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Leal Neto O, Paolotti D, Dalton C, Carlson S, Susumpow P, Parker M, Phetra P, Lau EHY, Colizza V, Jan van Hoek A, Kjelsø C, Brownstein JS, Smolinski MS. Enabling Multicentric Participatory Disease Surveillance for Global Health Enhancement: Viewpoint on Global Flu View. JMIR Public Health Surveill 2023; 9:e46644. [PMID: 37490846 PMCID: PMC10504624 DOI: 10.2196/46644] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 06/21/2023] [Accepted: 07/25/2023] [Indexed: 07/27/2023] Open
Abstract
Participatory surveillance (PS) has been defined as the bidirectional process of transmitting and receiving data for action by directly engaging the target population. Often represented as self-reported symptoms directly from the public, PS can provide evidence of an emerging disease or concentration of symptoms in certain areas, potentially identifying signs of an early outbreak. The construction of sets of symptoms to represent various disease syndromes provides a mechanism for the early detection of multiple health threats. Global Flu View (GFV) is the first-ever system that merges influenza-like illness (ILI) data from more than 8 countries plus 1 region (Hong Kong) on 4 continents for global monitoring of this annual health threat. GFV provides a digital ecosystem for spatial and temporal visualization of syndromic aggregates compatible with ILI from the various systems currently participating in GFV in near real time, updated weekly. In 2018, the first prototype of a digital platform to combine data from several ILI PS programs was created. At that time, the priority was to have a digital environment that brought together different programs through an application program interface, providing a real time map of syndromic trends that could demonstrate where and when ILI was spreading in various regions of the globe. After 2 years running as an experimental model and incorporating feedback from partner programs, GFV was restructured to empower the community of public health practitioners, data scientists, and researchers by providing an open data channel among these contributors for sharing experiences across the network. GFV was redesigned to serve not only as a data hub but also as a dynamic knowledge network around participatory ILI surveillance by providing knowledge exchange among programs. Connectivity between existing PS systems enables a network of cooperation and collaboration with great potential for continuous public health impact. The exchange of knowledge within this network is not limited only to health professionals and researchers but also provides an opportunity for the general public to have an active voice in the collective construction of health settings. The focus on preparing the next generation of epidemiologists will be of great importance to scale innovative approaches like PS. GFV provides a useful example of the value of globally integrated PS data to help reduce the risks and damages of the next pandemic.
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Affiliation(s)
- Onicio Leal Neto
- Ending Pandemics, San Francisco, CA, United States
- Department of Computer Science, ETH Zurich, Zurich, Switzerland
| | | | | | | | | | | | | | - Eric H Y Lau
- School of Public Health, University of Hong Kong, Hong Kong, China
| | - Vittoria Colizza
- Pierre Louis Institute of Epidemiology and Public Health, INSERM, Sorbonne Université, Paris, France
| | - Albert Jan van Hoek
- National Institute for Public Health and the Environment, Bilthoven, Netherlands
| | | | - John S Brownstein
- Boston Children Hospital, Harvard University, Boston, MA, United States
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2
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Abstract
Influenza is a common respiratory infection that causes considerable morbidity and mortality worldwide each year. In recent years, along with the improvement in computational resources, there have been a number of important developments in the science of influenza surveillance and forecasting. Influenza surveillance systems have been improved by synthesizing multiple sources of information. Influenza forecasting has developed into an active field, with annual challenges in the United States that have stimulated improved methodologies. Work continues on the optimal approaches to assimilating surveillance data and information on relevant driving factors to improve estimates of the current situation (nowcasting) and to forecast future dynamics.
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Affiliation(s)
- Sheikh Taslim Ali
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China;
| | - Benjamin J Cowling
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China;
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3
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Wu SL, Mertens AN, Crider YS, Nguyen A, Pokpongkiat NN, Djajadi S, Seth A, Hsiang MS, Colford JM, Reingold A, Arnold BF, Hubbard A, Benjamin-Chung J. Substantial underestimation of SARS-CoV-2 infection in the United States. Nat Commun 2020; 11:4507. [PMID: 32908126 PMCID: PMC7481226 DOI: 10.1038/s41467-020-18272-4] [Citation(s) in RCA: 238] [Impact Index Per Article: 59.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Accepted: 08/13/2020] [Indexed: 11/22/2022] Open
Abstract
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.
