Presentation Title

Modelling Disease Spillover Using Multipartite Networks

Project Collaborators

Laurent Hébert-Dufresne

Time

2:20 PM

Location

Williams Family Room

Abstract

The rise of Emergent Infectious Diseases (EIDs) is one of the primary modern stresses causing devastation to biological systems today. The widespread decline of bumble bees, concurrent with the detection of several new pathogens, indicates the presence of EIDs may be one of the factors leading to the decline of this biologically and economically important genus. One such pathogen recently found in bumble bees is Deformed Wing Virus (DWV). There is mounting experimental evidence that there is a spillover of DWV from commercial honey bees to neighboring bumble bee populations, and that a primary route of transmission between these groups is through flowers.

We model the spread of DWV between honey and bumble bees through flowers using a tripartite network, with edges connecting bees to flowers within their foraging range, and analyze disease spread for two cases of such networks. First we consider a fully-connected tripartite network, which corresponds to the classical mean-field Ross-Macdonald model with an additional vector population, and derive the steady states and reproductive number using a simple branching argument. Next, to incorporate spatially-explicit dynamics we construct the tripartite network using satellite images with the open-source software BEESCOUT, and compare our model's outbreak dynamics to observed infection densities. Together, these approaches demonstrate how a shared transmission route can lead to a major outbreak between two groups, even when either group may not appear at risk when studied in isolation.

Primary Faculty Mentor Name

Laurent Hébert-Dufresne

Status

Graduate

Student College

College of Engineering and Mathematical Sciences

Program/Major

Computer Science

Primary Research Category

Biological Sciences

Secondary Research Category

Engineering & Physical Sciences

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Modelling Disease Spillover Using Multipartite Networks

The rise of Emergent Infectious Diseases (EIDs) is one of the primary modern stresses causing devastation to biological systems today. The widespread decline of bumble bees, concurrent with the detection of several new pathogens, indicates the presence of EIDs may be one of the factors leading to the decline of this biologically and economically important genus. One such pathogen recently found in bumble bees is Deformed Wing Virus (DWV). There is mounting experimental evidence that there is a spillover of DWV from commercial honey bees to neighboring bumble bee populations, and that a primary route of transmission between these groups is through flowers.

We model the spread of DWV between honey and bumble bees through flowers using a tripartite network, with edges connecting bees to flowers within their foraging range, and analyze disease spread for two cases of such networks. First we consider a fully-connected tripartite network, which corresponds to the classical mean-field Ross-Macdonald model with an additional vector population, and derive the steady states and reproductive number using a simple branching argument. Next, to incorporate spatially-explicit dynamics we construct the tripartite network using satellite images with the open-source software BEESCOUT, and compare our model's outbreak dynamics to observed infection densities. Together, these approaches demonstrate how a shared transmission route can lead to a major outbreak between two groups, even when either group may not appear at risk when studied in isolation.