scholarly journals Relationships Between Several Quadrat-Based Statistical Measures Used to Characterize Spatial Aspects of Disease Incidence Data

2000 ◽  
Vol 90 (6) ◽  
pp. 568-575 ◽  
Author(s):  
M. S. Ridout ◽  
X.-M. Xu
Keyword(s):  
Spatial Autocorrelation ◽  
Binomial Distribution ◽  
Disease Incidence ◽  
Intraclass Correlation ◽  
Quadrat Size ◽  
Incidence Data ◽  
Statistical Measures ◽  
Index Of Dispersion ◽  
Rate Of Increase ◽  
The Mean

This article investigates the relationships between various statistical measures that are used to summarize spatial aspects of disease incidence data. The focus is on quadrat data in which each plant in a quadrat is classified as diseased or healthy. We show that spatial autocorrelation plays a central role via the mean intraclass correlation, ρ, which is defined as the average correlation of the disease status of all pairs of plants within the quadrat. The value of ρ determines the variance of the number of infected plants in the quadrat and, if this variable follows a beta-binomial distribution, the heterogeneity parameter of the beta-binomial distribution is directly related to the mean intraclass correlation. We consider in detail a model in which the spatial autocorrelation depends only on the distance between the plants. For illustration, we consider a specific autocorrelation model that was derived from simulated data. We show that this model leads, approximately, to the binary form of the power law relating the variance of the number of infected plants per quadrat to the mean. Using an approximation technique, we then show how the index of dispersion is related to quadrat size and shape. The index of dispersion increases with quadrat size. The rate of increase is dependent on quadrat shape, but the effect of quadrat shape is small in comparison to the effect of quadrat size. Finally, we note that if the spatial autocorrelation depends on the relative orientation of the plants, as well as the distance between them, there are connections with distance class methods.

scholarly journals Relationships Between Disease Incidence at Two Levels in a Spatial Hierarchy

1997 ◽  
Vol 87 (5) ◽  
pp. 542-550 ◽  
Author(s):  
G. Hughes ◽  
N. McRoberts ◽  
L. V. Madden ◽  
T. R. Gottwald
Keyword(s):  
Binomial Distribution ◽  
Spatial Scales ◽  
Disease Incidence ◽  
Random Pattern ◽  
Data Sets ◽  
Unknown Parameters ◽  
Incidence Data ◽  
Rate Of Increase ◽  
Grape Downy Mildew ◽  
Spatial Hierarchy

Relationships between disease incidence measured at two levels in a spatial hierarchy are derived. These relationships are based on the properties of the binomial distribution, the beta-binomial distribution, and an empirical power-law relationship that relates observed variance to theoretical binomial variance of disease incidence. Data sets for demonstrating and testing these relationships are based on observations of the incidence of grape downy mildew, citrus tristeza, and citrus scab. Disease incidence at the higher of the two scales is shown to be an asymptotic function of incidence at the lower scale, the degree of aggregation at that scale, and the size of the sampling unit. For a random pattern, the relationship between incidence measured at two spatial scales does not depend on any unknown parameters. In that case, an equation for estimating an approximate variance of disease incidence at the lower of the two scales from incidence measurements made at the higher scale is derived for use in the context of sampling. It is further shown that the effect of aggregation of incidence at the lower of the two scales is to reduce the rate of increase of disease incidence at the higher scale.

scholarly journals Spatial Pattern of Strawberry Powdery Mildew (Podosphaera aphanis) and Airborne Inoculum

Plant Disease ◽  
2014 ◽  
Vol 98 (1) ◽  
pp. 43-54 ◽  
Author(s):  
H. Van der Heyden ◽  
M. Lefebvre ◽  
L. Roberge ◽  
L. Brodeur ◽  
O. Carisse
Keyword(s):  
Powdery Mildew ◽  
Power Law ◽  
Negative Binomial Distribution ◽  
Binomial Distribution ◽  
Negative Binomial ◽  
Disease Incidence ◽  
Data Sets ◽  
Incidence Data ◽  
Airborne Inoculum ◽  
The Relationship

