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Simple discrete-time self-exciting models can describe complex dynamic processes: A case study of COVID-19

Hawkes processes are a form of self-exciting process that has been used in numerous applications, including neuroscience, seismology, and terrorism. While these self-exciting processes have a simple formulation, they can model incredibly complex phenomena. Traditionally Hawkes processes are a contin...

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Autores principales: Browning, Raiha, Sulem, Deborah, Mengersen, Kerrie, Rivoirard, Vincent, Rousseau, Judith
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8034752/
https://www.ncbi.nlm.nih.gov/pubmed/33836020
http://dx.doi.org/10.1371/journal.pone.0250015
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author Browning, Raiha
Sulem, Deborah
Mengersen, Kerrie
Rivoirard, Vincent
Rousseau, Judith
author_facet Browning, Raiha
Sulem, Deborah
Mengersen, Kerrie
Rivoirard, Vincent
Rousseau, Judith
author_sort Browning, Raiha
collection PubMed
description Hawkes processes are a form of self-exciting process that has been used in numerous applications, including neuroscience, seismology, and terrorism. While these self-exciting processes have a simple formulation, they can model incredibly complex phenomena. Traditionally Hawkes processes are a continuous-time process, however we enable these models to be applied to a wider range of problems by considering a discrete-time variant of Hawkes processes. We illustrate this through the novel coronavirus disease (COVID-19) as a substantive case study. While alternative models, such as compartmental and growth curve models, have been widely applied to the COVID-19 epidemic, the use of discrete-time Hawkes processes allows us to gain alternative insights. This paper evaluates the capability of discrete-time Hawkes processes by modelling daily mortality counts as distinct phases in the COVID-19 outbreak. We first consider the initial stage of exponential growth and the subsequent decline as preventative measures become effective. We then explore subsequent phases with more recent data. Various countries that have been adversely affected by the epidemic are considered, namely, Brazil, China, France, Germany, India, Italy, Spain, Sweden, the United Kingdom and the United States. These countries are all unique concerning the spread of the virus and their corresponding response measures. However, we find that this simple model is useful in accurately capturing the dynamics of the process, despite hidden interactions that are not directly modelled due to their complexity, and differences both within and between countries. The utility of this model is not confined to the current COVID-19 epidemic, rather this model could explain many other complex phenomena. It is of interest to have simple models that adequately describe these complex processes with unknown dynamics. As models become more complex, a simpler representation of the process can be desirable for the sake of parsimony.
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spelling pubmed-80347522021-04-15 Simple discrete-time self-exciting models can describe complex dynamic processes: A case study of COVID-19 Browning, Raiha Sulem, Deborah Mengersen, Kerrie Rivoirard, Vincent Rousseau, Judith PLoS One Research Article Hawkes processes are a form of self-exciting process that has been used in numerous applications, including neuroscience, seismology, and terrorism. While these self-exciting processes have a simple formulation, they can model incredibly complex phenomena. Traditionally Hawkes processes are a continuous-time process, however we enable these models to be applied to a wider range of problems by considering a discrete-time variant of Hawkes processes. We illustrate this through the novel coronavirus disease (COVID-19) as a substantive case study. While alternative models, such as compartmental and growth curve models, have been widely applied to the COVID-19 epidemic, the use of discrete-time Hawkes processes allows us to gain alternative insights. This paper evaluates the capability of discrete-time Hawkes processes by modelling daily mortality counts as distinct phases in the COVID-19 outbreak. We first consider the initial stage of exponential growth and the subsequent decline as preventative measures become effective. We then explore subsequent phases with more recent data. Various countries that have been adversely affected by the epidemic are considered, namely, Brazil, China, France, Germany, India, Italy, Spain, Sweden, the United Kingdom and the United States. These countries are all unique concerning the spread of the virus and their corresponding response measures. However, we find that this simple model is useful in accurately capturing the dynamics of the process, despite hidden interactions that are not directly modelled due to their complexity, and differences both within and between countries. The utility of this model is not confined to the current COVID-19 epidemic, rather this model could explain many other complex phenomena. It is of interest to have simple models that adequately describe these complex processes with unknown dynamics. As models become more complex, a simpler representation of the process can be desirable for the sake of parsimony. Public Library of Science 2021-04-09 /pmc/articles/PMC8034752/ /pubmed/33836020 http://dx.doi.org/10.1371/journal.pone.0250015 Text en © 2021 Browning et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Browning, Raiha
Sulem, Deborah
Mengersen, Kerrie
Rivoirard, Vincent
Rousseau, Judith
Simple discrete-time self-exciting models can describe complex dynamic processes: A case study of COVID-19
title Simple discrete-time self-exciting models can describe complex dynamic processes: A case study of COVID-19
title_full Simple discrete-time self-exciting models can describe complex dynamic processes: A case study of COVID-19
title_fullStr Simple discrete-time self-exciting models can describe complex dynamic processes: A case study of COVID-19
title_full_unstemmed Simple discrete-time self-exciting models can describe complex dynamic processes: A case study of COVID-19
title_short Simple discrete-time self-exciting models can describe complex dynamic processes: A case study of COVID-19
title_sort simple discrete-time self-exciting models can describe complex dynamic processes: a case study of covid-19
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8034752/
https://www.ncbi.nlm.nih.gov/pubmed/33836020
http://dx.doi.org/10.1371/journal.pone.0250015
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