Since December past year, a novel coronavirus has named COVID-19, which was discovered in Wuhan, China, and has spread to different cities in China as well as to 208 other countries and becomes as a global threat. The spread of virus COVID-19 can put all countries in situation of incapacity of how manage and face. In order to help people to make decisions in dealing this epidemic issue. This study proposes a forecasting model based on fuzzy time series (FTS) and Particle swarm optimization (PSO) for forecasting the number of confirmed cases of COVID-19 in Vietnam. First, the fuzzy relationship groups are utilized to overcome drawbacks of fuzzy relationship matrix in building of fuzzy forecasting model. Second, the PSO algorithm is used to find and adjust the proper number and length of intervals with an intent to achieve the best forecasting accuracy. To verify the effectiveness of the proposed model, a numerical COVID-19 dataset is selected for forecasting process. These forecasting results could be helpful in forecasting future confirmed cases if the spread of the virus did not change very strangely.