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Transboundary and emerging diseases2022; 69(5); e2757-e2768; doi: 10.1111/tbed.14627

Coupling spatial statistics with social network analysis to estimate distinct risk areas of disease circulation to improve risk-based surveillance.

Abstract: Most animal disease surveillance systems concentrate efforts in blocking transmission pathways and tracing back infected contacts while not considering the risk of transporting animals into areas with elevated disease risk. Here, we use a suite of spatial statistics and social network analysis to characterize animal movement among areas with an estimated distinct risk of disease circulation to ultimately enhance surveillance activities. Our model utilized equine infectious anemia virus (EIAV) outbreaks, between-farm horse movements, and spatial landscape data from 2015 through 2017. We related EIAV occurrence and the movement of horses between farms with climate variables that foster conditions for local disease propagation. We then constructed a spatially explicit model that allows the effect of the climate variables on EIAV occurrence to vary through space (i.e., non-stationary). Our results identified important areas in which in-going movements were more likely to result in EIAV infections and disease propagation. Municipalities were then classified as having high 56 (11.3%), medium 48 (9.66%), and low 393 (79.1%) spatial risk. The majority of the movements were between low-risk areas, altogether representing 68.68% of all animal movements. Meanwhile, 9.48% were within high-risk areas, and 6.20% were within medium-risk areas. Only 5.37% of the animals entering low-risk areas came from high-risk areas. On the other hand, 4.91% of the animals in the high-risk areas came from low- and medium-risk areas. Our results demonstrate that animal movements and spatial risk mapping could be used to make informed decisions before issuing animal movement permits, thus potentially reducing the chances of reintroducing infection into areas of low risk.
© 2022 The Authors. Transboundary and Emerging Diseases published by Wiley-VCH GmbH.
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Publication Date: 2022-06-25 PubMed ID: 35694801PubMed Central: PMC9796646DOI: 10.1111/tbed.14627Google Scholar: Lookup
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Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

The research uses spatial statistics and social network analysis to understand animal disease transmission and identify high-risk areas, using data of equine infectious anemia virus (EIAV) outbreaks from 2015 to 2017. The results help in informed decision-making regarding the issuance of animal movement permits and reducing infection risk.

Methods Used in the Study

  • The researchers used spatial statistics and social network analysis to understand disease transmission and areas of risk. This approach differs from traditional methods that focus on blocking transmission pathways and investigating contact history.
  • The study is based on the outbreak data of EIAV, along with information about between-farm horse movements, collected between 2015 and 2017.
  • The team connected the EIAV occurrence and horse movements with climate variables that influence local disease spread.
  • A spatially explicit model was developed to illustrate the impact of changing climate variables on EIAV distribution across different locations (non-stationarity).

Key Findings of the Research

  • The study identified specific locations where incoming animal movements are more prone to result in EIAV infections and disease spread.
  • Different risk zones were determined: 56 municipalities (11.3%) were classified as having a high spatial risk, 48 (9.66%) with a medium risk, and 393 (79.1%) with a low risk.
  • Most animal movements occurred between low-risk zones, accounting for 68.68% of all movements. Conversely, 9.48% were within high-risk areas, 6.20% within medium-risk areas, and a minor 5.37% of incoming animals in low-risk areas were from high-risk zones. Within the high-risk zones, 4.91% of the animals originated from low- and medium-risk areas.

Conclusions and Implications

  • The results show that evaluating animal movements in conjunction with spatial risk mapping can lead to better decision making around animal movement permits, potentially reducing the risk of disease reintroduction in low-risk zones.
  • The new approach can improve the effectiveness of disease surveillance activities and inform disease prevention efforts, especially in areas where livestock movement is a key factor in disease transmission.

Cite This Article

APA
Cardenas NC, Sanchez F, Lopes FPN, Machado G. (2022). Coupling spatial statistics with social network analysis to estimate distinct risk areas of disease circulation to improve risk-based surveillance. Transbound Emerg Dis, 69(5), e2757-e2768. https://doi.org/10.1111/tbed.14627

Publication

Transboundary and emerging diseases
ISSN: 1865-1682
NlmUniqueID: 101319538
Country: Germany
Language: English
Volume: 69
Issue: 5
Pages: e2757-e2768

Researcher Affiliations

Cardenas, Nicolas C
  • Department of Population Health and Pathobiology, College of Veterinary Medicine, North Carolina State University, Raleigh, North Carolina, USA.
Sanchez, Felipe
  • Department of Population Health and Pathobiology, College of Veterinary Medicine, North Carolina State University, Raleigh, North Carolina, USA.
  • Center for Geospatial Analytics, North Carolina State University, Raleigh, North Carolina, USA.
Lopes, Francisco P N
  • Departamento de Defesa Agropecuária, Secretaria da Agricultura, Pecuária e Desenvolvimento Rural (SEAPDR), Porto Alegre, Brazil.
Machado, Gustavo
  • Department of Population Health and Pathobiology, College of Veterinary Medicine, North Carolina State University, Raleigh, North Carolina, USA.

MeSH Terms

  • Animals
  • Disease Outbreaks / prevention & control
  • Disease Outbreaks / veterinary
  • Equine Infectious Anemia / epidemiology
  • Farms
  • Horse Diseases / epidemiology
  • Horses
  • Infectious Anemia Virus, Equine
  • Social Network Analysis

Conflict of Interest Statement

The authors declare no conflict of interest.

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