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Artificial Intelligence-Empowered Mobilization of Assessments in COVID-19-like Pandemics: A Case Study for Early Flattening of the Curve

Author

Listed:
  • Murat Simsek

    (School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada
    All authors contributed equally to this work.)

  • Burak Kantarci

    (School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada
    All authors contributed equally to this work.)

Abstract

The global outbreak of the Coronavirus Disease 2019 (COVID-19) pandemic has uncovered the fragility of healthcare and public health preparedness and planning against epidemics/pandemics. In addition to the medical practice for treatment and immunization, it is vital to have a thorough understanding of community spread phenomena as related research reports 17.9–30.8% confirmed cases to remain asymptomatic. Therefore, an effective assessment strategy is vital to maximize tested population in a short amount of time. This article proposes an Artificial Intelligence (AI)-driven mobilization strategy for mobile assessment agents for epidemics/pandemics. To this end, a self-organizing feature map (SOFM) is trained by using data acquired from past mobile crowdsensing (MCS) campaigns to model mobility patterns of individuals in multiple districts of a city so to maximize the assessed population with minimum agents in the shortest possible time. Through simulation results for a real street map on a mobile crowdsensing simulator and considering the worst case analysis, it is shown that on the 15th day following the first confirmed case in the city under the risk of community spread, AI-enabled mobilization of assessment centers can reduce the unassessed population size down to one fourth of the unassessed population under the case when assessment agents are randomly deployed over the entire city.

Suggested Citation

  • Murat Simsek & Burak Kantarci, 2020. "Artificial Intelligence-Empowered Mobilization of Assessments in COVID-19-like Pandemics: A Case Study for Early Flattening of the Curve," IJERPH, MDPI, vol. 17(10), pages 1-17, May.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:10:p:3437-:d:358298
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    Citations

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    Cited by:

    1. Gwanggil Jeon & Abdellah Chehri, 2021. "Computing Techniques for Environmental Research and Public Health," IJERPH, MDPI, vol. 18(18), pages 1-4, September.
    2. Kangwei Tu & Andras Reith, 2023. "Changes in Urban Planning in Response to Pandemics: A Comparative Review from H1N1 to COVID-19 (2009–2022)," Sustainability, MDPI, vol. 15(12), pages 1-20, June.
    3. Lorenzo Gianquintieri & Maria Antonia Brovelli & Andrea Pagliosa & Gabriele Dassi & Piero Maria Brambilla & Rodolfo Bonora & Giuseppe Maria Sechi & Enrico Gianluca Caiani, 2022. "Generating High-Granularity COVID-19 Territorial Early Alerts Using Emergency Medical Services and Machine Learning," IJERPH, MDPI, vol. 19(15), pages 1-19, July.
    4. Elena Belova & Ekaterina Shashina & Denis Shcherbakov & Yury Zhernov & Vitaly Sukhov & Nadezhda Zabroda & Valentina Makarova & Tatiana Isiutina-Fedotkova & Svetlana Mishina & Anton Simanovsky & Oleg M, 2021. "Sanitary Aspects of Countering the Spread of COVID-19 in Russia," IJERPH, MDPI, vol. 18(23), pages 1-9, November.

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