ArticlePhysical review research
Poisson Kalman filter for disease surveillance.
Article in Physical review research. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Determining disease attributes from epidemic trajectories.Infectious Disease Modelling · 2026Article
- Efficient sequential Bayesian inference for state-space epidemic models using ensemble data assimilation.PLoS computational biology · 2026Article
- Generalized unscented transformation for forecasting non-Gaussian processes.Physical review. E · 2025Article
- Development ofInternational journal of environmental research and public health · 2022Article
- Verification of operational numerical weather prediction model forecasts of precipitation using satellite rainfall estimates over Africa.Meteorological applicationsArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
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 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.
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Registered trials
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