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The CoronaSurveys System for COVID-19 Incidence Data Collection and Processing

Baquero, Carlos ; Casari, Paolo ; Fernandez Anta, Antonio ; García-García, Amanda ; Frey, Davide ; Garcia-Agundez, Augusto ; Georgiou, Chryssis ; Girault, Benjamin ; Ortega, Antonio ; Goessens, Mathieu ; Hernández-Roig, Harold A. ; Nicolaou, Nicolas ; Stavrakis, Efstathios ; Ojo, Oluwasegun ; Roberts, Julian C. ; Sanchez, Ignacio (2024)
The CoronaSurveys System for COVID-19 Incidence Data Collection and Processing.
In: Frontiers in Computer Science, 2021, 3
doi: 10.26083/tuprints-00022228
Article, Secondary publication, Publisher's Version

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Item Type: Article
Type of entry: Secondary publication
Title: The CoronaSurveys System for COVID-19 Incidence Data Collection and Processing
Language: English
Date: 19 January 2024
Place of Publication: Darmstadt
Year of primary publication: 2021
Place of primary publication: Lausanne
Publisher: Frontiers Media S.A.
Journal or Publication Title: Frontiers in Computer Science
Volume of the journal: 3
Collation: 10 Seiten
DOI: 10.26083/tuprints-00022228
Corresponding Links:
Origin: Secondary publication DeepGreen

CoronaSurveys is an ongoing interdisciplinary project developing a system to infer the incidence of COVID-19 around the world using anonymous open surveys. The surveys have been translated into 60 languages and are continuously collecting participant responses from any country in the world. The responses collected are pre-processed, organized, and stored in a version-controlled repository, which is publicly available to the scientific community. In addition, the CoronaSurveys team has devised several estimates computed on the basis of survey responses and other data, and makes them available on the project’s website in the form of tables, as well as interactive plots and maps. In this paper, we describe the computational system developed for the CoronaSurveys project. The system includes multiple components and processes, including the web survey, the mobile apps, the cleaning and aggregation process of the survey responses, the process of storage and publication of the data, the processing of the data and the computation of estimates, and the visualization of the results. In this paper we describe the system architecture and the major challenges we faced in designing and deploying it.

Uncontrolled Keywords: COVID-19, monitoring, survey, indirect reporting, visualization, network scale-up method, mobile app
Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-222286
Additional Information:

This article is part of the Research Topic Compelling COVID-19 Graphical Simulations

This article was submitted to Human-Media Interaction, a section of the journal Frontiers in Computer Science

Classification DDC: 000 Generalities, computers, information > 004 Computer science
600 Technology, medicine, applied sciences > 621.3 Electrical engineering, electronics
Divisions: 18 Department of Electrical Engineering and Information Technology > Institute of Computer Engineering > Multimedia Communications
Date Deposited: 19 Jan 2024 14:13
Last Modified: 06 Feb 2024 08:06
SWORD Depositor: Deep Green
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/22228
PPN: 515256579
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