Bridging Predictive Analytics and Mobile Crowdsensing for Future Risk Maps of Communities Against COVID-19

ABSTRACT

Crowd monitoring and management is an important application of Mobile Crowdsensing (MCS). The emergence of COVID-19 pandemic has made the modeling and simulation of community infection spread a vital activity in the battle against the disease. This paper provides insights for the utility of MCS to inform the decision support systems combating the pandemic. We present an MCS-driven community risk modeling solution against COVID-19 pandemic with the support of smart mobile device users (i.e., MCS participants), who opt-in to crowdsensing campaigns and grant access to their mobile device's built-in sensors (including GPS). Each community is defined by the spatio-temporal instances of MCS participants that are clustered based on the projected future movements of these participants. The MCS platform keeps track of the mobility patterns of the participants and utilizes unsupervised machine learning (ML) algorithms, more specifically k-means, Hidden Markov Model (HMM), and Expectation Maximization (EM) to predict a risk score of COVID-19 community spread for each community ahead of time. Through numerical results from simulating a metropolitan area (e.g., Paris), it is shown that communities? COVID-19 risk scores at the end of a set of MCS campaign can be predicted 20% ahead of time (i.e., upon completion of 80% of the MCS time commitments) with a dependability score up to 0.96 and an average of 0.93. Further tests with a larger population of participants show that community risk scores can be predicted 20% ahead of time with a dependability score up to 0.99 and an average of 0.98.

