1
|
Bokányi E, Vizi Z, Koltai J, Röst G, Karsai M. Real-time estimation of the effective reproduction number of COVID-19 from behavioral data. Sci Rep 2023; 13:21452. [PMID: 38052841 PMCID: PMC10698193 DOI: 10.1038/s41598-023-46418-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 10/31/2023] [Indexed: 12/07/2023] Open
Abstract
Monitoring the effective reproduction number [Formula: see text] of a rapidly unfolding pandemic in real-time is key to successful mitigation and prevention strategies. However, existing methods based on case numbers, hospital admissions or fatalities suffer from multiple measurement biases and temporal lags due to high test positivity rates or delays in symptom development or administrative reporting. Alternative methods such as web search and social media tracking are less directly indicating epidemic prevalence over time. We instead record age-stratified anonymous contact matrices at a daily resolution using a longitudinal online-offline survey in Hungary during the first two waves of the COVID-19 pandemic. This approach is innovative, cheap, and provides information in near real-time for estimating [Formula: see text] at a daily resolution. Moreover, it allows to complement traditional surveillance systems by signaling periods when official monitoring infrastructures are unreliable due to observational biases.
Collapse
Affiliation(s)
- Eszter Bokányi
- Institute of Logic, Language and Computation, University of Amsterdam, 1090GE, Amsterdam, The Netherlands
| | - Zsolt Vizi
- National Laboratory for Health Security, University of Szeged, Szeged, 6720, Hungary
| | - Júlia Koltai
- National Laboratory for Health Security, Centre for Social Sciences, Budapest, 1097, Hungary
- Faculty of Social Sciences, Eötvös Loránd University, Budapest, 1117, Hungary
| | - Gergely Röst
- National Laboratory for Health Security, University of Szeged, Szeged, 6720, Hungary
| | - Márton Karsai
- Department of Network and Data Science, Central European University, 1100, Vienna, Austria.
- National Laboratory for Health Security, Alfréd Rényi Institute of Mathematics, Budapest, 1053, Hungary.
| |
Collapse
|
2
|
Atkins N, Harikar M, Duggan K, Zawiejska A, Vardhan V, Vokey L, Dozier M, de los Godos EF, Mcswiggan E, Mcquillan R, Theodoratou E, Shi T. What are the characteristics of participatory surveillance systems for influenza-like-illness? J Glob Health 2023; 13:04130. [PMID: 37856769 PMCID: PMC10587643 DOI: 10.7189/jogh.13.04130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2023] Open
Abstract
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.
Collapse
Affiliation(s)
- Nadege Atkins
- Center for Population Health Sciences, Usher Institute, University of Edinburgh, Scotland, UK
- UNCOVER (Usher Network for COVID-19 Evidence Reviews) Usher Institute, University of Edinburgh, Edinburgh, UK
- Joint first authorship
| | - Mandara Harikar
- Center for Population Health Sciences, Usher Institute, University of Edinburgh, Scotland, UK
- UNCOVER (Usher Network for COVID-19 Evidence Reviews) Usher Institute, University of Edinburgh, Edinburgh, UK
- Joint first authorship
| | - Kirsten Duggan
- Center for Population Health Sciences, Usher Institute, University of Edinburgh, Scotland, UK
- UNCOVER (Usher Network for COVID-19 Evidence Reviews) Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Agnieszka Zawiejska
- Center for Population Health Sciences, Usher Institute, University of Edinburgh, Scotland, UK
- UNCOVER (Usher Network for COVID-19 Evidence Reviews) Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Vaishali Vardhan
- Center for Population Health Sciences, Usher Institute, University of Edinburgh, Scotland, UK
- UNCOVER (Usher Network for COVID-19 Evidence Reviews) Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Laura Vokey
- Center for Population Health Sciences, Usher Institute, University of Edinburgh, Scotland, UK
- UNCOVER (Usher Network for COVID-19 Evidence Reviews) Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Marshall Dozier
- Center for Population Health Sciences, Usher Institute, University of Edinburgh, Scotland, UK
- UNCOVER (Usher Network for COVID-19 Evidence Reviews) Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Emma F de los Godos
- Center for Population Health Sciences, Usher Institute, University of Edinburgh, Scotland, UK
- UNCOVER (Usher Network for COVID-19 Evidence Reviews) Usher Institute, University of Edinburgh, Edinburgh, UK
- Equal contribution
| | - Emilie Mcswiggan
- Center for Population Health Sciences, Usher Institute, University of Edinburgh, Scotland, UK
- UNCOVER (Usher Network for COVID-19 Evidence Reviews) Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Ruth Mcquillan
- Center for Population Health Sciences, Usher Institute, University of Edinburgh, Scotland, UK
- UNCOVER (Usher Network for COVID-19 Evidence Reviews) Usher Institute, University of Edinburgh, Edinburgh, UK
- Equal contribution
| | - Evropi Theodoratou
- Center for Population Health Sciences, Usher Institute, University of Edinburgh, Scotland, UK
- UNCOVER (Usher Network for COVID-19 Evidence Reviews) Usher Institute, University of Edinburgh, Edinburgh, UK
- Equal contribution
| | - Ting Shi
- Center for Population Health Sciences, Usher Institute, University of Edinburgh, Scotland, UK
- Equal contribution
| |
Collapse
|
3
|