The relationship between strawberry powdery mildew and airborne conidium concentration (ACC) of Podosphaera aphanis was studied using data collected from 2006 to 2009 in 15 fields, and spatial pattern was described using 2 years of airborne inoculum and disease incidence data collected in fields planted with the June-bearing strawberry (Fragaria × ananassa) cultivar Jewel. Disease incidence, expressed as the proportion of diseased leaflets, and ACC were monitored in fields divided into 3 × 8 grids containing 24 100 m2 quadrats. Variance-to-mean ratio, index of dispersion, negative binomial distribution, Poisson distribution, and binomial and beta-binomial distributions were used to characterize the level of spatial heterogeneity. The relationship between percent leaf area diseased and daily ACC was linear, while the relationship between ACC and disease incidence followed an exponential growth curve. The V/M ratios were significantly greater than 1 for 100 and 96% of the sampling dates for ACC sampled at 0.35 m from the ground (ACC0.35m) and for ACC sampled at 1.0 m from the ground (ACC1.0m), respectively. For disease incidence, the index of dispersion D was significantly greater than 1 for 79% of the sampling dates. The negative binomial distribution fitted 86% of the data sets for both ACC1.0m and ACC0.35m. For disease incidence data, the beta-binomial distribution provided a good fit of 75% of the data sets. Taylor's power law indicated that, for ACC at both sampling heights, heterogeneity increased with increasing mean ACC, whereas the binary form of the power law suggested that heterogeneity was not dependent on the mean for disease incidence. When the spatial location of each sampling location was taken into account, Spatial Analysis by Distance Indices showed low aggregation indices for both ACCs and disease incidence, and weak association between ACC and disease incidence. Based on these analyses, it was found that the distribution of strawberry powdery mildew was weakly aggregated. Although a higher level of heterogeneity was observed for airborne inoculum, the heterogeneity was low with no distinct foci, suggesting that epidemics are induced by well-distributed inoculum. This low level of heterogeneity allows mean airborne inoculum concentration to be estimated using only one sampler per field with an overall accuracy of at least 0.841. The results obtained in this study could be used to develop a sampling scheme that will improve strawberry powdery mildew risk estimation.

scholarly journals An Effective Sample Size for Predicting Plant Disease Incidence in a Spatial Hierarchy

1999 ◽  
Vol 89 (9) ◽  
pp. 770-781 ◽  
Author(s):  
L. V. Madden ◽  
G. Hughes
Keyword(s):  
Sample Size ◽  
Binomial Distribution ◽  
Disease Incidence ◽  
Simulated Data ◽  
Heterogeneous Data ◽  
Sampling Unit ◽  
Effective Sample Size ◽  
Incidence Data ◽  
Eutypa Dieback ◽  
Number Of Individuals

For aggregated or heterogeneous disease incidence, one can predict the proportion of sampling units diseased at a higher scale (e.g., plants) based on the proportion of diseased individuals and heterogeneity of diseased individuals at a lower scale (e.g., leaves) using a function derived from the beta-binomial distribution. Here, a simple approximation for the beta-binomial-based function is derived. This approximation has a functional form based on the binomial distribution, but with the number of individuals per sampling unit (n) replaced by a parameter (v) that has similar interpretation as, but is not the same as, the effective sample size (ndeff ) often used in survey sampling. The value of v is inversely related to the degree of heterogeneity of disease and generally is intermediate between ndeff and n in magnitude. The choice of v was determined iteratively by finding a parameter value that allowed the zero term (probability that a sampling unit is disease free) of the binomial distribution to equal the zero term of the beta-binomial. The approximation function was successfully tested on observations of Eutypa dieback of grapes collected over several years and with simulated data. Unlike the beta-binomial-based function, the approximation can be rearranged to predict incidence at the lower scale from observed incidence data at the higher scale, making group sampling for heterogeneous data a more practical proposition.

scholarly journals Quantitative and Molecular Epidemiology of Bacterial Blight of Onion in Seed Production Fields

2006 ◽  
Vol 96 (12) ◽  
pp. 1345-1354 ◽  
Author(s):  
L. Humeau ◽  
P. Roumagnac ◽  
Y. Picard ◽  
I. Robène-Soustrade ◽  
F. Chiroleu ◽  
...  
Keyword(s):  
Seed Production ◽  
Binomial Distribution ◽  
Bacterial Blight ◽  
Disease Incidence ◽  
Aflp Analysis ◽  
Data Sets ◽  
Spatial Dependency ◽  
Nested Polymerase Chain Reaction ◽  
Incidence Data ◽  
Polymerase Chain

