Using multinomial and space-time permutation models to understand the epidemiology of infectious bronchitis in California between 2008 and 2012

O. Alejandro Aleuy, Maurice Pitesky, Rodrigo A Gallardo

Research output: Contribution to journalReview article

Abstract

Although infectious bronchitis virus (IBV) has been described as one of the most economically important viral respiratory diseases in poultry, there are few analyses of outbreaks that use spatial statistics. In order to better understand how the different genotypes of IBV behave spatially and temporally, we used geographic information system-based mapping coupled with spatial and spatial-temporal statistics to identify statistically significant clustering of multiple strains of infectious bronchitis (IB) between 2008 and 2012 in California. Specifically, space-time permutation and multinomial models were used to identify spatial and spatial-temporal clusters of various genotypes of IBV. Using time permutations (i.e., windows) spanning days to years, we identified three statistically significant (P < 0.05) clusters. In contrast, multinomial models identified two statistically significant spatial-temporal clusters and one statistically significant spatial cluster. When comparing the space-time permutation and multinomial models against each other, we identified spatial and temporal overlap in two of the three statistically significant clusters. From a practical perspective, multinomial clustering approaches may be advantageous for studying IB because the model allows the different genotypes of IB to be independent nominal variables, thereby allowing for a more detailed spatial analysis. To that point, based on their risk ratios, the genotypes classified as vaccine-related were identified as the most significant contributor to two of the three mutinomial clusters. Additionally, statistically significant clusters were mapped and layered on a hot-spot analysis of commercial poultry farm density in order to qualitatively assess the relationship between farm density and clusters of IBV. Results showed that one of the three space-time permutations and one of the three multinomial clusters were spatially centered near the highest density farm areas, as determined by the hot-spot analysis.

Original languageEnglish (US)
Pages (from-to)226-232
Number of pages7
JournalAvian Diseases
Volume62
Issue number2
DOIs
StatePublished - Jun 1 2018

Fingerprint

infectious bronchitis
Infectious bronchitis virus
Bronchitis
space and time
epidemiology
Epidemiology
genotype
poultry
statistics
Genotype
farms
farm area
relative risk
Poultry
geographic information systems
respiratory tract diseases
Cluster Analysis
vaccines
Geographic Information Systems
Spatial Analysis

Keywords

  • GIS
  • IB outbreak
  • spatial statistics

ASJC Scopus subject areas

  • Food Animals
  • Animal Science and Zoology
  • Immunology and Microbiology(all)

Cite this

Using multinomial and space-time permutation models to understand the epidemiology of infectious bronchitis in California between 2008 and 2012. / Aleuy, O. Alejandro; Pitesky, Maurice; Gallardo, Rodrigo A.

In: Avian Diseases, Vol. 62, No. 2, 01.06.2018, p. 226-232.

Research output: Contribution to journalReview article

@article{61cf5e79c7a5432fb91318462a95e5d1,
title = "Using multinomial and space-time permutation models to understand the epidemiology of infectious bronchitis in California between 2008 and 2012",
abstract = "Although infectious bronchitis virus (IBV) has been described as one of the most economically important viral respiratory diseases in poultry, there are few analyses of outbreaks that use spatial statistics. In order to better understand how the different genotypes of IBV behave spatially and temporally, we used geographic information system-based mapping coupled with spatial and spatial-temporal statistics to identify statistically significant clustering of multiple strains of infectious bronchitis (IB) between 2008 and 2012 in California. Specifically, space-time permutation and multinomial models were used to identify spatial and spatial-temporal clusters of various genotypes of IBV. Using time permutations (i.e., windows) spanning days to years, we identified three statistically significant (P < 0.05) clusters. In contrast, multinomial models identified two statistically significant spatial-temporal clusters and one statistically significant spatial cluster. When comparing the space-time permutation and multinomial models against each other, we identified spatial and temporal overlap in two of the three statistically significant clusters. From a practical perspective, multinomial clustering approaches may be advantageous for studying IB because the model allows the different genotypes of IB to be independent nominal variables, thereby allowing for a more detailed spatial analysis. To that point, based on their risk ratios, the genotypes classified as vaccine-related were identified as the most significant contributor to two of the three mutinomial clusters. Additionally, statistically significant clusters were mapped and layered on a hot-spot analysis of commercial poultry farm density in order to qualitatively assess the relationship between farm density and clusters of IBV. Results showed that one of the three space-time permutations and one of the three multinomial clusters were spatially centered near the highest density farm areas, as determined by the hot-spot analysis.",
keywords = "GIS, IB outbreak, spatial statistics",
author = "Aleuy, {O. Alejandro} and Maurice Pitesky and Gallardo, {Rodrigo A}",
year = "2018",
month = "6",
day = "1",
doi = "10.1637/11788-122217-Reg.1",
language = "English (US)",
volume = "62",
pages = "226--232",
journal = "Avian Diseases",
issn = "0005-2086",
publisher = "American Association of Avian Pathologists",
number = "2",

