Agent-based modeling of COVID-19 outbreaks for New York state and UK: Parameter identification algorithm

This paper uses Covasim, an agent-based model (ABM) of COVID-19, to evaluate and scenarios of epidemic spread in New York State (USA) and the UK. Epidemiological parameters such as contagiousness (virus transmission rate), initial number of infected people, and probability of being tested depend on...

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Autores principales:	Olga Krivorotko, Mariia Sosnovskaia, Ivan Vashchenko, Cliff Kerr, Daniel Lesnic
Formato:	article
Lenguaje:	EN
Publicado:	KeAi Communications Co., Ltd. 2022
Materias:	Epidemiology Agent-based modeling COVID-19 Interventions analysis Coronavirus data analysis Forecasting scenarios Infectious and parasitic diseases RC109-216
Acceso en línea:	https://doaj.org/article/ae2af1b9e6074d45938df12c6c6b6311
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spelling	oai:doaj.org-article:ae2af1b9e6074d45938df12c6c6b63112021-12-04T04:35:28ZAgent-based modeling of COVID-19 outbreaks for New York state and UK: Parameter identification algorithm2468-042710.1016/j.idm.2021.11.004https://doaj.org/article/ae2af1b9e6074d45938df12c6c6b63112022-03-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2468042721000798https://doaj.org/toc/2468-0427This paper uses Covasim, an agent-based model (ABM) of COVID-19, to evaluate and scenarios of epidemic spread in New York State (USA) and the UK. Epidemiological parameters such as contagiousness (virus transmission rate), initial number of infected people, and probability of being tested depend on the region's demographic and geographical features, the containment measures introduced; they are calibrated to data about COVID-19 spread in the region of interest. At the first stage of our study, epidemiological data (numbers of people tested, diagnoses, critical cases, hospitalizations, and deaths) for each of the mentioned regions were analyzed. The data were characterized in terms of seasonality, stationarity, and dependency spaces, and were extrapolated using machine learning techniques to specify unknown epidemiological parameters of the model. At the second stage, the Optuna optimizer based on the tree Parzen estimation method for objective function minimization was applied to determine the model's unknown parameters. The model was validated with the historical data of 2020. The modeled results of COVID-19 spread in New York State and the UK have demonstrated that if the level of testing and containment measures is preserved, the number of positive cases in New York State remain the same during March of 2021, while in the UK it will reduce.Olga KrivorotkoMariia SosnovskaiaIvan VashchenkoCliff KerrDaniel LesnicKeAi Communications Co., Ltd.articleEpidemiologyAgent-based modelingCOVID-19Interventions analysisCoronavirus data analysisForecasting scenariosInfectious and parasitic diseasesRC109-216ENInfectious Disease Modelling, Vol 7, Iss 1, Pp 30-44 (2022)
institution	DOAJ
collection	DOAJ
language	EN
topic	Epidemiology Agent-based modeling COVID-19 Interventions analysis Coronavirus data analysis Forecasting scenarios Infectious and parasitic diseases RC109-216
spellingShingle	Epidemiology Agent-based modeling COVID-19 Interventions analysis Coronavirus data analysis Forecasting scenarios Infectious and parasitic diseases RC109-216 Olga Krivorotko Mariia Sosnovskaia Ivan Vashchenko Cliff Kerr Daniel Lesnic Agent-based modeling of COVID-19 outbreaks for New York state and UK: Parameter identification algorithm
description	This paper uses Covasim, an agent-based model (ABM) of COVID-19, to evaluate and scenarios of epidemic spread in New York State (USA) and the UK. Epidemiological parameters such as contagiousness (virus transmission rate), initial number of infected people, and probability of being tested depend on the region's demographic and geographical features, the containment measures introduced; they are calibrated to data about COVID-19 spread in the region of interest. At the first stage of our study, epidemiological data (numbers of people tested, diagnoses, critical cases, hospitalizations, and deaths) for each of the mentioned regions were analyzed. The data were characterized in terms of seasonality, stationarity, and dependency spaces, and were extrapolated using machine learning techniques to specify unknown epidemiological parameters of the model. At the second stage, the Optuna optimizer based on the tree Parzen estimation method for objective function minimization was applied to determine the model's unknown parameters. The model was validated with the historical data of 2020. The modeled results of COVID-19 spread in New York State and the UK have demonstrated that if the level of testing and containment measures is preserved, the number of positive cases in New York State remain the same during March of 2021, while in the UK it will reduce.
format	article
author	Olga Krivorotko Mariia Sosnovskaia Ivan Vashchenko Cliff Kerr Daniel Lesnic
author_facet	Olga Krivorotko Mariia Sosnovskaia Ivan Vashchenko Cliff Kerr Daniel Lesnic
author_sort	Olga Krivorotko
title	Agent-based modeling of COVID-19 outbreaks for New York state and UK: Parameter identification algorithm
title_short	Agent-based modeling of COVID-19 outbreaks for New York state and UK: Parameter identification algorithm
title_full	Agent-based modeling of COVID-19 outbreaks for New York state and UK: Parameter identification algorithm
title_fullStr	Agent-based modeling of COVID-19 outbreaks for New York state and UK: Parameter identification algorithm
title_full_unstemmed	Agent-based modeling of COVID-19 outbreaks for New York state and UK: Parameter identification algorithm
title_sort	agent-based modeling of covid-19 outbreaks for new york state and uk: parameter identification algorithm
publisher	KeAi Communications Co., Ltd.
publishDate	2022
url	https://doaj.org/article/ae2af1b9e6074d45938df12c6c6b6311
work_keys_str_mv	AT olgakrivorotko agentbasedmodelingofcovid19outbreaksfornewyorkstateandukparameteridentificationalgorithm AT mariiasosnovskaia agentbasedmodelingofcovid19outbreaksfornewyorkstateandukparameteridentificationalgorithm AT ivanvashchenko agentbasedmodelingofcovid19outbreaksfornewyorkstateandukparameteridentificationalgorithm AT cliffkerr agentbasedmodelingofcovid19outbreaksfornewyorkstateandukparameteridentificationalgorithm AT daniellesnic agentbasedmodelingofcovid19outbreaksfornewyorkstateandukparameteridentificationalgorithm
_version_	1718372933925076992

Agent-based modeling of COVID-19 outbreaks for New York state and UK: Parameter identification algorithm

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