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http://conacyt.repositorioinstitucional.mx/jspui/handle/1000/4360| A Poisson Kalman filter for disease surveillance | |
| Donald Ebeigbe. Tyrus Berry. Steven J. Schiff. Timothy Sauer. | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| https://arxiv.org/pdf/2003.11194v3.pdf | |
| 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. | |
| arxiv.org | |
| 2020 | |
| Artículo | |
| https://arxiv.org/pdf/2003.11194v3.pdf | |
| Inglés | |
| VIRUS RESPIRATORIOS | |
| Aparece en las colecciones: | Artículos científicos |
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| Fichero | Tamaño | Formato | |
|---|---|---|---|
| 1106380.pdf | 963.09 kB | Adobe PDF | Visualizar/Abrir |