Design and Feasibility Study of the Mobile Application StopTheSpread

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Abstract

The emergence of recent disease outbreaks calls for the design of new educational games aimed at increasing awareness in disease prevention. This article presents StopTheSpread, an educational mobile application that seeks to improve awareness about the best practices to prevent the spreading of seasonal flu in the general public. StopTheSpread integrates concepts in network science and epidemiology, within a freely available mobile application that provides a unique learning experience for free-choice learners about flu prevention. StopTheSpread teaches users basic concepts about flu prevention, within a series of games of increasing difficulty that maintain user engagement and offers a user-friendly design. StopTheSpread provides a summary of the best practices to prevent flu spreading according to the guidelines of the Centers for Disease Control and Prevention, and the World Health Organization, while connecting users to citizen science projects aimed at worldwide flu tracking. Through Facebook, Twitter, and email we reached volunteers during the COVID-19 confinement, to conduct an online feasibility study, toward assessing learning outcome in playing with our mobile application. Our results indicate that the use of StopTheSpread increased by 20% the awareness about the spreading mechanism of flu, compared with the baseline population.

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Most cited references 65

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Emergence of Scaling in Random Networks

Albert-László Barabási, Réka Albert (2000)

Systems as diverse as genetic networks or the World Wide Web are best described as networks with complex topology. A common property of many large networks is that the vertex connectivities follow a scale-free power-law distribution. This feature was found to be a consequence of two generic mechanisms: (i) networks expand continuously by the addition of new vertices, and (ii) new vertices attach preferentially to sites that are already well connected. A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.

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On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other

H. Mann, D. Whitney (1947)

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Is Open Access

Dynamics of Person-to-Person Interactions from Distributed RFID Sensor Networks

Ciro Cattuto, Wouter Van den Broeck, Alain Barrat … (2010)

Background Digital networks, mobile devices, and the possibility of mining the ever-increasing amount of digital traces that we leave behind in our daily activities are changing the way we can approach the study of human and social interactions. Large-scale datasets, however, are mostly available for collective and statistical behaviors, at coarse granularities, while high-resolution data on person-to-person interactions are generally limited to relatively small groups of individuals. Here we present a scalable experimental framework for gathering real-time data resolving face-to-face social interactions with tunable spatial and temporal granularities. Methods and Findings We use active Radio Frequency Identification (RFID) devices that assess mutual proximity in a distributed fashion by exchanging low-power radio packets. We analyze the dynamics of person-to-person interaction networks obtained in three high-resolution experiments carried out at different orders of magnitude in community size. The data sets exhibit common statistical properties and lack of a characteristic time scale from 20 seconds to several hours. The association between the number of connections and their duration shows an interesting super-linear behavior, which indicates the possibility of defining super-connectors both in the number and intensity of connections. Conclusions Taking advantage of scalability and resolution, this experimental framework allows the monitoring of social interactions, uncovering similarities in the way individuals interact in different contexts, and identifying patterns of super-connector behavior in the community. These results could impact our understanding of all phenomena driven by face-to-face interactions, such as the spreading of transmissible infectious diseases and information.

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Author and article information

Contributors

Rizzo Alessandro Rizzo Alessandro:

ORCID: https://orcid.org/0000-0002-2386-3146

Porfiri Maurizio Porfiri Maurizio:

ORCID: https://orcid.org/0000-0002-1480-3539

Journal

Journal ID (nlm-ta): IEEE Access

Journal ID (iso-abbrev): IEEE Access

Journal ID (publisher-id): 0063500

Journal ID (publisher-id): ACCESS

Journal ID (coden): IAECCG

Title: Ieee Access

Publisher: IEEE

ISSN (Electronic): 2169-3536

Publication date Collection: 2020

Publication date (Electronic): 08 September 2020

Volume: 8

Pages: 172105-172122

Affiliations

[1] departmentDepartment of Mechanical and Aerospace Engineering, institutionNew York University Tandon School of Engineering, institutionringgold 34242; Brooklyn NY 11201 USA

[2] departmentDipartimento di Elettronica e Telecomunicazioni, institutionPolitecnico di Torino, institutionringgold 19032; 10129 Turin Italy

[3] divisionOffice of Innovation, institutionNew York University Tandon School of Engineering, institutionringgold 34242; Brooklyn NY 11201 USA

[4] departmentDepartment of Biomedical Engineering, institutionNew York University Tandon School of Engineering, institutionringgold 34242; Brooklyn NY 11201 USA

Article

DOI: 10.1109/ACCESS.2020.3022740

PMC ID: 8675558

SO-VID: 836b3ba7-6f66-4ad1-850d-8a52622992ad

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

History

Date received : 25 August 2020

Date accepted : 05 September 2020

Date: 30 September 2020

Page count

Figures: 12, Tables: 9, Equations: 66, References: 82, Pages: 18

Funding

Funded by: National Science Foundation, fundref 10.13039/100000001;

Award ID: CMMI-1561134

Funded by: Compagnia di San Paolo, fundref 10.13039/100007388;

This work was supported in part by the National Science Foundation under Grant CMMI-1561134, and in part by the Compagnia di San Paolo.

Design and Feasibility Study of the Mobile Application StopTheSpread

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Abstract

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Novel Coronavirus Disease COVID-19

Most cited references 65

Emergence of Scaling in Random Networks

On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other

Dynamics of Person-to-Person Interactions from Distributed RFID Sensor Networks

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