Onion, a biennial plant species, is threatened by the emerging, seed-borne, and seed-transmitted Xanthomonas axonopodis pv. allii. Bacterial blight epidemics were monitored in seed production fields over two seasons. Temporal disease progress was different between the two seasons, with final incidence ranging from 0.04 to 0.06 in 2003 and from 0.44 to 0.61 in 2004. The number of hours with temperatures above 24°C was the best descriptor for predicting the number of days after inoculation for bacterial blight development on inoculated plants. Fitting the β-binomial distribution and binary power law analysis indicated aggregated patterns of disease incidence data. The β-binomial distribution was superior to the binomial distribution for 97% of the examined data sets. Spatial dependency ranged from 5.9 to 15.2 m, as determined by semivariance analysis. Based on amplified fragment length polymorphism (AFLP) analysis, it was concluded that plots predominantly were infected by the inoculated haplotype. A single other haplotype was identified by AFLP in all plots over the 2 years, and its detection in the field always followed wind-driven rains. X. axonopodis pv. allii-contaminated seed were detected by semiselective isolation and a nested polymerase chain reaction assay at levels up to 0.05% when final disease incidence was 0.61. Contaminated seed originated from both diseased and asymptomatic plants.

scholarly journals Effects of Quadrat Size and Shape, Initial Epidemic Conditions, and Spore Dispersal Gradient on Spatial Statistics of Plant Disease Epidemics

2000 ◽  
Vol 90 (7) ◽  
pp. 738-750 ◽  
Author(s):  
X.-M. Xu ◽  
M. S. Ridout
Keyword(s):  
Power Law ◽  
Initial Conditions ◽  
Disease Incidence ◽  
Intraclass Correlation ◽  
Plant Diseases ◽  
Parameter Estimates ◽  
Spore Dispersal ◽  
Quadrat Size ◽  
Size And Shape ◽  
Highly Correlated

The spatiotemporal spread of plant diseases was simulated using a stochastic model to study the effects of initial conditions (number of plants initially infected and their spatial pattern), spore dispersal gradient, and size and shape of sampling quadrats on statistics describing the spatiotemporal dynamics of epidemics. The spatial spread of disease was simulated using a half-Cauchy distribution with median dispersal distance μ (units of distance). A total of 54 different quadrat types, including 23 distinct sizes ranging from 4 to 144 plants, were used to sample the simulated epidemics. A symmetric form of the binary power law with two parameters (α, β) was fitted to the sampled epidemic data using each of the 54 quadrats for each replicate simulation run. The α and β estimates were highly correlated positively with each other, and their estimates were comparable to those estimated from observed epidemics. Intraclass correlation (κ) was calculated for each quadrat type; κ decreased exponentially with increasing quadrat size. An asymmetric form of the binary power law with three parameters (α 1, β1, β2) was used to relate κ to the disease incidence (p); β1 was highly correlated to β: β1 ≈ β - 1. In general, initial conditions and quadrat size affected α, β, α1, β1, and β2 greatly. The parameter estimates increased as quadrat size increased, and the relationships were described well by a linear regression model on the logarithm of quadrat size with the slope or intercept parameters dependent on initial conditions and μ. Compared with initial conditions and quadrat size, the overall effects of μ and quadrat shape were generally small, although within each quadrat size and initial condition they could be substantial. Quadrat shape had the greatest effect when the quadrat was long and thin. The relationship of the index of dispersion (D) to p and quadrat size was determined from the α and β estimates. D was greatest when p was 0.5 and decreased when p approached 0 or 1. It increased with quadrat size and the rate of the increase was maximum when p was 0.5 and decreased when p approached 0 or 1.

scholarly journals Spatial and Temporal Analyses of Citrus Sudden Death as a Tool to Generate Hypotheses Concerning Its Etiology