McNeil C, Verlander S, Divi N, Smolinski M. Straight from the source: Landscape of Participatory Surveillance Systems across the One Health Spectrum (Preprint). JMIR Public Health Surveill 2022; 8:e38551. [PMID: 35930345 PMCID: PMC9391976 DOI: 10.2196/38551] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 05/11/2022] [Accepted: 06/28/2022] [Indexed: 11/13/2022] Open
Affiliation(s)
| | | | - Nomita Divi
- Ending Pandemics, San Francisco, CA, United States
| | | |
Collapse
|
4
|
Marmara V, Marmara D, McMenemy P, Kleczkowski A. Cross-sectional telephone surveys as a tool to study epidemiological factors and monitor seasonal influenza activity in Malta. BMC Public Health 2021; 21:1828. [PMID: 34627201 PMCID: PMC8502089 DOI: 10.1186/s12889-021-11862-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Accepted: 09/27/2021] [Indexed: 11/29/2022] Open
Abstract
Background Seasonal influenza has major implications for healthcare services as outbreaks often lead to high activity levels in health systems. Being able to predict when such outbreaks occur is vital. Mathematical models have extensively been used to predict epidemics of infectious diseases such as seasonal influenza and to assess effectiveness of control strategies. Availability of comprehensive and reliable datasets used to parametrize these models is limited. In this paper we combine a unique epidemiological dataset collected in Malta through General Practitioners (GPs) with a novel method using cross-sectional surveys to study seasonal influenza dynamics in Malta in 2014–2016, to include social dynamics and self-perception related to seasonal influenza. Methods Two cross-sectional public surveys (n = 406 per survey) were performed by telephone across the Maltese population in 2014–15 and 2015–16 influenza seasons. Survey results were compared with incidence data (diagnosed seasonal influenza cases) collected by GPs in the same period and with Google Trends data for Malta. Information was collected on whether participants recalled their health status in past months, occurrences of influenza symptoms, hospitalisation rates due to seasonal influenza, seeking GP advice, and other medical information. Results We demonstrate that cross-sectional surveys are a reliable alternative data source to medical records. The two surveys gave comparable results, indicating that the level of recollection among the public is high. Based on two seasons of data, the reporting rate in Malta varies between 14 and 22%. The comparison with Google Trends suggests that the online searches peak at about the same time as the maximum extent of the epidemic, but the public interest declines and returns to background level. We also found that the public intensively searched the Internet for influenza-related terms even when number of cases was low. Conclusions Our research shows that a telephone survey is a viable way to gain deeper insight into a population’s self-perception of influenza and its symptoms and to provide another benchmark for medical statistics provided by GPs and Google Trends. The information collected can be used to improve epidemiological modelling of seasonal influenza and other infectious diseases, thus effectively contributing to public health. Supplementary Information The online version contains supplementary material available at 10.1186/s12889-021-11862-x.
Collapse
Affiliation(s)
- V Marmara
- Faculty of Economics, Management & Accountancy, University of Malta, Msida, MSD, 2080, Malta
| | - D Marmara
- Faculty of Health Sciences, Mater Dei Hospital, Block A, Level 1, University of Malta, Msida, MSD, 2090, Malta.
| | - P McMenemy
- Department of Mathematics, University of Stirling, Stirling, FK94LA, Scotland, UK
| | - A Kleczkowski
- Department of Mathematics and Statistics, University of Strathclyde, Rm. 1001, 26 Richmond Street, Glasgow, G1 1XH, Scotland
| |
Collapse
|
5
|
Huang W, Cao B, Yang G, Luo N, Chao N. Turn to the Internet First? Using Online Medical Behavioral Data to Forecast COVID-19 Epidemic Trend. Inf Process Manag 2021; 58:102486. [PMID: 33519039 PMCID: PMC7836698 DOI: 10.1016/j.ipm.2020.102486] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 12/21/2020] [Accepted: 12/26/2020] [Indexed: 12/23/2022]
Abstract
The surveillance and forecast of newly confirmed cases are important to mobilize medical resources and facilitate policymaking during a public health emergency. Digital surveillance using data available online has increasingly become a trend with the advancement of the Internet. In this study, we assessed the predictive value of multiple online medical behavioral data, including online medical consultation (OMC), online medical appointment (OMA), and online medical search (OMS) for the regional outbreak of coronavirus disease 2019 in Shenzhen, China during January 1, 2020 to March 5, 2020. Multivariate vector autoregression models were used for the prediction. The results identified a novel predictor, OMC, which can forecast the disease trend up to 2 days ahead of the official reports of confirmed cases from the local health department. OMS data had relatively weaker predictive power than OMC in our model, and OMA data failed to predict the confirmed cases. This study highlights the importance of OMC data and has implication in providing evidence-based guidelines for local authorities to evaluate risks and allocate resources during the pandemic.
