Brownstein, John, Chu, Shuyu, Marathe, Achla, Marathe, Madhav, Nguyen, Andre, Paolotti, Daniela, Perra, Nicola ORCID: https://orcid.org/0000-0002-5559-3064, Perrotta, Daniela, Santillana, Mauricio, Swarup, Samarth, Tizzoni, Michele, Vespignani, Alessandro and Ku, Anil (2017) Combining participatory influenza surveillance with modeling and forecasting. JMIR Public Health and Surveillance, 3 (4):e83. ISSN 2369-2960 (Online) (doi:10.2196/publichealth.7344)

Preview	PDF (Open Access Article) 17805 PERRA_Participatory_Influenza_Surveillance_(OA)_2017.pdf - Published Version Available under License Creative Commons Attribution. Download (1MB) | Preview
Preview	PDF (Author Accepted Manuscript) 17805 PERRA_Participatory_Influenza_Surveillance_2017.pdf - Accepted Version Download (3MB) | Preview
	PDF (Acknowlegement of Acceptance) 17805 PERRA_Acceptance_Acknowledgement_2017.pdf - Additional Metadata Restricted to Repository staff only Download (324kB) | Request a copy

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.

Objectives:
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. 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.

Item Type:	Article
Uncontrolled Keywords:	forecasting, disease surveillance, crowdsourcing, non-response bias
Subjects:	R Medicine > RA Public aspects of medicine
Faculty / School / Research Centre / Research Group:	Faculty of Business Faculty of Business > Department of International Business & Economics Faculty of Business > Networks and Urban Systems Centre (NUSC) Faculty of Business > Networks and Urban Systems Centre (NUSC) > Centre for Business Network Analysis (CBNA)
Related URLs:	Publisher
Last Modified:	21 Oct 2020 10:05
URI:	http://gala.gre.ac.uk/id/eprint/17805

Actions (login required)

View Item

Downloads