tetano
Editor, Senior Moderator
JMIR Public Health Surveill. 2017 Nov 1;3(4):e83. doi: 10.2196/publichealth.7344.
[h=1]Combining Participatory Influenza Surveillance with Modeling and Forecasting: Three Alternative Approaches.[/h] Brownstein JS[SUP]1,[/SUP][SUP]2,[/SUP][SUP]3[/SUP], Chu S[SUP]4[/SUP], Marathe A[SUP]4[/SUP], Marathe MV[SUP]4[/SUP], Nguyen AT[SUP]2,[/SUP][SUP]5[/SUP], Paolotti D[SUP]6[/SUP], Perra N[SUP]7[/SUP], Perrotta D[SUP]6[/SUP], Santillana M[SUP]1,[/SUP][SUP]2,[/SUP][SUP]3[/SUP], Swarup S[SUP]4[/SUP], Tizzoni M[SUP]6[/SUP], Vespignani A[SUP]8[/SUP], Vullikanti AKS[SUP]4[/SUP], Wilson ML[SUP]4[/SUP], Zhang Q[SUP]8[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] [h=4]BACKGROUND:[/h] 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.
[h=4]OBJECTIVE:[/h] 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.
[h=4]METHODS:[/h] 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).
[h=4]RESULTS:[/h] 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.
[h=4]CONCLUSIONS:[/h] 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.
[h=4]KEYWORDS:[/h] crowdsourcing; disease surveillance; forecasting; nonresponse bias
PMID: 29092812 DOI: 10.2196/publichealth.7344
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[h=1]Combining Participatory Influenza Surveillance with Modeling and Forecasting: Three Alternative Approaches.[/h] Brownstein JS[SUP]1,[/SUP][SUP]2,[/SUP][SUP]3[/SUP], Chu S[SUP]4[/SUP], Marathe A[SUP]4[/SUP], Marathe MV[SUP]4[/SUP], Nguyen AT[SUP]2,[/SUP][SUP]5[/SUP], Paolotti D[SUP]6[/SUP], Perra N[SUP]7[/SUP], Perrotta D[SUP]6[/SUP], Santillana M[SUP]1,[/SUP][SUP]2,[/SUP][SUP]3[/SUP], Swarup S[SUP]4[/SUP], Tizzoni M[SUP]6[/SUP], Vespignani A[SUP]8[/SUP], Vullikanti AKS[SUP]4[/SUP], Wilson ML[SUP]4[/SUP], Zhang Q[SUP]8[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] [h=4]BACKGROUND:[/h] 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.
[h=4]OBJECTIVE:[/h] 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.
[h=4]METHODS:[/h] 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).
[h=4]RESULTS:[/h] 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.
[h=4]CONCLUSIONS:[/h] 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.
[h=4]KEYWORDS:[/h] crowdsourcing; disease surveillance; forecasting; nonresponse bias
PMID: 29092812 DOI: 10.2196/publichealth.7344
Free full text