Fig. 3: SARS-CoV-2 evolution in silico (f = 0.3).

a Genetic algorithm (GA) exploring the evolutionary trajectories on the (β,p) plane until the generation of 300 is applied, depending on different values of the incubation period, \({T}^{*}\). Here β andp are the infection rate and the virus production rate, respectively. All symptomatic individuals lose their transmissibility by isolation after symptom onset (\({T}^{*} < t\)). The white dots represent the endpoint of 100 independent simulation runs, and the contour lines are the kernel density estimation of their distribution. The colored dot in each panel is the mean value of the white dots, which represents the set of evolutionary outcomes of (β,p) under the parameters we used. The colors used here are independent to them in Fig. 1. The black line is the mean trajectory of the GA through 300 generations. b The mean transmissibility fitness landscapes aggregated solely from the asymptomatic (top row) and symptomatic (bottom row) individuals are described, respectively, using 100 runs of GA. The white dot represents the maximum value of the mean transmissibility fitness, \({R}_{{TP}}\). Note the asymptomatic individuals have larger transmissibility fitness than symptomatic cases on average, regardless of \({T}^{*}\) values. c The trajectories of \({R}_{{TP}}\) along the course of GA with different \({T}^{*}\) are calculated. The gray dotted lines are the mean trajectory over 100 trials of colored lines. d The time-series patterns of viral load with the optimal parameters of (β,p) with different \({T}^{*}\), which were obtained in a, are shown. e The contour-plot for the timing of peak viral load (i.e., peak time) is shown. Each curve is colored accordingly. The gray region is the parameter range satisfying \({R}_{{TP}} \, < \, 1\).