Enabling Multicentric Participatory Disease Surveillance for Global Health Enhancement: Viewpoint on Global Flu View (Preprint)

Abstract

UNSTRUCTURED Participatory surveillance 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, participatory surveillance 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 is the first-ever system that merges 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 participatory surveillance programs was created, called Global Flu View (GFV). 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. Global Flu View 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 participatory surveillance 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 participatory surveillance. Global Flu View provides a useful example of the value of globally integrated PS data to help reduce the risks and damages of the next pandemic.

Visualization of Spatial–Temporal Epidemiological Data: A Scoping Review

“…The studies that used a map-style representation (79/114) adopted different strategies: static geographic 2D or 3D maps [14,15,19,20,22,31,33,36,38,[42][43][44]50,75,81,83,88,93,102,104,107,109,117,118,123], dynamic 2D maps [20][21][22]25,61,65,80,85,89,90,94,97], or choropleth maps [17,23,24,27,30,32,34,35,37,40,46,48,49,52,…”

“…Moreover, 24 of the studies that used a map-style representation combined a map with one or more types of charts, most of which were bar charts, pie charts, and networks [17,21,30,43,44,49,50,76,80,82,83,85,93,102,107,111,115,117,123]. An example of the application of bar charts over maps to create symbol maps is the representation of confirmed, death, or recovery cases [102,107].…”

“…In recent years, there has been an increasing use of choropleth maps compared to the rest, and building representations are starting to emerge in the study of epidemiological data, although the number is still low. [17,21,30,43,44,49,50,76,80,82,83,85,93,102,107,111,115,117,123]. An example of the application of bar charts over maps to create symbol maps is the representation of confirmed, death, or recovery cases [102,107].…”

“…According to our review, more than half of the 114 studies (67/114) used 2D or 3D line charts as depictions of time series to represent temporal data related to a pan-demic. They were not only used to depict the number of cases per week, month, or year [16,21,22,24,25,30,36,50,53,54,56,59,60,62,65,67,68,[71][72][73]75,79,[84][85][86][87]97,98,100,103,104,106,109,113,114,120,121,127] or cumulative cases [22,27,52,55,65,70,71,[98][99][100]105,…”

“…We also found other combinations, such as maps with color-coding: [107] used three colors to show areas within the map with variations in the number of cases, and [31] used disc areas with a degree of colors to show the areas in which the earliest and latest outbreaks had occurred. There were also maps with filters of time by day [21,61,79,89,102,105,[115][116][117][118][119], with a time range slider [113], or with timelines to show, for example, the daily progress of the death rate, rate of infection, and medical resource deficiency indices (MRDIs), along with their representation on a map [82]. To a lesser extent, some studies combined maps with bar charts [43,60,78,102] to compare different regions over time.…”