T.Y. Lim
@tylim.bsky.social
#DrugsAndBugs Postdoc at CCDD HSPH, PhD @mitsloan.bsky.social, formerly @YaleEnvironment. Complex systems & simulation modelling for public health. Standard disclaimers apply. @limtseyang in the other (worse) place.
Addendum - image in third post messed up, here's a clean version!
November 20, 2024 at 6:29 PM
Addendum - image in third post messed up, here's a clean version!
Thanks to our collaborators at MGB, Tufts Medical Center, and @countyofla.bsky.social Dept of Public Health for sharing data for this work!
@yhgrad.bsky.social @jameshay.bsky.social
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@yhgrad.bsky.social @jameshay.bsky.social
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November 20, 2024 at 4:31 PM
Thanks to our collaborators at MGB, Tufts Medical Center, and @countyofla.bsky.social Dept of Public Health for sharing data for this work!
@yhgrad.bsky.social @jameshay.bsky.social
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@yhgrad.bsky.social @jameshay.bsky.social
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Simple cubic spline regression models using daily reported Ct value distributions can nowcast epidemic [log] growth rates over two-week time horizons to reasonable accuracy (RMSE ~0.04 – for context, growth rates vary from approx. +/- 0.2)
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November 20, 2024 at 4:31 PM
Simple cubic spline regression models using daily reported Ct value distributions can nowcast epidemic [log] growth rates over two-week time horizons to reasonable accuracy (RMSE ~0.04 – for context, growth rates vary from approx. +/- 0.2)
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So in theory, viral loads / Ct values from routine or surveillance testing could tell us if outbreaks are growing or shrinking, separate from trends in case counts – providing early warning and an alternative source of information
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November 20, 2024 at 4:31 PM
So in theory, viral loads / Ct values from routine or surveillance testing could tell us if outbreaks are growing or shrinking, separate from trends in case counts – providing early warning and an alternative source of information
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