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Affiliation(s)
- Sean L Wu
- Division of Epidemiology and Biostatistics, University of California, 2121 Berkeley Way, Berkeley, CA, 94720-7360, USA
| | - Andrew N Mertens
- Division of Epidemiology and Biostatistics, University of California, 2121 Berkeley Way, Berkeley, CA, 94720-7360, USA
| | - Yoshika S Crider
- Division of Epidemiology and Biostatistics, University of California, 2121 Berkeley Way, Berkeley, CA, 94720-7360, USA
- Energy and Resources Group, University of California, 310 Barrows Hall, Berkeley, CA, 94720-3050, USA
| | - Anna Nguyen
- Division of Epidemiology and Biostatistics, University of California, 2121 Berkeley Way, Berkeley, CA, 94720-7360, USA
| | - Nolan N Pokpongkiat
- Division of Epidemiology and Biostatistics, University of California, 2121 Berkeley Way, Berkeley, CA, 94720-7360, USA
| | - Stephanie Djajadi
- Division of Epidemiology and Biostatistics, University of California, 2121 Berkeley Way, Berkeley, CA, 94720-7360, USA
| | - Anmol Seth
- Division of Epidemiology and Biostatistics, University of California, 2121 Berkeley Way, Berkeley, CA, 94720-7360, USA
| | - Michelle S Hsiang
- Department of Pediatrics, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, 75390-9003, USA
- Pandemic Community Response and Resilience Initiative, Global Health Group, University of California, San Francisco, Mission Hall, Box 1224, 550 16th Street, Third Floor, San Francisco, CA, 94158, USA
- Department of Pediatrics, University of California, San Francisco 550 16th Street, Box 0110, San Francisco, CA, 94143, USA
| | - John M Colford
- Division of Epidemiology and Biostatistics, University of California, 2121 Berkeley Way, Berkeley, CA, 94720-7360, USA
| | - Art Reingold
- Division of Epidemiology and Biostatistics, University of California, 2121 Berkeley Way, Berkeley, CA, 94720-7360, USA
| | - Benjamin F Arnold
- Francis I. Proctor Foundation, University of California, San Francisco 95 Kirkham Street, San Francisco, CA, 94143, USA
- Department of Ophthalmology, University of California, San Francisco 10 Koret Way, San Francisco, CA, 94143-0730, USA
| | - Alan Hubbard
- Division of Epidemiology and Biostatistics, University of California, 2121 Berkeley Way, Berkeley, CA, 94720-7360, USA
| | - Jade Benjamin-Chung
- Division of Epidemiology and Biostatistics, University of California, 2121 Berkeley Way, Berkeley, CA, 94720-7360, USA.
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4
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Rguig A, Cherkaoui I, McCarron M, Oumzil H, Triki S, Elmbarki H, Bimouhen A, El Falaki F, Regragui Z, Ihazmad H, Nejjari C, Youbi M. Establishing seasonal and alert influenza thresholds in Morocco. BMC Public Health 2020; 20:1029. [PMID: 32600376 DOI: 10.1186/s12889-020-09145-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Accepted: 06/18/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Several statistical methods of variable complexity have been developed to establish thresholds for influenza activity that may be used to inform public health guidance. We compared the results of two methods and explored how they worked to characterize the 2018 influenza season performance-2018 season. METHODS Historical data from the 2005/2006 to 2016/2018 influenza season performance seasons were provided by a network of 412 primary health centers in charge of influenza like illness (ILI) sentinel surveillance. We used the WHO averages and the moving epidemic method (MEM) to evaluate the proportion of ILI visits among all outpatient consultations (ILI%) as a proxy for influenza activity. We also used the MEM method to evaluate three seasons of composite data (ILI% multiplied by percent of ILI with laboratory-confirmed influenza) as recommended by WHO. RESULTS The WHO method estimated the seasonal ILI% threshold at 0.9%. The annual epidemic period began on average at week 46 and lasted an average of 18 weeks. The MEM model estimated the epidemic threshold (corresponding to the WHO seasonal threshold) at 1.5% of ILI visits among all outpatient consultations. The annual epidemic period began on week 49 and lasted on average 14 weeks. Intensity thresholds were similar using both methods. When using the composite measure, the MEM method showed a clearer estimate of the beginning of the influenza epidemic, which was coincident with a sharp increase in confirmed ILI cases. CONCLUSIONS We found that the threshold methodology presented in the WHO manual is simple to implement and easy to adopt for use by the Moroccan influenza surveillance system. The MEM method is more statistically sophisticated and may allow a better detection of the start of seasonal epidemics. Incorporation of virologic data into the composite parameter as recommended by WHO has the potential to increase the accuracy of seasonal threshold estimation.
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Affiliation(s)
- Ahmed Rguig
- Direction of Epidemiology and Disease Control, MoH, Rabat, Morocco
| | - Imad Cherkaoui
- Direction of Epidemiology and Disease Control, MoH, Rabat, Morocco.
| | | | - Hicham Oumzil
- National Institute of Hygiène, NIC, MoH, Rabat, Morocco
| | | | - Houria Elmbarki
- Direction of Epidemiology and Disease Control, MoH, Rabat, Morocco
| | | | | | | | | | - Chakib Nejjari
- University Mohammed VI of Health Sciences, Casablanca, Morocco
| | - Mohammed Youbi
- Direction of Epidemiology and Disease Control, MoH, Rabat, Morocco
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5
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Leal Neto O, Cruz O, Albuquerque J, Nacarato de Sousa M, Smolinski M, Pessoa Cesse EÂ, Libel M, Vieira de Souza W. Participatory Surveillance Based on Crowdsourcing During the Rio 2016 Olympic Games Using the Guardians of Health Platform: Descriptive Study. JMIR Public Health Surveill 2020; 6:e16119. [PMID: 32254042 PMCID: PMC7175192 DOI: 10.2196/16119] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Revised: 12/06/2019] [Accepted: 01/27/2020] [Indexed: 12/01/2022] Open
Abstract
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.
