaccination is associated with benefit over standard-dose quadrivalent vaccination in reducing CP events during periods of high, local influenza activity. This study was a prespecified secondary analysis of INVESTED, a multicenter, double-blind, active comparator randomized clinical trial conducted over 3 consecutive influenza seasons from September 2016 to July 2019. Follow-up was completed in July 2019, and data were analyzed from September 21, 2016, to July 31, 2019. Weekly Centers for Disease Control and Prevention (CDC)-reported, state-level ILI activity was ascertained to assess the weekly odds of the primary outcome. The study population included 3094 patients with high-risk CV disease from participating centers in the US. Participants were randomized to high-dose trivalent or standard-dose quadrivalent influenza vaccine and revaccinated for up to 3 seasons. The primary outcome was the time to composite of all-cause death or CP hospitalization within each season. Additional measures included weekly CDC-reported ILI activity data by state. Among 3094 participants (mean [SD] age, 65 [12] years; 2309 male [75%]), we analyzed 129 285 person-weeks of enrollment, including 1396 composite primary outcome events (1278 CP hospitalization, 118 deaths). A 1% ILI increase in the prior week was associated with an increased risk in the primary outcome (odds ratio [OR], 1.14; 95% CI, 1.07-1.21; P < .001), CP hospitalization (OR, 1.13; 95% CI, 1.06-1.21; P < .001), and CV hospitalization (OR, 1.12; 95% CI, 1.04-1.19; P = .001), after adjusting for state, demographic characteristics, enrollment strata, and CV risk factors. Increased ILI activity was not associated with all-cause death (OR, 1.00; 95% CI, 0.88-1.13; P > .99). High-dose compared with standard-dose vaccine did not significantly reduce the primary outcome, even when the analysis was restricted to weeks of high ILI activity (OR, 0.88; 95% CI, 0.65-1.20; P = .43). Traditionally warmer months in the US were associated with lower CV risk independent of local ILI activity. In this secondary analysis of a randomized clinical trial, ILI activity was temporally associated with increased CP events in patients with high-risk CV disease, and a higher influenza vaccine dose did not significantly reduce temporal CV risk. Other seasonal factors may play a role in the coincident high rates of ILI and CV events. ClinicalTrials.gov Identifier: NCT02787044.

LSTM-based recurrent neural network provides effective short term flu forecasting

BackgroundInfluenza virus is responsible for a yearly epidemic in much of the world. To better predict short-term, seasonal variations in flu infection rates and possible mechanisms of yearly infection variation, we trained a Long Short-Term Memory (LSTM)-based deep neural network on historical Influenza-Like-Illness (ILI), climate, and population data.MethodsData were collected from the Centers for Disease Control and Prevention (CDC), the National Center for Environmental Information (NCEI), and the United States Census Bureau. The model was initially built in Python using the Keras API and tuned manually. We explored the roles of temperature, precipitation, local wind speed, population size, vaccination rate, and vaccination efficacy. The model was validated using K-fold cross validation as well as forward chaining cross validation and compared to several standard algorithms. Finally, simulation data was generated in R and used for further exploration of the model.ResultsWe found that temperature is the strongest predictor of ILI rates, but also found that precipitation increased the predictive power of the network. Additionally, the proposed model achieved a +1 week prediction mean absolute error (MAE) of 0.1973. This is less than half of the MAE achieved by the next best performing algorithm. Additionally, the model accurately predicted simulation data. To test the role of temperature in the network, we phase-shifted temperature in time and found a predictable reduction in prediction accuracy.ConclusionsThe results of this study suggest that short term flu forecasting may be effectively accomplished using architectures traditionally reserved for time series analysis. The proposed LSTM-based model was able to outperform comparison models at the +1 week time point. Additionally, this model provided insight into the week-to-week effects of climatic and biotic factors and revealed potential patterns in data series. Specifically, we found that temperature is the strongest predictor of seasonal flu infection rates. This information may prove to be especially important for flu forecasting given the uncertain long-term impact of the SARS-CoV-2 pandemic on seasonal influenza.

Neural network models for influenza forecasting with associated uncertainty using Web search activity trends.

