scholarly journals Effects of Initial Epidemic Conditions, Sporulation Rate, and Spore Dispersal Gradient on the Spatio-Temporal Dynamics of Plant Disease Epidemics

1998 ◽  
Vol 88 (10) ◽  
pp. 1000-1012 ◽  
Author(s):  
X.-M. Xu ◽  
M. S. Ridout
Keyword(s):  
Spatial Pattern ◽  
Power Law ◽  
Temporal Dynamics ◽  
Initial Conditions ◽  
Disease Incidence ◽  
Disease Status ◽  
Spatio Temporal ◽  
Disease Epidemics ◽  
Sporulation Rate ◽  
The Relationship

A stochastic model that simulates the spread of disease over space and time was developed to study the effects of initial epidemic conditions (number of initial inocula and their spatial pattern), sporulation rate, and spore dispersal gradient on the spatio-temporal dynamics of plant disease epidemics. The spatial spread of disease was simulated using a half-Cauchy distribution with median dispersal distance μ (units of distance). The rate of temporal increase in disease incidence (βI, per day) was influenced jointly by μ and by the sporulation rate λ (spores per lesion per day). The relationship between βI and μ was nonlinear: the increase in βI with increasing μ was greatest when μ was small (i.e., when the dispersal gradient was steep). The rate of temporal increase in disease severity of diseased plants (βS) was affected mainly by λ: βS increased directly with increasing λ. Intraclass correlation (κt), the correlation of disease status of plants within quadrats, increased initially with disease incidence, reached a peak, and then declined as disease incidence approached 1.0. This relationship was well described by a power-law model that is consistent with the binary form of the variance power law. The amplitude of the model relating κt to disease incidence was affected mainly by μ: κt decreased with increasing μ. The shape of the curve was affected mainly by initial conditions, especially the spatial pattern of the initial inocula. Generally, the relationship of spatial autocorrelation (ρt,k), the correlation of disease status of plants at various distances apart, to disease incidence and distance was well described by a four-parameter power-law model. ρt,k increased with disease incidence to a maximum and then declined at higher values of disease incidence, in agreement with a power-law relationship. The amplitude of ρt,k was determined mainly by initial conditions and by μ: ρt,k decreased with increasing μ and was lower for regular patterns of initial inocula. The shape of the ρt,k curve was affected mainly by initial conditions, especially the spatial pattern of the initial inocula. At any level of disease incidence, autocorrelation declined exponentially with spatial lag; the degree of this decline was determined mainly by μ: it was steeper with decreasing μ.

scholarly journals Using Higher-Order Constructs to Estimate Health-Disease Status: The Effect of Health System Performance and Sustainability

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1228
Author(s):  
Alicia Ramírez-Orellana ◽  
María del Carmen Valls Martínez ◽  
Mayra Soledad Grasso
Keyword(s):  
Health System ◽  
Social Services ◽  
System Performance ◽  
Structural Equation ◽  
Disease Incidence ◽  
Disease Status ◽  
Well Being ◽  
Equation Modeling ◽  
Health System Performance ◽  
The Relationship

This article aims to provide information to public agencies and policymakers on the determinants of health systems and their relationships that influence citizens’ health–disease status. A total of 61 indicators for each of 17 Spanish autonomous communities were collected from the Spanish Ministry of Health, Social Services, and Equality between 2008 and 2017. The applied technique was partial least squares structural equation modeling (PLS-SEM). Concerning health–disease status, an influence of sustainability and performance on the health system was hypothesized. The findings revealed that health system sustainability had a negative effect on health–disease status, measured in terms of disease incidence. However, the relationship between health system performance and health–disease status is positive. Furthermore, health system performance mediates the relationship between sustainability and health–disease status. According to our study, if we consider the opposite poles that make up the definition of health–disease status (well-being and disease), this concept is defined more by the incidence of the negative aspect.

