Variation explained by the principal components obtained via sparse PCA of the 79 risk factors used to model population-normalized case-counts in the counties of New Mexico.

<p>We see that 12 principal components can cover 95% of the variations observed in the risk-factors.</p>

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Bibliographic Details
Main Author:	Cosmin Safta (11105442) (author)
Other Authors:	Jaideep Ray (11105439) (author), Wyatt Bridgman (21865902) (author)
Published:	2025
Subjects:	Biotechnology Ecology Infectious Diseases Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified three adjacent counties simple anomaly detector anomaly detector based accommodate multiple units various areal units model parameters condition calibrated epidemic model single areal unit div >< p detecting outbreaks using data comprises time areal unit epidemiological model symptomatic patients results suggest relies solely rates outperforms posterior distribution originally designed new mexico high variance framed within findings show field estimation field </ epidemiological dynamics count data conventional algorithm bayesian context
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