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.

流行病学模型已成为研究和了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中获得的几个输入参数值，其特点是由于生物变异导致的高不确定性水平。对于我们的案例研究中模拟登革热等媒介传播疾病传播的模型，这种情况很明显。因此，处理和建模这种不确定性对于确保设计模型的稳健性至关重要。为此，我们建议通过区间分析对不确定性进行建模，方法是用正向问题中的真实闭合区间表示输入参数和初始条件。这种方法的优点是对不确定性做出最少的假设，与传统方法（概率和模糊）不同。为了说明这种方法的性能，我们考虑了一个由七个状态变量和九个参数组成的耦合 ODE 系统，代表登革热在宿主-媒介种群之间的传播。此外，为了加强用于求解系统的数值方法的使用，不确定量（参数和初始条件）是根据 (i) 灵敏度分析的结果确定的$R_0$，（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）2016年爆发期间哥伦比亚伊塔圭和内瓦两个城市的登革热发病率数据。我们认为这里提出的通过区间分析在流行病学模型中选择和纳入不确定性的方法广泛适用于科学和工程中的其他现象和模型。