2003 ◽  
Vol 93 (4) ◽  
pp. 502-512 ◽  
Author(s):  
Renato B. Bassanezi ◽  
Armando Bergamin Filho ◽  
Lilian Amorim ◽  
Nelson Gimenes-Fernandes ◽  
Tim R. Gottwald ◽  
...  
Keyword(s):  
Sudden Death ◽  
Spatial Autocorrelation ◽  
Sweet Orange ◽  
Disease Incidence ◽  
Intraclass Correlation ◽  
Spatial Association ◽  
Unknown Etiology ◽  
Spatial And Temporal Patterns ◽  
Rangpur Lime ◽  
Area Of Influence

Citrus sudden death (CSD), a new disease of unknown etiology that affects sweet orange grafted on Rangpur lime, was visually monitored for 14 months in 41 groves in Brazil. Ordinary runs analysis of CSD-symptomatic trees indicated a departure from randomness of symptomatic trees status among immediately adjacent trees mainly within rows. The binomial index of dispersion (D) and the intraclass correlation (k) for various quadrat sizes suggested aggregation of CSD-symptomatic trees for almost all plots within the quadrat sizes tested. Estimated parameters of the binary form of Taylor's power law provided an overall measure of aggregation of CSD-symptomatic trees for all quadrat sizes tested. Aggregation in each plot was dependent on disease incidence. Spatial autocorrelation analysis of proximity patterns suggested that aggregation often existed among quadrats of various sizes up to three lag distances; however, significant lag positions discontinuous from main proximity patterns were rare, indicating a lack of spatial association among discrete foci. Some asymmetry was also detected for some spatial autocorrelation proximity patterns, indicating that within-row versus across-row distributions are not necessarily equivalent. These results were interpreted to mean that the cause of the disease was most likely biotic and its dissemination was common within a local area of influence that extended to approximately six trees in all directions, including adjacent trees. Where asymmetry was indicated, this area of influence was somewhat elliptical. Longer-distance patterns were not detected within the confines of the plot sizes tested. Annual rates of CSD progress based on the Gompertz model ranged from 0.37 to 2.02. Numerous similarities were found between the spatial patterns of CSD and Citrus tristeza virus (CTV) described in the literature, both in the presence of the aphid vector, Toxoptera citricida. CSD differs from CTV in that symptoms occur in sweet orange grafted on Rangpur lime. Based on the symptoms of CSD and on its spatial and temporal patterns, our hypothesis is that CSD may be caused by a similar but undescribed pathogen such as a virus and probably vectored by insects such as aphids by similar spatial processes to those affecting CTV.

Adaptation of the Connected Speech Test: Rerecording and Passage Equivalency

2020 ◽  
Vol 29 (2) ◽  
pp. 259-264 ◽  
Author(s):  
Hasan K. Saleh ◽  
Paula Folkeard ◽  
Ewan Macpherson ◽  
Susan Scollie
Keyword(s):  
Speech Perception ◽  
North American ◽  
First Language ◽  
Intraclass Correlation ◽  
Normal Hearing ◽  
Connected Speech ◽  
Internal Reliability ◽  
Speech Test ◽  
The Mean ◽  
Unit Standard

Purpose The original Connected Speech Test (CST; Cox et al., 1987) is a well-regarded and often utilized speech perception test. The aim of this study was to develop a new version of the CST using a neutral North American accent and to assess the use of this updated CST on participants with normal hearing. Method A female English speaker was recruited to read the original CST passages, which were recorded as the new CST stimuli. A study was designed to assess the newly recorded CST passages' equivalence and conduct normalization. The study included 19 Western University students (11 females and eight males) with normal hearing and with English as a first language. Results Raw scores for the 48 tested passages were converted to rationalized arcsine units, and average passage scores more than 1 rationalized arcsine unit standard deviation from the mean were excluded. The internal reliability of the 32 remaining passages was assessed, and the two-way random effects intraclass correlation was .944. Conclusion The aim of our study was to create new CST stimuli with a more general North American accent in order to minimize accent effects on the speech perception scores. The study resulted in 32 passages of equivalent difficulty for listeners with normal hearing.

scholarly journals Pilonidal sinus disease on the rise: a one-third incidence increase in inpatients in 13 years with substantial regional variation in Germany

