Spatial Stochastic Model Research Articles

What Are the Human Resources Required to Control a Foot-and-Mouth Disease Outbreak in Austria?

Contingency planning allows veterinary authorities to prepare a rapid response in the event of a disease outbreak. A recently published foot-and-mouth disease (FMD) simulation study indicated concerns whether capacity was sufficient to control a potential FMD epidemic in Austria. The objectives of the study presented here were to estimate the human resources required to implement FMD control measures and to identify areas of the operational activities that could potentially delay successful control of the disease. The stochastic spatial simulation model EuFMDiS (The European Foot-and-Mouth Disease Spread Model) was used to simulate a potential FMD outbreak and its economic impact, including different control scenarios based on variations of culling, vaccination, and pre-emptive depopulation. In this context, the utilization of human resources was assessed based on the associated EuFMDiS output regarding the performance of operational activities. The assessments show that the number of personnel needed in an outbreak with a stamping-out policy would reach the peak at the end of the second week of control with a median of 540 (257–926) individuals, out of which 31% would be veterinarians. Approximately 58% of these human resources would be attributable to surveillance, followed by staff for cleaning and disinfection activities. Our analysis demonstrates that, of the operational activities, surveillance personnel were the largest factor influencing the magnitude of the outbreak. The aim of the assessment presented here is to assist veterinary authorities in the contingency planning of required human resources to respond effectively to an outbreak of animal diseases such as FMD.

Transient and Persistent Efficiency and Spatial Spillovers: Evidence from the Portuguese Wine Industry

This paper investigates the presence of spatial spillovers in firms’ productive (in)efficiency. For this purpose, a spatial stochastic frontier model is specified and estimated, accounting for spatial dependence and persistent and transient (in)efficiency. This approach is applied to a panel dataset from 2014 to 2019 of Portuguese wineries. Apart from the traditional input and output quantities used in the estimation of a production function, the novelty of this study is the inclusion of information on the firms’ exact location, which allows incorporating the neighboring dependence in the productive efficiency analysis. Empirical findings show that despite the Portuguese wineries’ technological positive dependence on spatial closeness for both inputs and outputs, the geographic closeness is not strong enough to provide overall productive efficiency gains.

A multiscale compartment-based model of stochastic gene regulatory networks using hitting-time analysis.

Spatial stochastic models of single cell kinetics are capable of capturing both fluctuations in molecular numbers and the spatial dependencies of the key steps of intracellular regulatory networks. The spatial stochastic model can be simulated both on a detailed microscopic level using particle tracking and on a mesoscopic level using the reaction–diffusion master equation. However, despite substantial progress on simulation efficiency for spatial models in the last years, the computational cost quickly becomes prohibitively expensive for tasks that require repeated simulation of thousands or millions of realizations of the model. This limits the use of spatial models in applications such as multicellular simulations, likelihood-free parameter inference, and robustness analysis. Further approximation of the spatial dynamics is needed to accelerate such computational engineering tasks. We here propose a multiscale model where a compartment-based model approximates a detailed spatial stochastic model. The compartment model is constructed via a first-exit time analysis on the spatial model, thus capturing critical spatial aspects of the fine-grained simulations, at a cost close to the simple well-mixed model. We apply the multiscale model to a canonical model of negative-feedback gene regulation, assess its accuracy over a range of parameters, and demonstrate that the approximation can yield substantial speedups for likelihood-free parameter inference.

Advanced InSAR Tropospheric Corrections From Global Atmospheric Models that Incorporate Spatial Stochastic Properties of the Troposphere

AbstractTropospheric delays are still the main error source of satellite‐based Interferometric Synthetic Aperture Radar (InSAR) mapping of Earth’s surface movements. Recent studies have demonstrated the potential of global atmospheric models (GAMs) in reducing InSAR tropospheric delays. However, the importance of appropriate interpolation and weighting strategies in GAM corrections has largely been overlooked. Here we present a new GAM‐based tropospheric correction method that incorporates spatial stochastic models of the troposphere into the weighting strategy of the correction. The method determines the correlation between a pixel of interest and neighboring GAM grid locations (3D) according to the spatial variability of the tropospheric random field, instead of subjectively using an inverse distance method, a local spline function, or other standard interpolation scheme. Also, our new method considers horizontal heterogeneities of the tropospheric field by estimating the integral of the tropospheric delays along the satellite line‐of‐sight (LOS) direction, instead of calculating projected zenith‐delays. The method can be used with any GAM, but we here implement it with the latest ECMWF (European Center for Medium‐Range Weather Forecasts) ERA5 reanalysis products. We validate the new method with hundreds of Sentinel‐1 images from 2015 to 2020 over the island of Hawaii, a location with variable topography, surface conditions, local climate, and deformation, and explore the tropospheric corrections for both interferograms and time‐series analysis products (deformation velocities and time‐series solutions). Compared with other GAM corrections (PyAPS, d‐LOS, and GACOS), our new method yields a larger reduction of the average standard deviation of the corrected interferograms, i.e., from 2.55 to 1.91 cm, instead of 2.47 cm (PyAPS), 2.44 cm (d‐LOS), and 2.10 cm (GACOS). Also, a larger fraction of 87% of the interferograms (243 out of 280) is improved, compared with 52%, 53%, and 66% for the other GAM corrections, respectively. These results demonstrate the importance of considering (1) tropospheric stochastic models in GAM corrections, (2) horizontal heterogeneities when estimating the LOS delays, and (3) tropospheric delays when mapping long‐wavelength or small‐magnitude deformations using InSAR.