Collapse
Affiliation(s)
- Wensen Huang
- School of Media and Communication, Shenzhen University, No. 3688 Nanhai Avenue, Nanshan District, Shenzhen, China
| | - Bolin Cao
- School of Media and Communication, Shenzhen University, No. 3688 Nanhai Avenue, Nanshan District, Shenzhen, China
| | - Guang Yang
- School of Media and Communication, Shenzhen University, No. 3688 Nanhai Avenue, Nanshan District, Shenzhen, China
| | - Ningzheng Luo
- Health 160, Shenzhen Ningyuan Technology Co., Ltd., Shenzhen, China
| | - Naipeng Chao
- School of Media and Communication, Shenzhen University, No. 3688 Nanhai Avenue, Nanshan District, Shenzhen, China
| |
Collapse
|
6
|
Carter P, Megnin-Viggars O, Rubin GJ. What Factors Influence Symptom Reporting and Access to Healthcare During an Emerging Infectious Disease Outbreak? A Rapid Review of the Evidence. Health Secur 2021; 19:353-363. [PMID: 33416425 PMCID: PMC8403196 DOI: 10.1089/hs.2020.0126] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023] Open
Abstract
During any emerging infectious disease outbreak, people with symptoms of the illness are asked to report to a health service immediately to facilitate contact tracing. Several factors may influence a person's willingness to report symptoms and their ability to access healthcare services. Understanding these factors has become urgent during the COVID-19 pandemic. To determine which factors influence symptom reporting during an emerging infectious disease outbreak, we conducted a rapid review of the evidence. Studies included in the review were based on primary research, published in a peer-reviewed journal, written in English, included factors associated with symptom reporting or accessing healthcare, and were related to a major public health incident involving an infectious disease outbreak. Five themes were identified as facilitators of symptom reporting or accessing healthcare: accurate and informative communication about the disease and the need to seek help, symptom severity, concern about disease exposure, ease of access to healthcare facilities, and relationship with the healthcare provider. Seven themes were identified as barriers to symptom reporting or accessing healthcare: lack of knowledge of the disease and its treatment, fear of the disease and fear of subsequent treatments or requirements, stigmatization attached to having a disease, invasion of privacy, low concern about symptoms, economic consequences of disease diagnosis, and challenges related to attending a healthcare facility. For contract tracing services to be effective, members of the public need to have the capability, opportunity, and motivation to use them. The themes identified should be used to evaluate information provided to the public to ensure as many people as possible with relevant symptoms report them to a healthcare provider.
Collapse
Affiliation(s)
- Patrice Carter
- Patrice Carter, PhD, and Odette Megnin-Viggars, PhD, are Senior Systematic Reviewers; both at the Centre for Outcomes Research and Effectiveness, Research Department of Clinical, Educational and Health Psychology, University College London, London, UK. Patrice Carter is also a Senior Systematic Reviewer, Health Economics & Outcomes Research Ltd, Cardiff, UK. G. James Rubin, PhD, is Assistant Director, Health Protection Unit in Emergency Preparedness and Response, Department of Psychological Medicine, Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK
| | - Odette Megnin-Viggars
- Patrice Carter, PhD, and Odette Megnin-Viggars, PhD, are Senior Systematic Reviewers; both at the Centre for Outcomes Research and Effectiveness, Research Department of Clinical, Educational and Health Psychology, University College London, London, UK. Patrice Carter is also a Senior Systematic Reviewer, Health Economics & Outcomes Research Ltd, Cardiff, UK. G. James Rubin, PhD, is Assistant Director, Health Protection Unit in Emergency Preparedness and Response, Department of Psychological Medicine, Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK
| | - G James Rubin
- Patrice Carter, PhD, and Odette Megnin-Viggars, PhD, are Senior Systematic Reviewers; both at the Centre for Outcomes Research and Effectiveness, Research Department of Clinical, Educational and Health Psychology, University College London, London, UK. Patrice Carter is also a Senior Systematic Reviewer, Health Economics & Outcomes Research Ltd, Cardiff, UK. G. James Rubin, PhD, is Assistant Director, Health Protection Unit in Emergency Preparedness and Response, Department of Psychological Medicine, Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK
| |
Collapse
|
7
|
Richard A, Müller L, Wisniak A, Thiabaud A, Merle T, Dietrich D, Paolotti D, Jeannot E, Flahault A. Grippenet: A New Tool for the Monitoring, Risk-Factor and Vaccination Coverage Analysis of Influenza-Like Illness in Switzerland. Vaccines (Basel) 2020; 8:vaccines8030343. [PMID: 32605076 PMCID: PMC7565003 DOI: 10.3390/vaccines8030343] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 06/16/2020] [Accepted: 06/23/2020] [Indexed: 11/25/2022] Open
Abstract
Implemented in Switzerland in November 2016, Grippenet provides Internet-based participatory surveillance of influenza-like illness (ILI). The aim of this research is to test the feasibility of such a system and its ability to detect risk factors and to assess ILI-related behaviors. Participants filled in a web-based socio-demographic and behavioral questionnaire upon registration, and a weekly symptoms survey during the influenza season. ILI incidence was calculated weekly, and risk factors associated to ILI were analyzed at the end of each season. From November 2016 to May 2019, 1247 participants were included. The crossing of the Sentinel System (Sentinella) epidemic threshold was associated with an increase or decrease of Grippenet ILI incidence, within the same week or earlier. The number of active users varied according to ILI incidence. Factors associated with ILI were: ages 0–4 compared with 5–14 (adjusted odds ratio (AOR) 0.6, 95% confidence interval (CI) 0.19–0.99), 15–29 (AOR 0.29, 95% CI 0.15–0.60), and 65+ (AOR 0.38, 95% CI 0.16–0.93); female sex (male AOR 0.81, 95% CI 0.7–0.95); respiratory allergies (AOR 1.58, 95% CI 1.38–1.96), not being vaccinated (AOR 2.4, 95% CI 1.9–3.04); and self-employment (AOR 1.97, 95% CI 1.33–3.03). Vaccination rates were higher than those of the general population but not high enough to meet the Swiss recommendations. Approximately, 36.2% to 42.5% of users who reported one or more ILIs did not seek medical attention. These results illustrate the potential of Grippenet in complementing Sentinella for ILI monitoring in Switzerland.