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Affiliation(s)
- Onicio Leal Neto
- University of Zurich, Zurich, Switzerland.,Epitrack, Recife, Brazil
| | - Oswaldo Cruz
- Scientific Computation Program, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil
| | - Jones Albuquerque
- Epitrack, Recife, Brazil.,Immunopathology Lab Keizo Asami, Recife, Brazil
| | | | | | | | - Marlo Libel
- Ending Pandemics, San Francisco, CA, United States
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6
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Baltrusaitis K, Vespignani A, Rosenfeld R, Gray J, Raymond D, Santillana M. Differences in Regional Patterns of Influenza Activity Across Surveillance Systems in the United States: Comparative Evaluation. JMIR Public Health Surveill 2019; 5:e13403. [PMID: 31579019 PMCID: PMC6777281 DOI: 10.2196/13403] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2019] [Revised: 07/02/2019] [Accepted: 07/19/2019] [Indexed: 01/30/2023] Open
Abstract
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.
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Affiliation(s)
- Kristin Baltrusaitis
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, United States
| | | | - Roni Rosenfeld
- School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, United States
| | - Josh Gray
- athenaResearch at athenahealth, Watertown, MA, United States
| | - Dorrie Raymond
- athenaResearch at athenahealth, Watertown, MA, United States
| | - Mauricio Santillana
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, United States.,Department of Pediatrics, Harvard Medical School, Boston, MA, United States
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7
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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.
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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
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8
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Yang CY, Chen RJ, Chou WL, Lee YJ, Lo YS. An Integrated Influenza Surveillance Framework Based on National Influenza-Like Illness Incidence and Multiple Hospital Electronic Medical Records for Early Prediction of Influenza Epidemics: Design and Evaluation. J Med Internet Res 2019; 21:e12341. [PMID: 30707099 PMCID: PMC6376337 DOI: 10.2196/12341] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2018] [Revised: 12/18/2018] [Accepted: 01/20/2019] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Influenza is a leading cause of death worldwide and contributes to heavy economic losses to individuals and communities. Therefore, the early prediction of and interventions against influenza epidemics are crucial to reduce mortality and morbidity because of this disease. Similar to other countries, the Taiwan Centers for Disease Control and Prevention (TWCDC) has implemented influenza surveillance and reporting systems, which primarily rely on influenza-like illness (ILI) data reported by health care providers, for the early prediction of influenza epidemics. However, these surveillance and reporting systems show at least a 2-week delay in prediction, indicating the need for improvement. OBJECTIVE We aimed to integrate the TWCDC ILI data with electronic medical records (EMRs) of multiple hospitals in Taiwan. Our ultimate goal was to develop a national influenza trend prediction and reporting tool more accurate and efficient than the current influenza surveillance and reporting systems. METHODS First, the influenza expertise team at Taipei Medical University Health Care System (TMUHcS) identified surveillance variables relevant to the prediction of influenza epidemics. Second, we developed a framework for integrating the EMRs of multiple hospitals with the ILI data from the TWCDC website to proactively provide results of influenza epidemic monitoring to hospital infection control practitioners. Third, using the TWCDC ILI data as the gold standard for influenza reporting, we calculated Pearson correlation coefficients to measure the strength of the linear relationship between TMUHcS EMRs and regional and national TWCDC ILI data for 2 weekly time series datasets. Finally, we used the Moving Epidemic Method analyses to evaluate each surveillance variable for its predictive power for influenza epidemics. RESULTS Using this framework, we collected the EMRs and TWCDC ILI data of the past 3 influenza seasons (October 2014 to September 2017). On the basis of the EMRs of multiple hospitals, 3 surveillance variables, TMUHcS-ILI, TMUHcS-rapid influenza laboratory tests with positive results (RITP), and TMUHcS-influenza medication use (IMU), which reflected patients with ILI, those with positive results from rapid influenza diagnostic tests, and those treated with antiviral drugs, respectively, showed strong correlations with the TWCDC regional and national ILI data (r=.86-.98). The 2 surveillance variables-TMUHcS-RITP and TMUHcS-IMU-showed predictive power for influenza epidemics 3 to 4 weeks before the increase noted in the TWCDC ILI reports. CONCLUSIONS Our framework periodically integrated and compared surveillance data from multiple hospitals and the TWCDC website to maintain a certain prediction quality and proactively provide monitored results. Our results can be extended to other infectious diseases, mitigating the time and effort required for data collection and analysis. Furthermore, this approach may be developed as a cost-effective electronic surveillance tool for the early and accurate prediction of epidemics of influenza and other infectious diseases in densely populated regions and nations.
