Artificial Intelligence Prediction x41gorithms for Future Evolution of COVID-19 Cases

被引：0

作者：

Ksantini M. ^{[1
]}

Kadri N. ^{[2
]}

Ellouze A. ^{[3
]}

Turki S.H. ^{[4
]}

机构：

[1] Cem Lab, Department of Electrical Engineering. Enis, University of Sfax, Sfax

[2] Cem Lab, Enis, University of Sfax, ISIT'COM, Sfax

[3] Cem Lab, Department of Electrical Engineering, Enis, Isimg, University of Sfax, Sfax

[4] Miracl Laboratoiy, University of Sfax, Sfax

来源：

Ingenierie des Systemes d'Information | 2020年 / 25卷 / 03期

关键词：

Artificial intelligence; Belieffunctions; Covid-19; Deep learning; Dempster-shafer theory; Home isolation; Machine learning; Pandemic;

D O I：

10.18280/isi.250305

中图分类号：

学科分类号：

摘要：

Since the COVID-19 pandemic surges around the world and officially entered a dangerous new phase, one of the important concerns is when to take aggressive public health measures to slow the spread of COVID-19 and to know impact of the use of protection tools. Many studies have dealt with the prediction of the evolution of cases affected by the C'OVID-19 virus. Given the unreliability of the data collected about the number of new cases and the uncertainties in values, the results found cannot be accurate and present a bias. In this paper, we will present a study using artificial intelligence algorithms more precisely machine and deep learning algorithms to predict the evolution of cases reached by COVID-19 in the future given the application of confinement and the use of protection tools. To improve the accuracy of the results and to take into account the uncertain aspect of the data we will apply the theory of belief functions. Among objectives of this theory is the fusion of different sources of information, given by artificial intelligence algorithms in our case, in order to obtain a global knowledge in the form of a more precise and reinforced belief function. Results shows that applying the home isolation and the use of protection tools with the rate over of 80% can reduce considerably the number of cases. © 2020 International Information and Engineering Technology Association. All rights reserved.

引用

页码：319 / 325

页数：6

共 27 条

[1]

Wang C., Horby P.W., Hayden F.G., Gao G.F., A novel coronavirus outbreak of global health concern, The Lancet, 395, 10223, pp. 470-473, (2020)

[2]

Gorbalenya A.E., Baker S.C., Baric R., Groot R.J.D., Drosten C., Gulyaeva A.A., Haagmans B.L., Lauber C., Leontovich A.M., Neuman B.W., Penzar D., Perlman S., Poon L., Samborskiy D., Sidorov I.A., Sola Gurpegui I., Ziebuhr J., Severe acute respiratory syndrome-related coronavirus: The species and its viruses- A statement of the Coronavirus Study Group, (2020)

[3]

Prem K., Liu Y., Russell T.W., Kucharski A.J., Eggo R.M., Davies N., Jit M., Klepac P., The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: A modelling study, The Lancet Public Health. Elsevier, 5, 5, pp. E261-E270, (2020)

[4]

Li Y.C., Wang B.W., Peng R.Y., Zhou C., Zhan Y.L., Liu Z.X., Jiang X., Zhao B., Mathematical modeling and epidemic prediction of COVID-19 and its significance to epidemic prevention and control measures, Ann Infect Dis Epidemiol, 5, 1, (2020)

[5]

Bhatnagar M., COVID-19: Mathematical modeling and predictions, (2020)

[6]

Ivorra B., Ferrandez M.R., Vela M., Ramos A.M., Mathematical modeling of the spread of the coronavirus disease 2019 (COVID-19) taking into account the undetected infections, The case of China, pp. 1-28, (2020)

[7]

Nesteruk I., Statistics-based predictions of coronavirus epidemic spreading in mainland China, (2020)

[8]

Hu Z.X., Ge Q.Y., Jin L., Xiong M.M., Artificial intelligence forecasting of COVID-19 in China, pp. 1-20, (2020)

[9]

Punn N.S., Sonbhadra S.K., Agarwal S., COVID-19 epidemic analysis using machine learning and deep learning algorithms, (2020)

[10]

Wynants L., Calster B.V., Bonten M.M.J., Collins G.S., Debray T.P.A., Vos M.D., Et al., Prediction models for diagnosis and prognosis of COVID-19 infection: Systematic review and critical appraisal, BMJ. British Medical Journal Publishing Group, 369, (2020)

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