}

TY - JOUR

T1 - Using multinomial and space-time permutation models to understand the epidemiology of infectious bronchitis in California between 2008 and 2012

AU - Aleuy, O. Alejandro

AU - Pitesky, Maurice

AU - Gallardo, Rodrigo A

PY - 2018/6/1

Y1 - 2018/6/1

N2 - Although infectious bronchitis virus (IBV) has been described as one of the most economically important viral respiratory diseases in poultry, there are few analyses of outbreaks that use spatial statistics. In order to better understand how the different genotypes of IBV behave spatially and temporally, we used geographic information system-based mapping coupled with spatial and spatial-temporal statistics to identify statistically significant clustering of multiple strains of infectious bronchitis (IB) between 2008 and 2012 in California. Specifically, space-time permutation and multinomial models were used to identify spatial and spatial-temporal clusters of various genotypes of IBV. Using time permutations (i.e., windows) spanning days to years, we identified three statistically significant (P < 0.05) clusters. In contrast, multinomial models identified two statistically significant spatial-temporal clusters and one statistically significant spatial cluster. When comparing the space-time permutation and multinomial models against each other, we identified spatial and temporal overlap in two of the three statistically significant clusters. From a practical perspective, multinomial clustering approaches may be advantageous for studying IB because the model allows the different genotypes of IB to be independent nominal variables, thereby allowing for a more detailed spatial analysis. To that point, based on their risk ratios, the genotypes classified as vaccine-related were identified as the most significant contributor to two of the three mutinomial clusters. Additionally, statistically significant clusters were mapped and layered on a hot-spot analysis of commercial poultry farm density in order to qualitatively assess the relationship between farm density and clusters of IBV. Results showed that one of the three space-time permutations and one of the three multinomial clusters were spatially centered near the highest density farm areas, as determined by the hot-spot analysis.

AB - Although infectious bronchitis virus (IBV) has been described as one of the most economically important viral respiratory diseases in poultry, there are few analyses of outbreaks that use spatial statistics. In order to better understand how the different genotypes of IBV behave spatially and temporally, we used geographic information system-based mapping coupled with spatial and spatial-temporal statistics to identify statistically significant clustering of multiple strains of infectious bronchitis (IB) between 2008 and 2012 in California. Specifically, space-time permutation and multinomial models were used to identify spatial and spatial-temporal clusters of various genotypes of IBV. Using time permutations (i.e., windows) spanning days to years, we identified three statistically significant (P < 0.05) clusters. In contrast, multinomial models identified two statistically significant spatial-temporal clusters and one statistically significant spatial cluster. When comparing the space-time permutation and multinomial models against each other, we identified spatial and temporal overlap in two of the three statistically significant clusters. From a practical perspective, multinomial clustering approaches may be advantageous for studying IB because the model allows the different genotypes of IB to be independent nominal variables, thereby allowing for a more detailed spatial analysis. To that point, based on their risk ratios, the genotypes classified as vaccine-related were identified as the most significant contributor to two of the three mutinomial clusters. Additionally, statistically significant clusters were mapped and layered on a hot-spot analysis of commercial poultry farm density in order to qualitatively assess the relationship between farm density and clusters of IBV. Results showed that one of the three space-time permutations and one of the three multinomial clusters were spatially centered near the highest density farm areas, as determined by the hot-spot analysis.

KW - GIS

KW - IB outbreak

KW - spatial statistics

UR - http://www.scopus.com/inward/record.url?scp=85049189808&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85049189808&partnerID=8YFLogxK

U2 - 10.1637/11788-122217-Reg.1

DO - 10.1637/11788-122217-Reg.1

M3 - Review article

C2 - 29944405

AN - SCOPUS:85049189808

VL - 62

SP - 226

EP - 232

JO - Avian Diseases

JF - Avian Diseases

SN - 0005-2086

IS - 2

ER -