Collapse
Affiliation(s)
- Aude Richard
- Institute of Global Health, Faculty of Medicine, University of Geneva, 1202 Geneva, Switzerland; (L.M.); (A.W.); (A.T.); (T.M.); (D.D.); (E.J.); (A.F.)
- Correspondence:
| | - Laura Müller
- Institute of Global Health, Faculty of Medicine, University of Geneva, 1202 Geneva, Switzerland; (L.M.); (A.W.); (A.T.); (T.M.); (D.D.); (E.J.); (A.F.)
| | - Ania Wisniak
- Institute of Global Health, Faculty of Medicine, University of Geneva, 1202 Geneva, Switzerland; (L.M.); (A.W.); (A.T.); (T.M.); (D.D.); (E.J.); (A.F.)
| | - Amaury Thiabaud
- Institute of Global Health, Faculty of Medicine, University of Geneva, 1202 Geneva, Switzerland; (L.M.); (A.W.); (A.T.); (T.M.); (D.D.); (E.J.); (A.F.)
| | - Thibaut Merle
- Institute of Global Health, Faculty of Medicine, University of Geneva, 1202 Geneva, Switzerland; (L.M.); (A.W.); (A.T.); (T.M.); (D.D.); (E.J.); (A.F.)
| | - Damien Dietrich
- Institute of Global Health, Faculty of Medicine, University of Geneva, 1202 Geneva, Switzerland; (L.M.); (A.W.); (A.T.); (T.M.); (D.D.); (E.J.); (A.F.)
- Luxembourg Institute of Health, 1445 Strassen, Luxemburg
| | - Daniela Paolotti
- Institute for Scientific Interchange Foundation, 10126 Torino, Italy;
| | - Emilien Jeannot
- Institute of Global Health, Faculty of Medicine, University of Geneva, 1202 Geneva, Switzerland; (L.M.); (A.W.); (A.T.); (T.M.); (D.D.); (E.J.); (A.F.)
- Addiction Medicine, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, 1004 Lausanne, Switzerland
| | - Antoine Flahault
- Institute of Global Health, Faculty of Medicine, University of Geneva, 1202 Geneva, Switzerland; (L.M.); (A.W.); (A.T.); (T.M.); (D.D.); (E.J.); (A.F.)
| |
Collapse
|
8
|
Guerrisi C, Ecollan M, Souty C, Rossignol L, Turbelin C, Debin M, Goronflot T, Boëlle PY, Hanslik T, Colizza V, Blanchon T. Factors associated with influenza-like-illness: a crowdsourced cohort study from 2012/13 to 2017/18. BMC Public Health 2019; 19:879. [PMID: 31272411 PMCID: PMC6610908 DOI: 10.1186/s12889-019-7174-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2018] [Accepted: 06/17/2019] [Indexed: 11/22/2022] Open
Abstract
Background Influenza generates a significant societal impact on morbidity, mortality, and associated costs. The study objective was to identify factors associated with influenza-like-illness (ILI) episodes during seasonal influenza epidemics among the general population. Methods A prospective study was conducted with the GrippeNet.fr crowdsourced cohort between 2012/13 and 2017/18. After having completed a yearly profile survey detailing socio-demographic, lifestyle and health characteristics, participants reported weekly data on symptoms. Factors associated with at least one ILI episode per influenza epidemic, using the European Centre for Disease Prevention and Control case definition, were analyzed through a conditional logistic regression model. Results From 2012/13 to 2017/18, 6992 individuals participated at least once, and 61% of them were women (n = 4258). From 11% (n = 469/4140 in 2013/14) to 29% (n = 866/2943 in 2012/13) of individuals experienced at least one ILI during an influenza epidemic. Factors associated with higher risk for ILI were: gender female (OR = 1.29, 95%CI [1.20; 1.40]), young age (< 5 years old: 3.12 [2.05; 4.68]); from 5 to 14 years old: 1.53 [1.17; 2.00]), respiratory allergies (1.27 [1.18; 1.37]), receiving a treatment for chronic disease (1.20 [1.09; 1.32]), being overweight (1.18 [1.08; 1.29]) or obese (1.28 [1.14; 1.44]), using public transport (1.17 [1.07; 1.29]) and having contact with pets (1.18 [1.09; 1.27]). Older age (≥ 75 years old: 0.70 [0.56; 0.87]) and being vaccinated against influenza (0.91 [0.84; 0.99]) were found to be protective factors for ILI. Conclusions This ILI risk factors analysis confirms and further completes the list of factors observed through traditional surveillance systems. It indicates that crowdsourced cohorts are effective to study ILI determinants at the population level. These findings could be used to adapt influenza prevention messages at the population level to reduce the spread of the disease. Electronic supplementary material The online version of this article (10.1186/s12889-019-7174-6) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Caroline Guerrisi
- Sorbonne Université, INSERM, Institut Pierre Louis d'Épidémiologie et de Santé Publique (IPLESP), F-75012, Paris, France.
