Retrospect and Risk Analysis of Foot-and-Mouth Disease in China Based on Integrated Surveillance and Spatial Analysis Tools

Chen, Jiahui; Wang, Minjia; Liang, Ruirui; Lu, Yi; Zhang, Qiang; Chen, Qin; Niu, Bing

doi:10.3389/fvets.2019.00511

Cited by 19 publications

(15 citation statements)

References 46 publications

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“…In that case, branch lengths are restrained in the expected number of substitutions (Yuan et al 2020 ). The dissimilarity between transmission trees and phylogenetic trees correlate with the variance between species trees and phylogenetic trees (Chen et al 2020 ). Environmental infectiousness depends on the individual’s location and environment (Ramière et al 2019 ).…”

Section: Overview Of the Infectious Disease Transmission Patternmentioning

confidence: 99%

RETRACTED ARTICLE: Structural modeling and phylogenetic analysis for infectious disease transmission pattern based on maximum likelihood tree approach

Mahmoud

Alamery

et al. 2021

J Ambient Intell Human Comput

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Section: Overview Of the Infectious Disease Transmission Patternmentioning

confidence: 99%

RETRACTED ARTICLE: Structural modeling and phylogenetic analysis for infectious disease transmission pattern based on maximum likelihood tree approach

Mahmoud

Alamery

et al. 2021

J Ambient Intell Human Comput

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“…SDE mapping is a common method used to identify spatial direction trends of attribute data associated with geographical features. It has been widely used for geographically identifying disease and crime trends ( Chainey, Tompson & Uhlig, 2008 ; Wang, Shi & Miao, 2015 ; Leigh, Dunnett & Jackson, 2016 ; Al-Kindi et al, 2017 ; Ma et al, 2017 ; Polupan et al, 2017 ; Butkovic et al, 2019 ; Lu et al, 2019 ; Chen et al, 2020 ). We used SDE to identify the contiguous region that contained 1-standard deviation of WNV human incidence rates to focus on the counties in California that would most likely reveal previously unknown patterns of WNV risk and vulnerability, and defined that region as our study area.…”

Section: Methodsmentioning

confidence: 99%

Exploring the socio-economic and environmental components of infectious diseases using multivariate geovisualization: West Nile Virus

Kala

Atkinson

Tiwari

2020

PeerJ

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Background This study postulates that underlying environmental conditions and a susceptible population’s socio-economic status should be explored simultaneously to adequately understand a vector borne disease infection risk. Here we focus on West Nile Virus (WNV), a mosquito borne pathogen, as a case study for spatial data visualization of environmental characteristics of a vector’s habitat alongside human demographic composition for understanding potential public health risks of infectious disease. Multiple efforts have attempted to predict WNV environmental risk, while others have documented factors related to human vulnerability to the disease. However, analytical modeling that combines the two is difficult due to the number of potential explanatory variables, varying spatial resolutions of available data, and differing research questions that drove the initial data collection. We propose that the use of geovisualization may provide a glimpse into the large number of potential variables influencing the disease and help distill them into a smaller number that might reveal hidden and unknown patterns. This geovisual look at the data might then guide development of analytical models that can combine environmental and socio-economic data. Methods Geovisualization was used to integrate an environmental model of the disease vector’s habitat alongside human risk factors derived from socio-economic variables. County level WNV incidence rates from California, USA, were used to define a geographically constrained study area where environmental and socio-economic data were extracted from 1,133 census tracts. A previously developed mosquito habitat model that was significantly related to WNV infected dead birds was used to describe the environmental components of the study area. Self-organizing maps found 49 clusters, each of which contained census tracts that were more similar to each other in terms of WNV environmental and socio-economic data. Parallel coordinate plots permitted visualization of each cluster’s data, uncovering patterns that allowed final census tract mapping exposing complex spatial patterns contained within the clusters. Results Our results suggest that simultaneously visualizing environmental and socio-economic data supports a fuller understanding of the underlying spatial processes for risks to vector-borne disease. Unexpected patterns were revealed in our study that would be useful for developing future multilevel analytical models. For example, when the cluster that contained census tracts with the highest median age was examined, it was determined that those census tracts only contained moderate mosquito habitat risk. Likewise, the cluster that contained census tracts with the highest mosquito habitat risk had populations with moderate median age. Finally, the cluster that contained census tracts with the highest WNV human incidence rates had unexpectedly low mosquito habitat risk.

