198 research outputs found
Density Estimation of Spatio-Temporal Point Patterns Using Moran’s Statistics
Moran’s Index is a statistic that measures spatial autocorrelation, quantifying the degree of dispersion (or spread) of objects in space. When investigating data in an area, a single Moran statistic may not give a sufficient summary of the autocorrelation spread. However, by partitioning the area and taking the Moran statistic of each subarea, we discover patterns of the local neighbors not otherwise apparent. In this paper, we consider the model of the spread of an infectious disease, incorporate time factor, and simulate a multilevel Poisson process where the dependence among the levels is captured by the rate of increase of the disease spread over time, steered by a common factor in the scale. The main consequence of our results is that our Moran statistic is calculated from an explicit algorithm in a Monte Carlo simulation setting. Results are compared to Geary’s statistic and estimates of parameters under Poisson process are given
Current practices in cancer spatial data analysis: a call for guidance
There has long been a recognition that place matters in health, from recognition of clusters of yellow fever and cholera in the 1800s to modern day analyses of regional and neighborhood effects on cancer patterns. Here we provide a summary of discussions about current practices in the spatial analysis of georeferenced cancer data by a panel of experts recently convened at the National Cancer Institute
A Design and Analytic Strategy for Monitoring Disease Positivity and Case Characteristics in Accessible Closed Populations
We propose a monitoring strategy for efficient and robust estimation of
disease prevalence and case numbers within closed and enumerated populations
such as schools, workplaces, or retirement communities. The proposed design
relies largely on voluntary testing, notoriously biased (e.g., in the case of
COVID-19) due to non-representative sampling. The approach yields unbiased and
comparatively precise estimates with no assumptions about factors underlying
selection of individuals for voluntary testing, building on the strength of
what can be a small random sampling component. This component unlocks a
previously proposed "anchor stream" estimator, a well-calibrated alternative to
classical capture-recapture (CRC) estimators based on two data streams. We show
here that this estimator is equivalent to a direct standardization based on
"capture", i.e., selection (or not) by the voluntary testing program, made
possible by means of a key parameter identified by design. This equivalency
simultaneously allows for novel two-stream CRC-like estimation of general means
(e.g., of continuous variables such as antibody levels or biomarkers). For
inference, we propose adaptations of a Bayesian credible interval when
estimating case counts and bootstrapping when estimating means of continuous
variables. We use simulations to demonstrate significant precision benefits
relative to random sampling alone
Enhanced Inference for Finite Population Sampling-Based Prevalence Estimation with Misclassification Errors
Epidemiologic screening programs often make use of tests with small, but
non-zero probabilities of misdiagnosis. In this article, we assume the target
population is finite with a fixed number of true cases, and that we apply an
imperfect test with known sensitivity and specificity to a sample of
individuals from the population. In this setting, we propose an enhanced
inferential approach for use in conjunction with sampling-based bias-corrected
prevalence estimation. While ignoring the finite nature of the population can
yield markedly conservative estimates, direct application of a standard finite
population correction (FPC) conversely leads to underestimation of variance. We
uncover a way to leverage the typical FPC indirectly toward valid statistical
inference. In particular, we derive a readily estimable extra variance
component induced by misclassification in this specific but arguably common
diagnostic testing scenario. Our approach yields a standard error estimate that
properly captures the sampling variability of the usual bias-corrected maximum
likelihood estimator of disease prevalence. Finally, we develop an adapted
Bayesian credible interval for the true prevalence that offers improved
frequentist properties (i.e., coverage and width) relative to a Wald-type
confidence interval. We report the simulation results to demonstrate the
enhanced performance of the proposed inferential methods
New Approaches to Model Simulated Spatio-Temporal Moran\u27s Index
The Moran\u27s index is a statistic that measures spatial autocorrelation; it quantifies the degree of dispersion (or clustering) of objects in space. However, when investigating data over a general area, a single global Moran statistic may not give a sufficient summary of the spread, behavior, features or latent surfaces shared by neighboring areas; rather, by partitioning the area and taking the Moran statistic of each divided subareas, we can discover patterns of the local neighbors not otherwise apparent. In this paper, we present a simulation experiment where the local Moran values are computed and a time variable is added to a spatial Poisson point process. Changes in the Moran statistics over the neighboring areas are investigated and ideas on how to perform the analysis are proposed
A Bayesian Downscaler Model to Estimate Daily PM2.5 levels in the Continental US
There has been growing interest in extending the coverage of ground PM2.5
monitoring networks based on satellite remote sensing data. With broad spatial
and temporal coverage, satellite based monitoring network has a strong
potential to complement the ground monitor system in terms of the
spatial-temporal availability of the air quality data. However, most existing
calibration models focused on a relatively small spatial domain and cannot be
generalized to national-wise study. In this paper, we proposed a statistically
reliable and interpretable national modeling framework based on Bayesian
downscaling methods with the application to the calibration of the daily ground
PM2.5 concentrations across the Continental U.S. using satellite-retrieved
aerosol optical depth (AOD) and other ancillary predictors in 2011. Our
approach flexibly models the PM2.5 versus AOD and the potential related
geographical factors varying across the climate regions and yields spatial and
temporal specific parameters to enhance the model interpretability. Moreover,
our model accurately predicted the national PM2.5 with a R2 at 70% and
generates reliable annual and seasonal PM2.5 concentration maps with its SD.
Overall, this modeling framework can be applied to the national scale PM2.5
exposure assessments and also quantify the prediction errors.Comment: 14 pages, 6 figure
Tailoring Capture-Recapture Methods to Estimate Registry-Based Case Counts Based on Error-Prone Diagnostic Signals
Surveillance research is of great importance for effective and efficient
epidemiological monitoring of case counts and disease prevalence. Taking
specific motivation from ongoing efforts to identify recurrent cases based on
the Georgia Cancer Registry, we extend recently proposed "anchor stream"
sampling design and estimation methodology. Our approach offers a more
efficient and defensible alternative to traditional capture-recapture (CRC)
methods by leveraging a relatively small random sample of participants whose
recurrence status is obtained through a principled application of medical
records abstraction. This sample is combined with one or more existing
signaling data streams, which may yield data based on arbitrarily
non-representative subsets of the full registry population. The key extension
developed here accounts for the common problem of false positive or negative
diagnostic signals from the existing data stream(s). In particular, we show
that the design only requires documentation of positive signals in these
non-anchor surveillance streams, and permits valid estimation of the true case
count based on an estimable positive predictive value (PPV) parameter. We
borrow ideas from the multiple imputation paradigm to provide accompanying
standard errors, and develop an adapted Bayesian credible interval approach
that yields favorable frequentist coverage properties. We demonstrate the
benefits of the proposed methods through simulation studies, and provide a data
example targeting estimation of the breast cancer recurrence case count among
Metro Atlanta area patients from the Georgia Cancer Registry-based Cancer
Recurrence Information and Surveillance Program (CRISP) database
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