Poisson Kalman filter for disease surveillance
Poisson Kalman filter for disease surveillance
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DOI：
10.1103/physrevresearch.2.043028
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发表时间：
2020-10-06
影响因子：
4.2
通讯作者：
Sauer, Timothy
中科院分区：
文献类型：
--
作者：
Ebeigbe, Donald;Berry, Tyrus;Sauer, Timothy
泊松观测的最优滤波器开发作为传统的卡尔曼滤波器的一个变种。泊松分布是传染病的特征，它模拟了每天向卫生保健系统报告的患者数量。我们开发了一个线性和非线性（扩展）过滤器。该方法适用于新生儿败血症和感染后脑积水在非洲的案例研究，使用参数估计从公开的数据。我们的方法适用于广泛的疾病动态，包括非传染性和传染性传染病和流行病（如COVID-19）的固有非线性。
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 a 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.