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Affiliation(s)
- Cheng-Yi Yang
- Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei, Taiwan
| | - Ray-Jade Chen
- Department of Surgery, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.,Taipei Medical University Hospital, Taipei, Taiwan
| | - Wan-Lin Chou
- Taipei Medical University Hospital, Taipei, Taiwan
| | - Yuarn-Jang Lee
- Division of Infectious Disease, Department of Internal Medicine, Taipei Medical University Hospital, Taipei, Taiwan
| | - Yu-Sheng Lo
- Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei, Taiwan
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9
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An Evidence Synthesis Approach to Estimating the Proportion of Influenza Among Influenza-like Illness Patients. Epidemiology 2018; 28:484-491. [PMID: 28252453 DOI: 10.1097/ede.0000000000000646] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
BACKGROUND Estimation of the national-level incidence of seasonal influenza is notoriously challenging. Surveillance of influenza-like illness is carried out in many countries using a variety of data sources, and several methods have been developed to estimate influenza incidence. Our aim was to obtain maximally informed estimates of the proportion of influenza-like illness that is true influenza using all available data. METHODS We combined data on weekly general practice sentinel surveillance consultation rates for influenza-like illness, virologic testing of sampled patients with influenza-like illness, and positive laboratory tests for influenza and other pathogens, applying Bayesian evidence synthesis to estimate the positive predictive value (PPV) of influenza-like illness as a test for influenza virus infection. We estimated the weekly number of influenza-like illness consultations attributable to influenza for nine influenza seasons, and for four age groups. RESULTS The estimated PPV for influenza in influenza-like illness patients was highest in the weeks surrounding seasonal peaks in influenza-like illness rates, dropping to near zero in between-peak periods. Overall, 14.1% (95% credible interval [CrI]: 13.5%, 14.8%) of influenza-like illness consultations were attributed to influenza infection; the estimated PPV was 50% (95% CrI: 48%, 53%) for the peak weeks and 5.8% during the summer periods. CONCLUSIONS The model quantifies the correspondence between influenza-like illness consultations and influenza at a weekly granularity. Even during peak periods, a substantial proportion of influenza-like illness-61%-was not attributed to influenza. The much lower proportion of influenza outside the peak periods reflects the greater circulation of other respiratory pathogens relative to influenza.
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10
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Cowling BJ, Xu C, Tang F, Zhang J, Shen J, Havers F, Wendladt R, Leung NH, Greene C, Iuliano AD, Shifflett P, Song Y, Zhang R, Kim L, Chen Y, Chu DK, Zhu H, Shu Y, Yu H, Thompson MG. Cohort profile: the China Ageing REespiratory infections Study (CARES), a prospective cohort study in older adults in Eastern China. BMJ Open 2017; 7:e017503. [PMID: 29092901 PMCID: PMC5695487 DOI: 10.1136/bmjopen-2017-017503] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/05/2017] [Revised: 08/24/2017] [Accepted: 08/29/2017] [Indexed: 12/19/2022] Open
Abstract
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.
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Affiliation(s)
- Benjamin J Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, Li Ka Shing Faculty of Medicine, School of Public Health, University of Hong Kong, Hong Kong Special Administrative Region, Hong Kong, China
| | - Cuiling Xu
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, Li Ka Shing Faculty of Medicine, School of Public Health, University of Hong Kong, Hong Kong Special Administrative Region, Hong Kong, China
- Chinese National Influenza Center, National Institute for Viral Disease Control and Prevention, Collaboration Innovation Center for Diagnosis and Treatment of Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China
- Key Laboratory for Medical Virology, National Health and Family Planning Commission, Beijing, China
| | - Fenyang Tang
- Jiangsu Provincial Center for Disease Prevention and Control, Nanjing, China
| | - Jun Zhang
- Suzhou Center for Disease Prevention and Control, Suzhou, China
| | - Jinjin Shen
- Yancheng Center for Disease Prevention and Control, Yancheng, China
| | - Fiona Havers
- Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | | | - Nancy Hl Leung
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, Li Ka Shing Faculty of Medicine, School of Public Health, University of Hong Kong, Hong Kong Special Administrative Region, Hong Kong, China
| | - Carolyn Greene
- Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | | | | | - Ying Song
- Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Ran Zhang
- Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Lindsay Kim
- Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Yuyun Chen
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, Li Ka Shing Faculty of Medicine, School of Public Health, University of Hong Kong, Hong Kong Special Administrative Region, Hong Kong, China
| | - Daniel Kw Chu
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, Li Ka Shing Faculty of Medicine, School of Public Health, University of Hong Kong, Hong Kong Special Administrative Region, Hong Kong, China
| | - Huachen Zhu
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, Li Ka Shing Faculty of Medicine, School of Public Health, University of Hong Kong, Hong Kong Special Administrative Region, Hong Kong, China
| | - Yuelong Shu
- Chinese National Influenza Center, National Institute for Viral Disease Control and Prevention, Collaboration Innovation Center for Diagnosis and Treatment of Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China
- Key Laboratory for Medical Virology, National Health and Family Planning Commission, Beijing, China
| | - Hongjie Yu
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
- Division of Infectious Disease, Key Laboratory of Surveillance and Early-warning on Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Mark G Thompson
- Centers for Disease Control and Prevention, Atlanta, Georgia, USA
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11
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Quade P, Nsoesie EO. A Platform for Crowdsourced Foodborne Illness Surveillance: Description of Users and Reports. JMIR Public Health Surveill 2017; 3:e42. [PMID: 28679492 PMCID: PMC5517822 DOI: 10.2196/publichealth.7076] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2016] [Revised: 03/12/2017] [Accepted: 05/30/2017] [Indexed: 11/25/2022] Open
Abstract
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.
