Apathogenic proxies for transmission dynamics of a fatal virus

Marie L.J. Gilbertson, Nicholas M. Fountain-Jones, Jennifer L. Malmberg, Roderick B. Gagne, Justin S. Lee, Simona Kraberger, Sarah Kechejian, Raegan Petch, Elliott S. Chiu, Dave Onorato, Mark W. Cunningham, Kevin R. Crooks, W. Chris Funk, Scott Carver, Sue VandeWoude, Kimberly VanderWaal, Meggan E. Craft

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Identifying drivers of transmission—especially of emerging pathogens—is a formidable challenge for proactive disease management efforts. While close social interactions can be associated with microbial sharing between individuals, and thereby imply dynamics important for transmission, such associations can be obscured by the influences of factors such as shared diets or environments. Directly-transmitted viral agents, specifically those that are rapidly evolving such as many RNA viruses, can allow for high-resolution inference of transmission, and therefore hold promise for elucidating not only which individuals transmit to each other, but also drivers of those transmission events. Here, we tested a novel approach in the Florida panther, which is affected by several directly-transmitted feline retroviruses. We first inferred the transmission network for an apathogenic, directly-transmitted retrovirus, feline immunodeficiency virus (FIV), and then used exponential random graph models to determine drivers structuring this network. We then evaluated the utility of these drivers in predicting transmission of the analogously transmitted, pathogenic agent, feline leukemia virus (FeLV), and compared FIV-based predictions of outbreak dynamics against empirical FeLV outbreak data. FIV transmission was primarily driven by panther age class and distances between panther home range centroids. FIV-based modeling predicted FeLV dynamics similarly to common modeling approaches, but with evidence that FIV-based predictions captured the spatial structuring of the observed FeLV outbreak. While FIV-based predictions of FeLV transmission performed only marginally better than standard approaches, our results highlight the value of proactively identifying drivers of transmission—even based on analogously-transmitted, apathogenic agents—in order to predict transmission of emerging infectious agents. The identification of underlying drivers of transmission, such as through our workflow here, therefore holds promise for improving predictions of pathogen transmission in novel host populations, and could provide new strategies for proactive pathogen management in human and animal systems.

Original languageEnglish (US)
Article number940007
JournalFrontiers in Veterinary Science
StatePublished - Sep 8 2022

Bibliographical note

Funding Information:
This research was supported by the National Science Foundation (DEB-1413925, 1654609, and 2030509). MG was supported by the Office of the Director, National Institutes of Health (NIH T32OD010993), the University of Minnesota Informatics Institute MnDRIVE program, and the Van Sloun Foundation. JM was supported by the ACVP/STP Coalition for Veterinary Pathology Fellows and the Linda Munson Fellowship for Wildlife Pathology Research. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Publisher Copyright:
Copyright © 2022 Gilbertson, Fountain-Jones, Malmberg, Gagne, Lee, Kraberger, Kechejian, Petch, Chiu, Onorato, Cunningham, Crooks, Funk, Carver, VandeWoude, VanderWaal and Craft.


  • Florida panther
  • disease model
  • exponential random graph model
  • network modeling
  • transmission heterogeneity
  • transmission tree


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