A Poisson Kalman filter for disease surveillance
Methodology
2020-10-14 v5 Quantitative Methods
Abstract
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.
Cite
@article{arxiv.2003.11194,
title = {A Poisson Kalman filter for disease surveillance},
author = {Donald Ebeigbe and Tyrus Berry and Steven J. Schiff and Timothy Sauer},
journal= {arXiv preprint arXiv:2003.11194},
year = {2020}
}
Comments
19 Pages, 8 Figures