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BioStatistics
Creating SEIR, infection, deducing the impact of policies (or lack thereof) on infection data, prediction of future states, statistical inference from published data...
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.
Please check out the Datasets and Tools page.
Any new resources you might have in mind, please add them there directly.
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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
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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 )