tetano
Editor, Senior Moderator
JMIR Public Health Surveill
. 2023 Jul 25.
doi: 10.2196/46644. Online ahead of print. Global Flu View: enabling multi-centric participatory disease surveillance for global health enhancement: a viewpoint
Onicio Leal Neto[SUP] 1 2 [/SUP], Daniela Paolotti[SUP] 3 [/SUP], Craig Dalton[SUP] 4 [/SUP], Sandra Carlson[SUP] 4 [/SUP], Patipat Susumpow[SUP] 5 [/SUP], Matt Parker[SUP] 5 [/SUP], Polowat Phetra[SUP] 5 [/SUP], Eric Lau[SUP] 6 [/SUP], Vittoria Colizza[SUP] 7 [/SUP], Albert Jan van Hoek[SUP] 8 [/SUP], Charlotte Kjelsø[SUP] 9 [/SUP], John S Brownstein[SUP] 10 [/SUP], Mark S Smolinski[SUP] 11 [/SUP]
Affiliations
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 eight countries plus one region (Hong Kong) on four 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 (API), providing a real-time map of syndromic trends that could demonstrate where and when ILI was spreading in various regions of the globe. After two 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.
. 2023 Jul 25.
doi: 10.2196/46644. Online ahead of print. Global Flu View: enabling multi-centric participatory disease surveillance for global health enhancement: a viewpoint
Onicio Leal Neto[SUP] 1 2 [/SUP], Daniela Paolotti[SUP] 3 [/SUP], Craig Dalton[SUP] 4 [/SUP], Sandra Carlson[SUP] 4 [/SUP], Patipat Susumpow[SUP] 5 [/SUP], Matt Parker[SUP] 5 [/SUP], Polowat Phetra[SUP] 5 [/SUP], Eric Lau[SUP] 6 [/SUP], Vittoria Colizza[SUP] 7 [/SUP], Albert Jan van Hoek[SUP] 8 [/SUP], Charlotte Kjelsø[SUP] 9 [/SUP], John S Brownstein[SUP] 10 [/SUP], Mark S Smolinski[SUP] 11 [/SUP]
Affiliations
- PMID: 37490846
- DOI: 10.2196/46644
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 eight countries plus one region (Hong Kong) on four 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 (API), providing a real-time map of syndromic trends that could demonstrate where and when ILI was spreading in various regions of the globe. After two 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.