AI Article Synopsis

  • Disease transmission models often assume that individuals mix randomly, but this can lead to inaccurate predictions in populations with non-random contact patterns.
  • An SEIR model was used to analyze equine influenza, comparing both empirical contact networks (actual interactions) and theoretical networks (random mixing).
  • The study found that empirical networks produced different epidemic characteristics (bimodal curves and longer duration) compared to theoretical networks, underlining the importance of using real-world data to understand disease dynamics in non-randomly mixing populations.

Article 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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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6397169PMC
http://dx.doi.org/10.1038/s41598-019-40151-2DOI Listing

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