| | - Marie Ecollan
- Sorbonne Université, INSERM, Institut Pierre Louis d'Épidémiologie et de Santé Publique (IPLESP), F-75012, Paris, France.,Department of family Medicine, Faculté de Médecine, Université Paris Descartes, Paris, France
| | - Cécile Souty
- Sorbonne Université, INSERM, Institut Pierre Louis d'Épidémiologie et de Santé Publique (IPLESP), F-75012, Paris, France
| | - Louise Rossignol
- Sorbonne Université, INSERM, Institut Pierre Louis d'Épidémiologie et de Santé Publique (IPLESP), F-75012, Paris, France
| | - Clément Turbelin
- Sorbonne Université, INSERM, Institut Pierre Louis d'Épidémiologie et de Santé Publique (IPLESP), F-75012, Paris, France
| | - Marion Debin
- Sorbonne Université, INSERM, Institut Pierre Louis d'Épidémiologie et de Santé Publique (IPLESP), F-75012, Paris, France
| | - Thomas Goronflot
- Sorbonne Université, INSERM, Institut Pierre Louis d'Épidémiologie et de Santé Publique (IPLESP), F-75012, Paris, France
| | - Pierre-Yves Boëlle
- Sorbonne Université, INSERM, Institut Pierre Louis d'Épidémiologie et de Santé Publique (IPLESP), F-75012, Paris, France
| | - Thomas Hanslik
- Sorbonne Université, INSERM, Institut Pierre Louis d'Épidémiologie et de Santé Publique (IPLESP), F-75012, Paris, France.,APHP, Service de Médecine Interne, Hôpital Ambroise-Paré, 92100, Boulogne-Billancourt, France.,UFR des sciences de la santé Simone-Veil, Université de Versailles - Saint-Quentin-en-Yvelines, 78280, Versailles, France
| | - Vittoria Colizza
- Sorbonne Université, INSERM, Institut Pierre Louis d'Épidémiologie et de Santé Publique (IPLESP), F-75012, Paris, France
| | - Thierry Blanchon
- Sorbonne Université, INSERM, Institut Pierre Louis d'Épidémiologie et de Santé Publique (IPLESP), F-75012, Paris, France
| |
Collapse
|
9
|
Kalimeri K, Delfino M, Cattuto C, Perrotta D, Colizza V, Guerrisi C, Turbelin C, Duggan J, Edmunds J, Obi C, Pebody R, Franco AO, Moreno Y, Meloni S, Koppeschaar C, Kjelsø C, Mexia R, Paolotti D. Unsupervised extraction of epidemic syndromes from participatory influenza surveillance self-reported symptoms. PLoS Comput Biol 2019; 15:e1006173. [PMID: 30958817 PMCID: PMC6472822 DOI: 10.1371/journal.pcbi.1006173] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2018] [Revised: 04/18/2019] [Accepted: 03/01/2019] [Indexed: 11/18/2022] Open
Abstract
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.
Collapse
Affiliation(s)
| | | | | | | | - Vittoria Colizza
- INSERM, Sorbonne Université, Institut Pierre Louis d’Epidémiologie et de Santé Publique, IPLESP, Paris, France
| | - Caroline Guerrisi
- Sorbonne Université, INSERM, Institut Pierre Louis d’Epidémiologie et de Santé Publique, IPLESP, Paris, France
| | - Clement Turbelin
- Sorbonne Université, INSERM, Institut Pierre Louis d’Epidémiologie et de Santé Publique, IPLESP, Paris, France
| | - Jim Duggan
- School of Computer Science, National University of Ireland Galway, Galway, Ireland
| | - John Edmunds
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Chinelo Obi
- Immunisation and Countermeasures Division, National Infections Service, Public Health England, London, United Kingdom
| | - Richard Pebody
- Immunisation and Countermeasures Division, National Infections Service, Public Health England, London, United Kingdom
| | | | - Yamir Moreno
- ISI Foundation, Turin, Italy
- Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, Zaragoza, Spain
- Department of Theoretical Physics, University of Zaragoza, Zaragoza, Spain
| | - Sandro Meloni
- IFISC, Institute for Cross-Disciplinary Physics and Complex Systems (CSIC-UIB), Palma de Mallorca, Spain
| | | | | | - Ricardo Mexia
- Departamento de Epidemiologia, Instituto Nacional de Saúde Doutor Ricardo Jorge, Lisbon, Portugal
| | | |
Collapse
|
10
|
Geneviève LD, Wangmo T, Dietrich D, Woolley-Meza O, Flahault A, Elger BS. Research Ethics in the European Influenzanet Consortium: Scoping Review. JMIR Public Health Surveill 2018; 4:e67. [PMID: 30305258 PMCID: PMC6231872 DOI: 10.2196/publichealth.9616] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2017] [Revised: 06/04/2018] [Accepted: 06/28/2018] [Indexed: 11/28/2022] Open
Abstract
Background Influenzanet was launched in several European countries to monitor influenza-like illness during flu seasons with the help of volunteering participants and Web-based technologies. As in the case of developing fields, ethical approaches are not well developed in the collection, processing, and analysis of participants’ information. Existing controversies and varying national ethical regulations can, thus, hamper efficient cross-border research collaboration to the detriment of quality disease surveillance. Objective This scoping review characterizes current practices on how ethical, legal, and social issues (ELSIs) pertinent to research ethics are handled by different Influenzanet country groups to analyze similarities and identify the need for further harmonization of ethical approaches. Methods A literature search was carried out on PubMed, Web of Science, Global Digital Library on Ethics, and Bioethics Literature Database to identify ELSIs for Influenzanet country platforms. Only English-language papers were included with publication dates from 2003 to 2017. Publications were screened for the application of bioethics principles in the implementation of country platforms. Additional publications gathered from the Influenzanet Consortium website, reference screening, and conference proceeding were screened for ELSIs. Results We gathered 96 papers from our search methodology. In total, 28 papers that mentioned ELSIs were identified and included in this study. The Research Ethics Committee (REC) approvals were sought for recruiting participants and collecting their data in 8 of 11 country platforms and informed e-consent was sought from participants in 9 of 11 country platforms. Furthermore, personal data protection was ensured throughout the Consortium using data anonymization before processing and analysis and using aggregated data. Conclusions Epidemics forecasting activities, such as Influenzanet, are beneficial; however, its benefits could be further increased through the harmonization of data gathering and ethical requirements. This objective is achievable by the Consortium. More transparency should be promoted concerning REC-approved research for Influenzanet-like systems. The validity of informed e-consent could also be increased through the provision of a user friendly and standard information sheet across the Consortium where participants agree to its terms, conditions, and privacy policies before being able to fill in the questionnaire. This will help to build trust in the general public while preventing any decline in participation.
