![]() Since 1980, more than 30 emerging infectious diseases (EIDs) have appeared in the world, such as SARS, COVID-19, and so on. Finally, various experiments are conducted to validate the effectiveness of the proposed model and method. In the second module, the optimized parameters are used to predicate the spread of emerging infectious diseases. In the first module, we use a level-based learning swarm optimizer to optimize the parameters required in the epidemic mechanism. Third, based on the proposed model, we further develop a swarm-optimizer-assisted simulation and prediction method, which contains two modules. Moreover, an objective function is defined to minimize the error based on these data. Second, to determine suitable parameters for the model, we propose a data-driven approach, in which the public health data and population migration data are assembled. This model can provide a biological spread process for emerging infectious diseases. First, we combine a standard epidemic dynamic, the susceptible–exposed–infected–recovered (SEIR) model with population migration. In this paper, we intend to combine these two methods to develop a more comprehensive model for the simulation and prediction of emerging infectious diseases. We do not have sufficient evidence to say that the mean height of plants between the two populations is different.Mechanism-driven models based on transmission dynamics and statistic models driven by public health data are two main methods for simulating and predicting emerging infectious diseases. H A: µ 1 ≠µ 2 (the two population means are not equal)īecause the p-value of our test (0.53005) is greater than alpha = 0.05, we fail to reject the null hypothesis of the test. ![]() H 0: µ 1 = µ 2 (the two population means are equal) The two hypotheses for this particular two sample t-test are as follows: The t test statistic is -0.6337 and the corresponding two-sided p-value is 0.53005. Stats.ttest_ind(a=group1, b=group2, equal_var=True) #perform two sample t-test with equal variances Thus, we can proceed to perform the two sample t-test with equal variances: import scipy.stats as stats This means we can assume that the population variances are equal. The ratio of the larger sample variance to the smaller sample variance is 12.26 / 7.73 = 1.586, which is less than 4. As a rule of thumb, we can assume the populations have equal variances if the ratio of the larger sample variance to the smaller sample variance is less than 4:1. This is True by default.īefore we perform the test, we need to decide if we’ll assume the two populations have equal variances or not. If False, perform Welch’s t-test, which does not assume equal population variances.
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