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Community-Based Solution for a Community Spread Requires Incentive-Compatibility Considerations

Author

Listed:
  • Frank Lorne

    (New York Institute of Technology-Vancouver, Canada)

  • A.C. Lai

    (New York Institute of Technology-Vancouver, Canada)

  • Sairam Katla

    (New York Institute of Technology-Vancouver, Canada)

  • Krish Kale

    (New York Institute of Technology-Vancouver, Canada)

  • Daniel Bocanegra Diaz

    (New York Institute of Technology-Vancouver, Canada)

Abstract

The war on coronavirus is being fought on many fronts. Unquestionably, the battle is in the ICU rooms, but nonpharmaceutical interventions (NPIs) are equally important. Humans can win a battle but lose the war with the virus. If an increase in asymmetric information in human interactions is the social consequence of a spreading virus, that information problem has to be directly addressed. The challenge is more than a task in science. It is also a socio-economic institutional challenge. Health systems of the world, while largely adopting a TTT (Testing, Tracing, Treatment) procedure in managing the virus, has not sufficiently addressed to community spreads in designing mechanisms that can signal suspects and hotspots effectively. The need for examining the system outside the healthcare structure is essential once we understand the traveling journey of a virus. Contact tracing, while direct in identifying suspects of infected individuals, may not be effective even with technology such as AI embedded. Aside from privacy issues, digital contact tracing entails human execution that is a voluntary decision in a free society. There is no assurance that the public will necessarily cooperate. This paper articulates a reason for addressing community-based solutions that are outside the normal health system management. We argue that for a community-based solution to be effective, incentive-compatibility must be considered. Our paper proposes a solution that entails the least privacy intrusion.

Suggested Citation

  • Frank Lorne & A.C. Lai & Sairam Katla & Krish Kale & Daniel Bocanegra Diaz, 2020. "Community-Based Solution for a Community Spread Requires Incentive-Compatibility Considerations," ConScienS Conference Proceedings 002fl, Research Association for Interdisciplinary Studies.
  • Handle: RePEc:smo:conswp:002fl
    as

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    References listed on IDEAS

    as
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    5. Julia M. Puaschunder, 2020. "The Future of Artificial Intelligence in International Healthcare: An Index," Proceedings of the 17th International RAIS Conference, June 1-2, 2020 003jp, Research Association for Interdisciplinary Studies.
    Full references (including those not matched with items on IDEAS)

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