Web-based participatory surveillance of infectious diseases: the Influenzanet participatory surveillance experience

Paolotti, Daniela; Carnahan, AnnaSara; Colizza, Vittoria; Eames, Ken T. D.; Edmunds, John; Gomes, M. Gabriela M.; Koppeschaar, Carl; Rehn, Moa; Smallenburg, Ronald; Turbelin, Clément; Noort, S. van; Vespignani, Alessandro

doi:10.1111/1469-0691.12477

Cited by 165 publications

(155 citation statements)

References 10 publications

Supporting

Mentioning

153

Contrasting

Order By: Relevance

“…FluNearYou [645,646], and InfluenzaNet [647,648] are two examples. The first platform engages individuals living in USA or Canada.…”

Section: Participatory Platforms For Digital Surveillancementioning

confidence: 99%

“…In other words, the two platforms are systems for digital surveillance. Remarkably, several studies have shown that the ILI incidence estimated using these platforms is highly correlated with the classic ILI surveillance data [646,648,649]. With minimal participation from the subscribed users they allow to overcome several limitations of the classic systems.…”

Section: Participatory Platforms For Digital Surveillancementioning

confidence: 99%

See 1 more Smart Citation

Statistical physics of vaccination

et al. 2016

View full text Add to dashboard Cite

Historically, infectious diseases caused considerable damage to human societies, and they continue to do so today. To help reduce their impact, mathematical models of disease transmission have been studied to help understand disease dynamics and inform prevention strategies. Vaccination-one of the most important preventive measures of modern times-is of great interest both theoretically and empirically. And in contrast to traditional approaches, recent research increasingly explores the pivotal implications of individual behavior and heterogeneous contact patterns in populations. Our report reviews the developmental arc of theoretical epidemiology with emphasis on vaccination, as it led from classical models assuming homogeneously mixing (mean-field) populations and ignoring human behavior, to recent models that account for behavioral feedback and/or population spatial/social structure. Many of the methods used originated in statistical physics, such as lattice and network models, and their associated analytical frameworks. Similarly, the feedback loop between vaccinating behavior and disease propagation forms a coupled nonlinear system with analogs in physics. We also review the new paradigm of digital epidemiology, wherein sources of digital data such as online social media are mined for high-resolution information on epidemiologically relevant individual behavior. Armed with the tools and concepts of statistical physics, and further assisted by new sources of digital data, models that capture nonlinear interactions between behavior and disease dynamics offer a novel way of modeling real-world phenomena, and can help improve health outcomes. We conclude the review by discussing open problems in the field and promising directions for future research.

show abstract

“…FluNearYou [645,646], and InfluenzaNet [647,648] are two examples. The first platform engages individuals living in USA or Canada.…”

Section: Participatory Platforms For Digital Surveillancementioning

confidence: 99%

Section: Participatory Platforms For Digital Surveillancementioning

confidence: 99%

Statistical physics of vaccination

et al. 2016

View full text Add to dashboard Cite

show abstract

“…5 Media reports of case numbers and deaths contributed to a distorted view of severity and political pressure for robust control measures. Attempts were made to identify community case numbers through prospective internet surveys (Europe) 6,7 or retrospective telephone surveys (USA). 8,9 These helped to provide better denominator estimates for calculation of CFR.…”

Section: Fundingmentioning

confidence: 99%

Population-level susceptibility, severity and spread of pandemic influenza: design of, and initial results from, a pre-pandemic and hibernating pandemic phase study using cross-sectional data from the Health Survey for England (HSE)

Fragaszy

Quinlivan

Breuer³

et al. 2015

Public Health Res

View full text Add to dashboard Cite

BackgroundAssessing severity and spread of a novel influenza strain at the start of a pandemic is critical for informing a targeted and proportional response. It requires community-level studies to estimate the burden of infection and disease. Rapidly initiating such studies in a pandemic is difficult. The study aims to establish an efficient system allowing real-time assessment of population susceptibility, spread of infection and clinical attack rates in the event of a pandemic.MethodsWe developed and appended additional survey questions and specimen collection to the Health Survey for England (HSE) – a large, annual, rolling nationally representative general population survey recruiting throughout the year – to enable rapid population-based surveys of influenza infection and disease during a pandemic. Using these surveys we can assess the spread of the virus geographically, by age and through time. The data generated can also provide denominators for national estimates of case fatality and hospitalisation rates.Phase 1: we compared retrospectively collected HSE illness rates during the first two infection waves of the 2009 pandemic with the Flu Watch study (a prospective community cohort). Monthly and seasonal age-specific rates of illness and proportion vaccinated were compared.Phase 2: we piloted blood specimen and data collection alongside the 2012–13 HSE. We are developing laboratory methods and protocols for real-time serological assays of a novel pandemic influenza virus using these specimens, and automated programmes for analysing and reporting illness and infection rates.Phase 3: during inter-pandemic years, the study enters a holding phase, where it is included in the yearly HSE ethics application and planning procedures, allowing rapid triggering in a pandemic.Phase 4: once retriggered, the study will utilise the methods developed in phase 2 to monitor the severity and spread of the pandemic in real time.ResultsPhase 1: the rates of reported illness during the first two waves in the HSE underestimated the community burden as measured by Flu Watch, but the patterns of illness by age and time were broadly comparable. The extent of underestimation was greatest for HSE participants interviewed later in the year compared with those interviewed closer to the pandemic. Vaccine uptake in the HSE study was comparable to independent national estimates and the Flu Watch study.Phases 2 and 3: illness data and serological samples from 2018 participants were collected in the 2012–13 HSE and transferred to the University College London Hospital. In the 2013 HSE and onwards, this project was included in the annual HSE ethics and planning rounds.ConclusionsThe HSE’s underestimation of illness rates during the first two waves of the pandemic is probably due to recall bias and the limitation of being able to report only one illness when multiple illnesses per season can occur. Changes to the illness questions (reporting only recent illnesses) should help minimise these issues. Additional prospective follow-up could improve measurement of disease incidence. The representative nature of the HSE allows accurate measurements of vaccine uptake.Study registrationThis study is registered as ISRCTN80214280.FundingThis project was funded by the NIHR Public Health Research programme and will be published in full inPublic Health Research; Vol. 3, No. 6. See the NIHR Journals Library website for further project information.

show abstract

“…We are at the dawn of the digital revolution in epidemiology [7,8]. The new approach allows for the early detection of disease outbreaks [62], the real time monitoring of the evolution of a disease with an incredible geographical granularity [63][64][65], the access to health related behaviors, practices and sentiments at large scales [66,67], inform data-driven epidemic models [68,69], and development of statistical based models with prediction power [67,[70][71][72][73][74][75][76][77][78].…”

Section: Digital Epidemiologymentioning

confidence: 99%

“…In active data collection users are asked to share their health status using apps and web-based platforms [79]. Examples are influenzanet that is available in different European countries [64], and Flu near you in the USA [65] that engage tens of thousands of users that together provide the information necessary for the creation of interactive maps of ILI in almost real time. In passive data collection, instead, information about individuals health status is mined from other available sources that do not require the active participation of users.…”

Section: Digital Epidemiologymentioning

confidence: 99%