Data-assimilation and state estimation for contact-based spreading processes using the ensemble kalman filter: Application to COVID-19

A. Schaum, R. Bernal-Jaquez and L. Alarcon Ramos

Chaos, Solitons & Fractals, 2022, vol. 157, issue C

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.

Keywords: Epidemic spreading; COVID-19; Model identification; Data–assimilation; Ensemble kalman filter; Complex networks (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:157:y:2022:i:c:s0960077922000984

DOI: 10.1016/j.chaos.2022.111887

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