References

1. A. Capponi, C. Fiandrino, B. Kantarci, L. Foschini, D. Kliazovich, and P. Bouvry. 2019. A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities. IEEE Communications Surveys Tutorials, Vol. 21, 3 (thirdquarter 2019), 2419--2465.
2. J. M. Cecilia, J.-C. Cano, E. Hernández-Orallo, C. T. Calafate, and P. Manzoni. 2020. Mobile crowdsensing approaches to address the COVID-19 pandemic in Spain. IET Smart Cities, Vol. 2, 2 (2020), 58--63.
3. V. Chamola, V. Hassija, V. Gupta, and M. Guizani. 2020. A Comprehensive Review of the COVID-19 Pandemic and the Role of IoT, Drones, AI, Blockchain, and 5G in Managing its Impact. IEEE Access, Vol. 8 (2020), 90225--90265.
4. X. Ding, D. Clifton, N. JI, N. H. Lovell, P. Bonato, W. Chen, X. Yu, Z. Xue, T. Xiang, X. Long, K. Xu, X. Jiang, Q. Wang, B. Yin, G. Feng, and Y. Zhang. 2020. Wearable Sensing and Telehealth Technology with Potential Applications in the Coronavirus Pandemic. IEEE Reviews in Biomedical Engineering (2020), 1--1.
5. C. Fiandrino, A. Capponi, G. Cacciatore, D. Kliazovich, U. Sorger, P. Bouvry, B. Kantarci, F. Granelli, and S. Giordano. 2017. CrowdSenSim: a Simulation Platform for Mobile Crowdsensing in Realistic Urban Environments. IEEE Access, Vol. 5 (2017), 3490--3503.
6. L. Greco, G. Percannella, P. Ritrovato, F. Tortorella, and M. Vento. 2020. Trends in IoT based solutions for health care: Moving AI to the edge. Pattern Recognition Letters, Vol. 135 (2020), 346 -- 353.
7. L. A. Kalogiros, K. Lagouvardos, S. Nikoletseas, N. Papadopoulos, and P. Tzamalis. 2018. Allergymap: A Hybrid mHealth Mobile Crowdsensing System for Allergic Diseases Epidemiology : a multidisciplinary case study. In IEEE PerCom Workshops. 597--602.
8. H. Li, S.-M. Liu, X.-H. Yu, S.-L. Tang, and C.-K. Tang. 2020. Coronavirus disease 2019 (COVID-19): current status and future perspective. International Journal of Antimicrobial Agents (2020), 105951.
9. M. Nasajpour, S. Pouriyeh, R. M. Parizi, M. Dorodchi, M. Valero, and H. R. Arabnia. 2020. Internet of Things for Current COVID-19 and Future Pandemics: An Exploratory Study. ArXiv, Vol. abs/2007.11147 (2020).
10. P. C. Ng, P. Spachos, and K. Plataniotis. arXiv, 2020. COVID-19 and Your Smartphone: BLE-based Smart Contact Tracing. arxiv: 2005.13754 [cs.LG]
11. A. Polenta, P. Rignanese, P. Sernani, N. Falcionelli, D.N. Mekuria, S. Tomassini, and A.F. Dragoni. 2020. An Internet of Things Approach to Contact Tracing?The BubbleBox System. Information, Vol. 11 (2020), 347.1--347.12. Issue 7.
12. Md S. Rahman, N. C. Peeri, N. Shrestha, R. Zaki, U. Haque, and S. H. Ab Hamid. 2020. Defending against the Novel Coronavirus (COVID-19) Outbreak: How Can the Internet of Things (IoT) help to save the World? Health Policy and Technology, Vol. 9 (June 2020), 136--138. Issue 2.
13. C. C. Robusto. 1957. The cosine-haversine formula. The American Mathematical Monthly, Vol. 64, 1 (1957), 38--40.
14. M. Simsek and B. Kantarci. 2020. Artificial Intelligence-Empowered Mobilization of Assessments in COVID-19-like Pandemics: A Case Study for Early Flattening of the Curve. Int. Journal of Environmental Research and Public Health, Vol. 17 (2020), 3437.1--3437.17. Issue 10.
15. R. P. Singh, M. Javaid, A. Haleem, and R. Suman. 2020. Internet of things (IoT) applications to fight against COVID-19 pandemic. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, Vol. 14 (July-August 2020), 521--524. Issue 4.
16. G. Solmaz and D. Turgut. 2019. A survey of human mobility models. IEEE Access, Vol. 7, 1 (December 2019), 125711--125731. DOI: 10.1109/ACCESS.2019.2939203.
17. S. Swayamsiddha and M. Chandana. 2020. Application of cognitive Internet of Medical Things for COVID-19 pandemic. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, Vol. 14, 5 (2020), 911 -- 915.
18. D. S. W. Ting, L. Carin, V. Dzau, and T. Y. Wong. 2020. Digital technology and COVID-19. Nature medicine, Vol. 26, 4 (2020), 459--461.
19. D. Turgut and L. Bölöni. 2017. Value of Information and Cost of Privacy in the Internet of Things. IEEE Communications Magazine, Vol. 55, 9 (2017), 62--66.
20. L. van der Maaten and G. Hinton. 2008. Visualizing data using t-SNE. Journal of machine learning research, Vol. 9, Nov (2008), 2579--2605.
21. B. Wang, Y. Sun, T. Q. Duong, L. D. Nguyen, and L. Hanzo. 2020. Risk-Aware Identification of Highly Suspected COVID-19 Cases in Social IoT: A Joint Graph Theory and Reinforcement Learning Approach. IEEE Access, Vol. 8 (2020), 115655--115661.
22. D. Yang, G. Xue, X. Fang, and J. Tang. 2016. Incentive Mechanisms for Crowdsensing: Crowdsourcing With Smartphones. IEEE/ACM Transactions on Networking, Vol. 24, 3 (2016), 1732--1744.
23. X. Zhang, Z. Yang, W. Sun, Y. Liu, S. Tang, K. Xing, and X. Mao. 2016. Incentives for Mobile Crowd Sensing: A Survey. IEEE Communications Surveys Tutorials, Vol. 18, 1 (2016), 54--67.
24. Y. Zhang, Y. Li, B. Yang, X. Zheng, and M. Chen. 2020 a. Risk Assessment Of COVID-19 Based On Multisource Data From A Geographical View. IEEE Access (2020), 1--1.
25. Y. Zhang, M. Simsek, and B. Kantarci. 2020 b. Empowering Self-Organized Feature Maps for AI-Enabled Modelling of Fake Task Submissions to Mobile Crowdsensing Platforms. IEEE Internet of Things Journal (2020), 1--1.

Index Terms

• Computing methodologies

  • Machine learning

    • Learning paradigms

      • Unsupervised learning

        • Cluster analysis

• Human-centered computing

  • Ubiquitous and mobile computing

• Networks

  • Network services

    • Location based services