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Scientific reports2019; 9(1); 3227; doi: 10.1038/s41598-019-40151-2

Comparing the effects of non-homogenous mixing patterns on epidemiological outcomes in equine populations: A mathematical modelling study.

Abstract: Disease transmission models often assume homogenous mixing. This assumption, however, has the potential to misrepresent the disease dynamics for populations in which contact patterns are non-random. A disease transmission model with an SEIR structure was used to compare the effect of weighted and unweighted empirical equine contact networks to weighted and unweighted theoretical networks generated using random mixing. Equine influenza was used as a case study. Incidence curves generated with the unweighted empirical networks were similar in epidemic duration (5-8 days) and peak incidence (30.8-46.4%). In contrast, the weighted empirical networks resulted in a more pronounced difference between the networks in terms of the epidemic duration (8-15 days) and the peak incidence (5-25%). The incidence curves for the empirical networks were bimodal, while the incidence curves for the theoretical networks were unimodal. The incorporation of vaccination and isolation in the model caused a decrease in the cumulative incidence for each network, however, this effect was only seen at high levels of vaccination and isolation for the complete network. This study highlights the importance of using empirical networks to describe contact patterns within populations that are unlikely to exhibit random mixing such as equine populations.
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Publication Date: 2019-03-01 PubMed ID: 30824806PubMed Central: PMC6397169DOI: 10.1038/s41598-019-40151-2Google Scholar: Lookup
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  • Comparative Study
  • Journal Article
  • Research Support
  • Non-U.S. Gov't

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.

This scientific research focuses on disease transmission in horse populations and uses mathematical modeling to compare the effects of different types of mixing patterns (random and non-random) on disease outcomes. It specifically studies the spread of equine influenza under different contact assumptions and the impact of prevention measures like vaccination and isolation.

Research Objective and Background

  • The study aims to assess the effect of homogeneous and non-homogeneous (or random and non-random) mixing patterns on disease transmission in equine populations using a disease transmission model with an SEIR (Susceptible, Exposed, Infectious, and Recovered) structure.
  • Often, disease transmission models assume homogenous or random mixing patterns. However, this may misrepresent the actual disease dynamics in real-world scenarios where the contact patterns are non-random.

Methodology and Case Study

  • The researchers used both empirical (real-world data) and theoretical (artificially generated) equine contact networks, with weighted (more realistic, considering the differences in contact frequencies) and unweighted (all contacts equally likely) forms, to study their influence on outbreak scenarios.
  • Equine influenza, a common disease in horse populations, was used as a case study.

Results and Findings

  • The outcomes of unweighted empirical networks i.e., based on real-world data but where every contact has an equal chance of disease transmission, were found to be similar in terms of the duration of the epidemic (5-8 days) and peak incidence (30.8-46.4%).
  • However, when considering the weighted (frequency-based contact) empirical networks, a more pronounced difference was observed with a wider epidemic duration (8-15 days) and varied peak incidence (5-25%).
  • The incidence curves for empirical networks showed a bimodal distribution, whereas theoretical networks generated unimodal curves, implying different patterns of disease spread between real-world and imagined scenarios.
  • The incorporation of control measures like vaccination and isolation in the model led to a decrease in the cumulative incidence for each network. However, this reduction was only observed at high levels of implementation, especially in the complete network scenario indicating the need for thorough preventive actions.

Conclusion

  • The research re-emphasizes the importance of using empirical networks or real-world data to portray contact patterns in populations, especially where random mixing is unlikely as in the case of equine communities. It encourages the use of more complex models that take into account the true nature of population interactions for better disease control and management.

Cite This Article

APA
Milwid RM, O'Sullivan TL, Poljak Z, Laskowski M, Greer AL. (2019). Comparing the effects of non-homogenous mixing patterns on epidemiological outcomes in equine populations: A mathematical modelling study. Sci Rep, 9(1), 3227. https://doi.org/10.1038/s41598-019-40151-2

Publication

Scientific reports
ISSN: 2045-2322
NlmUniqueID: 101563288
Country: England
Language: English
Volume: 9
Issue: 1
Pages: 3227
PII: 3227

Researcher Affiliations

Milwid, Rachael M
  • Department of Population Medicine, University of Guelph, Guelph, ON, Canada.
O'Sullivan, Terri L
  • Department of Population Medicine, University of Guelph, Guelph, ON, Canada.
Poljak, Zvonimir
  • Department of Population Medicine, University of Guelph, Guelph, ON, Canada.
Laskowski, Marek
  • Department of Population Medicine, University of Guelph, Guelph, ON, Canada.
  • Department of Mathematics and Statistics, York University, Toronto, ON, Canada.
Greer, Amy L
  • Department of Population Medicine, University of Guelph, Guelph, ON, Canada. agreer@uoguelph.ca.

MeSH Terms

  • Animals
  • Contact Tracing / methods
  • Contact Tracing / veterinary
  • Epidemics / prevention & control
  • Horse Diseases / prevention & control
  • Horse Diseases / transmission
  • Horse Diseases / virology
  • Horses
  • Humans
  • Incidence
  • Influenza, Human / epidemiology
  • Influenza, Human / transmission
  • Influenza, Human / virology
  • Models, Theoretical
  • Neural Networks, Computer
  • Ontario
  • Orthomyxoviridae / physiology
  • Orthomyxoviridae Infections / epidemiology
  • Orthomyxoviridae Infections / transmission
  • Orthomyxoviridae Infections / virology
  • Time Factors
  • Vaccination / methods
  • Vaccination / veterinary

Conflict of Interest Statement

The authors declare no competing interests.

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Citations

This article has been cited 2 times.
  1. Gonzalez-Obando J, Forero JE, Zuluaga-Cabrera AM, Ruiz-Saenz J. Equine Influenza Virus: An Old Known Enemy in the Americas. Vaccines (Basel) 2022 Oct 14;10(10).
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  2. Fielding HR, Silk MJ, McKinley TJ, Delahay RJ, Wilson-Aggarwal JK, Gauvin L, Ozella L, Cattuto C, McDonald RA. Spatial and temporal variation in proximity networks of commercial dairy cattle in Great Britain. Prev Vet Med 2021 Sep;194:105443.
    doi: 10.1016/j.prevetmed.2021.105443pubmed: 34352518google scholar: lookup
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