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      Contact Tracing Incentive for COVID-19 and Other Pandemic Diseases From a Crowdsourcing Perspective

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          Abstract

          Governments of the world have invested a lot of manpower and material resources to combat COVID-19 this year. At this moment, the most efficient way that could stop the epidemic is to leverage the contact tracing system to monitor people’s daily contact information and isolate the close contacts of COVID-19. However, the contact tracing data usually contains people’s sensitive information that they do not want to share with the contact tracing system and government. Conversely, the contact tracing system could perform better when it obtains more detailed contact tracing data. In this article, we treat the process of collecting contact tracing data from a crowdsourcing perspective in order to motivate users to contribute more contact tracing data and propose the incentive algorithm named CovidCrowd. Different from previous works where they ask users to contribute their data voluntarily, the government offers some reward to users who upload their contact tracing data to reimburse the privacy and data processing cost. We formulate the problem as a Stackelberg game and show there exists a Nash equilibrium for any user given the fixed reward value. Then, CovidCrowd computes the optimal reward value which could maximize the utility of the system. Finally, we conduct a large-scale simulation with thousands of users and evaluation with real-world data set. Both results show that CovidCrowd outperforms the benchmarks, e.g., the user participating level is improved by at least 13.2% for all evaluation scenarios.

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          Convex Optimization

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            Response to COVID-19 in Taiwan: Big Data Analytics, New Technology, and Proactive Testing

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              Is Open Access

              Airborne Transmission Route of COVID-19: Why 2 Meters/6 Feet of Inter-Personal Distance Could Not Be Enough

              The COVID-19 pandemic caused the shutdown of entire nations all over the world. In addition to mobility restrictions of people, the World Health Organization and the Governments have prescribed maintaining an inter-personal distance of 1.5 or 2 m (about 6 feet) from each other in order to minimize the risk of contagion through the droplets that we usually disseminate around us from nose and mouth. However, recently published studies support the hypothesis of virus transmission over a distance of 2 m from an infected person. Researchers have proved the higher aerosol and surface stability of SARS-COV-2 as compared with SARS-COV-1 (with the virus remaining viable and infectious in aerosol for hours) and that airborne transmission of SARS-CoV can occur besides close-distance contacts. Indeed, there is reasonable evidence about the possibility of SARS-COV-2 airborne transmission due to its persistence into aerosol droplets in a viable and infectious form. Based on the available knowledge and epidemiological observations, it is plausible that small particles containing the virus may diffuse in indoor environments covering distances up to 10 m from the emission sources, thus representing a kind of aerosol transmission. On-field studies carried out inside Wuhan Hospitals showed the presence of SARS-COV-2 RNA in air samples collected in the hospitals and also in the surroundings, leading to the conclusion that the airborne route has to be considered an important pathway for viral diffusion. Similar findings are reported in analyses concerning air samples collected at the Nebraska University Hospital. On March 16th, we have released a Position Paper emphasizing the airborne route as a possible additional factor for interpreting the anomalous COVID-19 outbreaks in northern Italy, ranked as one of the most polluted areas in Europe and characterized by high particulate matter (PM) concentrations. The available information on the SARS-COV-2 spreading supports the hypothesis of airborne diffusion of infected droplets from person to person at a distance greater than two meters (6 feet). The inter-personal distance of 2 m can be reasonably considered as an effective protection only if everybody wears face masks in daily life activities.
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                Author and article information

                Contributors
                Journal
                IEEE Internet Things J
                IEEE Internet Things J
                0066700
                JIOT
                IITJAU
                Ieee Internet of Things Journal
                IEEE
                2327-4662
                01 November 2021
                04 January 2021
                : 8
                : 21
                : 15863-15874
                Affiliations
                [1] divisionSchool of Computer Science and Technology, institutionDalian University of Technology, institutionringgold 12399; Dalian 116024 China
                [2] divisionSchool of Software, institutionDalian University of Technology, institutionringgold 12399; Dalian 116600 China
                [3] divisionCollege of Computing and Informatics, institutionUniversity of Sharjah, institutionringgold 59105; Sharjah UAE
                [4] institutionKing Abdullah II School of Information Technology, University of Jordan, institutionringgold 54658; Amman 19328 Jordan
                [5] divisionSchool of Computer and Communication Engineering, institutionUniversity of Science and Technology Beijing, institutionringgold 12507; Beijing 100083 China
                [6] divisionSchool of Engineering, institutionAmity University—A Global University; Noida 201313 India
                [7] divisionSchool of Software, institutionTsinghua University, institutionringgold 12442; Beijing 100084 China
                Article
                10.1109/JIOT.2020.3049024
                8768981
                35782186
                47ba4b1a-bcb0-428e-bb7c-8b96aafe8a50
                Copyright @ 2021

                This article is free to access and download, along with rights for full text and data mining, re-use and analysis.