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Affiliation(s)
| | - Elaine Okanyene Nsoesie
- Institute for Health Metrics and Evaluation, University of Washington, Washington, WA, United States
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12
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Leal Neto O, Dimech GS, Libel M, de Souza WV, Cesse E, Smolinski M, Oliveira W, Albuquerque J. Saúde na Copa: The World's First Application of Participatory Surveillance for a Mass Gathering at FIFA World Cup 2014, Brazil. JMIR Public Health Surveill 2017; 3:e26. [PMID: 28473308 PMCID: PMC5438444 DOI: 10.2196/publichealth.7313] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2017] [Revised: 02/23/2017] [Accepted: 03/20/2017] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND The 2005 International Health Regulations (IHRs) established parameters for event assessments and notifications that may constitute public health emergencies of international concern. These requirements and parameters opened up space for the use of nonofficial mechanisms (such as websites, blogs, and social networks) and technological improvements of communication that can streamline the detection, monitoring, and response to health problems, and thus reduce damage caused by these problems. Specifically, the revised IHR created space for participatory surveillance to function, in addition to the traditional surveillance mechanisms of detection, monitoring, and response. Participatory surveillance is based on crowdsourcing methods that collect information from society and then return the collective knowledge gained from that information back to society. The spread of digital social networks and wiki-style knowledge platforms has created a very favorable environment for this model of production and social control of information. OBJECTIVE The aim of this study was to describe the use of a participatory surveillance app, Healthy Cup, for the early detection of acute disease outbreaks during the Fédération Internationale de Football Association (FIFA) World Cup 2014. Our focus was on three specific syndromes (respiratory, diarrheal, and rash) related to six diseases that were considered important in a mass gathering context (influenza, measles, rubella, cholera, acute diarrhea, and dengue fever). METHODS From May 12 to July 13, 2014, users from anywhere in the world were able to download the Healthy Cup app and record their health condition, reporting whether they were good, very good, ill, or very ill. For users that reported being ill or very ill, a screen with a list of 10 symptoms was displayed. Participatory surveillance allows for the real-time identification of aggregates of symptoms that indicate possible cases of infectious diseases. RESULTS From May 12 through July 13, 2014, there were 9434 downloads of the Healthy Cup app and 7155 (75.84%) registered users. Among the registered users, 4706 (4706/7155, 65.77%) were active users who posted a total of 47,879 times during the study period. The maximum number of users that signed up in one day occurred on May 30, 2014, the day that the app was officially launched by the Minister of Health during a press conference. During this event, the Minister of Health announced the special government program Health in the World Cup on national television media. On that date, 3633 logins were recorded, which accounted for more than half of all sign-ups across the entire duration of the study (50.78%, 3633/7155). CONCLUSIONS Participatory surveillance through community engagement is an innovative way to conduct epidemiological surveillance. Compared to traditional epidemiological surveillance, advantages include lower costs of data acquisition, timeliness of information collected and shared, platform scalability, and capacity for integration between the population being served and public health services.
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Affiliation(s)
- Onicio Leal Neto
- EpitrackRecifeBrazil
- SingularityU Recife ChapterRecifeBrazil
- Aggeu Magalhães Research CenterDepartament of Health CollectiveRecifeBrazil
| | | | - Marlo Libel
- Skoll Global Threats FundPandemics TeamSan Francisco, CAUnited States
| | | | - Eduarda Cesse
- Aggeu Magalhães Research CenterDepartament of Public HealthRecifeBrazil
| | - Mark Smolinski
- Skoll Global Threats FundPandemics TeamSan Francisco, CAUnited States
| | - Wanderson Oliveira
- Brazil's Ministry of HealthGeneral Coordination of Public Health Emergencies ResponseBrasiliaBrazil
| | - Jones Albuquerque
- EpitrackRecifeBrazil
- Federal Rural University of PernambucoInformatics DepartamentRecifeBrazil
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13
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Participatory Online Surveillance as a Supplementary Tool to Sentinel Doctors for Influenza-Like Illness Surveillance in Italy. PLoS One 2017; 12:e0169801. [PMID: 28076411 PMCID: PMC5226807 DOI: 10.1371/journal.pone.0169801] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2016] [Accepted: 12/21/2016] [Indexed: 11/20/2022] Open
Abstract
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.
