ArticleChaos, solitons, and fractals2022
Data-assimilation and state estimation for contact-based spreading processes using the ensemble kalman filter: Application to COVID-19.
Article in Chaos, solitons, and fractals, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- A semi-mechanistic modeling strategy for infectious diseases forecasting: Error correction and probabilistic prediction.Biosafety and health · 2026Article
- Combining data assimilation of states and parameters for more precise infectious disease prediction.One health (Amsterdam, Netherlands) · 2025Article
- Analysis of COVID-19 Spread in Tokyo through an Agent-Based Model with Data Assimilation.Journal of clinical medicine · 2022Article
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Authors and funding
3 authors.
Funding
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Abstract
The main aim of the present paper is threefold. First, it aims at presenting an extended contact-based model for the description of the spread of contagious diseases in complex networks with consideration of asymptomatic evolutions. Second, it presents a parametrization method of the considered model, including validation with data from the actual spread of COVID-19 in Germany, Mexico and the United States of America. Third, it aims at showcasing the fruitful combination of contact-based network spreading models with a modern state estimation and filtering technique to (i) enable real-time monitoring schemes, and (ii) efficiently deal with dimensionality and stochastic uncertainties. The network model is based on an interpretation of the states of the nodes as (statistical) probability densities samples, where nodes can represent individuals, groups or communities, cities or countries, enabling a wide field of application of the presented approach.
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Registered trials
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