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Open AccessArticle

Characterization of Pathogen Airborne Inoculum Density by Information Theoretic Analysis of Spore Trap Time Series Data

1
School of Earth, Environmental, and Marine Sciences, University of Texas, Rio Grande Valley, Edinburg, TX 78541, USA
2
Quantitative Biology and Epidemiology Group, Plant Pathology Department, University of California, Davis, Davis, CA 95616, USA
*
Author to whom correspondence should be addressed.
Entropy 2020, 22(12), 1343; https://doi.org/10.3390/e22121343
Received: 10 October 2020 / Revised: 23 November 2020 / Accepted: 23 November 2020 / Published: 27 November 2020
(This article belongs to the Special Issue Applications of Information Theory to Epidemiology)
In a previous study, air sampling using vortex air samplers combined with species-specific amplification of pathogen DNA was carried out over two years in four or five locations in the Salinas Valley of California. The resulting time series data for the abundance of pathogen DNA trapped per day displayed complex dynamics with features of both deterministic (chaotic) and stochastic uncertainty. Methods of nonlinear time series analysis developed for the reconstruction of low dimensional attractors provided new insights into the complexity of pathogen abundance data. In particular, the analyses suggested that the length of time series data that it is practical or cost-effective to collect may limit the ability to definitively classify the uncertainty in the data. Over the two years of the study, five location/year combinations were classified as having stochastic linear dynamics and four were not. Calculation of entropy values for either the number of pathogen DNA copies or for a binary string indicating whether the pathogen abundance data were increasing revealed (1) some robust differences in the dynamics between seasons that were not obvious in the time series data themselves and (2) that the series were almost all at their theoretical maximum entropy value when considered from the simple perspective of whether instantaneous change along the sequence was positive. View Full-Text
Keywords: time series; entropy; average mutual information; stochastic processes; deterministic dynamics time series; entropy; average mutual information; stochastic processes; deterministic dynamics
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MDPI and ACS Style

Choudhury, R.A.; McRoberts, N. Characterization of Pathogen Airborne Inoculum Density by Information Theoretic Analysis of Spore Trap Time Series Data. Entropy 2020, 22, 1343. https://doi.org/10.3390/e22121343

AMA Style

Choudhury RA, McRoberts N. Characterization of Pathogen Airborne Inoculum Density by Information Theoretic Analysis of Spore Trap Time Series Data. Entropy. 2020; 22(12):1343. https://doi.org/10.3390/e22121343

Chicago/Turabian Style

Choudhury, Robin A.; McRoberts, Neil. 2020. "Characterization of Pathogen Airborne Inoculum Density by Information Theoretic Analysis of Spore Trap Time Series Data" Entropy 22, no. 12: 1343. https://doi.org/10.3390/e22121343

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