Collapse
Affiliation(s)
| | - Tenzin Wangmo
- Institute for Biomedical Ethics, University of Basel, Basel, Switzerland
| | - Damien Dietrich
- Department of Radiology and Medical Informatics, Geneva University Hospitals, Geneva, Switzerland.,Institute of Global Health, University of Geneva, Geneva, Switzerland
| | | | - Antoine Flahault
- Institute of Global Health, University of Geneva, Geneva, Switzerland
| | - Bernice Simone Elger
- Institute for Biomedical Ethics, University of Basel, Basel, Switzerland.,University Center of Legal Medicine, University of Geneva, Geneva, Switzerland
| |
Collapse
|
11
|
Magumba MA, Nabende P, Mwebaze E. Design Choices for Automated Disease Surveillance in the Social Web. Online J Public Health Inform 2018; 10:e214. [PMID: 30349632 PMCID: PMC6194101 DOI: 10.5210/ojphi.v10i2.9312] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
The social web has emerged as a dominant information architecture accelerating technology innovation on an unprecedented scale. The utility of these developments to public health use cases like disease surveillance, information dissemination, outbreak prediction and so forth has been widely investigated and variously demonstrated in work spanning several published experimental studies and deployed systems. In this paper we provide an overview of automated disease surveillance efforts based on the social web characterized by their different high level design choices regarding functional aspects like user participation and language parsing approaches. We briefly discuss the technical rationale and practical implications of these different choices in addition to the key limitations associated with these systems within the context of operable disease surveillance. We hope this can offer some technical guidance to multi-disciplinary teams on how best to implement, interpret and evaluate disease surveillance programs based on the social web.
Collapse
Affiliation(s)
- Mark Abraham Magumba
- Department of Information Systems, Makerere
University Uganda, College of Computing and Information Sciences
| | - Peter Nabende
- Department of Information Systems, Makerere
University Uganda, College of Computing and Information Sciences
| | - Ernest Mwebaze
- Department of Computer Science, Makerere University
Uganda, College of Computing and Information Sciences
| |
Collapse
|
12
|
Ma W, Huo X, Zhou M. The healthcare seeking rate of individuals with influenza like illness: a meta-analysis. Infect Dis (Lond) 2018; 50:728-735. [PMID: 30009680 DOI: 10.1080/23744235.2018.1472805] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/28/2022] Open
Abstract
BACKGROUND Not all individuals with Influenza like illness (ILI) seek healthcare. Knowing the proportion that do is important to evaluate the actual burden and fatality rate of ILI-relevant diseases, such as seasonal influenza and human infection with avian influenza. A number of studies have investigated the healthcare seeking rate, but the results varied from 0.16 to 0.85. We conducted this analysis for better understanding the healthcare seeking rate for ILI, and providing fundamental data for researchers in relevant fields. METHODS In this meta-analysis, a total of 799 articles, published as of 13 December 2016, were retrieved from Pubmed, Embase, Web of Science and Cochrane, and 11 of them were included after screening. The pooled estimates and factors which influence healthcare seeking rates were analysed. RESULTS The overall pooled healthcare seeking rate was 0.52 (95% CI: 0.46-0.59). The rate was significantly higher during the H1N1 pandemic in 2009 (0.61, 95% CI: 0.51-0.74), in children (0.56, 95% CI: 0.55-0.57) and in patients with documented fever (0.62, 95% CI: 0.53-0.72) than during non-pandemic periods (0.39, 95% CI: 0.33-0.45), in adults (0.45, 95% CI: 0.42-0.48) and in patients without documented fever (0.44, 95% CI: 0.38-0.50). Meta-regression indicated that these three factors could jointly explain 70.1% of the total heterogeneity among published studies. CONCLUSION The healthcare seeking rate of ILI patients is needed for estimation of the burden of ILI in the general population based on data from routine ILI sentinel surveillance systems.
