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Predicting Pandemics in a Globally Connected World, Volume 1A Novel Point Process Model for COVID-19: Multivariate Recursive Hawkes Process

Predicting Pandemics in a Globally Connected World, Volume 1: A Novel Point Process Model for... [This chapter presents a novel point process model for COVID-19 transmission—the multivariate recursive Hawkes process, which is an extension of the recursive Hawkes model to the multivariate case. Equivalently the model can be viewed as an extension of the multivariate Hawkes model to allow for varying productivity as in the recursive model. Several theoretical properties of this process are stated and proved, including the existence of the multivariate recursive counting process and formulas for the mean and variance. EM-based algorithms are explored for estimating parameters of parametric and semi-parametric forms of the model. Additionally, an algorithm is presented to reconstruct the process from imprecise event times. The performance of the algorithms on both synthetic and real COVID-19 data sets is illustrated through several experiments.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Predicting Pandemics in a Globally Connected World, Volume 1A Novel Point Process Model for COVID-19: Multivariate Recursive Hawkes Process

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Publisher
Springer International Publishing
Copyright
© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
ISBN
978-3-030-96561-7
Pages
141 –182
DOI
10.1007/978-3-030-96562-4_5
Publisher site
See Chapter on Publisher Site

Abstract

[This chapter presents a novel point process model for COVID-19 transmission—the multivariate recursive Hawkes process, which is an extension of the recursive Hawkes model to the multivariate case. Equivalently the model can be viewed as an extension of the multivariate Hawkes model to allow for varying productivity as in the recursive model. Several theoretical properties of this process are stated and proved, including the existence of the multivariate recursive counting process and formulas for the mean and variance. EM-based algorithms are explored for estimating parameters of parametric and semi-parametric forms of the model. Additionally, an algorithm is presented to reconstruct the process from imprecise event times. The performance of the algorithms on both synthetic and real COVID-19 data sets is illustrated through several experiments.]

Published: Feb 18, 2022

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