scholarly journals Sampling for Plant Disease Incidence

1999 ◽  
Vol 89 (11) ◽  
pp. 1088-1103 ◽  
Author(s):  
L. V. Madden ◽  
G. Hughes
Keyword(s):  
Power Law ◽  
Binomial Distribution ◽  
Disease Incidence ◽  
Sequential Sampling ◽  
Cluster Sampling ◽  
Diseased Plant ◽  
Ratio Test ◽  
Wide Range ◽  
Sequential Probability ◽  
The Relationship

Knowledge of the distribution of diseased plant units (such as leaves, plants, or roots) or of the relationship between the variance and mean incidence is essential to efficiently sample for diseased plant units. Cluster sampling, consisting of N sampling units of n individuals each, is needed to determine whether the binomial or beta-binomial distribution describes the data or to estimate parameters of the binary power law for disease incidence. The precision of estimated disease incidence can then be evaluated under a wide range of settings including the hierarchical sampling of groups of individuals, the various levels of spatial heterogeneity of disease, and the situation when all individuals are disease free. Precision, quantified with the standard error or the width of the confidence interval for incidence, is directly related to N and inversely related to the degree of heterogeneity (characterized by the intracluster correlation, ρ). Based on direct estimates of ρ (determined from the θ parameter of the beta-binomial distribution or from the observed variance) or a model predicting ρ as a function of incidence (derived from the binary power law), one can calculate, before a sampling bout, the value of N needed to achieve a desired level of precision. The value of N can also be determined during a sampling bout using sequential sampling methods, either to estimate incidence with desired precision or to test a hypothesis about true disease incidence. In the latter case, the sequential probability ratio test is shown here to be useful for classifying incidence relative to a hypothesized threshold when the data follows the beta-binomial distribution with either a fixed ρ or a ρ that depends on incidence.

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 Analysing river network dynamics and active length - discharge relationship using water presence sensors

2021 ◽  
Author(s):  
Francesca Zanetti ◽  
Nicola Durighetto ◽  
Filippo Vingiani ◽  
Gianluca Botter
Keyword(s):  
Power Law ◽  
Cross Sections ◽  
Temporal Dynamics ◽  
River Network ◽  
Low Flow ◽  
Stream Network ◽  
Entire Study ◽  
Temporary Streams ◽  
Active Length ◽  
Spatio Temporal

Abstract. Despite the importance of temporary streams for the provision of key ecosystem services, their experimental monitoring remains challenging because of the practical difficulties in performing accurate high-frequency surveys of the flowing portion of river networks. In this study, about 30 electrical resistance (ER) sensors were deployed in a high relief 2.6 km2 catchment of the Italian Alps to monitor the spatio-temporal dynamics of the active river network during the fall of 2019. The set-up of the ER sensors was personalized to make them more flexible for the deployment in the field and more accurate under low flow conditions. Available ER data were analyzed, compared to field based estimates of the nodes' persistency and then used to generate a sequence of maps representing the active reaches of the stream network with a sub-daily temporal resolution. This allowed a proper estimate of the joint variations of active river network length (L) and catchment discharge (Q) during the entire study period. Our analysis revealed a high cross-correlation between the statistics of individual ER signals and the flow persistencies of the cross sections where the sensors were placed. The observed spatial and temporal dynamics of the actively flowing channels also revealed the diversity of the hydrological behaviour of distinct zones of the study catchment, which was attributed to differences in the catchment geology and stream-bed composition. The more pronounced responsiveness of the total active length to small precipitation events as compared to the catchment discharge led to important hysteresis in the L vs. Q relationship, thereby impairing the performances of a power-law model frequently used in the literature to relate these two quantities. Consequently, in our study site the adoption of a unique power-law L-Q relationship to infer flowing length variability from observed discharges would underestimate the actual variations of L by 40%. Our work emphasizes the potential of ER sensors for analysing spatio-temporal dynamics of active channels in temporary streams, discussing the major limitations of this type of technology emerging from the specific application presented herein.

scholarly journals Spatio-temporal control of mutualism in legumes helps spread symbiotic nitrogen fixation