Influenza affects millions of people every year. It causes a considerable amount of medical visits and hospitalisations as well as hundreds of thousands of deaths. Forecasting influenza prevalence with good accuracy can significantly help public health agencies to timely react to seasonal or novel strain epidemics. Although significant progress has been made, influenza forecasting remains a challenging modelling task. In this paper, we propose a methodological framework that improves over the state-of-the-art forecasting accuracy of influenza-like illness (ILI) rates in the United States. We achieve this by using Web search activity time series in conjunction with historical ILI rates as observations for training neural network (NN) architectures. The proposed models incorporate Bayesian layers to produce associated uncertainty intervals to their forecast estimates, positioning themselves as legitimate complementary solutions to more conventional approaches. The best performing NN, referred to as the iterative recurrent neural network (IRNN) architecture, reduces mean absolute error by 10.3% and improves skill by 17.1% on average in nowcasting and forecasting tasks across 4 consecutive flu seasons.

Wastewater Levels of Respiratory Syncytial Virus Associated with Influenza-like Illness Rates in Children-A Case Study in Larissa, Greece (October 2022-January 2023).

The emergence of the COVID-19 pandemic has led to significant progress in the field of wastewater-based surveillance (WBS) of respiratory pathogens and highlighted its potential for a wider application in public health surveillance. This study aimed to evaluate whether monitoring of respiratory syncytial virus (RSV) in wastewater can provide a comprehensive picture of disease transmission at the community level. The study was conducted in Larissa (Central Greece) between October 2022 and January 2023. Forty-six wastewater samples were collected from the inlet of the wastewater treatment plant of Larissa and analyzed with a real-time reverse transcription polymerase chain reaction (RT-PCR) based method. RSV and SARS-CoV-2 wastewater viral loads (genome copies/100,000 inhabitants) were analyzed against sentinel surveillance data on influenza-like illness (ILI) to identify potential associations. Univariate linear regression analysis revealed that RSV wastewater viral load (lagged by one week) and ILI notification rates in children up to 14 years old were strongly associated (std. Beta: 0.73 (95% CI: 0.31-1.14), p = 0.002, R2 = 0.308). A weaker association was found between SARS-CoV-2 viral load and ILI rates in the 15+ age group (std. Beta: 0.56 (95% CI: 0.06-1.05), p = 0.032, R2 = 0.527). The results support the incorporation of RSV monitoring into existing wastewater-based surveillance systems.

How COVID-19 shifted the seasonal flu in Korea.

Prior to 2021, Republic of Korea had experienced typical seasonal epidemics of influenza, with some seasons showing higher rates of influenza-like illness. Typically, seasonal flu epidemics in the country are reported between December and March.1 In the 2019–2020 season, there was a relatively high number of ILI rates. Notably, during the 2020–2021 and 2021–2022 seasons, the ILI rates had remained below the epidemic threshold, as Figure 1 illustrates. There had been continuous concern for the possible “twindemic” since the emergence of COVID-19. In the 2021–2022 season, this concern had grown with the ease of non-pharmaceutical interventions, including social distancing measures. “Twindemic” is a term used to describe the simultaneous occurrence of two different epidemics: COVID-19 and the seasonal influenza. It is used to stress the possible subsequent burden on healthcare systems and on the overall society. The 2022–2023 flu season in the Republic of Korea has shown a similar pattern to seasons prior to the COVID-19 pandemic. Despite an increase in the ILI baseline since Week 40, no early increase was observed. ILI rates increased above the seasonal threshold (4.9 ILI cases per 1000 outpatients) in Week 40, gradually increasing until Week 49, and showing a typical exponential increase starting from Week 50, similar to the pattern before the COVID-19 pandemic. The peak was observed in Week 53 and has been decreasing since then. This reoccurrence of the typical flu epidemic in the Republic of Korea is not surprising, given the lifting of overall social distancing measures and reduced population immunity to seasonal influenza due to the absence of a flu epidemic in the 2020–2021 and 2021–2022 seasons.2 Influenza vaccination rates have been stable during the COVID-19 pandemic.3 Nevertheless, the indoor mask mandate has been maintained, which may have prevented an early surge of the seasonal flu epidemic, unlike in other countries, including Australia and the United States.4-6 The impact of the resurgence of the seasonal flu in the midst of the Omicron variant-driven COVID-19 pandemic has not been as catastrophic as feared in the country. There is a chance for a spring season second peak since the indoor mask mandate is set to be lifted by the end of January 2023. Therefore, close monitoring of seasonal flu and COVID-19 trends, as well as continuous encouragement of vaccination for both highly transmissible pathogens, is needed. At present, the worst-case scenario appears to have been averted. Nevertheless, it is imperative that we continue to exercise caution, as it is currently too early to lower our guard. Seunghyun Lewis Kwon: Conceptualization; formal analysis; methodology; writing – original draft; writing – review and editing. Bryan Inho Kim: Methodology; supervision; validation; writing – original draft; writing – review and editing. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The peer review history for this article is available at https://publons.com/publon/10.1111/irv.13113. These data were derived from the following resources available in the public domain: https://www.kdca.go.kr/board/board.es?mid=a30504000000&bid=0033.