2021 ◽  
Author(s):  
Christina Oetzmann von Sochaczewski ◽  
Jan Gödeke
Keyword(s):  
Pilonidal Sinus ◽  
Regional Variation ◽  
Disease Incidence ◽  
Age Groups ◽  
Pilonidal Sinus Disease ◽  
National Guidelines ◽  
Reconstructive Procedures ◽  
Sinus Disease ◽  
The Mean ◽  
Almost All

Abstract Purpose Collective evidence from single-centre studies suggests an increasing incidence of pilonidal sinus disease in the last decades, but population-based data is scarce. Methods We analysed administrative case–based principal diagnoses of pilonidal sinus disease and its surgical therapy between 2005 and 2017 in inpatients. Changes were addressed via linear regression. Results The mean rate of inpatient episodes of pilonidal sinus disease per 100,000 men increased from 43 in 2005 to 56 in 2017. In females, the mean rate of inpatient episodes per 100,000 women rose from 14 in 2005 to 18 in 2017. In the whole population, for every case per 100,000 females, there were 3.1 cases per 100,000 males, but the numbers were highly variable between the age groups. There was considerable regional variation within Germany. Rates of inpatient episodes of pilonidal sinus disease were increasing in almost all age groups and both sexes by almost a third. Surgery was dominated by excision of pilonidal sinus without reconstructive procedures, such as flaps, whose share was around 13% of all procedures, despite recommendations of the national guidelines to prefer flap procedures. Conclusion Rates of inpatient episodes of pilonidal sinus disease in Germany rose across almost all age groups and both sexes with relevant regional variation. The underlying causative factors are unknown. Thus, patient-centred research is necessary to explore them. This should also take cases into account that are solely treated office-based in order to obtain a full-spectrum view of pilonidal sinus disease incidence rates.

scholarly journals In vitro validation of Digital Image Analysis Sequence (DIAS) for the assessment of the marginal fit of cement-retained implant-supported experimental crowns

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Aristeidis A. Villias ◽  
Stefanos G. Kourtis ◽  
Hercules C. Karkazis ◽  
Gregory L. Polyzois
Keyword(s):  
Image Analysis ◽  
Digital Image ◽  
Confidence Level ◽  
Digital Image Analysis ◽  
Statistical Tests ◽  
Interobserver Reliability ◽  
Intraclass Correlation ◽  
Marginal Fit ◽  
Smallest Detectable Change ◽  
The Mean

Abstract Background The replica technique with its modifications (negative replica) has been used for the assessment of marginal fit (MF). However, identification of the boundaries between prosthesis, cement, and abutment is challenging. The recently developed Digital Image Analysis Sequence (DIAS) addresses this limitation. Although DIAS is applicable, its reliability has not yet been proven. The purpose of this study was to verify the DIAS as an acceptable method for the quantitative assessment of MF at cemented crowns, by conducting statistical tests of agreement between different examiners. Methods One hundred fifty-one implant-supported experimental crowns were cemented. Equal negative replicas were produced from the assemblies. Each replica was sectioned in six parts, which were photographed under an optical microscope. From the 906 standardized digital photomicrographs (0.65 μm/pixel), 130 were randomly selected for analysis. DIAS included tracing the profile of the crown and the abutment and marking the margin definition points before cementation. Next, the traced and marked outlines were superimposed on each digital image, highlighting the components’ boundaries and enabling MF measurements. One researcher ran the analysis twice and three others once, independently. Five groups of 130 measurements were formed. Intra- and interobserver reliability was evaluated with intraclass correlation coefficient (ICC). Agreement was estimated with the standard error of measurement (SEM), the smallest detectable change at the 95% confidence level (SDC95%), and the Bland and Altman method of limits of agreement (LoA). Results Measured MF ranged between 22.83 and 286.58 pixels. Both the intra- and interobserver reliability were excellent, ICC = 1 at 95% confidence level. The intra- and interobserver SEM and SDC95% were less than 1 and 3 pixels, respectively. The Bland–Altman analysis presented graphically high level of agreement between the mean measurement of the first observer and each of the three other observers’ measurements. Differences between observers were normally distributed. In all three cases, the mean difference was less than 1 pixel and within ± 3 pixels LoA laid at least 95% of differences. T tests of the differences did not reveal any fixed bias (P > .05, not significant). Conclusion The DIAS is an objective and reliable method able to detect and quantify MF at ranges observed in clinical practice.

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