Collapse
Affiliation(s)
- Wang Ma
- a School of Public Health , Nanjing Medical University , Nanjing , China
| | - Xiang Huo
- b Jiangsu Provincial Center for Disease Control and Prevention , Nanjing , China
| | - Minghao Zhou
- a School of Public Health , Nanjing Medical University , Nanjing , China.,b Jiangsu Provincial Center for Disease Control and Prevention , Nanjing , China
| |
Collapse
|
13
|
Guerrisi C, Turbelin C, Souty C, Poletto C, Blanchon T, Hanslik T, Bonmarin I, Levy-Bruhl D, Colizza V. The potential value of crowdsourced surveillance systems in supplementing sentinel influenza networks: the case of France. Euro Surveill 2018; 23:1700337. [PMID: 29945696 PMCID: PMC6152237 DOI: 10.2807/1560-7917.es.2018.23.25.1700337] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2017] [Accepted: 02/15/2018] [Indexed: 11/20/2022] Open
Abstract
IntroductionParticipatory surveillance systems provide rich crowdsourced data, profiling individuals and their health status at a given time. We explored the usefulness of data from GrippeNet.fr, a participatory surveillance system, to estimate influenza-related illness incidence in France. Methods: GrippeNet.fr is an online cohort since 2012 averaging ca. 5,000 weekly participants reporting signs/symptoms suggestive of influenza. GrippeNet.fr has flexible criteria to define influenza-related illness. Different case definitions based on reported signs/symptoms and inclusions of criteria accounting for individuals' reporting and participation were used to produce influenza-related illness incidence estimates, which were compared to those from sentinel networks. We focused on the 2012/13 and 2013/14 seasons when two sentinel networks, monitoring influenza-like-illness (ILI) and acute respiratory infections (ARI) existed in France. Results: GrippeNet.fr incidence estimates agreed well with official temporal trends, with a higher accuracy for ARI than ILI. The influenza epidemic peak was often anticipated by one week, despite irregular participation of individuals. The European Centre for Disease Prevention and Control ILI definition, commonly used by participatory surveillance in Europe, performed better in tracking ARI than ILI when applied to GrippeNet.fr data. Conclusion: Evaluation of the epidemic intensity from crowdsourced data requires epidemic and intensity threshold estimations from several consecutive seasons. The study provides a standardised analytical framework for crowdsourced surveillance showing high sensitivity in detecting influenza-related changes in the population. It contributes to improve the comparability of epidemics across seasons and with sentinel systems. In France, GrippeNet.fr may supplement the ILI sentinel network after ARI surveillance discontinuation in 2014.
Collapse
Affiliation(s)
- Caroline Guerrisi
- Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique (IPLESP), Paris, France
| | - Clément Turbelin
- Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique (IPLESP), Paris, France
| | - Cécile Souty
- Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique (IPLESP), Paris, France
| | - Chiara Poletto
- Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique (IPLESP), Paris, France
| | - Thierry Blanchon
- Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique (IPLESP), Paris, France
| | - Thomas Hanslik
- Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique (IPLESP), Paris, France
- UFR des sciences de la santé Simone-Veil, Université Versailles-Saint-Quentin-en-Yvelines, Versailles, France
- AP-HP, Service de Médecine Interne, Hôpital Ambroise Paré, Boulogne Billancourt, France
| | - Isabelle Bonmarin
- Department of infectious diseases, Public Health France, Saint-Maurice, France
| | - Daniel Levy-Bruhl
- Department of infectious diseases, Public Health France, Saint-Maurice, France
| | - Vittoria Colizza
- Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique (IPLESP), Paris, France
| |
Collapse
|
14
|
Koppeschaar CE, Colizza V, Guerrisi C, Turbelin C, Duggan J, Edmunds WJ, Kjelsø C, Mexia R, Moreno Y, Meloni S, Paolotti D, Perrotta D, van Straten E, Franco AO. Influenzanet: Citizens Among 10 Countries Collaborating to Monitor Influenza in Europe. JMIR Public Health Surveill 2017; 3:e66. [PMID: 28928112 PMCID: PMC5627046 DOI: 10.2196/publichealth.7429] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2017] [Revised: 06/23/2017] [Accepted: 06/26/2017] [Indexed: 11/13/2022] Open
Abstract
Background The wide availability of the Internet and the growth of digital communication technologies have become an important tool for epidemiological studies and health surveillance. Influenzanet is a participatory surveillance system monitoring the incidence of influenza-like illness (ILI) in Europe since 2003. It is based on data provided by volunteers who self-report their symptoms via the Internet throughout the influenza season and currently involves 10 countries. Objective In this paper, we describe the Influenzanet system and provide an overview of results from several analyses that have been performed with the collected data, which include participant representativeness analyses, data validation (comparing ILI incidence rates between Influenzanet and sentinel medical practice networks), identification of ILI risk factors, and influenza vaccine effectiveness (VE) studies previously published. Additionally, we present new VE analyses for the Netherlands, stratified by age and chronic illness and offer suggestions for further work and considerations on the continuity and sustainability of the participatory system. Methods Influenzanet comprises country-specific websites where residents can register to become volunteers to support influenza surveillance and have access to influenza-related information. Participants are recruited through different communication channels. Following registration, volunteers submit an intake questionnaire with their postal code and sociodemographic and medical characteristics, after which they are invited to report their symptoms via a weekly electronic newsletter reminder. Several thousands of participants have been engaged yearly in Influenzanet, with over 36,000 volunteers in the 2015-16 season alone. Results In summary, for some traits and in some countries (eg, influenza vaccination rates in the Netherlands), Influenzanet participants were representative of the general population. However, for other traits, they were not (eg, participants underrepresent the youngest and oldest age groups in 7 countries). The incidence of ILI in Influenzanet was found to be closely correlated although quantitatively higher than that obtained by the sentinel medical practice networks. Various risk factors for acquiring an ILI infection were identified. The VE studies performed with Influenzanet data suggest that this surveillance system could develop into a complementary tool to measure the effectiveness of the influenza vaccine, eventually in real time. Conclusions Results from these analyses illustrate that Influenzanet has developed into a fast and flexible monitoring system that can complement the traditional influenza surveillance performed by sentinel medical practices. The uniformity of Influenzanet allows for direct comparison of ILI rates between countries. It also has the important advantage of yielding individual data, which can be used to identify risk factors. The way in which the Influenzanet system is constructed allows the collection of data that could be extended beyond those of ILI cases to monitor pandemic influenza and other common or emerging diseases.
