Estimating influenza attack rates in the United States using a participatory cohort

What are the characteristics of participatory surveillance systems for influenza-like-illness?

Background

Seasonal influenza causes significant morbidity and mortality, with an estimated 9.4 million hospitalisations and 290 000-650 000 respiratory related-deaths globally each year. Influenza can also cause mild illness, which is why not all symptomatic persons might necessarily be tested for influenza. To monitor influenza activity, healthcare facility-based syndromic surveillance for influenza-like illness is often implemented. Participatory surveillance systems for influenza-like illness (ILI) play an important role in influenza surveillance and can complement traditional facility-based surveillance systems to provide real-time estimates of influenza-like illness activity. However, such systems differ in designs between countries and contexts, making it necessary to identify their characteristics to better understand how they fit traditional surveillance systems. Consequently, we aimed to investigate the performance of participatory surveillance systems for ILI worldwide.

Methods

We systematically searched four databases for relevant articles on influenza participatory surveillance systems for ILI. We extracted data from the included, eligible studies and assessed their quality using the Joanna Briggs Critical Appraisal Tools. We then synthesised the findings using narrative synthesis.

Results

We included 39 out of 3797 retrieved articles for analysis. We identified 26 participatory surveillance systems, most of which sought to capture the burden and trends of influenza-like illness and acute respiratory infections among cohorts with risk factors for influenza-like illness. Of all the surveillance system attributes assessed, 52% reported on correlation with other surveillance systems, 27% on representativeness, and 21% on acceptability. Among studies that reported these attributes, all systems were rated highly in terms of simplicity, flexibility, sensitivity, utility, and timeliness. Most systems (87.5%) were also well accepted by users, though participation rates varied widely. However, despite their potential for greater reach and accessibility, most systems (90%) fared poorly in terms of representativeness of the population. Stability was a concern for some systems (60%), as was completeness (50%).

Conclusions

The analysis of participatory surveillance system attributes showed their potential in providing timely and reliable influenza data, especially in combination with traditional hospital- and laboratory led-surveillance systems. Further research is needed to design future systems with greater uptake and utility.

Calculating incidence of Influenza-like and COVID-like symptoms from Flutracking participatory survey data

This article describes a new method for estimating weekly incidence (new onset) of symptoms consistent with Influenza and COVID-19, using data from the Flutracking survey. The method mitigates some of the known self-selection and symptom-reporting biases present in existing approaches to this type of participatory longitudinal survey data. The key novel steps in the analysis are: 1) Identifying new onset of symptoms for three different Symptom Groupings: COVID-like illness (CLI1+, CLI2+), and Influenza-like illness (ILI), for responses reported in the Flutracking survey. 2) Adjusting for symptom reporting bias by restricting the analysis to a sub-set of responses from those participants who have consistently responded for a number of weeks prior to the analysis week. 3) Weighting responses by age to adjust for self-selection bias in order to account for the under- and over-representation of different age groups amongst the survey participants. This uses the survey package [22] in R [30]. 4) Constructing 95% point-wise confidence bands for incidence estimates using weighted logistic regression from the survey package [21] in R [28]. In addition to describing these steps, the article demonstrates an application of this method to Flutracking data for the 12 months from 27th April 2020 until 25th April 2021.

Topic-Emotion Propagation Mechanism of Public Emergencies in Social Networks

The information propagation of emergencies in social networks is often accompanied by the dissemination of the topic and emotion. As a virtual sensor of public emergencies, social networks have been widely used in data mining, knowledge discovery, and machine learning. From the perspective of network, this study aims to explore the topic and emotion propagation mechanism, as well as the interaction and communication relations of the public in social networks under four types of emergencies, including public health events, accidents and disasters, social security events, and natural disasters. Event topics were identified by Word2vec and K-means clustering. The biLSTM model was used to identify emotion in posts. The propagation maps of topic and emotion were presented visually on the network, and the synergistic relationship between topic and emotion propagation as well as the communication characteristics of multiple subjects were analyzed. The results show that there were similarities and differences in the propagation mechanism of topic and emotion in different types of emergencies. There was a positive correlation between topic and emotion of different types of users in social networks in emergencies. Users with a high level of topic influence were often accompanied by a high level of emotion appeal.

Participatory syndromic surveillance as a tool for tracking COVID-19 in Bangladesh

Limitations in laboratory diagnostic capacity and reporting delays have hampered efforts to mitigate and control the ongoing coronavirus disease 2019 (COVID-19) pandemic globally. To augment traditional lab and hospital-based surveillance, Bangladesh established a participatory surveillance system for the public to self-report symptoms consistent with COVID-19 through multiple channels. Here, we report on the use of this system, which received over 3 million responses within two months, for tracking the COVID-19 outbreak in Bangladesh. Although we observe considerable noise in the data and initial volatility in the use of the different reporting mechanisms, the self-reported syndromic data exhibits a strong association with lab-confirmed cases at a local scale. Moreover, the syndromic data also suggests an earlier spread of the outbreak across Bangladesh than is evident from the confirmed case counts, consistent with predicted spread of the outbreak based on population mobility data. Our results highlight the usefulness of participatory syndromic surveillance for mapping disease burden generally, and particularly during the initial phases of an emerging outbreak.

