Combining Participatory Influenza Surveillance with Modeling and Forecasting: Three Alternative Approaches
| dc.contributor.advisor | Chu, Shuyu | en |
| dc.contributor.advisor | Marathe, Madhav V. | en |
| dc.contributor.advisor | Nguyen, Andre T. | en |
| dc.contributor.advisor | Paolotti, Daniela | en |
| dc.contributor.advisor | Perra, Nicola | en |
| dc.contributor.advisor | Santillana, Mauricio | en |
| dc.contributor.advisor | Swarup, Samarth | en |
| dc.contributor.advisor | Tizzoni, Michele | en |
| dc.contributor.advisor | Vespignani, Alessandro | en |
| dc.contributor.advisor | Vullikanti, Anil Kumar S. | en |
| dc.contributor.advisor | Wilson, Mandy L. | en |
| dc.contributor.advisor | Zhang, Qian | en |
| dc.contributor.author | Brownstein, John S. | en |
| dc.contributor.author | Marathe, Achla | en |
| dc.date.accessioned | 2018-09-07T15:29:59Z | en |
| dc.date.available | 2018-09-07T15:29:59Z | en |
| dc.date.issued | 2017 | en |
| dc.description.abstract | Background: Influenza outbreaks affect millions of people every year and its surveillance is usually carried out in developed countries through a network of sentinel doctors who report the weekly number of Influenza-like Illness cases observed among the visited patients. Monitoring and forecasting the evolution of these outbreaks supports decision makers in designing effective interventions and allocating resources to mitigate their impact. Objective: Describe the existing participatory surveillance approaches that have been used for modeling and forecasting of the seasonal influenza epidemic, and how they can help strengthen real-time epidemic science and provide a more rigorous understanding of epidemic conditions. Methods: We describe three different participatory surveillance systems, WISDM (Widely Internet Sourced Distributed Monitoring), Influenzanet and Flu Near You (FNY), and show how modeling and simulation can be or has been combined with participatory disease surveillance to: i) measure the non-response bias in a participatory surveillance sample using WISDM; and ii) nowcast and forecast influenza activity in different parts of the world (using Influenzanet and Flu Near You). Results: WISDM-based results measure the participatory and sample bias for three epidemic metrics i.e. attack rate, peak infection rate, and time-to-peak, and find the participatory bias to be the largest component of the total bias. The Influenzanet platform shows that digital participatory surveillance data combined with a realistic data-driven epidemiological model can provide both short-term and long-term forecasts of epidemic intensities, and the ground truth data lie within the 95 percent confidence intervals for most weeks. The statistical accuracy of the ensemble forecasts increase as the season progresses. The Flu Near You platform shows that participatory surveillance data provide accurate short-term flu activity forecasts and influenza activity predictions. The correlation of the HealthMap Flu Trends estimates with the observed CDC ILI rates is 0.99 for 2013-2015. Additional data sources lead to an error reduction of about 40% when compared to the estimates of the model that only incorporates CDC historical information. Conclusions: While the advantages of participatory surveillance, compared to traditional surveillance, include its timeliness, lower costs, and broader reach, it is limited by a lack of control over the characteristics of the population sample. Modeling and simulation can help overcome this limitation as well as provide real-time and long-term forecasting of influenza activity in data-poor parts of the world. | en |
| dc.description.sponsorship | DP, DP, and MT acknowledge support from H2020 Future and Emerging Technologies Proactive: Global Systems Science CIMPLEX grant number 641191. AV and QZ acknowledge funding from the Models of Infectious Disease Agent Study (MIDAS)–National Institute of General Medical Sciences U54GM111274. DP would like to thank all the Influenzanet volunteers and the Influenzanet investigators for the platforms whose data have been used in this study, in particular Yamir Moreno, John Edmunds, Charlotte Kjelsø, and Carl Koppeschaar. The Influenzanet Spanish platform has been partially supported by the European Commission Future and Emerging Technologies–Proactive Project Multiplex (grant 317532). A M, MLW , SC, SS, ASV, and MVM acknowledge support from the Defense Threat Reduction Agency Comprehensive National Incident Management System contract HDTRA1-11-D-0016-0001, National Institutes of Health (NIH) MIDAS grant 5U01GM070694, NIH grant 1R01GM109718, National Science Foundation (NSF) Interface between Computer Science and Economics and Social Science grant CCF-1216000, NSF Research Traineeship Program Data-Enabled Science and Engineering grant DGE-154362, and NSF Data Infrastructure Building Blocks grant ACI-1443054. MS, AN, and JSB acknowledge support from the Skoll Global Threats Fund and thank all of the FNY participants who contributed their time and information to the FNY system. | en |
| dc.identifier.doi | https://doi.org/10.2196/publichealth.7344 | en |
| dc.identifier.issue | 4 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/84976 | en |
| dc.identifier.volume | 3 | en |
| dc.language.iso | en_US | en |
| dc.publisher | JMIR Publications | en |
| dc.rights | Creative Commons Attribution 3.0 United States | en |
| dc.rights.uri | http://creativecommons.org/licenses/by/3.0/us/ | en |
| dc.subject | forecasting | en |
| dc.subject | disease surveillance | en |
| dc.subject | crowdsourcing | en |
| dc.subject | nonresponse bias | en |
| dc.title | Combining Participatory Influenza Surveillance with Modeling and Forecasting: Three Alternative Approaches | en |
| dc.title.serial | JMIR Public Health and Surveillance | en |
| dc.type | Article - Refereed | en |