README.md
In gfalbery/ViralSharingPhylogeography: What the Package Does (One Line, Title Case)
Predicting the global mammalian viral sharing network using phylogeography
Gregory F. Albery, Evan A. Eskew, Noam Ross, and Kevin J. Olival
Work for EcoHealth Alliance for a three-month internship, intercalated within Greg's PhD. Internship began fully 07 Jan 2019, and ended March 22nd.
The project examines mammalian viral sharing patterns and their phylogeographic correlates. Using mammal species pairs as the unit of analysis, we constructed Generalised Additive Mixed Models (GAMMs) to untangle the roles of spatial overlap and phylogenetic relatedness while accounting for species-level sampling biases in the network.
Simulating with the resulting estimates produced a "neutral" network of global viral sharing patterns, which we used to uncover multiple taxonomic and geographic patterns of viral sharing. We validated its use as a predictive tool by recapitulating trends in the Enhanced Infectious Diseases Database (EID2) and by simulating a reservoir identification process.
Preprint available via bioRxiv
The predicted network itself is listed as PredictedNetwork.rds and as a .csv in PredictedNetwork.zip.
It comprises a square matrix of sharing probabilities, where every row and column is a species of mammal.
R scripts are numbered according to the order of use:
- Scripts beginning with
0_ will import and structure datasets.
- All other scripts will:
-- run viral sharing GAMMs,
-- validate the GAMMs,
-- use the estimates to predict the global mammalian viral sharing network,
-- summarise traits of the network,
-- use it to predict sharing patterns, and
-- make figures.
Data come from three main sources:
- The IUCN (https://www.iucnredlist.org/resources/spatial-data-download)
- A mammalian supertree (https://onlinelibrary.wiley.com/doi/10.1111/j.1461-0248.2009.01307.x)
- A mammal-virus association dataset (https://github.com/ecohealthalliance/hp3)
Our validation set was the Enhanced Infectious Diseases Database (EID2) (https://www.nature.com/articles/sdata201549)
gfalbery/ViralSharingPhylogeography documentation built on April 24, 2020, 12:41 p.m.
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Predicting the global mammalian viral sharing network using phylogeography
Gregory F. Albery, Evan A. Eskew, Noam Ross, and Kevin J. Olival
Work for EcoHealth Alliance for a three-month internship, intercalated within Greg's PhD. Internship began fully 07 Jan 2019, and ended March 22nd.
The project examines mammalian viral sharing patterns and their phylogeographic correlates. Using mammal species pairs as the unit of analysis, we constructed Generalised Additive Mixed Models (GAMMs) to untangle the roles of spatial overlap and phylogenetic relatedness while accounting for species-level sampling biases in the network.
Simulating with the resulting estimates produced a "neutral" network of global viral sharing patterns, which we used to uncover multiple taxonomic and geographic patterns of viral sharing. We validated its use as a predictive tool by recapitulating trends in the Enhanced Infectious Diseases Database (EID2) and by simulating a reservoir identification process.
Preprint available via bioRxiv
The predicted network itself is listed as PredictedNetwork.rds and as a .csv in PredictedNetwork.zip.
It comprises a square matrix of sharing probabilities, where every row and column is a species of mammal.
R scripts are numbered according to the order of use:
- Scripts beginning with
0_will import and structure datasets. - All other scripts will: -- run viral sharing GAMMs, -- validate the GAMMs, -- use the estimates to predict the global mammalian viral sharing network, -- summarise traits of the network, -- use it to predict sharing patterns, and -- make figures.
Data come from three main sources:
- The IUCN (https://www.iucnredlist.org/resources/spatial-data-download)
- A mammalian supertree (https://onlinelibrary.wiley.com/doi/10.1111/j.1461-0248.2009.01307.x)
- A mammal-virus association dataset (https://github.com/ecohealthalliance/hp3)
Our validation set was the Enhanced Infectious Diseases Database (EID2) (https://www.nature.com/articles/sdata201549)
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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