Thread by @maiamajumder

Our latest preprint from the #COVID19 Dispersed Volunteer Research Network is now live at [biorxiv.org/content/10.110…]!

We use machine learning techniques to map the existing #coronavirus literature & identify research needs for #SARSCoV2 (compared to #MERS- & #SARS-CoV here).

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(This study was led by @AnhvinhDoanvo in collaboration with @Money_qxl, @qramjee, @EvolvingEpitope, @angeldesaimd, & myself. It started off as one of our research hackathon projects in late March, & it's been such a delight to oversee its development over the last 11 weeks!)

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Perhaps unsurprisingly to those familiar with scientific funding mechanisms, we find via PCA & LDA that #COVID19 studies (both preprints & peer-reviewed) have primarily been dominated by clinical, modeling, & field-based – as opposed to laboratory-based – research to date.

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(...The irony of this study isn't lost on me, BTW. I'm a computational epidemiologist, & this is a modeling paper – but that doesn't change the fact that laboratory-based research is crucial to understanding host cell entry & development of therapeutics, among other things!)

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With this in mind, we hope that the work we present in this paper (though not yet peer-reviewed) will offer a data-driven defense for the argument that we need better mechanisms for funding laboratory-based research if we want to be prepared for rapid response to pandemics.

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I need to get back to work, but I'll try to return to this thread in the future to elaborate further. For now, I welcome y'all to take a look at the @biorxivpreprint URL for additional (provisional) results as well as the limitations & assumptions associated with this work!

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