Optimization of Nucleic Acid Detection Method under the New Epidemic Situation in Macao based on Experimental and Mathematical Statistics Analysis

A large number of COVID-19 nucleic acid tests are performed every day in the world. If the testing is performed individually, the testing workload and testing cost would be high. In this paper, we proposed a simulated model to check the efficiency of group testing methods given four typical situations and find the best grouping number for different predicted infection rates. Maximum likelihood Estimation (MLE) is applied for model estimation and Inversion Method is used to generate pseudo random variables for simulation. The simulation results point to the conclusions: (1) Given a high infection rate (p>0.1), both the false-positive and the false-negative possibilities have a poor relation with the grouping number. (2) However, given a low infection rate (p<0.1), the false-positive and false-negative possibilities will become important factors to reduce the grouping number. Especially, if p is close to 0.01, the grouping number is significantly affected. (3) With a 10% infection rate, it is recommended to group 5 participants in one test given no false-positive or false-negative situations. (4) If p<0.3, the group testing approach has the potential to improve overall testing efficiency.