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Modeling Heterogeneity in Direct Infectious Disease Transmission in a Compartmental Model

机译：modeling Heterogeneity in Direct Infectious Disease Transmission in a Compartmental model

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Mathematical models have been used to understand the transmission dynamics of infectious diseases and to assess the impact of intervention strategies. Traditional mathematical models usually assume a homogeneous mixing in the population, which is rarely the case in reality. Here, we construct a new transmission function by using as the probability density function a negative binomial distribution, and we develop a compartmental model using it to model the heterogeneity of contact rates in the population. We explore the transmission dynamics of the developed model using numerical simulations with different parameter settings, which characterize different levels of heterogeneity. The results show that when the reproductive number, [GRAPHICS] , is larger than one, a low level of heterogeneity results in dynamics similar to those predicted by the homogeneous mixing model. As the level of heterogeneity increases, the dynamics become more different. As a test case, we calibrated the model with the case incidence data for severe acute respiratory syndrome (SARS) in Beijing in 2003, and the estimated parameters demonstrated the effectiveness of the control measures taken during that period.
机译：已经使用数学模型来了解传染病的传播动态并评估干预策略的影响。传统的数学模型通常假定总体中存在均匀混合，而实际情况很少。在这里，我们通过使用负二项式分布作为概率密度函数来构造新的传递函数，并使用该模型来建立隔间模型，以对总体中接触率的异质性进行建模。我们使用具有不同参数设置的数值模拟探索开发模型的传输动力学，这些数值模拟表征了不同级别的异质性。结果表明，当生殖数[GRAPHICS]大于1时，较低的异质性会导致动力学与均质混合模型所预测的相似。随着异质性水平的提高，动力学变得越来越不同。作为测试案例，我们使用2003年北京严重急性呼吸综合征（SARS）的发病率数据对模型进行了校准，估计的参数证明了在此期间采取的控制措施的有效性。

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