A Mobile App and Dashboard for Early Detection of Infectious Disease Outbreaks: Development Study

BACKGROUND

Outbreaks of infectious diseases pose great risks, including hospitalization and death, to public health. Therefore, improving the management of outbreaks is important for preventing widespread infection and mitigating associated risks. Mobile health technology provides new capabilities that can help better capture, monitor, and manage infectious diseases, including the ability to quickly identify potential outbreaks.

OBJECTIVE

This study aims to develop a new infectious disease surveillance (IDS) system comprising a mobile app for accurate data capturing and dashboard for better health care planning and decision making.

METHODS

We developed the IDS system using a 2-pronged approach: a literature review on available and similar disease surveillance systems to understand the fundamental requirements and face-to-face interviews to collect specific user requirements from the local public health unit team at the Nepean Hospital, Nepean Blue Mountains Local Health District, New South Wales, Australia.

RESULTS

We identified 3 fundamental requirements when designing an electronic IDS system, which are the ability to capture and report outbreak data accurately, completely, and in a timely fashion. We then developed our IDS system based on the workflow, scope, and specific requirements of the public health unit team. We also produced detailed design and requirement guidelines. In our system, the outbreak data are captured and sent from anywhere using a mobile device or a desktop PC (web interface). The data are processed using a client-server architecture and, therefore, can be analyzed in real time. Our dashboard is designed to provide a daily, weekly, monthly, and historical summary of outbreak information, which can be potentially used to develop a future intervention plan. Specific information about certain outbreaks can also be visualized interactively to understand the unique characteristics of emerging infectious diseases.

CONCLUSIONS

We demonstrated the design and development of our IDS system. We suggest that the use of a mobile app and dashboard will simplify the overall data collection, reporting, and analysis processes, thereby improving the public health responses and providing accurate registration of outbreak information. Accurate data reporting and collection are a major step forward in creating a better intervention plan for future outbreaks of infectious diseases.

An Online Influenza Surveillance System for Primary Care Workers in Switzerland: Observational Prospective Pilot Study

Background

A better understanding of the influenza epidemiology among primary care workers could guide future recommendations to prevent transmission in primary care practices. Therefore, we designed a pilot study to assess the feasibility of using a work-based online influenza surveillance system among primary care workers. Such an approach is of particular relevance in the context of the coronavirus disease (COVID-19) pandemic, as its findings could apply to other infectious diseases with similar mechanisms of transmission.

Objective

This study aims to determine the feasibility of using a work-based online influenza surveillance system for primary care workers in Switzerland.

Methods

Physicians and staff of one walk-in clinic and two selected primary care practices were enrolled in this observational prospective pilot study during the 2017-2018 influenza season. They were invited to record symptoms of influenza-like illness in a weekly online survey sent by email and to self-collect a nasopharyngeal swab in case any symptoms were recorded. Samples were tested by real-time polymerase chain reaction for influenza A, influenza B, and a panel of respiratory pathogens.

Results

Among 67 eligible staff members, 58% (n=39) consented to the study and 53% (n=36) provided data. From the time all participants were included, the weekly survey response rate stayed close to 100% until the end of the study. Of 79 symptomatic episodes (mean 2.2 episodes per participant), 10 episodes in 7 participants fitted the definition of an influenza-like illness case (attack rate: 7/36, 19%). One swab tested positive for influenza A H1N1 (attack rate: 3%, 95% CI 0%-18%). Swabbing was considered relatively easy.

Conclusions

A work-based online influenza surveillance system is feasible for use among primary care workers. This promising methodology could be broadly used in future studies to improve the understanding of influenza epidemiology and other diseases such as COVID-19. This could prove to be highly useful in primary care settings and guide future recommendations to prevent transmission. A larger study will also help to assess asymptomatic infections.

Substantial underestimation of SARS-CoV-2 infection in the United States

Accurate estimates of the burden of SARS-CoV-2 infection are critical to informing pandemic response. Confirmed COVID-19 case counts in the U.S. do not capture the total burden of the pandemic because testing has been primarily restricted to individuals with moderate to severe symptoms due to limited test availability. Here, we use a semi-Bayesian probabilistic bias analysis to account for incomplete testing and imperfect diagnostic accuracy. We estimate 6,454,951 cumulative infections compared to 721,245 confirmed cases (1.9% vs. 0.2% of the population) in the United States as of April 18, 2020. Accounting for uncertainty, the number of infections during this period was 3 to 20 times higher than the number of confirmed cases. 86% (simulation interval: 64-99%) of this difference is due to incomplete testing, while 14% (0.3-36%) is due to imperfect test accuracy. The approach can readily be applied in future studies in other locations or at finer spatial scale to correct for biased testing and imperfect diagnostic accuracy to provide a more realistic assessment of COVID-19 burden.

Participatory Surveillance Based on Crowdsourcing During the Rio 2016 Olympic Games Using the Guardians of Health Platform: Descriptive Study

Background

With the evolution of digital media, areas such as public health are adding new platforms to complement traditional systems of epidemiological surveillance. Participatory surveillance and digital epidemiology have become innovative tools for the construction of epidemiological landscapes with citizens’ participation, improving traditional sources of information. Strategies such as these promote the timely detection of warning signs for outbreaks and epidemics in the region.

