Artificial Intelligence-Empowered Mobilization of Assessments in Covid-19-like Pandemics
A Case Study for Early Flattening of the Curve
Bibliographic Data
| ID | 15513952 |
|---|---|
| Authors | Murat Şimşek (0000-0003-3156-5760, University of Ottawa), Burak Kantarcı (0000-0003-0220-7956, University of Ottawa, corresponding author) |
| Year | 2020 |
| Volume | 17 |
| Issue | 10 |
| Pages | 3437-3437 |
| Publication date | 2020-05-14 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Environmental Research and Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1661-7827 • E-ISSN: 1660-4601 |
| Publisher | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/ijerph17103437 |
| PMID | 32423150 |
| OpenAlex | W3025404847 |
| Language | EN |
| Citations received | 6 |
| References cited | 25 |
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
2019-20 coronavirus outbreak · Aeronautics · Coronavirus disease 2019 (COVID-19 · Flattening · Pandemic · Political science · Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 · Disaster Response and Management · Engineering · Medicine · Occupational Health and Safety Research · Psychology · Supply Chain Resilience and Risk Management · Mechanical Engineering · Virology
Feasibility of AI-driven disease surveillance systems at international airports in sub-Saharan Africa
Mapping Spatiotemporal Diffusion of Covid-19 in Lombardy (Italy) on the Base of Emergency Medical Services Activities
Sanitary Aspects of Countering the Spread of Covid-19 in Russia
Generating High-Granularity Covid-19 Territorial Early Alerts Using Emergency Medical Services and Machine Learning
The application framework of big data technology in the Covid-19 epidemic emergency management in local government—a case study of Hainan Province, China
Respiratory pandemics, urban planning and design
Transmission potential and severity of Covid-19 in South Korea
Self-organized formation of topologically correct feature maps
Estimating the asymptomatic proportion of coronavirus disease 2019 (Covid-19) cases on board the Diamond Princess cruise ship, Yokohama, Japan, 2020
Predicting the impacts of epidemic outbreaks on global supply chains
| Unique citing works | 6 |
|---|---|
| Citations per year | 1 |
| Citation span | 2020 - 2026 (7) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 6 |