An Artificial Intelligence Technique used in Mathematical Model for Predictions Symptoms of COVID-19 Pandemic

Namrata Tripathi, Gurusharan Kaur, R K Sharma
COVID-19; MERS; Pandemic; Artificial Intelligence; Prediction
COVID-19 is a virus still has the potential to spread across countries with no vaccination developed around the world. Artificial intelligence techniques can be instilled to help design better strategies and make productive decisions. These techniques evaluate the situations of the past thus allowing better predictions on the situation that will occur in the future. These mathematical predictions can help prepare for potential threats and consequences. Artificial intelligence techniques play a very important role in obtaining accurate prediction. To calculate the risk factor in the community by using random samples. The predictions of cumulative positive cases of MERS-COVID-19 are by probability methods. Analysis and assumption of outbreak of the disease will be improved by estimating the random sample. Forecast of a pandemic can be made on different parameters like the impact of environmental factors, the effect of quarantine, the incubation period, age, gender and many other factors. Decision making process will be improved. This study examines these challenges and also provides a number of recommendations for people currently battling the global COVID-19 pandemic.
[1] Dey N, Rajinikant V, Fong SJ, Kaiser MS, “Mahmud M. Socialgroup-optimization assisted kapur’s entropy and morphological segmentation for automated detection of COVID-19 infection from computed tomography images”. 2020.
[2] Dorigatti I, Okell L, Cori A, et al. Report 4,” severity of 2019- novel coronavirus (nCoV). Imperial College Report”, June, 4, 2020.
[3] Ferguson NM, Cummings DAT, Cauchemez S, et al. ,”Strategies for containing an emerging influenza pandemic in Southeast Asia. Nature”, 437(7056),2005; 209–14.
[4] Ferguson N, Laydon D, Nedjati-Gilani G, et al. Report 9: “impact of non-pharmaceutical interventions (NPIs) to reduce COVID19 mortality and healthcare demand”. Imperial College June, 4, 2020
[5] H. Sugita, “Robust numerical integration and pairwise independent random variables”, Jour. Comput. Appl. Math., 139, (2002), 1–8.
[6] H. Sugita, “Dynamic random Weyl sampling for drastic reduction of randomness in Monte Carlo integration”, Math. Comput. Simulation, 62 ,2003, 529–537.
[7] Hu S, Liu M, Fong S, Song W, Dey N, Wong R. “Forecasting China future MNP by deep learning. In: Behavior engineering and applications”,Springer, Cham ,2018 ,169–210.
[8] Long C, Ying Q, Fu X, Li Z, Gao Y. “Forecasting the cumulative number of COVID-19 deaths in China: a Boltzmann functionbased modeling study”. 2020.
[9] Lourenco J, Paton R, Ghafari M, et al.” Fundamental principles of epidemic spread highlight the immediate need for large-scale serological surveys to assess the stage of the SARS-CoV-2 epidemic”, 2020.
[10] P. Martin-Lof, “The definition of random sequences”, Inform. Control, 9 ,1966, 602– 619.
[11] Sameni R. Mathematical modeling of epidemic diseases; a case study of the COVID-19 coronavirus. 11371, 2020.
[12] Singh N, Mohanty SR,”Short term price forecasting using adaptive generalized neuron model”, Int J Ambient ComputIntell (IJACI),9(3),2018,44–56.
[13] Wagh CS, Mahalle PN, Wagh SJ. “Epidemic peak for COVID19 in India”, 2020.
[14] Wu Z, McGoogan JM.,” Characteristics of and important lessons from the coronavirus disease 2019 (COVID19) outbreak in China”, summary of a report of 72 314 cases from the Chinese. JAMA 2020,323-1239.
[15] Weber A, Ianelli F, Goncalves S. ,”Trend analysis of the COVID-19 pandemic in China and the rest of the world”. . 2020.
Received : 15 September 2020
Accepted : 17 December 2020
Published : 04 January 2021
DOI: 10.30726/esij/v7.i4.2020.74021

Download “An Artificial Intelligence Technique used in Mathematical Model for Predictions Symptoms of COVID-19 Pandemic”

21.-Artificial-Intelligence-for-Predictions-Symptoms-of-COVID-19.pdf – Downloaded 455 times – 204.11 KB