GO-COVID: AN INTERACTIVE CROSS-PLATFORM BASED DASHBOARD FOR REAL-TIME TRACKING OF COVID-19 USING DATA ANALYTICS

Authors:

Sagnick Biswas,Labhvam Kumar Sharma,Ravi Ranjan,Jyoti Sekhar Banerjee,

DOI NO:

https://doi.org/10.26782/jmcms.2020.06.00001

Keywords:

COVID-19,pneumonia,mobile application,Artificial Intelligence-Machine Learning (AI-ML) tool,chat-bot,geo-location,

Abstract

Currently, COVID-19 is the biggest obstacle for the survival of the human race. Again, as mobile technology is now an essential component of human life, hence it is possible to utilize the power of mobile technology against the treat of COVID-19. Every nation is now trying to deploy an interactive platform for creating public awareness and share the necessary information related to COVID-19. Keeping all of these in mind, authors have deployed an interactive cross-platform (web/mobile) application GO-COVID for the ease of the users, specifically in India. This dashboard is featured with all the real-time attributes regarding the novel coronavirus disease and its measures and controls. The system deliberately aims to maintain the digital well-being of the society, create public awareness, and not create any panic situation among the individuals of the society. The application uses modern AI-ML tools to analyze the disease among the individuals with the help of an informative test and has also deployed a chat-bot for user ease of interaction. The application also collects the geo-location and other necessary historical data to ensure your safety and distancing from the affected personals. The same is also used to backtrack the ones affected and perform tests. All of these features enable the app to compete with the pandemic in this modern world.

Refference:

I. A. Martin, J. Nateqi, S. Gruarin, N. Munsch, I. Abdarahmane, & B.Knapp, “An artificial intelligence-based first-line defence against COVID-19: digitally screening citizens for risks via a chatbot”. bioRxiv, 2020

II. A. Chakraborty, J.S. Banerjee, A. Chattopadhyay, Malicious node restricted quantized data fusion scheme for trustworthy spectrum sensing in cognitive radio networks. Journal of Mechanics of Continua and Mathematical Sciences,15(1), 39–56, 2020

III. A.Chakraborty, and J.S.Banerjee, “An Advance Q Learning (AQL) Approach for Path Planning and Obstacle Avoidance of a Mobile Robot”. International Journal of Intelligent Mechatronics and Robotics, 3(1), pp 53-73 2013

IV. A.Chakraborty, J. S. Banerjee, and A.Chattopadhyay, “Non-Uniform Quantized Data Fusion Rule Alleviating Control Channel Overhead for Cooperative Spectrum Sensing in Cognitive Radio Networks”. In: Proc. IACC, pp 210-215 2017

V. A.Chakraborty, J. S. Banerjee, and A.Chattopadhyay, “Non-uniform quantized data fusion rule for data rate saving and reducing control channel overhead for cooperative spectrum sensing in cognitive radio networks”,Wireless Personal Communications,Springer, 104(2), 837-851, 2019

VI. D. Das, et. al., “Analysis of Implementation Factors of 3D Printer: The Key Enabling Technology for making Prototypes of the Engineering Design and Manufacturing”, International Journal of Computer Applications, pp.8-14, 2017

VII. D. Das, et. al., “An in-depth Study of Implementation Issues of 3D Printer”, in Proc. MICRO 2016 Conference on Microelectronics, Circuits and Systems (pp. 45-49), 2016

VIII. E.Dong, H.Du, &L. Gardner, “An interactive web-based dashboard to track COVID-19 in real time”. The Lancet infectious diseases, 2020

IX. F.Andry, L. Wan, D. Nicholson, “A mobile application accessing patients’ health records through a rest API”, In Proceedings of the 4th International Conference, scitepress.org, 2011

X. H. L. Semigran, J. A. Linder, C. Gidengil, & A. Mehrotra, “Evaluation of symptom checkers for self diagnosis and triage: audit study”. bmj, 351, h3480, 2015

XI. https://www.bing.com/covid

XII. https://covid.apollo247.com/

XIII. https://covindia.com/

XIV. https://www.mygov.in/aarogya-setu-app/

XV. I. Pandey, et. al., “WBAN: A Smart Approach to Next Generation e-healthcare System”, In 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC), pp. 344-349, IEEE, 2019

XVI. J. Luo, “Mobile computing in healthcare: the dreams and wishes of clinicians”. In Proceedings of the 2nd International Workshop on Systems and Networking Support for Health Care and Assisted Living Environments (pp. 1-4), 2008

XVII. J. Banerjee, et. al., “Impact of machine learning in various network security applications”, In 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC), pp. 276-281, IEEE, 2019

XVIII. J. Chattopadhyay, S. Kundu, A. Chakraborty, J. S.Banerjee, “Facial expression recognition for human computer interaction”, in Proceedings of ICCVBIC 2018, Springer (press), 2020

XIX. J. S. Banerjee,A.Chakraborty, and A.Chattopadhyay,“Relay node selection using analytical hierarchy process (AHP) for secondary transmission in multi-user cooperative cognitive radio systems”, in Proc. ETAEERE 2016, LNEE-Springer, Dec. 2016

XX. J. S. Banerjee,A.Chakraborty, and A.Chattopadhyay,“Fuzzy based relay selection for secondary transmission in cooperative cognitive radio networks”, in Proc. OPTRONIX 2016, Springer, India, Aug. 2016

XXI. J. S. Banerjee,A.Chakraborty, and A.Chattopadhyay,“Reliable best-relay selection for secondary transmission in co-operation based cognitive radio systems: A multi-criteria approach”, Journal of Mechanics of Continua and Mathematical Sciences, 13(2), 24-42, 2018

