Modeling the uncertainty in epidemiological models through interval analysis considering actual data from two municipalities in Colombia affected by dengue
Modeling the uncertainty in epidemiological models through interval analysis considering actual data from two municipalities in Colombia affected by dengue
复制标题
考虑哥伦比亚两个受登革热影响城市的实际数据，通过区间分析对流行病学模型的不确定性进行建模
DOI：
10.1016/j.apm.2022.07.006
复制
发表时间：
2022
影响因子：
5
通讯作者：
Puerta-Yepes, María Eugenia
中科院分区：
文献类型：
--
作者：
Lizarralde-Bejarano, Diana Paola;Gulbudak, Hayriye;Kearfott, Ralph Baker;Puerta-Yepes, María Eugenia
流行病学模型已成为研究和理解人群中传播疾病的特征和影响的有力工具。然而，这些模型通常需要指定从实验数据获得的输入参数的几个值，其特征在于由于生物变异的高不确定性水平。这种情况对于模拟媒介传播疾病（如我们的案例研究登革热）传播的模型是显而易见的。因此，处理和建模这种不确定性是必不可少的，以确保设计的模型的鲁棒性。为此，我们建议通过区间分析来建模的不确定性，表示输入参数和初始条件的真实的封闭区间的正问题。与传统方法（概率和模糊）不同，这种方法的优点是对不确定性的假设最少。为了说明这种方法的性能，我们考虑了一个耦合的ODE系统的7个状态变量和9个参数，代表宿主-媒介人群之间的传播登革热。此外，为了提高用于求解系统的数值方法的使用，（参数和初始条件）是根据以下结果确定的：（i）R 0的灵敏度分析，（ii）模型的结构可识别性分析，（iii）关于蚊子种群的可用信息的特征，以及（iv）哥伦比亚两个城市的登革热发病率数据，Itagüí和Neiva，在2016年爆发期间。我们相信，这里提出的方法来选择和纳入流行病学模型中的不确定性，通过区间分析是广泛适用于其他现象和模型在科学和工程。
Epidemiological models have become powerful tools for studying and understanding the characteristics and impact of transmitted diseases in a population. However, these models usually require specifying several values of input parameters obtained from experimental data, characterized by high uncertainty levels due to biological variation. This situation is evident for models that simulate the transmission of vector-borne diseases such as dengue, our case study. Therefore, treating and modeling this uncertainty is essential to ensure the robustness of designed models. For this, we propose to model the uncertainty through interval analysis by representing the input parameters and initial conditions by real closed intervals in the forward problem. This approach has the advantage of making a minimal number of assumptions concerning uncertainties, unlike the traditional methods (probabilistic and fuzzy). To illustrate the performance of this methodology, we consider a coupled ODE system of seven state variables and nine parameters, representing the transmission of Dengue between host-vector populations. Additionally, to enhance the use of the numerical method utilized for solving the system, the uncertain quantities (parameters and initial conditions) are determined based on the results of (i) the sensitivity analysis of R 0,(ii) the structural identifiability analysis of the model,(iii) the characteristics of the available information about mosquito population, and (iv) dengue incidence data in two municipalities in Colombia, Itagüí and Neiva, during the outbreaks in 2016. We believe that the methodology proposed here to select and incorporate uncertainty in epidemiological models through interval analysis is widely applicable to other phenomena and models in science and engineering.