论文标题
用于疾病监测的泊松卡尔曼过滤器
A Poisson Kalman filter for disease surveillance
论文作者
论文摘要
用于泊松观测的最佳过滤器是作为传统卡尔曼过滤器的变体开发的。泊松分布是传染病的特征，该疾病对每天呈现给医疗保健系统的患者数量进行了建模。我们开发了线性和非线性（扩展）滤波器。这些方法应用于非洲的新生儿败血症和感染后脑积水的案例研究，使用从公开可公开的数据中估算的参数。我们的方法适用于广泛的疾病动态，包括非传染性传染病和流行病的固有非线性，例如Covid-19。
An optimal filter for Poisson observations is developed as a variant of the traditional Kalman filter. Poisson distributions are characteristic of infectious diseases, which model the number of patients recorded as presenting each day to a health care system. We develop both a linear and nonlinear (extended) filter. The methods are applied to a case study of neonatal sepsis and postinfectious hydrocephalus in Africa, using parameters estimated from publicly available data. Our approach is applicable to a broad range of disease dynamics, including both noncommunicable and the inherent nonlinearities of communicable infectious diseases and epidemics such as from COVID-19.