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14
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Da'ar OB, Yunus F, Md Hossain N, Househ M. Impact of Twitter intensity, time, and location on message lapse of bluebird's pursuit of fleas in Madagascar. J Infect Public Health 2016; 10:396-402. [PMID: 27423931 DOI: 10.1016/j.jiph.2016.06.011] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2016] [Revised: 05/25/2016] [Accepted: 06/24/2016] [Indexed: 10/21/2022] Open
Abstract
BACKGROUND The recent outbreak of bubonic plague in Madagascar reminds us of the continuing public health challenges posed by such deadly diseases in various parts of the world years after their eradication. This study examines the role of Twitter in public health disease surveillance with special focus on how Twitter intensity, time, and location issues explain Twitter plague message delay. METHOD We retrospectively analyzed the Twitter feeds of the 2014 bubonic plague outbreak in Madagascar. The analyses are based on the plague-related data available in the public domain between November 19th and 27th 2014. The data were compiled in March 2015. We calculated the time differential between the tweets and retweets, and analyzed various characteristics of the Tweets including Twitter intensity of the users. RESULTS A total of 6873 Twitter users were included in the study, of which 52% tweeted plague-related information during the morning hours (before mid-day), and 87% of the tweets came from the west of the epicenter of the plague. More importantly, while session of tweet lease and relative location had effect on message lapse, absolute location did not. Additionally, we found no evidence of differential effect of location on message lapse based on relative location i.e. tweets from west or east nor number of following. However, there is evidence that more intense Twitter use appears to have significant effect on message lapse such that as the number of tweets became more intense, time differential between the tweets and retweets increased while higher number of retweets diminished message lapse. CONCLUSION This study affirms that Twitter can play an important role in ongoing disease surveillance and the timely dissemination of information during public health emergencies independent of the time and space restrictions. Further ways should be explored to embed social media channels in routine public health practice.
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Affiliation(s)
- Omar B Da'ar
- College of Public Health and Health Informatics, King Saud Bin Abdulaziz University for Health Sciences, Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia.
| | - Faisel Yunus
- Institute of Public Health, College of Medicine and Health Sciences, UAE University, Al-Ain, United Arab Emirates
| | - Nassif Md Hossain
- King Abdulaziz Medical City, Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Mowafa Househ
- College of Public Health and Health Informatics, King Saud Bin Abdulaziz University for Health Sciences, Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia
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15
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van Noort SP, Codeço CT, Koppeschaar CE, van Ranst M, Paolotti D, Gomes MGM. Ten-year performance of Influenzanet: ILI time series, risks, vaccine effects, and care-seeking behaviour. Epidemics 2015; 13:28-36. [PMID: 26616039 DOI: 10.1016/j.epidem.2015.05.001] [Citation(s) in RCA: 48] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2014] [Revised: 04/27/2015] [Accepted: 05/31/2015] [Indexed: 12/20/2022] Open
Abstract
Recent public health threats have propelled major innovations on infectious disease monitoring, culminating in the development of innovative syndromic surveillance methods. Influenzanet is an internet-based system that monitors influenza-like illness (ILI) in cohorts of self-reporting volunteers in European countries since 2003. We investigate and confirm coherence through the first ten years in comparison with ILI data from the European Influenza Surveillance Network and demonstrate country-specific behaviour of participants with ILI regarding medical care seeking. Using regression analysis, we determine that chronic diseases, being a child, living with children, being female, smoking and pets at home, are all independent predictors of ILI risk, whereas practicing sports and walking or bicycling for locomotion are associated with a small risk reduction. No effect for using public transportation or living alone was found. Furthermore, we determine the vaccine effectiveness for ILI for each season.
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Affiliation(s)
| | - Cláudia T Codeço
- Instituto Gulbenkian de Ciência, Oeiras, Portugal; Oswaldo Cruz Foundation, Rio de Janeiro, Brazil
| | | | - Marc van Ranst
- Rega Institute for Medical Research, University of Leuven, Leuven, Belgium
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16
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Stein ML, van Steenbergen JE, Buskens V, van der Heijden PGM, Koppeschaar CE, Bengtsson L, Thorson A, Kretzschmar MEE. Enhancing Syndromic Surveillance With Online Respondent-Driven Detection. Am J Public Health 2015; 105:e90-7. [PMID: 26066940 DOI: 10.2105/ajph.2015.302717] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
OBJECTIVES We investigated the feasibility of combining an online chain recruitment method (respondent-driven detection) and participatory surveillance panels to collect previously undetected information on infectious diseases via social networks of participants. METHODS In 2014, volunteers from 2 large panels in the Netherlands were invited to complete a survey focusing on symptoms of upper respiratory tract infections and to invite 4 individuals they had met in the preceding 2 weeks to take part in the study. We compared sociodemographic characteristics among panel participants, individuals who volunteered for our survey, and individuals recruited via respondent-driven detection. RESULTS Starting from 1015 panel members, the survey spread through all provinces of the Netherlands and all age groups in 83 days. A total of 433 individuals completed the survey via peer recruitment. Participants who reported symptoms were 6.1% (95% confidence interval = 5.4, 6.9) more likely to invite contact persons than were participants who did not report symptoms. Participants with symptoms invited more symptomatic recruits to take part than did participants without symptoms. CONCLUSIONS Our findings suggest that online respondent-driven detection can enhance identification of symptomatic patients by making use of individuals' local social networks.
