Project D.A.D.O. (Data and Analysis for Decisions and Operations) was developed in partnership with Prefeitura do Recife and Porto Digital to support the development of Business Intelligence and Analytics strategies in the fight against the pandemic. The working groups, formed by researchers, engineers and epidemiologists, had the support of companies in Porto Digital, such as In Loco and Neurotech.
For better management and monitoring of an epidemic spread it is crucial to develop spatiotemporal analysis tools. Many dashboards encountered in the literature do not consider how the geolocation characteristics and travel patterns may influence the spread of the virus. This work brings interactive widgets that are capable to cross information about mobility patterns, geolocation characteristics and epidemiological variables so that health officials can understand how these factors act during an ongoing pandemic to manage it, and to make better decisions to minimize the damage of future outbreaks.
To do so, our system uses a mobility network, generated through anonymized mobile location data, which enables the division of a region into representative clusters. The clusters' aggregated socioeconomic, demographic, and epidemiological indicators can be analyzed through multiple coordinated views. We demonstrate the use of this tool through a case study based on a region that has been significantly affected by the pandemic. The proposed tool was built and verified by experts assembled to give scientific recommendations to policymakers. Our analysis shows how a policymaker could use the tool to evaluate different isolation scenarios considering the trade-off between economic activity and the pandemic's spread, where the practical insights can also be used in a future outbreak.
After cloning the project, the user can create and configure an application and import the required datasets. Here, the bairrosRecife application (https://nivan.github.io/covidClusters/bairrosRecife/) will be used as an example. Each application is constituted of two files:
- index.html: mainly used to import libraries and datasets
- main.js: contains the code of the multiple coordinated views widgets.
It is also necessary to create a 'data' directory containing the required datasets (Note that a data directory already exists in this repository). Our approach requires two datasets:
- Boundaries of a location (BoL): Each location have geographical boundaries defined by a polygon, which constitute basic types of the leaflet map. For the bairrosRecife application, /data/boundariesBairros.js contains the boundaries of each district of Recife. This dataset is included in the index.html as <script src="../data/boundariesBairros.js"></script>.
- Location variables (LV): Each location contains mobility, socioeconomic, demographic and epidemiological variables (MSDE). For the bairrosRecife application, /data/graphBairrosEcon3.js contains the MSDE variables of each district of Recife. This dataset is included in the index.html as <script src="../data/graphBairrosEcon3.js"></script>.
The name of the locations must be the same in both 'BoL' and 'LV' datasets. It is necessary to configure the names of the variables to integrate the LV dataset and the main.js code. This will require that the user modify the code in the 'buildCoords()' function of the main.js script.
The final step is to update the central coordinates and zoom properties of the leaflet map, according to the region that is being studied. This can be configured in the 'loadInterface()' function of the main.js script.