Time Series Methods and Repeated Sample Surveys

<p>When applied to a sequence of repeated surveys, the traditional sample survey estimators of means or totals for one time period only, fail to take advantage of any time series structure. Such structure may result from correlation between successive responses for resampled individuals, or from time series properties in the parameters of interest. Historically, the initial published papers on time series improvement of repeated sample survey estimates allowed only the first possibility, treating the sum over the population of the individual responses as fixed; individual responses were seen as having stochastic properties only with respect to the sampling scheme. The alternative and later development allowed that both individual responses and their sum have stochastic properties with respect to a superpopulation from which the population of individual responses are drawn. Superpopulations allowed the application of mainstream time series techniques, including signal extraction and stochastic least squares, to repeated sample survey data. These developments in their historical perspective are the topic of Chapter 1. Superpopulation models may also be applied to sample surveys from a single time period, and superpopulation and design properties of the one period linear non-homogeneous sample survey estimator form the topic of Chapter 2; this estimator is sufficiently general to subsume almost all single period non-informative sample survey estimators, and Chapter 2 allows systematisation of a wide range of previously disparate results. This linear estimator may also be extended beyond one time period to include the known estimators for repeated surveys, and this topic, together with a consideration of the effects of data agqregation on non-stochastic and stochastic least squares, is the subject of Chapter 3. Given the central role of the general linear model, and the time series nature of repeated surveys, projection and parameter updating formulae for linear models should form an integral part of repeated survey analysis. The correlation of sample survey errors however, invalidates the formulae appropriate to the known iid error case, and Chapters 4 and 5 develop the general formulae to allow correlated error structure. Chapter 4 considers parameter vectors of fixed length, as for example, for polynomial models, and provides formulae for estimating the length of the parameter vector, and for calculating independent recursive residuals and cusums when further data are added to the model. Chapter 5 considers updating and projection formulae in a wider context, and allows that the parameter vector may be stochastic or non-stochastic and that its length may increase with additional data; it consequently provides a general extension of the Kalman filter to the case of coloured noise over time. The paucity of suitable data has limited data analysis to that contained in Chapter 6, where a simulation study and an analysis of medical data gauge the efficacy of polynomial models in time with multiple observations per time point and autocorrelated errors. The formulae of Chapter 4 allow testing for the constancy of the regression relationships over time. The appendix details SAS computer programs for fitting the polynomial models of Chapter 6.</p>

Time Series Methods and Repeated Sample Surveys

<p>When applied to a sequence of repeated surveys, the traditional sample survey estimators of means or totals for one time period only, fail to take advantage of any time series structure. Such structure may result from correlation between successive responses for resampled individuals, or from time series properties in the parameters of interest. Historically, the initial published papers on time series improvement of repeated sample survey estimates allowed only the first possibility, treating the sum over the population of the individual responses as fixed; individual responses were seen as having stochastic properties only with respect to the sampling scheme. The alternative and later development allowed that both individual responses and their sum have stochastic properties with respect to a superpopulation from which the population of individual responses are drawn. Superpopulations allowed the application of mainstream time series techniques, including signal extraction and stochastic least squares, to repeated sample survey data. These developments in their historical perspective are the topic of Chapter 1. Superpopulation models may also be applied to sample surveys from a single time period, and superpopulation and design properties of the one period linear non-homogeneous sample survey estimator form the topic of Chapter 2; this estimator is sufficiently general to subsume almost all single period non-informative sample survey estimators, and Chapter 2 allows systematisation of a wide range of previously disparate results. This linear estimator may also be extended beyond one time period to include the known estimators for repeated surveys, and this topic, together with a consideration of the effects of data agqregation on non-stochastic and stochastic least squares, is the subject of Chapter 3. Given the central role of the general linear model, and the time series nature of repeated surveys, projection and parameter updating formulae for linear models should form an integral part of repeated survey analysis. The correlation of sample survey errors however, invalidates the formulae appropriate to the known iid error case, and Chapters 4 and 5 develop the general formulae to allow correlated error structure. Chapter 4 considers parameter vectors of fixed length, as for example, for polynomial models, and provides formulae for estimating the length of the parameter vector, and for calculating independent recursive residuals and cusums when further data are added to the model. Chapter 5 considers updating and projection formulae in a wider context, and allows that the parameter vector may be stochastic or non-stochastic and that its length may increase with additional data; it consequently provides a general extension of the Kalman filter to the case of coloured noise over time. The paucity of suitable data has limited data analysis to that contained in Chapter 6, where a simulation study and an analysis of medical data gauge the efficacy of polynomial models in time with multiple observations per time point and autocorrelated errors. The formulae of Chapter 4 allow testing for the constancy of the regression relationships over time. The appendix details SAS computer programs for fitting the polynomial models of Chapter 6.</p>

Impact of repeated nasal sampling on detection and quantification of SARS-CoV-2

The impact of repeated sample collection on COVID-19 test performance is unknown. The FDA and CDC currently recommend the primary collection of diagnostic samples to minimize the perceived risk of false-negative findings. We therefore evaluated the association between repeated sample collection and test performance among 325 symptomatic patients undergoing COVID-19 testing in Atlanta, GA. High concordance was found between consecutively collected mid-turbinate samples with both molecular (n = 74, 100% concordance) and antigen-based (n = 147, 97% concordance, kappa = 0.95, CI = 0.88–1.00) diagnostic assays. Repeated sample collection does not decrease COVID-19 test performance, demonstrating that multiple samples can be collected for assay validation and clinical diagnosis.