Objective

This study aims to describe the participatory surveillance platform Guardians of Health, which was used in a project conducted during the 2016 Olympic and Paralympic Games in Rio de Janeiro, Brazil, and officially used by the Brazilian Ministry of Health for the monitoring of outbreaks and epidemics.

Methods

This is a descriptive study carried out using secondary data from Guardians of Health available in a public digital repository. Based on syndromic signals, the information subsidy for decision making by policy makers and health managers becomes more dynamic and assertive. This type of information source can be used as an early route to understand the epidemiological scenario.

Results

The main result of this research was demonstrating the use of the participatory surveillance platform as an additional source of information for the epidemiological surveillance performed in Brazil during a mass gathering. The platform Guardians of Health had 7848 users who generated 12,746 reports about their health status. Among these reports, the following were identified: 161 users with diarrheal syndrome, 68 users with respiratory syndrome, and 145 users with rash syndrome.

Conclusions

It is hoped that epidemiological surveillance professionals, researchers, managers, and workers become aware of, and allow themselves to use, new tools that improve information management for decision making and knowledge production. This way, we may follow the path for a more intelligent, efficient, and pragmatic disease control system.

Reconciling randomized trial evidence on proximal versus distal outcomes, with application to trials of influenza vaccination for healthcare workers

When synthesizing the body of evidence concerning a clinical intervention, impacts on both proximal and distal outcome variables may be relevant. Assessments will be more defensible if results concerning a proximal outcome align with those concerning a corresponding distal outcome. We present a method to assess the coherence of empirical clinical trial results with biologic and mathematical first principles in situations where the intervention can only plausibly impact the distal outcome indirectly via the proximal outcome. The method comprises a probabilistic sensitivity analysis, where plausible ranges for key parameters are specified, resulting in a constellation of plausible pairs of estimated intervention effects, for the proximal and distal outcomes, respectively. Both outcome misclassification and sampling variability are reflected in the method. We apply our methodology in the context of cluster randomized trials to evaluate the impacts of vaccinating healthcare workers on the health of elderly patients, where the proximal outcome is suspected influenza and the distal outcome is death. However, there is scope to apply the method for other interventions in other disease areas.

ZMapp Reinforces the Airway Mucosal Barrier Against Ebola Virus

Filoviruses, including Ebola, have the potential to be transmitted via virus-laden droplets deposited onto mucus membranes. Protecting against such emerging pathogens will require understanding how they may transmit at mucosal surfaces and developing strategies to reinforce the airway mucus barrier. Here, we prepared Ebola pseudovirus (with Zaire strain glycoproteins) and used high-resolution multiple-particle tracking to track the motions of hundreds of individual pseudoviruses in fresh and undiluted human airway mucus isolated from extubated endotracheal tubes. We found that Ebola pseudovirus readily penetrates human airway mucus. Addition of ZMapp, a cocktail of Ebola-binding immunoglobulin G antibodies, effectively reduced mobility of Ebola pseudovirus in the same mucus secretions. Topical delivery of ZMapp to the mouse airways also facilitated rapid elimination of Ebola pseudovirus. Our work demonstrates that antibodies can immobilize virions in airway mucus and reduce access to the airway epithelium, highlighting topical delivery of pathogen-specific antibodies to the lungs as a potential prophylactic or therapeutic approach against emerging viruses or biowarfare agents.

Differences in Regional Patterns of Influenza Activity Across Surveillance Systems in the United States: Comparative Evaluation

BACKGROUND

The Centers for Disease Control and Prevention (CDC) tracks influenza-like illness (ILI) using information on patient visits to health care providers through the Outpatient Influenza-like Illness Surveillance Network (ILINet). As participation in this system is voluntary, the composition, coverage, and consistency of health care reports vary from state to state, leading to different measures of ILI activity between regions. The degree to which these measures reflect actual differences in influenza activity or systematic differences in the methods used to collect and aggregate the data is unclear.

OBJECTIVE

The objective of our study was to qualitatively and quantitatively compare national and region-specific ILI activity in the United States across 4 surveillance data sources-CDC ILINet, Flu Near You (FNY), athenahealth, and HealthTweets.org-to determine whether these data sources, commonly used as input in influenza modeling efforts, show geographical patterns that are similar to those observed in CDC ILINet's data. We also compared the yearly percentage of FNY participants who sought health care for ILI symptoms across geographical areas.

METHODS

We compared the national and regional 2018-2019 ILI activity baselines, calculated using noninfluenza weeks from previous years, for each surveillance data source. We also compared measures of ILI activity across geographical areas during 3 influenza seasons, 2015-2016, 2016-2017, and 2017-2018. Geographical differences in weekly ILI activity within each data source were also assessed using relative mean differences and time series heatmaps. National and regional age-adjusted health care-seeking percentages were calculated for each influenza season by dividing the number of FNY participants who sought medical care for ILI symptoms by the total number of ILI reports within an influenza season. Pearson correlations were used to assess the association between the health care-seeking percentages and baselines for each surveillance data source.