XXII. J. S. Banerjee, and A. Chakraborty, “Fundamentals of Software Defined Radio and Cooperative Spectrum Sensing: A Step Ahead of Cognitive Radio Networks”. In Handbook of Research on Software-Defined and Cognitive Radio Technologies for Dynamic Spectrum Management, IGI Global, pp 499-543 2015

XXIII. J.S.Banerjee, A.Chakraborty, and K.Karmakar, “Architecture of Cognitive Radio Networks”. In N. Meghanathan& Y.B.Reddy (Ed.), Cognitive Radio Technology Applications for Wireless and Mobile Ad Hoc Networks, IGI Global, pp 125-152 2013

XXIV. J. S. Banerjee, and A.Chakraborty, “Modeling of Software Defined Radio Architecture & Cognitive Radio, the Next Generation Dynamic and Smart Spectrum Access Technology”. In M.H. Rehmani& Y. Faheem (Ed.), Cognitive Radio Sensor Networks: Applications, Architectures, and Challenges, IGI Global, pp. 127-158 2014

XXV. J.S.Banerjee, et. al., “A Comparative Study on Cognitive Radio Implementation Issues”, International Journal of Computer Applications, vol.45, no.15, pp. 44-51, May.2012

XXVI. J. S. Banerjee,A.Chakraborty, and A.Chattopadhyay, “A novel best relay selection protocol for cooperative cognitive radio systems using fuzzy AHP”, Journal of Mechanics of Continua and Mathematical Sciences, 13(2), 72-87, 2018

XXVII. J. S. Banerjee, D. Goswami, and S. Nandi, “OPNET: a new paradigm for simulation of advanced communication systems”, in Proc. International Conference on Contemporary Challenges in Management, Technology & Social Sciences, SEMS, India, (pp. 319-328), 2014

XXVIII. J. S. Banerjee, et. al., “A Survey on Agri-Crisis in India Based on Engineering Aspects”, Int. J. of Data Modeling and Knowledge Management, 3(1–2), pp.71-76, 2013

XXIX. K. Karmakar, J.S. Banerjee, “Different network micro-mobility protocols and their performance analysis”. Int. J. Comput. Sci. Inf. Technol. 2(5), 2165–2175, 2011

XXX. J.Kaminski, “Informatics in the time of COVID-19”, 2020

XXXI. M. N. K. Boulos, & E. M. Geraghty, “Geographical tracking and mapping of coronavirus disease COVID-19/severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic and associated events around the world: how 21st century GIS technologies are supporting the global fight against outbreaks and epidemics”, Int J Health Geogr 19, 8 , 2020

XXXII. M. Cascella, M. Rajnik, A. Cuomo, S. C. Dulebohn, & R. Di Napoli, “Features, evaluation and treatment coronavirus (COVID-19)”. In Statpearls [internet]. StatPearls Publishing, 2020

XXXIII. O.Saha; A. Chakraborty, and J. S. Banerjee, “A Decision Framework of IT-Based Stream Selection Using Analytical Hierarchy Process (AHP) for Admission in Technical Institutions”, In: Proc. OPTRONIX 2017, IEEE, pp. 1-6, Nov. 2017

XXXIV. O. Saha; A. Chakraborty, and J. S. Banerjee, “A Fuzzy AHP Approach to IT-Based Stream Selection for Admission in Technical Institutions in India”, In: Proc. IEMIS, AISC-Springer, pp. 847-858, 2019

XXXV. R. Roy, S.Dutta, S. Biswas, & J. S. Banerjee, “Android Things: A Comprehensive Solution from Things to Smart Display and Speaker”. In Proceedings of International Conference on IoT Inclusive Life (ICIIL 2019), NITTTR Chandigarh, India, 339-352, Springer, Singapore,2020

XXXVI. S. Paul, A. Chakraborty, and J. S. Banerjee, “A Fuzzy AHP-Based Relay Node Selection Protocol for Wireless Body Area Networks (WBAN)”, In: Proc. OPTRONIX 2017, IEEE, pp. 1-6, Nov. 2017

XXXVII. S. Paul, A. Chakraborty, and J. S. Banerjee, “The Extent Analysis Based Fuzzy AHP Approach for Relay Selection in WBAN”, In: Proc. CISC, (pp. 331-341). Springer, Singapore, 2019

XXXVIII. S. Guhathakurata, S. Kundu, A. Chakraborty, J. S.Banerjee, “A Novel Approach to Predict COVID-19 Using Support Vector Machine”.In Data Science for COVID-19, Elsevier (press), 2020

XXXIX. WHO-China Joint Mission, Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19), (2020). https://www.who.int/docs/default-source/coronaviruse/who-china-joint-mission-on-covid-19-final-report.pdf (accessed March 1, 2020)

XL. World Health Organization, Coronavirus disease 2019 (COVID-19) Situation Report – 47, (2020). https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200307-sitrep-47-covid-19.pdf?sfvrsn=27c364a4_2 (accessed March 7, 2020)

XLI. W. Wang, J. Tang, &F. Wei, “Updated understanding of the outbreak of 2019 novel coronavirus (2019‐nCoV) in Wuhan, China”. Journal of medical virology, 92(4), 441-447, 2020

XLII. Z. Y.Zu, M. D. Jiang, P. P. Xu, W. Chen, Q. Q. Ni, G. M. Lu, & L. J. Zhang, “Coronavirus disease 2019 (COVID-19): a perspective from China”. Radiology, 200490, 2020

View Download