An intercountry comparison of the impact of the paediatric live attenuated influenza vaccine (LAIV) programme across the UK and the Republic of Ireland (ROI), 2010 to 2017.

The universal paediatric live attenuated influenza vaccine (LAIV) programme commenced in the United Kingdom (UK) in 2013/2014. Since 2014/2015, all pre-school and primary school children in Scotland and Northern Ireland have been offered the vaccine. England and Wales incrementally introduced the programme with additional school age cohorts being vaccinated each season. The Republic of Ireland (ROI) had no universal paediatric programme before 2017. We evaluated the potential population impact of vaccinating primary school-aged children across the five countries up to the 2016/2017 influenza season. We compared rates of primary care influenza-like illness (ILI) consultations, confirmed influenza intensive care unit (ICU) admissions, and all-cause excess mortality using standardised methods. To further quantify the impact, a scoring system was developed where each weekly rate/z-score was scored and summed across each influenza season according to the weekly respective threshold experienced in each country. Results highlight ILI consultation rates in the four seasons' post-programme, breached baseline thresholds once or not at all in Scotland and Northern Ireland; in three out of the four seasons in England and Wales; and in all four seasons in ROI. No differences were observed in the seasons' post-programme introduction between countries in rates of ICU and excess mortality, although reductions in influenza-related mortality were seen. The scoring system also reflected similar results overall. Findings of this study suggest that LAIV vaccination of primary school age children is associated with population-level benefits, particularly in reducing infection incidence in primary care.

SARS-CoV-2 Surveillance Through China Influenza Surveillance Information System - China, December 1, 2022 to February 12, 2023.

The World Health Organization (WHO) proposed using influenza surveillance systems to carry out coronavirus disease 2019 (COVID-19) surveillance due to the similarity between the two diseases in some respiratory symptoms. To assess the prevalence of COVID-19, we analyzed the influenza-like illness (ILI) and positive rate of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) detections in ILI patients reported to the influenza Surveillance Information System (CNISIS) since late 2022. Data related to ILI were reported by national surveillance sentinel hospitals. Positive testing for SARS-CoV-2 and influenza viruses was conducted using real-time reverse transcription polymerase chain reaction (rRT-PCR) detection by the national influenza surveillance network laboratories. Surveillance data were reported to CNISIS. Beginning on December 12, 2022 (Week 50), the ILI percentage increased dramatically, peaking in Week 51 at 12.1%. Subsequently, the ILI percentage began to decline rapidly from Week 52, 2022, and by Week 6, 2023 (February 6-12), the ILI and ILI percentage had returned to the levels observed at the beginning of December 2022. From December 1, 2022 to February 12, 2023, 115,844 specimens were tested for both SARS-CoV-2 and influenza virus. Of these, 30,381 (26.2%) were positive for SARS-CoV-2 and 1,763 (1.5%) were positive for influenza virus. The positive rate of SARS-CoV-2 tests peaked at 74.1% around December 23 and 25. Sentinel-based surveillance, previously established for influenza, is an effective way to track the circulation trend of SARS-CoV-2 during community-level epidemics. There was no co-prevalence of SARS-CoV-2 and influenza virus during the outbreak of SARS-CoV-2, even during the winter influenza season. However, it is important to remain vigilant for the potential rise of influenza activities following the COVID-19 epidemic.