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Affiliation(s)
- Mart L Stein
- Mart L. Stein and Mirjam E. E. Kretzschmar are with the Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands. Jim E. van Steenbergen is with the Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands. Vincent Buskens is with the Department of Sociology, Faculty of Social and Behavioural Sciences, Utrecht University, Utrecht. Peter G. M. van der Heijden is with the Department of Methodology and Statistics, Faculty of Social and Behavioural Sciences, Utrecht University, Utrecht. Carl E. Koppeschaar is with Science in Action BV, Amsterdam, the Netherlands. Linus Bengtsson and Anna Thorson are with the Department of Public Health Sciences-Global Health, Karolinska Institutet, Stockholm, Sweden
| | - Jim E van Steenbergen
- Mart L. Stein and Mirjam E. E. Kretzschmar are with the Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands. Jim E. van Steenbergen is with the Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands. Vincent Buskens is with the Department of Sociology, Faculty of Social and Behavioural Sciences, Utrecht University, Utrecht. Peter G. M. van der Heijden is with the Department of Methodology and Statistics, Faculty of Social and Behavioural Sciences, Utrecht University, Utrecht. Carl E. Koppeschaar is with Science in Action BV, Amsterdam, the Netherlands. Linus Bengtsson and Anna Thorson are with the Department of Public Health Sciences-Global Health, Karolinska Institutet, Stockholm, Sweden
| | - Vincent Buskens
- Mart L. Stein and Mirjam E. E. Kretzschmar are with the Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands. Jim E. van Steenbergen is with the Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands. Vincent Buskens is with the Department of Sociology, Faculty of Social and Behavioural Sciences, Utrecht University, Utrecht. Peter G. M. van der Heijden is with the Department of Methodology and Statistics, Faculty of Social and Behavioural Sciences, Utrecht University, Utrecht. Carl E. Koppeschaar is with Science in Action BV, Amsterdam, the Netherlands. Linus Bengtsson and Anna Thorson are with the Department of Public Health Sciences-Global Health, Karolinska Institutet, Stockholm, Sweden
| | - Peter G M van der Heijden
- Mart L. Stein and Mirjam E. E. Kretzschmar are with the Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands. Jim E. van Steenbergen is with the Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands. Vincent Buskens is with the Department of Sociology, Faculty of Social and Behavioural Sciences, Utrecht University, Utrecht. Peter G. M. van der Heijden is with the Department of Methodology and Statistics, Faculty of Social and Behavioural Sciences, Utrecht University, Utrecht. Carl E. Koppeschaar is with Science in Action BV, Amsterdam, the Netherlands. Linus Bengtsson and Anna Thorson are with the Department of Public Health Sciences-Global Health, Karolinska Institutet, Stockholm, Sweden
| | - Carl E Koppeschaar
- Mart L. Stein and Mirjam E. E. Kretzschmar are with the Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands. Jim E. van Steenbergen is with the Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands. Vincent Buskens is with the Department of Sociology, Faculty of Social and Behavioural Sciences, Utrecht University, Utrecht. Peter G. M. van der Heijden is with the Department of Methodology and Statistics, Faculty of Social and Behavioural Sciences, Utrecht University, Utrecht. Carl E. Koppeschaar is with Science in Action BV, Amsterdam, the Netherlands. Linus Bengtsson and Anna Thorson are with the Department of Public Health Sciences-Global Health, Karolinska Institutet, Stockholm, Sweden
| | - Linus Bengtsson
- Mart L. Stein and Mirjam E. E. Kretzschmar are with the Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands. Jim E. van Steenbergen is with the Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands. Vincent Buskens is with the Department of Sociology, Faculty of Social and Behavioural Sciences, Utrecht University, Utrecht. Peter G. M. van der Heijden is with the Department of Methodology and Statistics, Faculty of Social and Behavioural Sciences, Utrecht University, Utrecht. Carl E. Koppeschaar is with Science in Action BV, Amsterdam, the Netherlands. Linus Bengtsson and Anna Thorson are with the Department of Public Health Sciences-Global Health, Karolinska Institutet, Stockholm, Sweden
| | - Anna Thorson
- Mart L. Stein and Mirjam E. E. Kretzschmar are with the Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands. Jim E. van Steenbergen is with the Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands. Vincent Buskens is with the Department of Sociology, Faculty of Social and Behavioural Sciences, Utrecht University, Utrecht. Peter G. M. van der Heijden is with the Department of Methodology and Statistics, Faculty of Social and Behavioural Sciences, Utrecht University, Utrecht. Carl E. Koppeschaar is with Science in Action BV, Amsterdam, the Netherlands. Linus Bengtsson and Anna Thorson are with the Department of Public Health Sciences-Global Health, Karolinska Institutet, Stockholm, Sweden
| | - Mirjam E E Kretzschmar
