Cargando…
Intervention decision model to prevent spiking mortality of turkeys
Based on the daily records on turkeys' mortalities for the series of flocks placed on different farms in a relatively compact geographical area for the period of approximately 2 yr and other relevant explanatory variables, the goal of the research was to design a decision model to determine whe...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Poultry Science Association Inc. Published by Elsevier Inc.
1998
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7172810/ https://www.ncbi.nlm.nih.gov/pubmed/9657602 http://dx.doi.org/10.1093/ps/77.7.950 |
_version_ | 1783524329790636032 |
---|---|
author | Vukina, T Barnes, HJ Solakoglu, MN |
author_facet | Vukina, T Barnes, HJ Solakoglu, MN |
author_sort | Vukina, T |
collection | PubMed |
description | Based on the daily records on turkeys' mortalities for the series of flocks placed on different farms in a relatively compact geographical area for the period of approximately 2 yr and other relevant explanatory variables, the goal of the research was to design a decision model to determine whether or not to use the fluorquinolone antibiotic, sarafloxacin, to prevent spiking mortality of turkeys. The core of the designed decision model is the forecasting model that attempts to ex-ante predict the cumulative flock mortality for the period between 8 and 28 d of age. Forecasts were generated with the parameters of the linear regression model where continuous values of daily mortalities served as a dependent variable. The decision variable is a binary (yes/no) choice variable, where “yes” means “go ahead with treatment” and “no” means “do nothing”. If the predicted cumulative mortality for the period between 8 and 28 d of age exceeds 9% of the total initial placement, the model generates a “yes” signal. If the predicted cumulative mortality for the same period is below 9% of the total initial placement, the model generates a “no” signal. The results indicate a reasonable accuracy of the prediction model where the number of correct prediction increases and the number of incorrect predictions falls very fast as the forecasting window shortens. The intervention decision model could help veterinarians in making decisions on whether or not to treat the suspect flocks. |
format | Online Article Text |
id | pubmed-7172810 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 1998 |
publisher | Poultry Science Association Inc. Published by Elsevier Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-71728102020-04-22 Intervention decision model to prevent spiking mortality of turkeys Vukina, T Barnes, HJ Solakoglu, MN Poult Sci Articles Based on the daily records on turkeys' mortalities for the series of flocks placed on different farms in a relatively compact geographical area for the period of approximately 2 yr and other relevant explanatory variables, the goal of the research was to design a decision model to determine whether or not to use the fluorquinolone antibiotic, sarafloxacin, to prevent spiking mortality of turkeys. The core of the designed decision model is the forecasting model that attempts to ex-ante predict the cumulative flock mortality for the period between 8 and 28 d of age. Forecasts were generated with the parameters of the linear regression model where continuous values of daily mortalities served as a dependent variable. The decision variable is a binary (yes/no) choice variable, where “yes” means “go ahead with treatment” and “no” means “do nothing”. If the predicted cumulative mortality for the period between 8 and 28 d of age exceeds 9% of the total initial placement, the model generates a “yes” signal. If the predicted cumulative mortality for the same period is below 9% of the total initial placement, the model generates a “no” signal. The results indicate a reasonable accuracy of the prediction model where the number of correct prediction increases and the number of incorrect predictions falls very fast as the forecasting window shortens. The intervention decision model could help veterinarians in making decisions on whether or not to treat the suspect flocks. Poultry Science Association Inc. Published by Elsevier Inc. 1998-07-01 2019-12-11 /pmc/articles/PMC7172810/ /pubmed/9657602 http://dx.doi.org/10.1093/ps/77.7.950 Text en © 1998 Poultry Science Association Inc. 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 | Articles Vukina, T Barnes, HJ Solakoglu, MN Intervention decision model to prevent spiking mortality of turkeys |
title | Intervention decision model to prevent spiking mortality of turkeys |
title_full | Intervention decision model to prevent spiking mortality of turkeys |
title_fullStr | Intervention decision model to prevent spiking mortality of turkeys |
title_full_unstemmed | Intervention decision model to prevent spiking mortality of turkeys |
title_short | Intervention decision model to prevent spiking mortality of turkeys |
title_sort | intervention decision model to prevent spiking mortality of turkeys |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7172810/ https://www.ncbi.nlm.nih.gov/pubmed/9657602 http://dx.doi.org/10.1093/ps/77.7.950 |
work_keys_str_mv | AT vukinat interventiondecisionmodeltopreventspikingmortalityofturkeys AT barneshj interventiondecisionmodeltopreventspikingmortalityofturkeys AT solakoglumn interventiondecisionmodeltopreventspikingmortalityofturkeys |