The goal of this project is to come up with a reasonable way to predict pathogen sharing patterns between countries, using the DAME (dynamic additive and multiplicative effects model) for dyadic networks.
This follows on two previous preprints:
- Poisot, T., Nunn, C., & Morand, S. (2014). Ongoing worldwide homogenization of human pathogens. bioRxiv, 009977.
- Dallas, T. A., Carlson, C. J., & Poisot, T. (2018). Leveraging pathogen community distributions to understand outbreak and emergence potential. bioRxiv, 336065.
The GIDEON data is available as a list of countries, years, and pathogens. The goal is currently to come up with appropriate dyadic predictors for pairs of countries.
- Can we appropriately describe pathogen geography?
- Which predictors work best overall?
- Do different predictors work better for different agents (bacterial, viral, fungal, protozoan)?
- Do different predictors work better for different transmission routes (zoonotic, vector-borne)?
- Are there any detectably-weird years?
- Are there any detectably-weird country associations?
- What does this teach us about forecasting disease emergence?
So far there are three categories that broadly include a few different ideas, which draw on a few specific data sources very heavily:
- GBIF (www.gbif.org): for animal occurrence data
- CEPII GeoDist Database
- Geographic distance between countries: several options available in GeoDist
- dist: distance between most important cities
- distcap: distance between capital cities
- distw: distance weighted by population (gravity model)
- distwces: distance weighted by population (gravity model, usually used for bilateral trade flow)
- Contiguity between countries (shared border)
- contig in GeoDist (a binary variable)
- Shared continent
- can be derived from continent in GeoDist
- Shared language
- can derive from GeoDist either for official language, or language spoken by more than 9% of the population
- Shared colonial history: a few options in GeoDist
- comcol: common colonizer after 1945
- colony: have ever had a colonial link
- curcol: currently in a colonial relationship
- Traveler data
- flight data? Shweta could help design this part I think
- Trade data?
- Bomin included some in the original DAME paper that I think would work nicely here, probably
- Dissimilarity in wildlife community (GBIF)
- all vertebrates?
- all mammals
- key mammal groups: bats, primates, rodents
- use Jaccard dissimilarity like Dallas et al.
- Dissimilarity in vector community
- Data available, last updated in the 1970s, for mosquitoes: https://academic.oup.com/jme/article/44/4/554/875111
- same Jaccard dissimilarity idea (or potentially just number of species in common)
- Climatic dissimilarity
- Could adapt the protocol from this paper but instead use major cities or capitols: http://rspb.royalsocietypublishing.org/content/274/1617/1489.short
- Number of shared invasive species
- Can be derived from GRIIS (http://www.griis.org/); data paper at https://www.nature.com/articles/sdata2017202
- List of all invasive terrestrial animals and plants by country