- Mart L. Stein and Mirjam E. E. Kretzschmar are with the Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands. Jim E. van Steenbergen is with the Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands. Vincent Buskens is with the Department of Sociology, Faculty of Social and Behavioural Sciences, Utrecht University, Utrecht. Peter G. M. van der Heijden is with the Department of Methodology and Statistics, Faculty of Social and Behavioural Sciences, Utrecht University, Utrecht. Carl E. Koppeschaar is with Science in Action BV, Amsterdam, the Netherlands. Linus Bengtsson and Anna Thorson are with the Department of Public Health Sciences-Global Health, Karolinska Institutet, Stockholm, Sweden
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17
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Chunara R, Goldstein E, Patterson-Lomba O, Brownstein JS. Estimating influenza attack rates in the United States using a participatory cohort. Sci Rep 2015; 5:9540. [PMID: 25835538 PMCID: PMC4894435 DOI: 10.1038/srep09540] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2014] [Accepted: 03/09/2015] [Indexed: 11/09/2022] Open
Abstract
We considered how participatory syndromic surveillance data can be used to estimate influenza attack rates during the 2012-2013 and 2013-2014 seasons in the United States. Our inference is based on assessing the difference in the rates of self-reported influenza-like illness (ILI, defined as presence of fever and cough/sore throat) among the survey participants during periods of active vs. low influenza circulation as well as estimating the probability of self-reported ILI for influenza cases. Here, we combined Flu Near You data with additional sources (Hong Kong household studies of symptoms of influenza cases and the U.S. Centers for Disease Control and Prevention estimates of vaccine coverage and effectiveness) to estimate influenza attack rates. The estimated influenza attack rate for the early vaccinated Flu Near You members (vaccination reported by week 45) aged 20-64 between calendar weeks 47-12 was 14.7%(95% CI(5.9%,24.1%)) for the 2012-2013 season and 3.6%(-3.3%,10.3%) for the 2013-2014 season. The corresponding rates for the US population aged 20-64 were 30.5% (4.4%, 49.3%) in 2012-2013 and 7.1%(-5.1%, 32.5%) in 2013-2014. The attack rates in women and men were similar each season. Our findings demonstrate that participatory syndromic surveillance data can be used to gauge influenza attack rates during future influenza seasons.
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Affiliation(s)
- Rumi Chunara
- The Global Institute of Public Health, New york University and Computer science &Engineering, New york University
| | - Edward Goldstein
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, United States of America
| | - Oscar Patterson-Lomba
- Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, United States of America
| | - John S Brownstein
- 1] Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, United States of America [2] Informatics Program, Division of Emergency Medicine, Department of Medicine, Boston Children's Hospital, Boston, Massachusetts, United States of America
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18
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Wójcik OP, Brownstein JS, Chunara R, Johansson MA. Public health for the people: participatory infectious disease surveillance in the digital age. Emerg Themes Epidemiol 2014; 11:7. [PMID: 24991229 PMCID: PMC4078360 DOI: 10.1186/1742-7622-11-7] [Citation(s) in RCA: 84] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2014] [Accepted: 06/09/2014] [Indexed: 11/20/2022] Open
Abstract
The 21st century has seen the rise of Internet-based participatory surveillance systems for infectious diseases. These systems capture voluntarily submitted symptom data from the general public and can aggregate and communicate that data in near real-time. We reviewed participatory surveillance systems currently running in 13 different countries. These systems have a growing evidence base showing a high degree of accuracy and increased sensitivity and timeliness relative to traditional healthcare-based systems. They have also proven useful for assessing risk factors, vaccine effectiveness, and patterns of healthcare utilization while being less expensive, more flexible, and more scalable than traditional systems. Nonetheless, they present important challenges including biases associated with the population that chooses to participate, difficulty in adjusting for confounders, and limited specificity because of reliance only on syndromic definitions of disease limits. Overall, participatory disease surveillance data provides unique disease information that is not available through traditional surveillance sources.
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Affiliation(s)
- Oktawia P Wójcik
- Harvard Medical School and Boston Children's Hospital, 1 Autumn St., Boston, MA 02215, USA
| | - John S Brownstein
- Harvard Medical School and Boston Children's Hospital, 1 Autumn St., Boston, MA 02215, USA
| | - Rumi Chunara
- Harvard Medical School and Boston Children's Hospital, 1 Autumn St., Boston, MA 02215, USA
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