BioStatistics

Creating SEIR, infection, deducing the impact of policies (or lack thereof) on infection data, prediction of future states, statistical inference from published data...

Communication

For the time being, there is a #biostats channel on the Slack group (check out the [email protected] group for the invitation link). During the BioHackathon, we'll update this section.

Resources

Please check out the Datasets and Tools page.

Any new resources you might have in mind, please add them there directly.

Ideas for projects

• Create reference libraries that read the Johns Hopkins repository and put data in the format of a particular language.

  • There is now an R package for the data colated by Johns Hopkins

• Implement a SEIR model (similar to here https://gabgoh.github.io/COVID/) as a function, and tested against the various data points we have (e.g. vs country or other geographical split). This can be consequently tested against the different policies in place (of which we know at least), trying to identify "interesting" cases. An interesting case could be characterized as the case of two geographical locations that implemented similar measures/policies across time, but have significantly different outcome (i.e. the SEIR model parameters do not align). For these cases, the corresponding entries of the COVID-19 phylogeny can be tested for molecular markers, as they could be of potential value. Also try to correlate the SEIR parameters to environmental data (to confirm the "evidence that a 1C increase in local temperature reduces transmission by 13%" - article here )

Participants

• Fotis Psomopoulos (coordinating, until someone else comes forward)
• Janne Solanpää (co-coordinating?)
• JJ Merelo
• Bonface Munyoki
• Felizitas Eichner
• Noushin Nabavi
• Maciej Bak
• Ceci Valenzuela
• Arvon Clemons II
• Saeed Omidi
• Paul Lassmann-Klee
• Sara Vilella
• Gonzalo Colmenarejo
• Thanasis Vergoulis
• Kostis Zagganas
• Laura Zaragoza Infante
• Festus Nyasimi
• Nikolaos Pechlivanis