Weak species in rock-paper-scissors models

In this letter, we investigate the population dynamics in a May-Leonard formulation of the rock-paper-scissors (RPS) game in which one or two species, which we shall refer to as “weak”, have a reduced predation or reproduction probability. We show that in a non-spatial model the stationary solution where all three species coexist is always unstable, while in a spatial stochastic model coexistence is possible for a wide parameter space. We find that a reduced predation probability results in a significantly higher abundance of “weak” species, in models with either one or two “weak” species, as long as the simulation lattices are sufficiently large for coexistence to prevail. On the other hand, we show that a reduced reproduction probability has a smaller impact on the abundance of “weak” species, generally leading to a slight decrease of its population size —the increase of the population size of one of the “weak” species being more than compensated by the reduction of the other, in the two-species case. We further show that the species abundances in models where both predation and reproduction probabilities are simultaneously reduced may be accurately estimated from the results obtained considering only a reduction of either the predation or the reproduction probability.

Upscaling bimolecular reactive transport in highly heterogeneous porous media with the LAgrangian Transport Eulerian Reaction Spatial (LATERS) Markov model

The LAgrangian Transport Eulerian Reaction Spatial (LATERS) Markov model was developed to predict upscaled bimolecular reactive transport in a flow around an array of solid cylinders. This method combines the stochastic Lagrangian Spatial Markov model (SMM) to predict transport and a volume averaged reaction rate equation to predict reactions of the form $$A+B\rightarrow \emptyset$$ . Here, we extend the LATERS Markov model to upscale bimolecular reactive transport in a Darcy flow through an idealized heterogeneous porous medium. In agreement with previous literature, the accuracy of the prediction is a function of the Damkohler (Da) numbers, i.e., high Da are more challenging because of incomplete mixing. It was found that a key component which must be incorporated into the upscaled model in these high Da systems is the idea that nearby A and B particles should be more likely to react than those that are farther apart. This is here achieved by appropriately reducing the resolution of the spatial grid employed to resolve the reactive process.

Sheep scab transmission: a spatially explicit dynamic metapopulation model

Psoroptic mange (sheep scab), caused by the parasitic mite, Psoroptes ovis, is an important disease of sheep worldwide. It causes chronic animal welfare issues and economic losses. Eradication of scab has proved impossible in many sheep-rearing areas and recent reports of resistance to macrocyclic lactones, a key class of parasiticide, highlight the importance of improving approaches to scab management. To allow this, the current study aimed to develop a stochastic spatial metapopulation model for sheep scab transmission which can be adapted for use in any geographical region, exhibited here using data for Great Britain. The model uses agricultural survey and sheep movement data to geo-reference farms and capture realistic movement patterns. Reported data on sheep scab outbreaks from 1973 to 1991 were used for model fitting with Sequential Monte Carlo Approximate Bayesian Computation methods. The outbreak incidence predicted by the model was from the same statistical distribution as the reported outbreak data ({chi }^{2} = 115.3, p = 1) and the spatial location of sheep scab outbreaks predicted was positively correlated with the observed outbreak data by county (tau = 0.55, p < 0.001), confirming that the model developed is able to accurately capture the number of farms infected in a year, the seasonality of scab incidence and the spatial patterns seen in the data. This model gives insight into the transmission dynamics of sheep scab and will allow the exploration of more effective control strategies.

Bayesian Inference in Spatial Stochastic Volatility Models: An Application to House Price Returns in Chicago*

AbstractIn this study, we propose a spatial stochastic volatility model in which the latent log‐volatility terms follow a spatial autoregressive process. Though there is no spatial correlation in the outcome equation (the mean equation), the spatial autoregressive process defined for the log‐volatility terms introduces spatial dependence in the outcome equation. To introduce a Bayesian Markov chain Monte Carlo (MCMC) estimation algorithm, we transform the model so that the outcome equation takes the form of log‐squared terms. We approximate the distribution of the log‐squared error terms of the outcome equation with a finite mixture of normal distributions so that the transformed model turns into a linear Gaussian state‐space model. Our simulation results indicate that the Bayesian estimator has satisfactory finite sample properties. We investigate the practical usefulness of our proposed model and estimation method by using the price returns of residential properties in the broader Chicago Metropolitan area.

