DOI: http://dx.doi.org/10.18203/2394-6040.ijcmph20210029

Safely opening schools: artificial intelligence techniques to control transmission of COVID-19

Azza Sarfraz, Zouina Sarfraz, Donald Hathaway III, Sarabjot Singh Makkar, Trissa Paul, Alanna Barrios, Muzna Sarfraz, Gaurav Patel, Nazma Hanif, Hafiza Hussain, Zainab Nadeem, Michael Talalaev, Marcos A. Sanchez-Gonzalez

Abstract


Artificial intelligence techniques and similar digital technologies are promising applications for surveillance systems, contact tracing, and pandemic planning amid the COVID-19 pandemic. With no long-term effective treatment or vaccinations available, it is highly important to scale intelligence solutions to promote detection, school-level screening, monitoring, reducing burden of staff, and prediction potential COVID-19 outbreaks at schools. The objectives of this paper were to present the artificial intelligence for safely opening schools model, and build a solidifying analysis of current literature for applications of the system. The applications are imminent to promoting school health by maximizing the potential of AI technologies. While the AISOS model is not a silver bullet, the improvement in school transmission will be particularly useful as an emergent temporary, potentially permanent, measure of transmission control and monitoring.


Keywords


School health, Artificial intelligence, Machine learning, Disease transmission, COVID-19

Full Text:

PDF

References


Whitelaw S, Mamas MA, Topol E, Van Spall HGC. Applications of digital technology in COVID-19 pandemic planning and response. Lancet Digit Heal. 2020;2(8):e435-40.

Allam Z, Dey G, Jones DS. Artificial intelligence provided early detection of the coronavirus (COVID-19) in China and will influence future urban health policy internationally. AI. 2020;1(2):156-65.

Tuli S, Tuli S, Tuli R, Gill SS. Predicting the growth and trend of COVID-19 pandemic using machine learning and cloud computing. Internet Things. 2020; 11:100222.

Luengo-Oroz M, Hoffmann Pham K, Bullock J. Artificial intelligence cooperation to support the global response to COVID-19. Nat Mach Intell. 2020;2:295-7.

Adly AS, Adly AS, Adly MS. Approaches based on artificial intelligence and the internet of intelligent things to prevent the spread of COVID-19: Scoping review. J Med Internet Res. 2020;22(8):e19104.

Massaroni C, Lopes DS, Lo Presti D, Schena E, Silvestri S. Contactless monitoring of breathing patterns and respiratory rate at the pit of the neck: A single camera approach. J Sensors. 2018.

Ferretti L, Wymant C, Kendall M, Zhao L, Nurtay A, Abeler-Dörner L, et al. Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing. Science. 2020;368(6491):eabb6936.

Stokes EK, Zambrano LD, Anderson KN. Coronavirus Disease 2019 Case Surveillance, United States, January 22-May 30, 2020. Morb Mortal Wkly Rep. 2020; 69(24);759-765

CDC COVID data tracker. Available at: https://www.cdc.gov/coronavirus/2019-ncov/cases-updates/index.html. Accessed on 20 October 2020.

Mehta NS, Mytton OT, Mullins EWS, Fowler TA, Falconer CL, Murphy OB, et al. SARS-CoV-2 (COVID-19): What do we know about children? a systematic review. Clin Infect Dis. 2020;71(9):2469-79.

Medeiros GCBS, Nunes ACF AK et al. The control and prevention of COVID-19 transmission in children: a protocol for systematic review and meta-analysis. Medicine (Baltimore). 2020;99(31):e21393.

Bi Q, Wu Y, Mei S. Epidemiology and transmission of COVID-19 in 391 cases and 1286 of their close contacts in Shenzhen, China: a retrospective cohort study. Lancet Infect Dis. 2020;20(8):911-9.

Lopez AS, Hill M, Antezano J, et al. Transmission dynamics of COVID-19 outbreaks associated with child care facilities-salt lake city, Utah, April-July 2020. MMWR Morb Mortal Wkly Rep. 2020;69(37); 1319-23

Guilamo-Ramos V, Benzekri A, Thimm-Kaiser M, Hidalgo A, Perlman DC. Reconsidering assumptions of adolescent and young adult SARS-CoV-2 transmission dynamics. Clin Infect Dis. 2020: ciaa1348.

