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Modeling the spread of bird flu and predicting outbreak diversity
Avian influenza, commonly known as bird flu, is an epidemic caused by H5N1 virus that primarily affects birds like chickens, wild water birds, etc. On rare occasions, these can infect other species including pigs and humans. In the span of less than a year, the lethal strain of bird flu is spreading...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd.
2008
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7105027/ https://www.ncbi.nlm.nih.gov/pubmed/32288641 http://dx.doi.org/10.1016/j.nonrwa.2007.04.009 |
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author | Upadhyay, Ranjit Kumar Kumari, Nitu Rao, V. Sree Hari |
author_facet | Upadhyay, Ranjit Kumar Kumari, Nitu Rao, V. Sree Hari |
author_sort | Upadhyay, Ranjit Kumar |
collection | PubMed |
description | Avian influenza, commonly known as bird flu, is an epidemic caused by H5N1 virus that primarily affects birds like chickens, wild water birds, etc. On rare occasions, these can infect other species including pigs and humans. In the span of less than a year, the lethal strain of bird flu is spreading very fast across the globe mainly in South East Asia, parts of Central Asia, Africa and Europe. In order to study the patterns of spread of epidemic, we made an investigation of outbreaks of the epidemic in one week, that is from February 13–18, 2006, when the deadly virus surfaced in India. We have designed a statistical transmission model of bird flu taking into account the factors that affect the epidemic transmission such as source of infection, social and natural factors and various control measures are suggested. For modeling the general intensity coefficient [Formula: see text] , we have implemented the recent ideas given in the article Fitting the Bill, Nature [R. Howlett, Fitting the bill, Nature 439 (2006) 402], which describes the geographical spread of epidemics due to transportation of poultry products. Our aim is to study the spread of avian influenza, both in time and space, to gain a better understanding of transmission mechanism. Our model yields satisfactory results as evidenced by the simulations and may be used for the prediction of future situations of epidemic for longer periods. We utilize real data at these various scales and our model allows one to generalize our predictions and make better suggestions for the control of this epidemic. |
format | Online Article Text |
id | pubmed-7105027 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2008 |
publisher | Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-71050272020-03-31 Modeling the spread of bird flu and predicting outbreak diversity Upadhyay, Ranjit Kumar Kumari, Nitu Rao, V. Sree Hari Nonlinear Anal Real World Appl Article Avian influenza, commonly known as bird flu, is an epidemic caused by H5N1 virus that primarily affects birds like chickens, wild water birds, etc. On rare occasions, these can infect other species including pigs and humans. In the span of less than a year, the lethal strain of bird flu is spreading very fast across the globe mainly in South East Asia, parts of Central Asia, Africa and Europe. In order to study the patterns of spread of epidemic, we made an investigation of outbreaks of the epidemic in one week, that is from February 13–18, 2006, when the deadly virus surfaced in India. We have designed a statistical transmission model of bird flu taking into account the factors that affect the epidemic transmission such as source of infection, social and natural factors and various control measures are suggested. For modeling the general intensity coefficient [Formula: see text] , we have implemented the recent ideas given in the article Fitting the Bill, Nature [R. Howlett, Fitting the bill, Nature 439 (2006) 402], which describes the geographical spread of epidemics due to transportation of poultry products. Our aim is to study the spread of avian influenza, both in time and space, to gain a better understanding of transmission mechanism. Our model yields satisfactory results as evidenced by the simulations and may be used for the prediction of future situations of epidemic for longer periods. We utilize real data at these various scales and our model allows one to generalize our predictions and make better suggestions for the control of this epidemic. Elsevier Ltd. 2008-09 2007-05-08 /pmc/articles/PMC7105027/ /pubmed/32288641 http://dx.doi.org/10.1016/j.nonrwa.2007.04.009 Text en Copyright © 2007 Elsevier Ltd. All rights reserved. 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 Upadhyay, Ranjit Kumar Kumari, Nitu Rao, V. Sree Hari Modeling the spread of bird flu and predicting outbreak diversity |
title | Modeling the spread of bird flu and predicting outbreak diversity |
title_full | Modeling the spread of bird flu and predicting outbreak diversity |
title_fullStr | Modeling the spread of bird flu and predicting outbreak diversity |
title_full_unstemmed | Modeling the spread of bird flu and predicting outbreak diversity |
title_short | Modeling the spread of bird flu and predicting outbreak diversity |
title_sort | modeling the spread of bird flu and predicting outbreak diversity |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7105027/ https://www.ncbi.nlm.nih.gov/pubmed/32288641 http://dx.doi.org/10.1016/j.nonrwa.2007.04.009 |
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