Spatial Dependence Evaluation of Agricultural Technical Efficiency—Based on the Stochastic Frontier and Spatial Econometric Model

In recent years, through the implementation of a series of policies, such as the delimitation of major grain producing areas and the construction of advantageous and characteristic agricultural product areas, the spatial distribution of agriculture in China has changed significantly; however, research on the impact of such changes on the efficiency of agricultural technology is still lacking. Taking 11 cities in Hebei Province as the research object, this study examines the spatial dependence of regional agricultural technical efficiency using the stochastic frontier analysis and spatial econometric analysis. The results show that the improvement in agricultural technical efficiency is evident in all cities in Hebei Province from 2008 to 2017, but there is scope for further improvement. Industrial agglomeration has statistical significance in improving the efficiency of agricultural technology. Further, there is an obvious spatial correlation and difference in agricultural technical efficiency. Optimizing the spatial distribution of agricultural production, promoting the innovation, development, and application of agricultural technology, and promoting the expansion of regional elements can contribute to improving agricultural technical efficiency.

Spatial autoregressive stochastic frontier model with application to Indonesia’s aquaculture

Stochastic frontier analysis (SFA) is one of the popular methods for measuring efficiency and productivity. SFA generally assumes that decision making units are independent. However, this assumption may be inappropriate since decision making units are probable interdependent depending on their spatial proximities. This paper aims to incorporate spatial lag into standard SFA model. The performance of spatial autoregressive stochastic frontier model (SAR-SFA) is compared to standard SFA using simulation and real data. Simulation study is undertaken using various degrees of spatial autocorrelation. Furthermore, SAR-SFA is applied to estimate technical efficiency of aquaculture in Indonesia. Simulation results showed that the SAR-SFA generally produces better performance than the standard SFA. If spatial dependencies are significant, then the estimation of production frontier from standard SFA could be misleading. It affects the distribution of technical efficiency and its ranking. Moran’s I test indicated that Indonesia’s aquaculture has significant positive spatial autocorrelation. If standard SFA is applied to estimate technical efficiency of Indonesia’s aquaculture, then the resulted technical efficiency is implausible. This issue is well handled by the SAR-SFA model. Moreover, SAR-SFA model serves procedure to estimate efficiency spillovers.

Could a rabies incursion spread in the northern Australian dingo population? Development of a spatial stochastic simulation model.

Australia, home to the iconic dingo, is currently free from canine rabies. However northern Australia, including Indigenous communities with large free-roaming domestic dog populations, is at increased risk of rabies incursion from nearby Indonesia. We developed a novel agent-based stochastic spatial rabies spread model to evaluate the potential spread of rabies within the dingo population of the Northern Peninsula Area (NPA) region of northern Australia. The model incorporated spatio-temporal features specific to this host-environment system, including landscape heterogeneity, demographic fluctuations, dispersal movements and dingo ecological parameters—such as home range size and density—derived from NPA field studies. Rabies spread between dingo packs in nearly 60% of simulations. In such situations rabies would affect a median of 22 dingoes (approximately 14% of the population; 2.5–97.5 percentiles: 2–101 dingoes) within the study area which covered 1,131 km2, and spread 0.52 km/week for 191 days. Larger outbreaks occurred in scenarios in which an incursion was introduced during the dry season (vs. wet season), and close to communities (vs. areas with high risk of interaction between dingoes and hunting community dogs). Sensitivity analyses revealed that home range size and duration of infectious clinical period contributed most to the variance of outputs. Although conditions in the NPA would most likely not support a sustained propagation of the disease in the dingo population, due to the predicted number of infected dingoes following a rabies incursion and the proximity of Indigenous communities to dingo habitat, we conclude that the risk for human transmission could be substantial.

Institutional quality and its spatial spillover effects on energy efficiency

Several studies have studied the determinants of energy efficiency. However, the influence of government institutions was mostly ignored as only few studies have provided evidence on the role of institutions in enhancing domestic energy efficiency. In this paper, we extend the previous literature by asking whether neighboring country's institutions could also have an impact on domestic energy efficiency. Thus, with the stochastic frontier approach and spatial econometric model, we investigate the spatial effect of institutional quality on energy efficiency in a panel of 99 countries (economies) for the period 1995–2016. Our results, first confirm the presence of spatial correlations in energy efficiency across countries. Secondly, we discover that the direct positive effect of institutional quality on energy efficiency is such that it overcomes the insignificant indirect negative effect, which suggests a total positive and significant effect. Our results, therefore, indicate that institutional quality matters in energy efficiency improvement and being close to countries with good institutional framework has a positive effect on domestic energy efficiency. Finally, the energy efficiency estimates demonstrate that global energy issue can only be addressed with long-term policies that increases technological progress.

