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SARS-CoV-2 has the ability to form large multi-nucleated cells known as syncytia. Little is known about how syncytia affect the dynamics of the infection or severity of the disease. In this manuscript, we extend a mathematical model of cell–cell fusion assays to estimate both the syncytia formation rate and the average duration of the fusion phase for five strains of SARS-CoV-2. We find that the original Wuhan strain has the slowest rate of syncytia formation (6. 4 × 1 0 − 4 / h), but takes only 4.0 h to complete the fusion process, while the Alpha strain has the fastest rate of syncytia formation (0.36 /h), but takes 7.6 h to complete the fusion process. The Beta strain also has a fairly fast syncytia formation rate (9. 7 × 1 0 − 2 / h), and takes the longest to complete fusion (8.4 h). The D614G strain has a fairly slow syncytia formation rate (2. 8 × 1 0 − 3 / h), but completes fusion in 4.0 h. Finally, the Delta strain is in the middle with a syncytia formation rate of 3. 2 × 1 0 − 2 / h and a fusing time of 6.1 h. We note that for these SARS-CoV-2 strains, there appears to be a tradeoff between the ease of forming syncytia and the speed at which they complete the fusion process. • We use mathematical models to estimate the syncytia formation rate and fusion rate of five VOCs. • Wuhan and D614G strains form syncytia slowly, but complete the fusion process quickly. • Alpha and beta strains form syncytia rapidly, but complete the fusion process slowly. • There appears to be a trade-off between ease of forming syncytia and how quickly fusion is completed. [ABSTRACT FROM AUTHOR]

• A new stochastic fractional Coronavirus (2019-nCov) mathematical model with modified parameters are presented. • Milstein's higher order method is constructed to study the model problem • Mean square stability of Milstein algorithm is proved. • Comparative studies and numerical simulations are implemented In this paper, a new stochastic fractional Coronavirus (2019-nCov) model with modified parameters is presented. The proposed stochastic COVID-19 model describes well the real data of daily confirmed cases in Wuhan. Moreover, a novel fractional order operator is introduced, it is a linear combination of Caputo's fractional derivative and Riemann-Liouville integral. Milstein's higher order method is constructed with the new fractional order operator to study the model problem. The mean square stability of Milstein algorithm is proved. Numerical results and comparative studies are introduced. [ABSTRACT FROM AUTHOR]