                History
                : 14 October 2020
                : 08 December 2020
                : 23 December 2020
                : 22 October 2021
                Page count
                Figures: 6, Tables: 1, Equations: 197, References: 49, Pages: 12
                Funding
                Funded by: National Key Research and Development Program of China, fundref 10.13039/501100012166;
                Award ID: 2018YFC0910500
                Funded by: Fundamental Research Funds for the Central Universities, fundref 10.13039/501100012226;
                Award ID: DUT20RC(3)039
                Award ID: DUT20RC(4)005
                Award ID: DUT19JC39
                Award ID: N182608004
                Award ID: N180101028
                Funded by: Liaoning Key Research and Development Program;
                Award ID: 2019JH210100030
                Award ID: 2020JH210100046
                Funded by: National Key Research and Development Projects;
                Award ID: 2019YFB1802600
                Funded by: Liaoning United Foundation;
                Award ID: U1908214
                Funded by: National Natural Science Foundation of China, fundref 10.13039/501100001809;
                Award ID: 62002045
                Award ID: 62072094
                Award ID: 61872052
                Award ID: 61872073
                Award ID: 61672148
                Funded by: Natural Science Foundation of Liaoning Province, fundref 10.13039/501100005047;
                Award ID: 2019-MS-055
                Funded by: Liaoning Province Science and Technology Fund Project;
                Award ID: 2020MS086
                Funded by: CERNET Innovation Project;
                Award ID: NGII20190504
                Funded by: Youth Science and Technology Star of Dalian;
                Award ID: 2018RQ45
                Funded by: Opening Project of Shanghai Trusted Industrial Control Platform;
                Award ID: TICPSH202003017-ZC
                Funded by: Program for Liaoning Innovative Research Term in University;
                Award ID: LT2016007
                Funded by: Program for the Liaoning Distinguished Professor, Program for Innovative Research Team in University of Liaoning Province, fundref 10.13039/501100012467;
                Funded by: Science and Technology Innovation Fund of Dalian, fundref 10.13039/501100017683;
                Award ID: 2020JJ25CY001
                Funded by: PR of China Ministry of Education Distinguished Possessor Grant given to Prof. Obaidat;
                Award ID: MS2017BJKJ003
                This work was supported in part by the National Key Research and Development Program of China under Grant 2018YFC0910500; in part by the Fundamental Research Funds for the Central Universities under Grant DUT20RC(3)039, Grant DUT20RC(4)005, Grant DUT19JC39, Grant N182608004, and Grant N180101028; in part by the Liaoning Key Research and Development Program under Grant 2019JH210100030 and Grant 2020JH210100046; in part by the National Key Research and Development Projects under Grant 2019YFB1802600; in part by the Liaoning United Foundation under Grant U1908214; in part by the National Natural Science Foundation of China under Grant 62002045, Grant 62072094, Grant 61872052, Grant 61872073, and Grant 61672148; in part by the Natural Science Foundation of Liaoning Province under Grant 2019-MS-055; in part by the Liaoning Province Science and Technology Fund Project under Grant 2020MS086; in part by the CERNET Innovation Project under Grant NGII20190504; in part by the Youth Science and Technology Star of Dalian under Grant 2018RQ45; in part by the Opening Project of Shanghai Trusted Industrial Control Platform under Grant TICPSH202003017-ZC; in part by the Program for Liaoning Innovative Research Term in University under Grant LT2016007; in part by the Program for the Liaoning Distinguished Professor, Program for Innovative Research Team in University of Liaoning Province; in part by the Science and Technology Innovation Fund of Dalian under Grant 2020JJ25CY001; and in part by the PR of China Ministry of Education Distinguished Possessor Grant given to Prof. Obaidat under Grant MS2017BJKJ003.
                Categories
                Article

                contact tracing,covid-19,incentive algorithm,nash equilibrium,pandemic diseases,utility maximization

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