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Benoit Daubech ◽  
Philippe Remigi ◽  
Ginaini Doin de Moura ◽  
Marta Marchetti ◽  
Cécile Pouzet ◽  
...  
Keyword(s):  
N2 Fixation ◽  
Temporal Dynamics ◽  
Root Nodules ◽  
Bacterial Species ◽  
Ecological Factors ◽  
Initial Inoculum ◽  
Selective Forces ◽  
Spatio Temporal ◽  
Plant Population Size ◽  
The Relationship

Mutualism is of fundamental importance in ecosystems. Which factors help to keep the relationship mutually beneficial and evolutionarily successful is a central question. We addressed this issue for one of the most significant mutualistic interactions on Earth, which associates plants of the leguminosae family and hundreds of nitrogen (N2)-fixing bacterial species. Here we analyze the spatio-temporal dynamics of fixers and non-fixers along the symbiotic process in the Cupriavidus taiwanensis–Mimosa pudica system. N2-fixing symbionts progressively outcompete isogenic non-fixers within root nodules, where N2-fixation occurs, even when they share the same nodule. Numerical simulations, supported by experimental validation, predict that rare fixers will invade a population dominated by non-fixing bacteria during serial nodulation cycles with a probability that is function of initial inoculum, plant population size and nodulation cycle length. Our findings provide insights into the selective forces and ecological factors that may have driven the spread of the N2-fixation mutualistic trait.

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 Pattern Analysis of Strawberry Leaf Blight in Perennial Production Systems

1999 ◽  
Vol 89 (5) ◽  
pp. 421-433 ◽  
Author(s):  
W. W. Turechek ◽  
L. V. Madden
Keyword(s):  
Spatial Pattern ◽  
Disease Incidence ◽  
Spatial Association ◽  
Disease Status ◽  
Leaf Blight ◽  
Data Sets ◽  
Ratio Test ◽  
Spatial Pattern Analysis ◽  
Test Statistic ◽  
Degree Of Heterogeneity

Spatial pattern of the incidence of strawberry leaf blight, caused by Phomopsis obscurans, was quantified in commercial strawberry fields in Ohio using statistics for heterogeneity and spatial correlation. For each strawberry planting, two transects were randomly chosen and the proportion of leaflets (out of 15) and leaves (out of five) with leaf blight symptoms was determined from N = 49 to 106 (typically 75) evenly spaced sampling units, thus establishing a natural spatial hierarchy to compare patterns of disease. The beta-binomial distribution fitted the data better than the binomial in 92 and 26% of the 121 data sets over 2 years at the leaflet and leaf levels, respectively, based on a likelihood ratio test. Heterogeneity in individual data sets was measured with the index of dispersion (variance ratio), C(α) test, a standard normal-based test statistic, and estimated θ parameter of the beta-binomial. Using these indices, overdispersion was detected in approximately 94 and 36% of the data sets at the leaflet and leaf levels, respectively. Estimates of the slope from the binary power law were significantly (P < 0.01) greater than 1 and estimates of the intercept were significantly greater than 0 (P < 0.01) at both the leaflet and leaf levels for both years, indicating that degree of heterogeneity was a function of incidence. A covariance analysis indicated that cultivar, time, and commercial farm location of sampling had little influence on the degree of heterogeneity. The measures of heterogeneity indicated that there was a positive correlation of disease status of leaflets (or leaves) within sampling units. Measures of spatial association in disease incidence among sampling units were determined based on autocorrelation coefficients, runs analysis, and a new class of tests known as spatial analysis by distance indices (SADIE). In general, from 9 to 22% of the data sets had a significant nonrandom spatial arrangement of disease incidence among sampling units, depending on which test was used. When significant associations existed, the magnitude of the association was small but was about the same for leaflets and leaves. Comparing test results, SADIE analysis was found to be a viable alternative to spatial autocorrelation analysis and has the advantage of being an extension of heterogeneity analysis rather than a separate approach. Collectively, results showed that incidence of Phomopsis leaf blight was primarily characterized by small, loosely aggregated clusters of diseased leaflets, typically confined within the borders of the sampling units.