2204. Pathogen Co-infections and Trends in Influenza-like Illness in PAIVED

Abstract Background The Pragmatic Assessment of Influenza Vaccine Effectiveness in the Department of Defense (DoD) (PAIVED) is a multicenter, multiservice study assessing influenza vaccine effectiveness in active-duty service members, retirees, and dependents. In its fourth season (2021/22), PAIVED offers a unique opportunity to examine influenza-like illness (ILI) trends prior to and during the COVID-19 pandemic in a prospectively followed, well-defined cohort. Methods Over the past 4 influenza seasons, PAIVED has enrolled DoD beneficiaries who were randomized to receive egg-based, cell-based, or recombinant-derived influenza vaccine. Participants provided some basic demographic information and were then sent a weekly text or email that inquired about ILI symptoms, defined as 1) having cough or sore throat, plus 2) feeling feverish/having chills or having body aches/fatigue. Participants with ILI completed a daily symptom diary for one week and submitted a nasal swab for PCR-based pathogen detection. Table 1.Demographic characteristics of PAIVED participants over four seasonsFigure 1.Percent of PAIVED participants with influenza-like illness, SARS-CoV-2, influenza, and rhinovirus identified in swab samples collected over four seasons. Results Over the 4 seasons, 15,449 participants were followed for ILI (Table 1) with 3,407 participants reporting a total of 3,985 ILIs. For the 2021/22 season, ILI reports peaked in January (Figure 1). Overall, 4.7% of episodes had more than one pathogen identified (Table 2). Among the 122 coinfections identified to date, most were coinfections with rhinoviruses (91/122, 75%), including rhinovirus coinfections with seasonal coronaviruses (29, 24%), metapneumovirus (18, 15%), SARS-CoV-2 (17, 14%), and influenza (14, 11%). SARS-CoV-2 and influenza were found together in one sample. The lab data will continue to be processed for the current season (2021/22). Table 2.Pathogens identified in PAIVED nasal swabs over four seasons Conclusion ILI rates were lowest during the third year (2020/21), consistent with national influenza surveillance reports of influenza and outpatient ILI activity, suggesting that measures taken to reduce transmission of SARS-CoV-2 reduced the spread of other respiratory viruses. The emergence of the SARS-CoV-2 omicron variant in December 2021 was associated with higher ILI rates. Among those individuals for whom a sample was collected, coinfections were highest in 2018/19. Data collection and specimen analysis are ongoing for 2021/22. Disclosures Jitendrakumar Modi, MD, GlaxoSmithKline: I am a paid speaker for GSK. I do not speak for their flu brand. Timothy Burgess, MD, MPH, AstraZeneca: The HJF, in support of the USU IDCRP, was funded to conduct or augment unrelated Phase III Mab and vaccine trials as part of US Govt. COVID19 response.

Paid sick leave policy impacts on health and care utilization in the United States: why policy design matters.

The link between policy design choices and health is an important, yet understudied area of public health research. I investigate the impact of the generosity, inclusion, and autonomy of state paid sick leave laws on influenza-like-illness (ILI) rates and its components using data from the Centers for Disease Control and state-level paid sick leave statutes. I found that paid sick leave policies that include small firms and that allow for a larger number of medical uses have lower ILI rates, relative to states with less comprehensive policies. States with policies that had more generous accrual rates and that included a wide variety of worker types (temporary, part-time, students) increased the total number of reported medical cases, relative to states with less comprehensive policies. Policymakers contemplating paid sick leave policies should consider these design choices in their goals to incentivize health care utilization and to reduce contagion.Supplementary InformationThe online version contains supplementary material available at 10.1057/s41271-022-00371-9.

Leveraging Google Search Data and Artificial Intelligence Methods for Provincial-level Influenza Forecasting: A South African Case Study

This paper investigates the usefulness of Google search patterns with Artificial Intelligence (AI) techniques for timely influenza-like illness (ILI) forecasting for each of the nine South African provinces. Traditional surveillance methods are limited by delays in reporting. Existing digital disease surveillance studies that employ alternative online data have scarcely explored sub-Saharan African countries. In South Africa, Google search data has only been recently studied for ILI surveillance at the national level. Meanwhile, the differences in socio-economic and technological conditions across provinces call for a finer spatial investigation. We perform correlation analysis between Google trends (GT) data for 21 ILI-related terms and real-life ILI surveillance data for each province. Next, we develop models to assess the predictive performance of these GT data for forecasting ILI rates, using time series, machine learning, and deep learning methods. We observe sufficient correlation for only two of the nine provinces: Gauteng and Western Cape. Thus, GT data could only be used to forecast ILI in these two provinces. Interestingly, these two provinces are regarded as the most economically developed. In the other seven provinces, LSTM, a deep learning technique, gives more accurate predictions than a baseline autoregressive model when only past ILI data are used for forecasting future ILI trends. The results reveal that, for provinces for which GT data is sufficiently available, it is not only free and fast, but is an effective predictor on its own as well as when added to past ILI data for forecasting future ILI infection rates. The correlation analysis suggests an association between provincial socio-economic development and the use of digital platforms for disease surveillance. Overall, the study established the need for finer scale ILI forecasting which will inform targeted planning for disease surveillance and interventions.