As the Pandemic Progresses, How Does Willingness to Vaccinate against COVID-19 Evolve?

Controversy around the safety and efficacy of COVID-19 vaccines may lead to low vaccination rates. Survey data were collected in April and August 2020 from a total of 2343 Australian adults. A quarter (n = 575, 24%) completed both surveys. A generalized linear mixed model analysis was conducted to determine whether willingness to vaccinate changed in the repeated sample, and a multinominal logistic regression was conducted in all participants to determine whether willingness to vaccinate was associated with demographics, chronic disease, or media use. Willingness to vaccinate slightly decreased between April (87%) and August (85%) but this was not significant. Willingness to vaccinate was lower in people with a certificate or diploma (79%) compared to those with a Bachelor degree (87%), p < 0.01 and lower in infrequent users of traditional media (78%) compared to frequent users of traditional media (89%), p < 0.001. Women were more likely to be unsure if they would be willing to vaccinate (10%) compared to men (7%), p < 0.01. There were no associations between willingness to vaccinate and age, chronic disease, or social media use. Promotion of a COVID-19 vaccine should consider targeting women, and people with a certificate or diploma, via non-traditional media channels.

Dimethyl 3,3′-dithiobispropionimidate-functionalized diatomaceous earth particles for efficient biomolecule separation

The early diagnosis and monitoring of cancers are key factors in effective cancer treatment. Particularly, the separation of biomolecules is an essential step for both diagnostic and analytical purposes. However, the current techniques used to isolate biomolecules are intensive, laborious, and require multiple instruments as well as repeated sample preparations to separate each biomolecule. Thus, an efficient separation system that can simultaneously separate biomolecules from scarce samples is highly desirable. Hence, in this study, we developed a biosilica-based syringe filtration system for the efficient separation of biomolecules from cancer samples using amine-modified diatomaceous earth (AD) with dimethyl 3,3′-dithiobispropionimidate (DTBP). The syringe filter can be an efficient and rapid tool for use in various procedures without complex instruments. The DTBP-based AD system was combined with the syringe filter system for nucleic acid and protein separation from various cancer cells. We demonstrated the efficacy of the DTBP-based AD in a single-filter system for the efficient separation of DNA and proteins within 40 min. This DTBP-based AD syringe filter system showed good rapidity, efficiency, and affordability in the separation of biomolecules from single samples for the early diagnosis and clinical analysis of cancers.

Streaming of repeated noise in primary and secondary fields of auditory cortex

Statistical regularities in natural sounds facilitate the perceptual segregation of auditory sources, or streams. Repetition is one cue that drives stream segregation in humans, but the neural basis of this perceptual phenomenon remains unknown. We demonstrated a similar perceptual ability in animals by training ferrets to detect a stream of repeating noise samples (foreground) embedded in a stream of random samples (background). During passive listening, we recorded neural activity in primary (A1) and secondary (PEG) fields of auditory cortex. We used two context-dependent encoding models to test for evidence of streaming of the repeating stimulus. The first was based on average evoked activity per noise sample and the second on the spectro-temporal receptive field (STRF). Both approaches tested whether changes in the neural response to repeating versus random stimuli were better modeled by scaling the response to both streams equally (global gain) or by separately scaling the response to the foreground versus background stream (stream-specific gain). Consistent with previous observations of adaptation, we found an overall reduction in global gain when the stimulus began to repeat. However, when we measured stream-specific changes in gain, responses to the foreground were enhanced relative to the background. This enhancement was stronger in PEG than A1. In A1, enhancement was strongest in units with low sparseness (i.e., broad sensory tuning) and with tuning selective for the repeated sample. Enhancement of responses to the foreground relative to the background provides evidence for stream segregation that emerges in A1 and is refined in PEG.

Added insight from image-based wettability characterization

Microtomographic rock and fluid imaging under in-situ conditions is applied for reservoir wettability characterization. The investigation entails careful sample preparation and cleaning of mini-plugs, operation with reservoir fluids, wettability restoration, centrifuge wettability testing cycles, repeated sample scanning and image analysis, parametrization of wettability and digital rocks simulation for input into reservoir modeling. The results are compared to conventional Amott testing performed in core laboratories. Determination of saturations from image analysis, instead of centrifuge production, allows the use of stock tank crude, rather than exchanged mineral oil. Doping of the synthetic formation water (here with 1 M sodium iodide) was applied for enhancement of the X-ray contrast. The digital imaging workflow offers insight on the liquid distributions from the plug scale down to the pore-scale, linked to applied pressure gradients and resulting pore fluid occupancies in the sequence of displacement states. An example is given with the investigation of a North-German oil field, where the image-based workflow led to a revised view of the reservoir conditions for spontaneous imbibition and drainage, and the overall wetting behavior.