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Modelling infectious diseases with herd immunity in a randomly mixed population

The conventional susceptible-infectious-recovered (SIR) model tends to magnify the transmission dynamics of infectious diseases, and thus the estimated total infections and immunized population may be higher than the threshold required for infection control and eradication. The study developed a new...

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Autores principales: Law, Kian Boon, M. Peariasamy, Kalaiarasu, Mohd Ibrahim, Hishamshah, Abdullah, Noor Hisham
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8523531/
https://www.ncbi.nlm.nih.gov/pubmed/34663839
http://dx.doi.org/10.1038/s41598-021-00013-2
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author Law, Kian Boon
M. Peariasamy, Kalaiarasu
Mohd Ibrahim, Hishamshah
Abdullah, Noor Hisham
author_facet Law, Kian Boon
M. Peariasamy, Kalaiarasu
Mohd Ibrahim, Hishamshah
Abdullah, Noor Hisham
author_sort Law, Kian Boon
collection PubMed
description The conventional susceptible-infectious-recovered (SIR) model tends to magnify the transmission dynamics of infectious diseases, and thus the estimated total infections and immunized population may be higher than the threshold required for infection control and eradication. The study developed a new SIR framework that allows the transmission rate of infectious diseases to decline along with the reduced risk of contact infection to overcome the limitations of the conventional SIR model. Two new SIR models were formulated to mimic the declining transmission rate of infectious diseases at different stages of transmission. Model A utilized the declining transmission rate along with the reduced risk of contact infection following infection, while Model B incorporated the declining transmission rate following recovery. Both new models and the conventional SIR model were then used to simulate an infectious disease with a basic reproduction number (r(0)) of 3.0 and a herd immunity threshold (HIT) of 0.667 with and without vaccination. Outcomes of simulations were assessed at the time when the total immunized population reached the level predicted by the HIT, and at the end of simulations. Further, all three models were used to simulate the transmission dynamics of seasonal influenza in the United States and disease burdens were projected and compared with estimates from the Centers for Disease Control and Prevention. For the simulated infectious disease, in the initial phase of the outbreak, all three models performed expectedly when the sizes of infectious and recovered populations were relatively small. As the infectious population increased, the conventional SIR model appeared to overestimate the infections even when the HIT was achieved in all scenarios with and without vaccination. For the same scenario, Model A appeared to attain the level predicted by the HIT and in comparison, Model B projected the infectious disease to be controlled at the level predicted by the HIT only at high vaccination rates. For infectious diseases with high r(0), and at low vaccination rates, the level at which the infectious disease was controlled cannot be accurately predicted by the current theorem. Transmission dynamics of infectious diseases with herd immunity can be accurately modelled by allowing the transmission rate of infectious diseases to decline along with the reduction of contact infection risk after recovery or vaccination. Model B provides a credible framework for modelling infectious diseases with herd immunity in a randomly mixed population.
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spelling pubmed-85235312021-10-20 Modelling infectious diseases with herd immunity in a randomly mixed population Law, Kian Boon M. Peariasamy, Kalaiarasu Mohd Ibrahim, Hishamshah Abdullah, Noor Hisham Sci Rep Article The conventional susceptible-infectious-recovered (SIR) model tends to magnify the transmission dynamics of infectious diseases, and thus the estimated total infections and immunized population may be higher than the threshold required for infection control and eradication. The study developed a new SIR framework that allows the transmission rate of infectious diseases to decline along with the reduced risk of contact infection to overcome the limitations of the conventional SIR model. Two new SIR models were formulated to mimic the declining transmission rate of infectious diseases at different stages of transmission. Model A utilized the declining transmission rate along with the reduced risk of contact infection following infection, while Model B incorporated the declining transmission rate following recovery. Both new models and the conventional SIR model were then used to simulate an infectious disease with a basic reproduction number (r(0)) of 3.0 and a herd immunity threshold (HIT) of 0.667 with and without vaccination. Outcomes of simulations were assessed at the time when the total immunized population reached the level predicted by the HIT, and at the end of simulations. Further, all three models were used to simulate the transmission dynamics of seasonal influenza in the United States and disease burdens were projected and compared with estimates from the Centers for Disease Control and Prevention. For the simulated infectious disease, in the initial phase of the outbreak, all three models performed expectedly when the sizes of infectious and recovered populations were relatively small. As the infectious population increased, the conventional SIR model appeared to overestimate the infections even when the HIT was achieved in all scenarios with and without vaccination. For the same scenario, Model A appeared to attain the level predicted by the HIT and in comparison, Model B projected the infectious disease to be controlled at the level predicted by the HIT only at high vaccination rates. For infectious diseases with high r(0), and at low vaccination rates, the level at which the infectious disease was controlled cannot be accurately predicted by the current theorem. Transmission dynamics of infectious diseases with herd immunity can be accurately modelled by allowing the transmission rate of infectious diseases to decline along with the reduction of contact infection risk after recovery or vaccination. Model B provides a credible framework for modelling infectious diseases with herd immunity in a randomly mixed population. Nature Publishing Group UK 2021-10-18 /pmc/articles/PMC8523531/ /pubmed/34663839 http://dx.doi.org/10.1038/s41598-021-00013-2 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Law, Kian Boon
M. Peariasamy, Kalaiarasu
Mohd Ibrahim, Hishamshah
Abdullah, Noor Hisham
Modelling infectious diseases with herd immunity in a randomly mixed population
title Modelling infectious diseases with herd immunity in a randomly mixed population
title_full Modelling infectious diseases with herd immunity in a randomly mixed population
title_fullStr Modelling infectious diseases with herd immunity in a randomly mixed population
title_full_unstemmed Modelling infectious diseases with herd immunity in a randomly mixed population
title_short Modelling infectious diseases with herd immunity in a randomly mixed population
title_sort modelling infectious diseases with herd immunity in a randomly mixed population
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8523531/
https://www.ncbi.nlm.nih.gov/pubmed/34663839
http://dx.doi.org/10.1038/s41598-021-00013-2
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