About: Lies, Gosh Darn Lies, and Not Enough Good Statistics: Why Epidemic Model Parameter Estimation Fails

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Lies, Gosh Darn Lies, and Not Enough Good Statistics: Why Epidemic Model Parameter Estimation Fails
Creator	Zalloua, Pierre Parida, Laxmi Platt, Daniel
source	MedRxiv
abstract	An opportunity exists in exploring epidemic modeling as a novel way to determine physiological and demic parameters for genetic association studies on a population/environmental (quasi) epidemiological study level. First, the spread of SARS-COV-2 has produced population specific lineages; second, epidemic spread model parameters are tied directly to these physiological and demic rates (e. g. incubation time, recovery time, transmission rate); and third, these parameters may serve as novel phenotypes to associate with region-specific genetic mutations as well as demic characteristics (e. g. age structure, cultural observance of personal space, crowdedness). Therefore, we sought to understand whether the parameters of epidemic models could be determined from the trajectory of infections, recovery, and hospitalizations prior to peak, and also to evaluate the quality and comparability of data between jurisdictions reporting their statistics necessary for the analysis of model parameters across populations. We found that, analytically, the pre-peak growth of an epidemic is limited by a subset of the model variates, and that the rate limiting variables are dominated by the expanding eigenmode of their equations. The variates quickly converge to the ratio of eigenvector components of the positive growth rate, which determines the doubling time. There are 9 parameters and 4 independent components in the eigenmode, leaving 5 undetermined parameters. Those parameters can be strikingly population dependent, and can have significant impact on estimates of hospital loads downstream. Without a sound framework, measurements of infection rates and other parameters are highly corrupted by uneven testing rates to uneven counting and reporting of relevant values. From the standpoint of phenotype parameters, this means that structured experiments must be performed to estimate these parameters in order to perform genetic association studies, or to construct viable models that accurately predict critical quantities such as hospitalization loads.
has issue date	2020-04-21(xsd:dateTime)
bibo:doi	10.1101/2020.04.20.20071928
has license	medrxiv
sha1sum (hex)	c1e789ce7be399ec9901b0ee90a9dcbe2c77e80b
schema:url	https://doi.org/10.1101/2020.04.20.20071928
resource representing a document's title	Lies, Gosh Darn Lies, and Not Enough Good Statistics: Why Epidemic Model Parameter Estimation Fails
resource representing a document's body	covid:c1e789ce7be399ec9901b0ee90a9dcbe2c77e80b#body_text
is schema:about of	named entity 'epidemic' named entity 'exists' named entity 'models' named entity 'performed' named entity 'time' »more»

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