Breakthrough, natural immune, partial immune, variants of the virus such as delta and omicron, social density, transportations, cultural differences, variations of γ values, and population are all ignored during the analysis to simplify the model. However, they can be added and create more reaction pathways with modifications to the calculations, which is beyond the scope of this data analysis lab course. With information on what number of daily cases represent separate variants in the total number of daily new cases and the number of reinfections, The model can be expanded to account for the impact of reported higher rates of reinfection by the Omicron variant. The model provides the students some prediction power on the spread trend of COVID-19 (Fig. ). For example, if a mask can block 50% of the virus from spreading, then we can reduce the Rt value by 50% if everybody has put on the mask; reducing 50% of social activity further halves the Rt value as seen in the first several months of the pandemic (Figure -5 piecewise Rt). In general, the Rt value is unlikely to exceed the R0 value and must be larger or equal to zero for the same virus which set the limits on the upper and lower boundary of the spreading rate. The effect of the vaccination is readily visible in the simulation on the spread rate assuming no change in the social behaviors. There is no obvious evidence observed for the different spreading rates among the different variants such as delta and omicron, which will need data on the variants among the positive cases to be analyzed.