Multi‐syndrome analysis of time series using PCA: A new concept for outbreak investigation

Mohtashemi, Mojdeh; Kleinman, Ken; Yih, W. Katherine

doi:10.1002/sim.2872

Cited by 12 publications

(5 citation statements)

References 18 publications

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“…A related question, but one that we do not address here, is how to combine multiple streams of data, in order to increase detection power and to provide greater situational awareness. Recent statistical methods such as the multivariate Poisson spatial scan [ 2 ], multivariate Bayesian spatial scan [ 3 , 4 ], PANDA [ 5 , 6 ], and multivariate time series analysis [ 7 - 9 ] address this more difficult question, but for simplicity we focus here on the case of spatial outbreak detection using a single data stream.…”

Section: Introductionmentioning

confidence: 99%

An empirical comparison of spatial scan statistics for outbreak detection

Neill

2009

Int J Health Geogr

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Background: The spatial scan statistic is a widely used statistical method for the automatic detection of disease clusters from syndromic data. Recent work in the disease surveillance community has proposed many variants of Kulldorff's original spatial scan statistic, including expectation-based Poisson and Gaussian statistics, and incorporates a variety of time series analysis methods to obtain expected counts. We evaluate the detection performance of twelve variants of spatial scan, using synthetic outbreaks injected into four real-world public health datasets.

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Section: Introductionmentioning

confidence: 99%

An empirical comparison of spatial scan statistics for outbreak detection

Neill

2009

Int J Health Geogr

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“…This agglomeration of cases is necessary since the notion of early detection of outbreaks of a chronic disease, such as TB, with long incubation period [20] requires a longer time scale. This is in contrast to syndromic surveillance of acute infectious disease (such as influenza-like illnesses), where early detection encompasses only hours to days after the start of an outbreak (see [21]–[25]) and the case counts are much greater for this shorter time period. The scan statistic is shown to handle both densities of cases.…”

Section: Methodsmentioning

confidence: 95%

Early Detection of Tuberculosis Outbreaks among the San Francisco Homeless: Trade-Offs Between Spatial Resolution and Temporal Scale

et al. 2007

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BackgroundSan Francisco has the highest rate of tuberculosis (TB) in the U.S. with recurrent outbreaks among the homeless and marginally housed. It has been shown for syndromic data that when exact geographic coordinates of individual patients are used as the spatial base for outbreak detection, higher detection rates and accuracy are achieved compared to when data are aggregated into administrative regions such as zip codes and census tracts. We examine the effect of varying the spatial resolution in the TB data within the San Francisco homeless population on detection sensitivity, timeliness, and the amount of historical data needed to achieve better performance measures.Methods and FindingsWe apply a variation of space-time permutation scan statistic to the TB data in which a patient's location is either represented by its exact coordinates or by the centroid of its census tract. We show that the detection sensitivity and timeliness of the method generally improve when exact locations are used to identify real TB outbreaks. When outbreaks are simulated, while the detection timeliness is consistently improved when exact coordinates are used, the detection sensitivity varies depending on the size of the spatial scanning window and the number of tracts in which cases are simulated. Finally, we show that when exact locations are used, smaller amount of historical data is required for training the model.ConclusionSystematic characterization of the spatio-temporal distribution of TB cases can widely benefit real time surveillance and guide public health investigations of TB outbreaks as to what level of spatial resolution results in improved detection sensitivity and timeliness. Trading higher spatial resolution for better performance is ultimately a tradeoff between maintaining patient confidentiality and improving public health when sharing data. Understanding such tradeoffs is critical to managing the complex interplay between public policy and public health. This study is a step forward in this direction.

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“…To compare the time-series trend of the estimated anthocyanin content, we compiled data on the genotype average of the estimated anthocyanin content from three seasons from two sites into one dataset. Following previous studies analyzing time-series data, we conducted dimension reduction by PCA using the package base and vegan [73][74][75] . To explore the time points that likely influenced the emerging pattern, we plotted the PC1 and PC2 scores over time using the R package tidyverse.…”

Section: Dimension Reduction With Pcamentioning

confidence: 99%

PlantServation: time-series phenotyping using machine learning revealed seasonal pigment fluctuation in diploid and polyploidArabidopsis

Akiyama

Goto²,

Tameshige

et al. 2022

Preprint

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Long-term field monitoring of leaf pigment content is informative for understanding plant responses to environments distinct from regulated chambers, but is impractical by conventional destructive measurements. We developed PlantServation, a method incorporating robust image-acquisition hardware and deep learning-based software to analyze field images, where the plant shape, color, and background vary over months. We estimated the anthocyanin contents of small individuals of four Arabidopsis species using color information and verified the results experimentally. We obtained > 4 million plant images over three field seasons to study anthocyanin fluctuations. We found significant effects of past radiation, coldness, and precipitation on the anthocyanin content in the field. The synthetic allopolyploid A. kamchatica recapitulated the fluctuations of natural polyploids by integrating diploid responses. The data support a long-standing hypothesis stating that allopolyploids can inherit and combine the traits of progenitors. PlantServation pipeline facilitates the study of plant responses to complex environments termed "in natura."

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Multi‐syndrome analysis of time series using PCA: A new concept for outbreak investigation

Cited by 12 publications

References 18 publications

An empirical comparison of spatial scan statistics for outbreak detection

An empirical comparison of spatial scan statistics for outbreak detection

Early Detection of Tuberculosis Outbreaks among the San Francisco Homeless: Trade-Offs Between Spatial Resolution and Temporal Scale

PlantServation: time-series phenotyping using machine learning revealed seasonal pigment fluctuation in diploid and polyploidArabidopsis

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