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Mathematical modeling of COVID-19 in British Columbia: An age-structured model with time-dependent contact rates

Following the emergence of COVID-19 at the end of 2019, several mathematical models have been developed to study the transmission dynamics of this disease. Many of these models assume homogeneous mixing in the underlying population. However, contact rates and mixing patterns can vary dramatically am...

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Autores principales: Iyaniwura, Sarafa A., Falcão, Rebeca C., Ringa, Notice, Adu, Prince A., Spencer, Michelle, Taylor, Marsha, Colijn, Caroline, Coombs, Daniel, Janjua, Naveed Z., Irvine, Michael A., Otterstatter, Michael
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Author(s). Published by Elsevier B.V. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8993502/
https://www.ncbi.nlm.nih.gov/pubmed/35447505
http://dx.doi.org/10.1016/j.epidem.2022.100559
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author Iyaniwura, Sarafa A.
Falcão, Rebeca C.
Ringa, Notice
Adu, Prince A.
Spencer, Michelle
Taylor, Marsha
Colijn, Caroline
Coombs, Daniel
Janjua, Naveed Z.
Irvine, Michael A.
Otterstatter, Michael
author_facet Iyaniwura, Sarafa A.
Falcão, Rebeca C.
Ringa, Notice
Adu, Prince A.
Spencer, Michelle
Taylor, Marsha
Colijn, Caroline
Coombs, Daniel
Janjua, Naveed Z.
Irvine, Michael A.
Otterstatter, Michael
author_sort Iyaniwura, Sarafa A.
collection PubMed
description Following the emergence of COVID-19 at the end of 2019, several mathematical models have been developed to study the transmission dynamics of this disease. Many of these models assume homogeneous mixing in the underlying population. However, contact rates and mixing patterns can vary dramatically among individuals depending on their age and activity level. Variation in contact rates among age groups and over time can significantly impact how well a model captures observed trends. To properly model the age-dependent dynamics of COVID-19 and understand the impacts of interventions, it is essential to consider heterogeneity arising from contact rates and mixing patterns. We developed an age-structured model that incorporates time-varying contact rates and population mixing computed from the ongoing BC Mix COVID-19 survey to study transmission dynamics of COVID-19 in British Columbia (BC), Canada. Using a Bayesian inference framework, we fit four versions of our model to weekly reported cases of COVID-19 in BC, with each version allowing different assumptions of contact rates. We show that in addition to incorporating age-specific contact rates and mixing patterns, time-dependent (weekly) contact rates are needed to adequately capture the observed transmission dynamics of COVID-19. Our approach provides a framework for explicitly including empirical contact rates in a transmission model, which removes the need to otherwise model the impact of many non-pharmaceutical interventions. Further, this approach allows projection of future cases based on clear assumptions of age-specific contact rates, as opposed to less tractable assumptions regarding transmission rates.
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spelling pubmed-89935022022-04-11 Mathematical modeling of COVID-19 in British Columbia: An age-structured model with time-dependent contact rates Iyaniwura, Sarafa A. Falcão, Rebeca C. Ringa, Notice Adu, Prince A. Spencer, Michelle Taylor, Marsha Colijn, Caroline Coombs, Daniel Janjua, Naveed Z. Irvine, Michael A. Otterstatter, Michael Epidemics Article Following the emergence of COVID-19 at the end of 2019, several mathematical models have been developed to study the transmission dynamics of this disease. Many of these models assume homogeneous mixing in the underlying population. However, contact rates and mixing patterns can vary dramatically among individuals depending on their age and activity level. Variation in contact rates among age groups and over time can significantly impact how well a model captures observed trends. To properly model the age-dependent dynamics of COVID-19 and understand the impacts of interventions, it is essential to consider heterogeneity arising from contact rates and mixing patterns. We developed an age-structured model that incorporates time-varying contact rates and population mixing computed from the ongoing BC Mix COVID-19 survey to study transmission dynamics of COVID-19 in British Columbia (BC), Canada. Using a Bayesian inference framework, we fit four versions of our model to weekly reported cases of COVID-19 in BC, with each version allowing different assumptions of contact rates. We show that in addition to incorporating age-specific contact rates and mixing patterns, time-dependent (weekly) contact rates are needed to adequately capture the observed transmission dynamics of COVID-19. Our approach provides a framework for explicitly including empirical contact rates in a transmission model, which removes the need to otherwise model the impact of many non-pharmaceutical interventions. Further, this approach allows projection of future cases based on clear assumptions of age-specific contact rates, as opposed to less tractable assumptions regarding transmission rates. The Author(s). Published by Elsevier B.V. 2022-06 2022-04-09 /pmc/articles/PMC8993502/ /pubmed/35447505 http://dx.doi.org/10.1016/j.epidem.2022.100559 Text en © 2022 The Author(s) Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Iyaniwura, Sarafa A.
Falcão, Rebeca C.
Ringa, Notice
Adu, Prince A.
Spencer, Michelle
Taylor, Marsha
Colijn, Caroline
Coombs, Daniel
Janjua, Naveed Z.
Irvine, Michael A.
Otterstatter, Michael
Mathematical modeling of COVID-19 in British Columbia: An age-structured model with time-dependent contact rates
title Mathematical modeling of COVID-19 in British Columbia: An age-structured model with time-dependent contact rates
title_full Mathematical modeling of COVID-19 in British Columbia: An age-structured model with time-dependent contact rates
title_fullStr Mathematical modeling of COVID-19 in British Columbia: An age-structured model with time-dependent contact rates
title_full_unstemmed Mathematical modeling of COVID-19 in British Columbia: An age-structured model with time-dependent contact rates
title_short Mathematical modeling of COVID-19 in British Columbia: An age-structured model with time-dependent contact rates
title_sort mathematical modeling of covid-19 in british columbia: an age-structured model with time-dependent contact rates
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8993502/
https://www.ncbi.nlm.nih.gov/pubmed/35447505
http://dx.doi.org/10.1016/j.epidem.2022.100559
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