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Real-time forecasting of the COVID 19 using fuzzy grey Markov: a different approach in decision-making. (English) Zbl 1513.62234

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62P10 Applications of statistics to biology and medical sciences; meta analysis
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[1] Arji, G.; Ahmadi, H.; Nilashi, M.; Rashid, TA; Ahmed, OH; Aliojo, N.; Zainol, A., Fuzzy logic approach for infectious disease diagnosis: a methodical evaluation, literature and classification, Biocybern Biomed Eng, 39, 4, 937-955 (2019) · doi:10.1016/j.bbe.2019.09.004
[2] Baz, A.; Alhakami, A.; Alshareef, E., A framework of computational model for predicting the spread of COVID-19 pandemic in Saudi Arabia, Int J Intell Eng Syst, 13, 5, 463-475 (2020) · doi:10.22266/ijies2020.1031.41
[3] Bherwani, H., Understanding Covid-19 transmission through Bayesian probabilistic modeling and GIS-based Voronoi approach: a policy perspective, Environ Dev Sustain, 23, 5846-5864 (2020) · doi:10.1007/s10668-020-00849-0
[4] Boccaletti, S.; Ditto, W.; Mindlin, G.; Atangana, A., Modeling and forecasting of epidemic spreading: the case of Covid-19 and beyond, Chaos Solitons Fractals, 135 (2020) · doi:10.1016/j.chaos.2020.109794
[5] Castillo, O.; Melin, P., Forecasting of Covid-19 time series for countries in the world based on a hybrid approach combining the fractal dimension and fuzzy logic, Chaos Solitons Fractals, 140 (2020) · Zbl 1495.92076 · doi:10.1016/j.chaos.2020.110242
[6] Castillo, O.; Melin, P., A novel method for a Covid-19 classification of countries based on an intelligent fuzzy fractal approach, Healthcare, 9, 2, 196 (2021) · doi:10.3390/healthcare9020196
[7] Ceylan, Z., Short-term prediction of Covid-19 spread using grey rolling model optimized by particle swarm optimization, Appl Soft Comput, 109 (2021) · doi:10.1016/j.asoc.2021.107592
[8] Chowdhury, AA; Hasan, KT; Hoque, KKS, Analysis and prediction of Covid-19 pandemic in Bangladesh by using ANFIS and LSTM network, Cogn Comput, 13, 761-770 (2021) · doi:10.1007/s12559-021-09859-0
[9] Dattner, I.; Huppert, A., Modern statistical tools for inference and prediction of infectious diseases using mathematical models, Stat Methods Med Res, 27, 7, 1927-1929 (2018) · doi:10.1177/0962280217746456
[10] Deepak, P.; Divya, M.; Suyash, B.; Mayank, A., Fuzzy rule based system to predict Covid19—a deadly virus, Int J Manag Humanit, 4, 8, 78-82 (2020) · doi:10.35940/ijmh.H0781.044820
[11] Dhamodharavadhani, S.; Rathipriya, R.; Chatterjee, JM, Covid-19 mortality rate prediction for Indian using statistical neural network models, Front Public Health, 8, 441 (2020) · doi:10.3389/fpubh.2020.00441
[12] Ding, C.; Chen, Y.; Liu, Z.; Liu, T., Prediction on transmission trajectory of Covid-19 based on particle swarm algorithm, Pattern Recognit Lett, 152, 70-78 (2021) · doi:10.1016/j.patrec.2021.09.003
[13] Gao, J.; Li, J.; Wang, M., Time series analysis of cumulative incidences of typhoid and paratyphoid fevers in China using both Grey and SARIMA models, PLoS One, 15, 10 (2020) · doi:10.1371/journal.pone.0241217
[14] Geng, N.; Yong, Z.; Sun, Y.; Jiang, Y.; Chen, D., Forecasting China’s annual biofuel production using an improved grey model, Energies, 8, 10, 12080-12099 (2015) · doi:10.3390/en81012080
[15] Innocent, PR; John, RI; Garibald, GM, Fuzzy methods for medical diagnosis, Appl Artif Intell, 19, 1, 69-98 (2005) · doi:10.1080/08839510590887414
[16] Iqelan, BM, Forecasts of female breast cancer referrals using Grey prediction model GM(1,1), Appl Math Sci, 11, 54, 2647-2662 (2017) · doi:10.12988/ams.2017.79273
[17] Kumar, N.; Susan, S., Particle swarm optimization of partitions and fuzzy order for fuzzy time series forecasting of COVID-19, Appl Soft Comput, 110, 107611 (2021) · doi:10.1016/j.asoc.2021.107611
[18] Li, H.; Zeng, B.; Wang, J.; Wu, H., Forecasting the number of new coronavirus infections using an improved grey prediction model, Iran J Public Health, 50, 9, 1842-1853 (2021) · doi:10.18502/ijph.v50i9.7057
[19] Malavika, B.; Marimuthu, S.; Joy, M.; Nadaraj, A.; Asirvatham, ES; Jeyaseelan, L., Forecasting Covid-19 epidemic in India and high incidence states using SIR and logistic growth models, Clin Epidemiol Glob Health, 9, 26-33 (2020) · doi:10.1016/j.cegh.2020.06.006