scholarly journals CONTRIBUTION OF SATELLITE IMAGERY TO THE CHARACTERIZATION OF THE RELATIONSHIP BETWEEN CLIMATE AND PYROLOGICAL VARIABLES OF BUSH FIRES IN THE SAVANNAH ZONE (case of the BOUNKANI REGION)

2021 ◽  
Vol 5 (1) ◽  
pp. 64-72
Author(s):  
Kambire Sie ◽  
Talnan Jean Honore Coulibaly ◽  
Naga Coulibaly ◽  
Issiaka Savane ◽  
Lanciné Droh Gone ◽  
...  
Keyword(s):  
Temporal Dynamics ◽  
Fire Regime ◽  
Pearson Correlation ◽  
Climatic Variables ◽  
Fire Season ◽  
Burned Area ◽  
Fire Control ◽  
Burned Areas ◽  
Spatio Temporal ◽  
The Relationship

The present study was undertaken to characterize the bushfire regime and the climatic factors influencing its propagation in the Bounkani region. Thus, this work analyzes the spatio-temporal dynamics of fires and the relationship between climatic variables and pyrological variables. First, it exploits time series of active fires and burned areas from MODIS Active fires (MCD14ML) and MODIS Burned area (MCD60A1) data for the period from 2000 to 2017. The methodology is based on the evaluation of seasonality and fire occurrences, and on the spatio-temporal evolution of fires. The results obtained indicate that, on average, the fire season occurs between the months of November and March, generally corresponding to the dry season. Also, the number of fires and the area burned follow a decreasing trend during the 17 years of study. The months of December and January recorded the highest peaks of burned areas and fire outbreaks respectively. Finally, the analysis of the dependence between climatic variables and pyrological variables by the Pearson correlation method showed the influence of climatic parameters in the outbreak and spread of bushfires in the study area. Precipitation and relative humidity are the best predictors with a negative influence on fire activity, while the positive predictors remain temperature. These variables directly impact fire regime in general. The results of this study will assist policy makers and managers in decision making for the implementation of fire control strategies.

scholarly journals An Integrated Approach for Spatio-Temporal Cholera Disease Hotspot Relation Mining for Public Health Management in Punjab, Pakistan

2020 ◽  
Vol 17 (11) ◽  
pp. 3763
Author(s):  
Fatima Khalique ◽  
Shoab Ahmed Khan ◽  
Wasi Haider Butt ◽  
Irum Matloob
Keyword(s):  
Public Health ◽  
Temporal Dynamics ◽  
Complex Dynamics ◽  
Health Management ◽  
Disease Incidence ◽  
Integrated Approach ◽  
Geographic Area ◽  
Disease Dynamics ◽  
Spatio Temporal ◽  
Public Health Management

Public health management can generate actionable results when diseases are studied in context with other candidate factors contributing to disease dynamics. In order to fully understand the interdependent relationships of multiple geospatial features involved in disease dynamics, it is important to construct an effective representation model that is able to reveal the relationship patterns and trends. The purpose of this work is to combine disease incidence spatio-temporal data with other features of interest in a mutlivariate spatio-temporal model for investigating characteristic disease and feature patterns over identified hotspots. We present an integrated approach in the form of a disease management model for analyzing spatio-temporal dynamics of disease in connection with other determinants. Our approach aligns spatio-temporal profiles of disease with other driving factors in public health context to identify hotspots and patterns of disease and features of interest in the identified locations. We evaluate our model against cholera disease outbreaks from 2015–2019 in Punjab province of Pakistan. The experimental results showed that the presented model effectively address the complex dynamics of disease incidences in the presence of other features of interest over a geographic area representing populations and sub populations during a given time. The presented methodology provides an effective mechanism for identifying disease hotspots in multiple dimensions and relation between the hotspots for cost-effective and optimal resource allocation as well as a sound reference for further predictive and forecasting analysis.

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