Maltezou HC, Vorou R, Papadima K, Kossyvakis A, Spanakis N, Gioula G, et al. Transmission dynamics of SARS-CoV-2 within families with children in Greece: A study of 23 clusters. J Med Virol. 2020; 10:1002.

Danis K, Epaulard O, Bénet T. Cluster of Coronavirus Disease 2019 (COVID-19) in the French Alps, February 2020. Clin Infect Dis. 2020;71(15): 825-32.

Merckx J, Labrecque JA, Kaufman JS. Transmission of SARS-CoV-2 by Children. Dtsch Arztebl Int. 2020;117(33-34):553-60.

COVID policy sequencing. Available at: https://github.com/mfalkenheim/covid-policy-sequencing/blob/master/SIR_Node.py. Accessed on 20 October 2020.

Wang Q, Xie S, Wang Y, Zeng D. Survival-convolution models for predicting COVID-19 Cases and assessing effects of mitigation strategies. medRxiv. 2020;2020.20067306.

Farcomeni A, Maruotti A, Divino F, Lasinio GJ LG. An ensemble approach to short-term forecast of COVID-19 intensive care occupancy in Italian Regions. 2020;1-13.

Fintzi J, Bayer D, Goldstein I. Using multiple data streams to estimate and forecast SARS-CoV-2 transmission dynamics, with application to the virus spread in Orange County, California. ArXiv. 2020.

Khanh NC, Thai PQ, Quach H-L. Transmission of Severe Acute Respiratory Syndrome Coronavirus 2 During Long Flight. Emerg Infect Dis. 2020;26(11): 2617-24.

Quarantine Management Team, COVID-19 National Emergency Response Center. Coronavirus Disease-19: Quarantine framework for travelers entering Korea. Osong Public Health Res Perspect. 2020;11 (3):133-139.

Fielding-Miller R, Sundaram M, Brouwer K. Social determinants of COVID-19 mortality at the county level. medRxiv Prepr Serv Heal Sci. 2020.

Jiang S, Zhou Q, Zhan X, Li Q. Bayesian segmentation modeling for longitudinal epidemiological studies. 2020.

Wang G, Gu Z, Li X. Comparing and integrating US COVID-19 daily data from multiple sources: a county-level dataset with local characteristics. ArXiv. 2020.

Tran DT, Kiranyaz S, Gabbouj M, Iosifidis A. Heterogeneous multilayer generalized operational perceptron. IEEE Trans Neural Networks Learn Syst. 2020;31(3):724-4.

Abdo MS, Shah K, Wahash HA, Panchal SK. On a comprehensive model of the novel coronavirus (COVID-19) under Mittag-Leffler derivative. Chaos Solitons Fractals. 2020;135:109867.

Atangana A. Modelling the spread of COVID-19 with new fractal-fractional operators: Can the lockdown save mankind before vaccination? Chaos Solitons Fractals. 2020;136:109860.

Hadid SB, Ibrahim RW, Altulea D, Momani S. Solvability and stability of a fractional dynamical system of the growth of COVID-19 with approximate solution by fractional Chebyshev polynomials. Adv Differ Equations. 2020;2020(1): 338.

Panovska-Griffiths J, Kerr CC, Stuart RM, et al. Determining the optimal strategy for reopening schools, the impact of test and trace interventions, and the risk of occurrence of a second COVID-19 epidemic wave in the UK: a modelling study. Lancet Child Adolesc Heal. 2020;4(11):817-27.

Dhama K, Khan S, Tiwari R, Sircar S, Bhat S, Malik YS, et al. Coronavirus Disease 2019-COVID-19. Clin Microbiol Rev. 2020;33(4):e00028-20.

Mizumoto K, Chowell G. Transmission potential of the novel coronavirus (COVID-19) onboard the diamond Princess Cruises Ship, 2020. Infect Dis Model. 2020;5:264-70.

Cheng HY, Jian SW, Liu DP, Ng TC, Huang WT, Lin HH, et al. Contact tracing assessment of covid-19 transmission dynamics in Taiwan and risk at different exposure periods before and after symptom onset. JAMA Intern Med. 2020;180(9):1156-63.

Song P, Karako T. COVID-19: Real-time dissemination of scientific information to fight a public health emergency of international concern. Biosci Trends. 2020;14(1):1-2.