Collapse
Affiliation(s)
| | - Vittoria Colizza
- UPMC Univ Paris 06, Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Pierre Louis d'épidémiologie et de Santé Publique (IPLESP UMRS 1136), Sorbonne Universités, Paris, France
| | - Caroline Guerrisi
- UPMC Univ Paris 06, Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Pierre Louis d'épidémiologie et de Santé Publique (IPLESP UMRS 1136), Sorbonne Universités, Paris, France
| | - Clément Turbelin
- UPMC Univ Paris 06, Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Pierre Louis d'épidémiologie et de Santé Publique (IPLESP UMRS 1136), Sorbonne Universités, Paris, France
| | - Jim Duggan
- School of Engineering and Informatics, National University of Ireland, Galway, Ireland
| | - W John Edmunds
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | | | - Ricardo Mexia
- Instituto Nacional de Saúde Doutor Ricardo Jorge, Lisbon, Portugal
| | - Yamir Moreno
- Institute for Biocomputation and Physics of Complex Systems, Department of Theoretical Physics, University of Zaragoza, Zaragoza, Spain
| | - Sandro Meloni
- Institute for Biocomputation and Physics of Complex Systems, Department of Theoretical Physics, University of Zaragoza, Zaragoza, Spain
| | | | | | | | - Ana O Franco
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom.,Instituto Gulbenkian de Ciência, Oeiras, Portugal
| |
Collapse
|
15
|
Guerrisi C, Turbelin C, Blanchon T, Hanslik T, Bonmarin I, Levy-Bruhl D, Perrotta D, Paolotti D, Smallenburg R, Koppeschaar C, Franco AO, Mexia R, Edmunds WJ, Sile B, Pebody R, van Straten E, Meloni S, Moreno Y, Duggan J, Kjelsø C, Colizza V. Participatory Syndromic Surveillance of Influenza in Europe. J Infect Dis 2017; 214:S386-S392. [PMID: 28830105 DOI: 10.1093/infdis/jiw280] [Citation(s) in RCA: 68] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
The growth of digital communication technologies for public health is offering an unconventional means to engage the general public in monitoring community health. Here we present Influenzanet, a participatory system for the syndromic surveillance of influenza-like illness (ILI) in Europe. Through standardized online surveys, the system collects detailed profile information and self-reported symptoms volunteered by participants resident in the Influenzanet countries. Established in 2009, it now includes 10 countries representing more than half of the 28 member states of the European Union population. The experience of 7 influenza seasons illustrates how Influenzanet has become an adjunct to existing ILI surveillance networks, offering coherence across countries, inclusion of nonmedically attended ILI, flexibility in case definition, and facilitating individual-level epidemiological analyses generally not possible in standard systems. Having the sensitivity to timely detect substantial changes in population health, Influenzanet has the potential to become a viable instrument for a wide variety of applications in public health preparedness and control.
Collapse
Affiliation(s)
- Caroline Guerrisi
- Sorbonne Universités, UPMC Univ Paris 06, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique
| | - Clément Turbelin
- Sorbonne Universités, UPMC Univ Paris 06, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique
| | - Thierry Blanchon
- Sorbonne Universités, UPMC Univ Paris 06, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique
| | - Thomas Hanslik
- Sorbonne Universités, UPMC Univ Paris 06, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique.,Assistance Publique Hopitaux de Paris, Service de Medecine Interne, Hopital Ambroise Pare, Boulogne Billancourt
| | - Isabelle Bonmarin
- Department of infectious diseases, Public Health France, Saint-Maurice, France
| | - Daniel Levy-Bruhl
- Department of infectious diseases, Public Health France, Saint-Maurice, France
| | | | | | | | | | | | - Ricardo Mexia
- Instituto Nacional de Saúde Doutor Ricardo Jorge, Lisbon, Portugal
| | | | | | | | | | - Sandro Meloni
- Institute for Biocomputation and Physics and Complex Systems, University of Zaragoza, Spain
| | - Yamir Moreno
- Institute for Biocomputation and Physics and Complex Systems, University of Zaragoza, Spain
| | - Jim Duggan
- College of Engineering and Informatics, National University of Ireland, Galway
| | | | - Vittoria Colizza
- Sorbonne Universités, UPMC Univ Paris 06, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique.,Institute for Scientific Interchange, Turin, Italy
| |
Collapse
|
16
|
Chunara R, Wisk LE, Weitzman ER. Denominator Issues for Personally Generated Data in Population Health Monitoring. Am J Prev Med 2017; 52:549-553. [PMID: 28012811 PMCID: PMC5362284 DOI: 10.1016/j.amepre.2016.10.038] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/05/2016] [Revised: 10/13/2016] [Accepted: 10/31/2016] [Indexed: 01/14/2023]
Affiliation(s)
- Rumi Chunara
- Department of Computer Science and Engineering, New York University Tandon School of Engineering, Brooklyn, New York; College of Global Public Health, New York University, New York, New York.
| | - Lauren E Wisk
- Division of Adolescent/Young Adult Medicine, Boston Children's Hospital, Boston, Massachusetts; Department of Pediatrics, Harvard Medical School, Harvard University, Boston, Massachusetts
| | - Elissa R Weitzman
- Division of Adolescent/Young Adult Medicine, Boston Children's Hospital, Boston, Massachusetts; Department of Pediatrics, Harvard Medical School, Harvard University, Boston, Massachusetts; Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts
| |
Collapse
|