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Modification of the Conventional Influenza Epidemic Models Using Environmental Parameters in Iran
OBJECTIVES: The association between the spread of infectious diseases and climate parameters has been widely studied in recent decades. In this paper, we formulate, exploit, and compare three variations of the susceptible-infected-recovered (SIR) model incorporating climate data. The SIR model is a...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Korean Society of Medical Informatics
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6372465/ https://www.ncbi.nlm.nih.gov/pubmed/30788178 http://dx.doi.org/10.4258/hir.2019.25.1.27 |
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author | Naserpor, Ahmad Niakan Kalhori, Sharareh R. Ghazisaeedi, Marjan Azizi, Rasoul Hosseini Ravandi, Mohammad Sharafie, Sajad |
author_facet | Naserpor, Ahmad Niakan Kalhori, Sharareh R. Ghazisaeedi, Marjan Azizi, Rasoul Hosseini Ravandi, Mohammad Sharafie, Sajad |
author_sort | Naserpor, Ahmad |
collection | PubMed |
description | OBJECTIVES: The association between the spread of infectious diseases and climate parameters has been widely studied in recent decades. In this paper, we formulate, exploit, and compare three variations of the susceptible-infected-recovered (SIR) model incorporating climate data. The SIR model is a well-studied model to investigate the dynamics of influenza viruses; however, the improved versions of the classic model have been developed by introducing external factors into the model. METHODS: The modification models are derived by multiplying a linear combination of three complementary factors, namely, temperature (T), precipitation (P), and humidity (H) by the transmission rate. The performance of these proposed models is evaluated against the standard model for two outbreak seasons. RESULTS: The values of the root-mean-square error (RMSE) and the Akaike information criterion (AIC) improved as they declined from 8.76 to 7.05 and from 98.12 to 93.01 for season 2013/14, respectively. Similarly, for season 2014/15, the RMSE and AIC decreased from 8.10 to 6.45 and from 117.73 to 107.91, respectively. The estimated values of R(t) in the framework of the standard and modified SIR models are also compared. CONCLUSIONS: Through simulations, we determined that among the studied environmental factors, precipitation showed the strongest correlation with the transmission dynamics of influenza. Moreover, the SIR+P+T model is the most efficient for simulating the behavioral dynamics of influenza in the area of interest. |
format | Online Article Text |
id | pubmed-6372465 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Korean Society of Medical Informatics |
record_format | MEDLINE/PubMed |
spelling | pubmed-63724652019-02-20 Modification of the Conventional Influenza Epidemic Models Using Environmental Parameters in Iran Naserpor, Ahmad Niakan Kalhori, Sharareh R. Ghazisaeedi, Marjan Azizi, Rasoul Hosseini Ravandi, Mohammad Sharafie, Sajad Healthc Inform Res Original Article OBJECTIVES: The association between the spread of infectious diseases and climate parameters has been widely studied in recent decades. In this paper, we formulate, exploit, and compare three variations of the susceptible-infected-recovered (SIR) model incorporating climate data. The SIR model is a well-studied model to investigate the dynamics of influenza viruses; however, the improved versions of the classic model have been developed by introducing external factors into the model. METHODS: The modification models are derived by multiplying a linear combination of three complementary factors, namely, temperature (T), precipitation (P), and humidity (H) by the transmission rate. The performance of these proposed models is evaluated against the standard model for two outbreak seasons. RESULTS: The values of the root-mean-square error (RMSE) and the Akaike information criterion (AIC) improved as they declined from 8.76 to 7.05 and from 98.12 to 93.01 for season 2013/14, respectively. Similarly, for season 2014/15, the RMSE and AIC decreased from 8.10 to 6.45 and from 117.73 to 107.91, respectively. The estimated values of R(t) in the framework of the standard and modified SIR models are also compared. CONCLUSIONS: Through simulations, we determined that among the studied environmental factors, precipitation showed the strongest correlation with the transmission dynamics of influenza. Moreover, the SIR+P+T model is the most efficient for simulating the behavioral dynamics of influenza in the area of interest. Korean Society of Medical Informatics 2019-01 2019-01-31 /pmc/articles/PMC6372465/ /pubmed/30788178 http://dx.doi.org/10.4258/hir.2019.25.1.27 Text en © 2019 The Korean Society of Medical Informatics http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Article Naserpor, Ahmad Niakan Kalhori, Sharareh R. Ghazisaeedi, Marjan Azizi, Rasoul Hosseini Ravandi, Mohammad Sharafie, Sajad Modification of the Conventional Influenza Epidemic Models Using Environmental Parameters in Iran |
title | Modification of the Conventional Influenza Epidemic Models Using Environmental Parameters in Iran |
title_full | Modification of the Conventional Influenza Epidemic Models Using Environmental Parameters in Iran |
title_fullStr | Modification of the Conventional Influenza Epidemic Models Using Environmental Parameters in Iran |
title_full_unstemmed | Modification of the Conventional Influenza Epidemic Models Using Environmental Parameters in Iran |
title_short | Modification of the Conventional Influenza Epidemic Models Using Environmental Parameters in Iran |
title_sort | modification of the conventional influenza epidemic models using environmental parameters in iran |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6372465/ https://www.ncbi.nlm.nih.gov/pubmed/30788178 http://dx.doi.org/10.4258/hir.2019.25.1.27 |
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