RESULTS

We observed consistent differences in ILI activity across geographical areas for CDC ILINet and athenahealth data. ILI activity for FNY displayed little variation across geographical areas, whereas differences in ILI activity for HealthTweets.org were associated with the total number of tweets within a geographical area. The percentage of FNY participants who sought health care for ILI symptoms differed slightly across geographical areas, and these percentages were positively correlated with CDC ILINet and athenahealth baselines.

CONCLUSIONS

Our findings suggest that differences in ILI activity across geographical areas as reported by a given surveillance system may not accurately reflect true differences in the prevalence of ILI. Instead, these differences may reflect systematic collection and aggregation biases that are particular to each system and consistent across influenza seasons. These findings are potentially relevant in the real-time analysis of the influenza season and in the definition of unbiased forecast models.

Flucast: A Real-Time Tool to Predict Severity of an Influenza Season

Background

Influenza causes serious illness requiring annual health system surge capacity, yet annual seasonal variation makes it difficult to forecast and plan for the severity of an upcoming season. Research shows that hospital and health system stakeholders indicate a preference for forecasting tools that are easy to use and understand to assist with surge capacity planning for influenza.

Objective

This study aimed to develop a simple risk prediction tool, Flucast, to predict the severity of an emerging influenza season.

Methods

Study data were obtained from the National Notifiable Diseases Surveillance System and Australian Influenza Surveillance Reports from the Department of Health, Australia. We tested Flucast using retrospective seasonal data for 11 Australian influenza seasons. We compared five different models using parameters known early in the season that may be associated with the severity of the season. To calibrate the tool, the resulting estimates of seasonal severity were validated against independent reports of influenza-attributable morbidity and mortality. The model with the highest predictive accuracy against retrospective seasonal activity was chosen as a best-fit model to develop the Flucast tool. The tool was prospectively tested against the 2018 and the emerging 2019 influenza season.

Results

The Flucast tool predicted the severity of all retrospectively studied years correctly for influenza seasonal activity in Australia. With the use of real-time data, the tool provided a reasonable early prediction of a low to moderate season for the 2018 and severe seasonal activity for the upcoming 2019 season. The tool meets stakeholder preferences for simplicity and ease of use to assist with surge capacity planning.

Conclusions

The Flucast tool may be useful to inform future health system influenza preparedness planning, surge capacity, and intervention programs in real time, and can be adapted for different settings and geographic locations.

Google Trends in Infodemiology and Infoveillance: Methodology Framework

Internet data are being increasingly integrated into health informatics research and are becoming a useful tool for exploring human behavior. The most popular tool for examining online behavior is Google Trends, an open tool that provides information on trends and the variations of online interest in selected keywords and topics over time. Online search traffic data from Google have been shown to be useful in analyzing human behavior toward health topics and in predicting disease occurrence and outbreaks. Despite the large number of Google Trends studies during the last decade, the literature on the subject lacks a specific methodology framework. This article aims at providing an overview of the tool and data and at presenting the first methodology framework in using Google Trends in infodemiology and infoveillance, including the main factors that need to be taken into account for a strong methodology base. We provide a step-by-step guide for the methodology that needs to be followed when using Google Trends and the essential aspects required for valid results in this line of research. At first, an overview of the tool and the data are presented, followed by an analysis of the key methodological points for ensuring the validity of the results, which include selecting the appropriate keyword(s), region(s), period, and category. Overall, this article presents and analyzes the key points that need to be considered to achieve a strong methodological basis for using Google Trends data, which is crucial for ensuring the value and validity of the results, as the analysis of online queries is extensively integrated in health research in the big data era.

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

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

Impact of demographic disparities in social distancing and vaccination on influenza epidemics in urban and rural regions of the United States

Background

Self-protective behaviors of social distancing and vaccination uptake vary by demographics and affect the transmission dynamics of influenza in the United States. By incorporating the socio-behavioral differences in social distancing and vaccination uptake into mathematical models of influenza transmission dynamics, we can improve our estimates of epidemic outcomes. In this study we analyze the impact of demographic disparities in social distancing and vaccination on influenza epidemics in urban and rural regions of the United States.

Methods

We conducted a survey of a nationally representative sample of US adults to collect data on their self-protective behaviors, including social distancing and vaccination to protect themselves from influenza infection. We incorporated this data in an agent-based model to simulate the transmission dynamics of influenza in the urban region of Miami Dade county in Florida and the rural region of Montgomery county in Virginia.

Results

We compare epidemic scenarios wherein the social distancing and vaccination behaviors are uniform versus non-uniform across different demographic subpopulations. We infer that a uniform compliance of social distancing and vaccination uptake among different demographic subpopulations underestimates the severity of the epidemic in comparison to differentiated compliance among different demographic subpopulations. This result holds for both urban and rural regions.

Conclusions

By taking into account the behavioral differences in social distancing and vaccination uptake among different demographic subpopulations in analysis of influenza epidemics, we provide improved estimates of epidemic outcomes that can assist in improved public health interventions for prevention and control of influenza.

Electronic supplementary material

The online version of this article (10.1186/s12879-019-3703-2) contains supplementary material, which is available to authorized users.

