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A Data-informed Public Health Policy-Makers Platform
Article

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

by and *,†
School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada
*
Author to whom correspondence should be addressed.
All authors contributed equally to this work.
Int. J. Environ. Res. Public Health 2020, 17(10), 3437; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17103437
Received: 5 April 2020 / Revised: 6 May 2020 / Accepted: 11 May 2020 / Published: 14 May 2020
(This article belongs to the Special Issue Computing Techniques for Environmental Research and Public Health)
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. View Full-Text
Keywords: Artificial Intelligence; public health; COVID-19; mobile assessment centers; pandemics; epidemics; self-organizing feature map; neural networks; optimum route planning Artificial Intelligence; public health; COVID-19; mobile assessment centers; pandemics; epidemics; self-organizing feature map; neural networks; optimum route planning
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MDPI and ACS Style

Simsek, M.; Kantarci, B. Artificial Intelligence-Empowered Mobilization of Assessments in COVID-19-like Pandemics: A Case Study for Early Flattening of the Curve. Int. J. Environ. Res. Public Health 2020, 17, 3437. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17103437

AMA Style

Simsek M, Kantarci B. Artificial Intelligence-Empowered Mobilization of Assessments in COVID-19-like Pandemics: A Case Study for Early Flattening of the Curve. International Journal of Environmental Research and Public Health. 2020; 17(10):3437. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17103437

Chicago/Turabian Style

Simsek, Murat, and Burak Kantarci. 2020. "Artificial Intelligence-Empowered Mobilization of Assessments in COVID-19-like Pandemics: A Case Study for Early Flattening of the Curve" International Journal of Environmental Research and Public Health 17, no. 10: 3437. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17103437

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