[20] Marfak, A.; Achak, D.; Azizi, A.; Nejjari, C.; Aboudi, K.; Saad, E.; Hilali, A.; Marfak, IY, The hidden Markov chain modeling of the Covid-19 spreading using Moroccan dataset, Data Brief, 32, 106067 (2020) · doi:10.1016/j.dib.2020.106067
[21] Melin, P.; Monica, JC; Sanchez, D.; Castillo, O., Analysis of spatial spread relationships of coronavirus (Covid-19) pandemic in the world using self organizing maps, Chaos Solitons Fractals, 138 (2020) · doi:10.1016/j.chaos.2020.109917
[22] Melin, P.; Monica, JC; Sanchez, D.; Castillo, O., Multiple ensemble neural network models with fuzzy response aggregation for predicting Covid-19 time series: the case of Mexico, Healthcare, 8, 2, 181 (2020) · doi:10.3390/healthcare8020181
[23] Nieszporska, S., Grey systems in the management of demand for palliative care services in Poland, Health Econ Rev, 12, 3 (2022) · doi:10.1186/s13561-021-00349-5
[24] Nitesh, D.; Sharma, MK, Fuzzy logic inference system for identification and prevention of coronavirus (Covid-19), Int J Innov Technol Explor Eng, 9, 6, 1575-1580 (2020) · doi:10.35940/ijitee.F4642.049620
[25] Overton, CE; Stage, HB; Ahmad, S., Using statistics and mathematical modeling to understand infectious disease outbreaks: Covid-19 as an example, Infect Dis Model, 5, 409-441 (2020) · doi:10.1016/j.idm.2020.06.008
[26] Palash, D.; Soumendra, G., Fuzzy decision making in medical diagnosis using an advanced distance measure on intuitionistic fuzzy sets, Open Cybern Syst J, 12, 136-149 (2018) · doi:10.2174/1874110X01812010136
[27] Roda, WC; Varughese, MB; Han, D.; Li, MY, Why is it difficult to accurately predict the Covid-19 epidemic?, Infect Dis Model, 5, 271-281 (2020) · doi:10.1016/j.idm.2020.03.001
[28] Ruben, RC; Olivas, JA; Romero, FP; Francisco, AG; Jesus, SG, An application of fuzzy prototypes to the diagnosis and treatment of fuzzy diseases, Int J Intell Syst, 32, 2, 194-210 (2016) · doi:10.1002/int.21836
[29] Sahin, U.; Sahin, T., Forecasting the cumulative number of confirmed cases of Covid-19 in Italy, UK and USA using fractional nonlinear grey Bernoulli model, Chaos Solitons Fractals, 138 (2020) · doi:10.1016/j.chaos.2020.109948
[30] Sha, H.; Tang, S.; Rong, L., A discrete stochastic model of the Covid-19 outbreak: forecast and control, Math Biosci Eng, 17, 4, 2792-2804 (2020) · Zbl 1467.92200 · doi:10.3934/mbe.2020153
[31] Sun, T.; Wang, Y., Modeling Covid-19 epidemic in Heilongjiang province, China, Chaos Solitons Fractals, 138 (2020) · doi:10.1016/j.chaos.2020.109949
[32] Varela-Santos, S.; Melin, P., A new approach for classifying coronavirus Covid-19 based on its manifestation on chest X-rays using texture features and neural networks, InfSci, 545, 403-414 (2020) · doi:10.1016/j.ins.2020.09.041
[33] Wang, Y.; Wei, F.; Sun, C.; Li, Q., The research of improved Grey GM(1,1) model to predict the postprandial glucose in Type-2 diabetes, Biomed Res Int, 2016, 6837052 (2016) · doi:10.1155/2016/6837052
[34] Yang, X.; Zou, J.; Kong, D.; Jiang, G., The analysis of GM(1,1) grey model to predict the incidence trend of typhoid and paratyphoid fevers in Wuhan City, China, Medicine, 97, 34 (2018) · doi:10.1097/MD.0000000000011787
[35] Zadeh, LA, Fuzzy sets, Inf Control, 8, 3, 338-353 (1965) · Zbl 0139.24606 · doi:10.1016/S0019-9958(65)90241-X
[36] Zadeh LA (1969) Biological application of the theory of fuzzy sets and systems. In: Biocybernetics of the central nervous system, Little Brown, Boston, Mass, pp 199-212
[37] Zeng, B.; Ma, X.; Shi, J., Modeling method of the grey GM(1,1) model with interval grey action quantity and its application, Complexity, 2020, 6514236 (2020) · Zbl 1435.93084 · doi:10.1155/2020/6514236
[38] Zhao, Y.; Shou, M.; Wang, Z., Prediction of the number of patients infected with Covid-19 based on rolling grey Verhulst models, Int J Environ Res Public Health, 17, 12, 4582 (2020) · doi:10.3390/ijerph17124582
[39] Zhou, X.; Guo, L.; Zhang, J.; Qin, S.; Zhu, Y., Prediction of mine dust concentration based on grey Markov model, Shock Vib, 2021, 5859249 (2021) · doi:10.1155/2021/5859249
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