Influenza and Viral Pneumonia

Influenza and other respiratory viruses are commonly identified in patients with community-acquired pneumonia, hospital-acquired pneumonia, and in immunocompromised patients with pneumonia. Clinically, it is difficult to differentiate viral from bacterial pneumonia. Similarly, the radiological findings of viral infection are nonspecific. The advent of polymerase chain reaction testing has enormously facilitated the identification of respiratory viruses, which has important implications for infection control measures and treatment. Currently, treatment options for patients with viral infection are limited, but there is ongoing research on the development and clinical testing of new treatment regimens and strategies.

Transmissibility and severity of influenza virus by subtype

The characteristics of influenza might vary depending on the disease subtype. This review includes previous studies on the transmissibility and severity of influenza and summarizes them by subtype. The attack rate and incubation period of influenza A were 2.3-12.3% and 1.4 days, respectively, and those of influenza B were 0.6-5.5% and 0.6 days, respectively. The five subtypes of influenza A virus, namely, H1N1, H2N2, H3N3, H5N1, and H7N9, are reviewed. The indexes related to transmissibility (reproduction number, attack rate, serial interval, latent period, incubation period, infectious period) and severity (hospitalization rate, case fatality rate) differed by influenza subtype. Generally, H3N2 showed a higher attack rate than H1N1 and H2N2. Additionally, H5N1 and H7N9 showed higher mortality rates than the other subtypes, including H1N1, H2N2, H3N2.

Strengthening Global Public Health Surveillance through Data and Benefit Sharing

Equitable sharing of public health surveillance data can help prevent or mitigate the effect of infectious diseases. Equitable data sharing includes working toward more equitable sharing of the public health benefits that data sharing brings and requires the engagement of those providing the data, those interpreting and using the data generated by others, those facilitating the data-sharing process, and those deriving and contributing to the benefit. An expert consultation conducted by Chatham House outlined 7 principles to encourage the process of equitable data sharing: 1) building trust; 2) articulating the value; 3) planning for data sharing; 4) achieving quality data; 5) understanding the legal context; 6) creating data-sharing agreements; and 7) monitoring and evaluation. Sharing of public health surveillance data is best done taking into account these principles, which will help to ensure data are shared optimally and ethically, while fulfilling stakeholder expectations and facilitating equitable distribution of benefits.

Annual estimates of the burden of seasonal influenza in the United States: A tool for strengthening influenza surveillance and preparedness

Background

Estimates of influenza disease burden are broadly useful for public health, helping national and local authorities monitor epidemiologic trends, plan and allocate resources, and promote influenza vaccination. Historically, estimates of the burden of seasonal influenza in the United States, focused mainly on influenza‐related mortality and hospitalization, were generated every few years. Since the 2010‐2011 influenza season, annual US influenza burden estimates have been generated and expanded to include estimates of influenza‐related outpatient medical visits and symptomatic illness in the community.

Methods

We used routinely collected surveillance data, outbreak field investigations, and proportions of people seeking health care from survey results to estimate the number of illnesses, medical visits, hospitalizations, and deaths due to influenza during six influenza seasons (2010‐2011 through 2015‐2016).

Results

We estimate that the number of influenza‐related illnesses that have occurred during influenza season has ranged from 9.2 million to 35.6 million, including 140 000 to 710 000 influenza‐related hospitalizations.

Discussion

These annual efforts have strengthened public health communications products and supported timely assessment of the impact of vaccination through estimates of illness and hospitalizations averted. Additionally, annual estimates of influenza burden have highlighted areas where disease surveillance needs improvement to better support public health decision making for seasonal influenza epidemics as well as future pandemics.

Cohort profile: the China Ageing REespiratory infections Study (CARES), a prospective cohort study in older adults in Eastern China

PURPOSE

This study was established to provide direct evidence on the incidence of laboratory-confirmed influenza virus and respiratory syncytial virus (RSV) infections in older adults in two cities in Jiangsu Province, China, and the potential impact of acute respiratory infections on frailty.

PARTICIPANTS

The cohort was enrolled in Suzhou and Yancheng, two cities in Jiangsu Province in Eastern China. Between November 2015 and March 2016, we enrolled 1532 adults who were 60-89 years of age, and collected blood samples along with baseline data on demographics, general health, chronic diseases, functional status and cognitive function through face-to-face interviews using a standardised questionnaire. Participants are being followed weekly throughout the year to identify acute respiratory illnesses. We schedule home visits to ill participants to collect mid-turbinate nasal and oropharyngeal swabs for laboratory testing and detailed symptom information for the acute illness. Regular follow-up including face-to-face interviews and further blood draws will take place every 6-12 months.

FINDINGS TO DATE

As of 3 September 2016, we had identified 339 qualifying acute respiratory illness events and 1463 (95%) participants remained in the study. Laboratory testing is ongoing.

FUTURE PLANS

We plan to conduct laboratory testing to estimate the incidence of influenza virus and RSV infections in older adults. We plan to investigate the impact of these infections on frailty and functional status to determine the association of pre-existing immune status with protection against influenza and RSV infection in unvaccinated older adults, and to assess the exposure to avian influenza viruses in this population.