FluTracking: Weekly online community based surveillance of influenza-like illness in Australia, 2018 Annual Report.

FluTracking experienced major growth in 2018, with participation numbers increasing 34.1% from 2017. The addition of 16,881 new participants brought the total number of participants for 2018 to 45,532. A majority of participants continued to complete their survey within 24 hours of the email being sent (mean 74.3% responses received in 24 hours). The rate of influenza-like illness (ILI) in 2018 was the lowest since FluTracking commenced in 2007 and was consistently low across all ages. The peak weekly ILI rate was consistent with previous years, occurring during the week ending 19 August. This preceded the peak in laboratory-confirmed influenza notifications by three weeks. During the peak week of FluTracking, 2.1% of unvaccinated, and 1.9% of vaccinated participants reported fever and cough. By the final survey of 2018, 65.6% of participants had received the annual influenza vaccine, compared with 60.2% in 2017. Vaccination rates in participants under five years of age doubled from 23.7% in 2017, to 55.6% in 2018. During the peak four weeks of reported ILI, a lower percentage of participants sought medical care in 2018 compared to 2017 (36.7% and 42.3% respectively), and fewer participants reported a positive laboratory test for influenza (0.8% and 4.8%). Overall the severity of the 2018 season was one of the lowest FluTracking has recorded. Rates of both influenza laboratory notifications and general practitioner (GP) ILI consultations were lower in 2018 than most prior years. We found a reduction in the percentage of FluTracking participants with ILI who were tested for influenza (3.2% compared with 5.0% in 2017), and who visited a medical practitioner (36.7% compared with 42.3% in 2017). The drop in laboratory-confirmed cases and Australian Sentinel Practices Research Network (ASPREN) reported GP consultations concurs with our survey results that 2018 was a milder influenza season than many previous.

Approximation of Influenza-like illness rates using sleep and cardiorespiratory data from a smart bed

Approximation of Influenza-like illness rates using sleep and cardiorespiratory data from a smart bed

Dynamics of influenza-like illness under urbanization procedure and COVID-19 pandemic in the subcenter of Beijing during 2013-2021.

Influenza‐like illness (ILI) varies in intensity year by year, generally keeping a stable pattern except for great changes of its epidemic pattern. Of the most impacting factors, urbanization has been suggested as shaping the intensity of influenza epidemics. Besides, growing evidence indicates the nonpharmaceutical interventions (NPIs) to severe acute respiratory syndrome coronavirus 2 offer great advantages in controlling infectious diseases. The present study aimed to evaluate the impact of urbanization and NPIs on the dynamic of ILI in Tongzhou, Beijing, during January 2013 to March 2021. ILI epidemiological surveillance data in Tongzhou district were obtained from Beijing Influenza Surveillance Network and separated into three periods of urbanization and four intervals of coronavirus disease 2019 pandemic. Standardized average incidence rates of ILI in each separate stages were calculated and compared by using Wilson method and time series model of seasonal ARIMA. Influenza seasonal outbreaks showed similar epidemic size and intensity before urbanization during 2013–2016. Increased ILI activity was found during the process of Tongzhou's urbanization during 2017–2019, with the rate difference of 2.48 (95% confidence interva [CI]: 2.44, 2.52) and the rate ratio of 1.75 (95% CI: 1.74, 1.76) of ILI incidence between preurbanization and urbanization periods. ILI activity abruptly decreased from the beginning of 2020 and kept at the bottom level almost in every epidemic interval. The top decrease in ILI activity by NPIs was shown in 5–14 years group in 2020–2021 influenza season, as 92.2% (95% CI: 78.3%, 95.2%). The results indicated that both urbanization and NPIs interrupted the epidemic pattern of ILI. We should pay more attention to public health when facing increasing population density, human contact, population mobility, and migration in the process of urbanization. NPIs and influenza vaccination should be implemented as necessary measures to protect people from common infectious diseases like ILI.