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“…SDE mapping is a common method used to identify spatial direction trends of attribute data associated with geographical features. It has been widely used for geographically identifying disease and crime trends (Chainey, Tompson et al 2008, Wang, Shi et al 2015, Leigh, Dunnett et al 2016, Al-Kindi, Kwan et al 2017, Ma, Xiao et al 2017, Polupan, Bezymennyi et al 2017, Butkovic, Mrdovic et al 2019, Lu, Deng et al 2019, Chen, Wang et al 2020. We used SDE to identify the contiguous region that contained 1-standard deviation of WNV human incidence rates to focus on the counties in California that would most likely reveal previously unknown patterns of WNV risk and vulnerability, and defined that region as our study area.…”

Section: Methodsmentioning

confidence: 99%

Peer Review #1 of "Exploring the socio-economic and environmental components of infectious diseases using multivariate geovisualization: West Nile Virus (v0.1)"

2020

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Background. This study postulates that underlying environmental conditions and a susceptible population's socioeconomic status should be explored simultaneously to adequately understand a vector borne disease infection risk. Here we focus on West Nile Virus (WNV), a mosquito borne pathogen, as a case study for spatial data visualization of environmental characteristics of a vector's habitat alongside human demographic composition for understanding potential public health risks of infectious disease. Multiple efforts have attempted to predict WNV environmental risk, while others have documented factors related to human vulnerability to the disease. However, analytical modeling that combines the two is difficult due to the number of potential explanatory variables, varying spatial resolutions of available data, and differing research questions that drove the initial data collection. We propose that the use of geovisualization may provide a glimpse into the large number of potential variables influencing the disease and help distill them into a smaller number that might reveal hidden and unknown patterns. This geovisual look at the data might then guide development of analytical models that can combine environmental and socioeconomic data. Methods. Geovisualization was used to integrate an environmental model of the disease vector's habitat alongside human risk factors derived from socioeconomic variables. County level WNV incidence rates from California, USA, were used to define aa geographically constrained study area where environmental and socioeconomic data were extracted from 1,133 census tracts. A previously developed mosquito habitat model that was significantly related to WNV infected dead birds was used to describe the environmental components of the study area. Self-organizing maps found 49 clusters, each of which contained census tracts that were more similar to each other in terms of WNV environmental and socioeconomic data. Parallel coordinate plots permitted visualization of each cluster's data, uncovering patterns that allowed final census tract mapping exposing complex spatial patterns contained within the clusters. Results. Our results suggest that simultaneously visualizing environmental and socioeconomic data supports a fuller understanding of the underlying spatial processes for risks to vector-borne disease. Unexpected patterns were revealed in our study that would be useful for developing future multilevel analytical models. For example, when the cluster that contained census tracts with the highest median age was examined, it was determined that those census tracts only contained moderate mosquito habitat risk. Likewise, the cluster that contained census tracts with the highest mosquito habitat risk had populations with moderate median age. Finally, the cluster that contained census tracts with the highest WNV human incidence rates had unexpectedly low mosquito habitat risk.

show abstract

Retrospect and Risk Analysis of Foot-and-Mouth Disease in China Based on Integrated Surveillance and Spatial Analysis Tools

Cited by 19 publications

References 46 publications

RETRACTED ARTICLE: Structural modeling and phylogenetic analysis for infectious disease transmission pattern based on maximum likelihood tree approach

RETRACTED ARTICLE: Structural modeling and phylogenetic analysis for infectious disease transmission pattern based on maximum likelihood tree approach

Exploring the socio-economic and environmental components of infectious diseases using multivariate geovisualization: West Nile Virus

Peer Review #1 of "Exploring the socio-economic and environmental components of infectious diseases using multivariate geovisualization: West Nile Virus (v0.1)"

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