Modeling and Forecasting Influenza-like Illness (ILI) in Houston, Texas Using Three Surveillance Data Capture Mechanisms

Objective The objective was to forecast and validate prediction estimates of influenza activity in Houston, TX using four years of historical influenza-like illness (ILI) from three surveillance data capture mechanisms. Background Using novel surveillance methods and historical data to estimate future trends of influenza-like illness can lead to early detection of influenza activity increases and decreases. Anticipating surges gives public health professionals more time to prepare and increase prevention efforts. Methods Data was obtained from three surveillance systems, Flu Near You, ILINet, and hospital emergency center (EC) visits, with diverse data capture mechanisms. Autoregressive integrated moving average (ARIMA) models were fitted to data from each source for week 27 of 2012 through week 26 of 2016 and used to forecast influenza-like activity for the subsequent 10 weeks. Estimates were then compared to actual ILI percentages for the same period. Results Forecasted estimates had wide confidence intervals that crossed zero. The forecasted trend direction differed by data source, resulting in lack of consensus about future influenza activity. ILINet forecasted estimates and actual percentages had the least differences. ILINet performed best when forecasting influenza activity in Houston, TX. Conclusion Though the three forecasted estimates did not agree on the trend directions, and thus, were considered imprecise predictors of long-term ILI activity based on existing data, pooling predictions and careful interpretations may be helpful for short term intervention efforts. Further work is needed to improve forecast accuracy considering the promise forecasting holds for seasonal influenza prevention and control, and pandemic preparedness.

Digital Pharmacovigilance and Disease Surveillance: Combining Traditional and Big-Data Systems for Better Public Health

The digital revolution has contributed to very large data sets (ie, big data) relevant for public health. The two major data sources are electronic health records from traditional health systems and patient-generated data. As the two data sources have complementary strengths-high veracity in the data from traditional sources and high velocity and variety in patient-generated data-they can be combined to build more-robust public health systems. However, they also have unique challenges. Patient-generated data in particular are often completely unstructured and highly context dependent, posing essentially a machine-learning challenge. Some recent examples from infectious disease surveillance and adverse drug event monitoring demonstrate that the technical challenges can be solved. Despite these advances, the problem of verification remains, and unless traditional and digital epidemiologic approaches are combined, these data sources will be constrained by their intrinsic limits.

Coughing, sneezing, and aching online: Twitter and the volume of influenza-like illness in a pediatric hospital

This study investigates the relation of the incidence of georeferenced tweets related to respiratory illness to the incidence of influenza-like illness (ILI) in the emergency department (ED) and urgent care clinics (UCCs) of a large pediatric hospital. We collected (1) tweets in English originating in our hospital’s primary service area between 11/1/2014 and 5/1/2015 and containing one or more specific terms related to respiratory illness and (2) the daily number of patients presenting to our hospital’s EDs and UCCs with ILI, as captured by ICD-9 codes. A Support Vector Machine classifier was applied to the set of tweets to remove those unlikely to be related to ILI. Time series of the pooled set of remaining tweets involving any term, of tweets involving individual terms, and of the ICD-9 data were constructed, and temporal cross-correlation between the social media and clinical data was computed. A statistically significant correlation (Spearman ρ = 0.23) between tweets involving the term flu and ED and UCC volume related to ILI 11 days in the future was observed. Tweets involving the terms coughing (Spearman ρ = 0.24) and headache (Spearman ρ = 0.19) individually were also significantly correlated to ILI-related clinical volume four and two days in the future, respectively. In the 2014–2015 cold and flu season, the incidence of local tweets containing the terms flu, coughing, and headache were early indicators of the incidence of ILI-related cases presenting to EDs and UCCs at our children’s hospital.

A Platform for Crowdsourced Foodborne Illness Surveillance: Description of Users and Reports

Background

Underreporting of foodborne illness makes foodborne disease burden estimation, timely outbreak detection, and evaluation of policies toward improving food safety challenging.

Objective

The objective of this study was to present and evaluate Iwaspoisoned.com, an openly accessible Internet-based crowdsourcing platform that was launched in 2009 for the surveillance of foodborne illness. The goal of this system is to collect data that can be used to augment traditional approaches to foodborne disease surveillance.

Methods

Individuals affected by a foodborne illness can use this system to report their symptoms and the suspected location (eg, restaurant, hotel, hospital) of infection. We present descriptive statistics of users and businesses and highlight three instances where reports of foodborne illness were submitted before the outbreaks were officially confirmed by the local departments of health.

Results

More than 49,000 reports of suspected foodborne illness have been submitted on Iwaspoisoned.com since its inception by individuals from 89 countries and every state in the United States. Approximately 95.51% (42,139/44,119) of complaints implicated restaurants as the source of illness. Furthermore, an estimated 67.55% (3118/4616) of users who responded to a demographic survey were between the ages of 18 and 34, and 60.14% (2776/4616) of the respondents were female. The platform is also currently used by health departments in 90% (45/50) of states in the US to supplement existing programs on foodborne illness reporting.

Conclusions

Crowdsourced disease surveillance through systems such as Iwaspoisoned.com uses the influence and familiarity of social media to create an infrastructure for easy reporting and surveillance of suspected foodborne illness events. If combined with traditional surveillance approaches, these systems have the potential to lessen the problem of foodborne illness underreporting and aid in early detection and monitoring of foodborne disease outbreaks.

Use of a Digital Health Application for Influenza Surveillance in China

OBJECTIVES

To examine whether a commercial digital health application could support influenza surveillance in China.