Nowcasting influenza‐like illness (ILI) via a deep learning approach using google search data: An empirical study on Taiwan ILI

Influenza outbreaks have brought increasing challenges to public health systems globally. The effective and efficient tracking of influenza can help authorities make informed and proactive decisions. In this study, we focus on nowcasting influenza epidemics at regional level. To alleviate the information lag between the release of the Centers for Disease Control's influenza-like illness (ILI) reports and real-time influenza activity, we incorporate Google search data and holiday effects to facilitate the ILI nowcasting. We develop a deep learning framework by extending the spatiotemporal residual network (ST-ResNet) to nowcast ILI rates in irregular-shaped region. We investigate the effect of temporal and spatial dependencies among irregular-shaped regions as well as external influence on ILI nowcasting. Various forecasting models, including time series models, penalized regression models, and other deep learning models, are employed for evaluating the performance of the proposed framework. Moreover, based on city-level ILI data in Taiwan, we conduct extensive experiments for methods validation. The results show that the extended ST-ResNet can effectively capture the complex spatiotemporal dependencies of ILI activity and the effects of external variables. Additionally, the strategy of incorporating Google search data and holiday effects improves the prediction. The findings may provide insights into the utility of various statistical and deep learning methods for effective ILI tracking at regional level and facilitate informed decision making for public health authorities.

Dietary Supplementation with Biobran/MGN-3 Increases Innate Resistance and Reduces the Incidence of Influenza-like Illnesses in Elderly Subjects: A Randomized, Double-Blind, Placebo-Controlled Pilot Clinical Trial.

Influenza-like illness (ILI) remains a major cause of severe mortality and morbidity in the elderly. Aging is associated with a decreased ability to sense pathogens and mount effective innate and adaptive immune responses, thus mandating the development of protective nutraceuticals. Biobran/MGN-3, an arabinoxylan from rice bran, has potent anti-aging and immunomodulatory effects, suggesting that it may be effective against ILI. The objective of the current study was to investigate the effect of Biobran/MGN-3 on ILI incidence, natural killer (NK) cell activity, and the expressions of RIG-1 (retinoic acid-inducible gene 1), MDA5 (melanoma differentiation-associated protein 5), and their downstream signaling genes ISG-15 (interferon-stimulated genes 15) and MX1 (myxovirus (influenza) resistance 1, interferon-inducible). A double-blind, placebo-controlled clinical trial included eighty healthy older adults over 55 years old, 40 males and 40 females, who received either a placebo or Biobran/MGN-3 (500 mg/day) for 3 months during known ILI seasonality (peak incidence) in Egypt. The incidence of ILI was confirmed clinically according to the WHO case definition criteria. Hematological, hepatic, and renal parameters were assessed in all subjects, while the activity of NK and NKT (natural killer T) cells was assessed in six randomly chosen subjects in each group by the degranulation assay. The effect of Biobran/MGN-3 on RIG-1 and MDA5, as well as downstream ISG15 and MX1, was assessed in BEAS-2B pulmonary epithelial cells using flow cytometry. The incidence rate and incidence density of ILI in the Biobran/MGN-3 group were 5.0% and 0.57 cases per 1000 person-days, respectively, compared to 22.5% and 2.95 cases per 1000 person-days in the placebo group. Furthermore, Biobran/MGN-3 ingestion significantly enhanced NK activity compared to the basal levels and to the placebo group. In addition, Biobran/MGN-3 significantly upregulated the expression levels of RIG-1, MDA5, ISG15, and MX1 in the human pulmonary epithelial BEAS-2B cell lines. No side effects were observed. Taken together, Biobran/MGN-3 supplementation enhanced the innate immune response of elderly subjects by upregulating the NK activity associated with reduction of ILI incidence. It also upregulated the intracellular RIG-1, MDA5, ISG15, and MX1 expression in pulmonary epithelial tissue cultures. Biobran/MGN-3 could be a novel agent with prophylactic effects against a wide spectrum of respiratory viral infections that warrants further investigation.