METHODS

We retrieved data from the Thermia online and mobile educational tool, which allows parents to monitor their children's fever and infectious febrile illnesses including influenza. We modeled monthly aggregated influenza-like illness case counts from Thermia users over time and compared them against influenza monthly case counts obtained from the National Health and Family Planning Commission of the People's Republic of China by using time series regression analysis. We retrieved 44 999 observations from January 2014 through July 2016 from Thermia China.

RESULTS

Thermia appeared to predict influenza outbreaks 1 month earlier than the National Health and Family Planning Commission influenza surveillance system (P = .046). Being younger, not having up-to-date immunizations, and having an underlying health condition were associated with participant-reported influenza-like illness.

CONCLUSIONS

Digital health applications could supplement traditional influenza surveillance systems in China by providing access to consumers' symptom reporting. Growing popularity and use of commercial digital health applications in China potentially affords opportunities to support disease detection and monitoring and rapid treatment mobilization.

Determinants of Participants' Follow-Up and Characterization of Representativeness in Flu Near You, A Participatory Disease Surveillance System

Background

Flu Near You (FNY) is an Internet-based participatory surveillance system in the United States and Canada that allows volunteers to report influenza-like symptoms using a brief weekly symptom report.

Objective

Our objective was to evaluate the representativeness of the FNY population compared with the general population of the United States, explore the demographic and behavioral characteristics associated with FNY’s high-participation users, and summarize results from a user survey of a cohort of FNY participants.

Methods

We compared (1) the representativeness of sex and age groups of FNY participants during the 2014-2015 flu season versus the general US population and (2) the distribution of Human Development Index (HDI) scores of FNY participants versus that of the general US population. We analyzed associations between demographic and behavioral factors and the level of participant follow-up (ie, high vs low). Finally, descriptive statistics of responses from FNY’s 2015 and 2016 end-of-season user surveys were calculated.

Results

During the 2014-2015 influenza season, 47,234 unique participants had at least one FNY symptom report that was either self-reported (users) or submitted on their behalf (household members). The proportion of female FNY participants was significantly higher than that of the general US population (n=28,906, 61.2% vs 51.1%, P<.001). Although each age group was represented in the FNY population, the age distribution was significantly different from that of the US population (P<.001). Compared with the US population, FNY had a greater proportion of individuals with HDI >5.0, signaling that the FNY user distribution was more affluent and educated than the US population baseline. We found that high-participation use (ie, higher participation in follow-up symptom reports) was associated with sex (females were 25% less likely than men to be high-participation users), higher HDI, not reporting an influenza-like illness at the first symptom report, older age, and reporting for household members (all differences between high- and low-participation users P<.001). Approximately 10% of FNY users completed an additional survey at the end of the flu season that assessed detailed user characteristics (3217/33,324 in 2015; 4850/44,313 in 2016). Of these users, most identified as being either retired or employed in the health, education, and social services sectors and indicated that they achieved a bachelor’s degree or higher.

Conclusions

The representativeness of the FNY population and characteristics of its high-participation users are consistent with what has been observed in other Internet-based influenza surveillance systems. With targeted recruitment of underrepresented populations, FNY may improve as a complementary system to timely tracking of flu activity, especially in populations that do not seek medical attention and in areas with poor official surveillance data.

Universal influenza virus vaccines and therapeutic antibodies

BACKGROUND

Current influenza virus vaccines are effective when well matched to the circulating strains. Unfortunately, antigenic drift and the high diversity of potential emerging zoonotic and pandemic viruses make it difficult to select the right strains for vaccine production. This problem causes vaccine mismatches, which lead to sharp drops in vaccine effectiveness and long response times to manufacture matched vaccines in case of novel pandemic viruses.

AIMS

To provide an overview of universal influenza virus vaccines and therapeutic antibodies in preclinical and clinical development.

SOURCES

PubMed and clinicaltrials.gov were used as sources for this review.

CONTENT

Universal influenza virus vaccines that target conserved regions of the influenza virus including the haemagglutinin stalk domain, the ectodomain of the M2 ion channel or the internal matrix and nucleoproteins are in late preclinical and clinical development. These vaccines could confer broad protection against all influenza A and B viruses including drift variants and thereby abolish the need for annual re-formulation and re-administration of influenza virus vaccines. In addition, these novel vaccines would enhance preparedness against emerging influenza virus pandemics. Finally, novel therapeutic antibodies against the same conserved targets are in clinical development and could become valuable tools in the fight against influenza virus infection.

IMPLICATIONS

Both universal influenza virus vaccines and therapeutic antibodies are potential future options for the control of human influenza infections.

Participatory Online Surveillance as a Supplementary Tool to Sentinel Doctors for Influenza-Like Illness Surveillance in Italy

The monitoring of seasonal influenza yearly epidemics remains one of the main activity of national syndromic surveillance systems. The development of internet-based surveillance tools has brought an innovative approach to seasonal influenza surveillance by directly involving self-selected volunteers among the general population reporting their health status on a weekly basis throughout the flu season. In this paper, we explore how Influweb, an internet-based monitoring system for influenza surveillance, deployed in Italy since 2008 has performed during three years from 2012 to 2015 in comparison with data collected during the same period by the Italian sentinel doctors surveillance system.

Universal or Specific? A Modeling-Based Comparison of Broad-Spectrum Influenza Vaccines against Conventional, Strain-Matched Vaccines

Despite the availability of vaccines, influenza remains a major public health challenge. A key reason is the virus capacity for immune escape: ongoing evolution allows the continual circulation of seasonal influenza, while novel influenza viruses invade the human population to cause a pandemic every few decades. Current vaccines have to be updated continually to keep up to date with this antigenic change, but emerging ‘universal’ vaccines—targeting more conserved components of the influenza virus—offer the potential to act across all influenza A strains and subtypes. Influenza vaccination programmes around the world are steadily increasing in their population coverage. In future, how might intensive, routine immunization with novel vaccines compare against similar mass programmes utilizing conventional vaccines? Specifically, how might novel and conventional vaccines compare, in terms of cumulative incidence and rates of antigenic evolution of seasonal influenza? What are their potential implications for the impact of pandemic emergence? Here we present a new mathematical model, capturing both transmission dynamics and antigenic evolution of influenza in a simple framework, to explore these questions. We find that, even when matched by per-dose efficacy, universal vaccines could dampen population-level transmission over several seasons to a greater extent than conventional vaccines. Moreover, by lowering opportunities for cross-protective immunity in the population, conventional vaccines could allow the increased spread of a novel pandemic strain. Conversely, universal vaccines could mitigate both seasonal and pandemic spread. However, where it is not possible to maintain annual, intensive vaccination coverage, the duration and breadth of immunity raised by universal vaccines are critical determinants of their performance relative to conventional vaccines. In future, conventional and novel vaccines are likely to play complementary roles in vaccination strategies against influenza: in this context, our results suggest important characteristics to monitor during the clinical development of emerging vaccine technologies.

Influenza vaccines used today offer good protection, but have limitations: they have to be updated regularly, to remain effective in the face of ongoing virus evolution, and they cannot be used in advance of an influenza pandemic. In this study we considered how such ‘conventional’ vaccines might compare on the population level against new ‘universal’ vaccines currently being developed, that may protect against a broad spectrum of influenza viruses. We developed a mathematical model to capture the interactions between vaccination, influenza transmission, and viral evolution. The model suggests that annual vaccination with universal vaccines could control annual influenza epidemics more efficiently than conventional vaccines. In doing so they could slow viral evolution, rather than promoting it, while maintaining the broadly protective immunity that could mitigate against the emergence of a pandemic. These effects depend sensitively on the duration of protection that universal vaccines can afford, an important quantity to monitor in their development. In future, it is likely that conventional and universal vaccines would be deployed in tandem: we suggest that they could fulfill distinct roles, with universal vaccines being prioritised for managing transmission and evolution, and conventional vaccines being focused on protecting specific risk groups.

Evaluating Google, Twitter, and Wikipedia as Tools for Influenza Surveillance Using Bayesian Change Point Analysis: A Comparative Analysis

Background

Traditional influenza surveillance relies on influenza-like illness (ILI) syndrome that is reported by health care providers. It primarily captures individuals who seek medical care and misses those who do not. Recently, Web-based data sources have been studied for application to public health surveillance, as there is a growing number of people who search, post, and tweet about their illnesses before seeking medical care. Existing research has shown some promise of using data from Google, Twitter, and Wikipedia to complement traditional surveillance for ILI. However, past studies have evaluated these Web-based sources individually or dually without comparing all 3 of them, and it would be beneficial to know which of the Web-based sources performs best in order to be considered to complement traditional methods.

Objective

The objective of this study is to comparatively analyze Google, Twitter, and Wikipedia by examining which best corresponds with Centers for Disease Control and Prevention (CDC) ILI data. It was hypothesized that Wikipedia will best correspond with CDC ILI data as previous research found it to be least influenced by high media coverage in comparison with Google and Twitter.

Methods

Publicly available, deidentified data were collected from the CDC, Google Flu Trends, HealthTweets, and Wikipedia for the 2012-2015 influenza seasons. Bayesian change point analysis was used to detect seasonal changes, or change points, in each of the data sources. Change points in Google, Twitter, and Wikipedia that occurred during the exact week, 1 preceding week, or 1 week after the CDC’s change points were compared with the CDC data as the gold standard. All analyses were conducted using the R package “bcp” version 4.0.0 in RStudio version 0.99.484 (RStudio Inc). In addition, sensitivity and positive predictive values (PPV) were calculated for Google, Twitter, and Wikipedia.

Results

During the 2012-2015 influenza seasons, a high sensitivity of 92% was found for Google, whereas the PPV for Google was 85%. A low sensitivity of 50% was calculated for Twitter; a low PPV of 43% was found for Twitter also. Wikipedia had the lowest sensitivity of 33% and lowest PPV of 40%.

Conclusions

Of the 3 Web-based sources, Google had the best combination of sensitivity and PPV in detecting Bayesian change points in influenza-related data streams. Findings demonstrated that change points in Google, Twitter, and Wikipedia data occasionally aligned well with change points captured in CDC ILI data, yet these sources did not detect all changes in CDC data and should be further studied and developed.