Approximate Bayesian inference for a spatial point process model exhibiting regularity and random aggregation

AbstractIn this article, we propose a doubly stochastic spatial point process model with both aggregation and repulsion. This model combines the ideas behind Strauss processes and log Gaussian Cox processes. The likelihood for this model is not expressible in closed form but it is easy to simulate realizations under the model. We therefore explain how to use approximate Bayesian computation (ABC) to carry out statistical inference for this model. We suggest a method for model validation based on posterior predictions and global envelopes. We illustrate the ABC procedure and model validation approach using both simulated point patterns and a real data example.

Modeling Multi-Frequency Characteristics for Classroom and Hall Scenarios at 2-4, 9-11 and 27-29 GHz Bands

This paper investigates the wideband channel characteristics obtained in a classroom and a hall indoor scenarios at 2-4, 9–11 and 27-29GHz. A virtual uniform circular array (UCA) based channel sounding system was utilized to capture wideband spatial channel characteristics. The propagation parameters of multipath components (MPCs) were estimated by using a high resolution parameter estimation (HRPE) algorithm. The estimated MPCs are further grouped into clusters via a novel clustering identification algorithm based on the KPowerMeans algorithm. The comparison of the composite and cluster-level characteristics at multiple frequency bands in both scenarios is investigated. Moreover, the impact of the indoor room environment, i.e. dimension and furniture, on the propagation channel is also analyzed. The statistics of channel parameters at multiple frequency bands extracted constitute a stochastic clustered spatial channel model.

Yardstick Competition and Spatial Interdependence of Cost Efficiency in Local Governments: Development and Application of an Interpretable Spatial Inefficiency Stochastic Frontier Model

Yardstick Competition and Spatial Interdependence of Cost Efficiency in Local Governments: Development and Application of an Interpretable Spatial Inefficiency Stochastic Frontier Model

A Spatial Durbin Stochastic Frontier Model Introducing Spillover Effects in the Determinants of Firms’ Efficiency

A Spatial Durbin Stochastic Frontier Model Introducing Spillover Effects in the Determinants of Firms’ Efficiency

QoS‐aware stochastic spatial PLS model for analysing secrecy performance under eavesdropping and jamming

Securing wireless communication, being inherently vulnerable to eavesdropping and jamming attacks, becomes more challenging in resource-constrained networks such as internet-of-things. Towards this, physical layer security (PLS) has gained significant attention due to its low complexity. The authors address the issue of random inter-node distances in secrecy analysis and develop a comprehensive quality-of-service (QoS) aware PLS framework for the analysis of both eavesdropping and jamming capabilities of the attacker. The proposed solution covers the spatially stochastic deployment of nodes. The secrecy performance is characterised against both attacks using inter-node distance-based probabilistic distribution functions. The model takes into account the practical limits arising out of underlying QoS requirements, which include the maximum distance between legitimate users driven by transmit power and receiver sensitivity. A novel concept of eavesdropping zone is introduced, and the relative impact of jamming power is investigated. Closed-form expressions for asymptotic secrecy outage probability are derived offering insights into the design of optimal system parameters for desired security level against the attacker's capability of both attacks. The analytical framework, validated by numerical results, establishes that the proposed solution offers a potentially accurate characterisation of the PLS performance and key design perspective from point-of-view of both legitimate user and attacker.

Community context and dispersal stochasticity drive variation in spatial spread.

Dispersal is a key process in shaping species spatial distributions. Species interactions and variation in dispersal probabilities may jointly influence species spatial dynamics. However, many studies examine dispersal as a neutral process, independent of community context or intraspecific variation in dispersal behaviour. Here, we use controlled, replicated communities of two Tribolium species (T. castaneum and T. confusum) to examine how intraspecific variation in dispersal behaviour and community context influence dispersal dynamics in simple experimental landscapes composed of homogeneous habitat patches. We found considerable individual-level variation in dispersal probability that was unrelated to body size variation. Further, the context of dispersal mattered, as T. castaneum dispersal was reduced in two-species communities, while T. confusum dispersal was unaffected by community composition. Incorporating individual-level variation into a two-species stochastic spatial Ricker model, we provide evidence that individual-level variability in dispersal behaviour results in more variable spatial spread than assuming individuals have the same dispersal probability. Further, interspecific competition resulted in more variable spatial spread. The variability in spatial spread observed in our tightly controlled and replicated experimental system and in our stochastic model simulations points to potential fundamental limitations in forecasting species shifting ranges without considering potential interspecific interactions and demographic variability in dispersal behaviour.

Extinction threshold in the spatial stochastic logistic model: space homogeneous case

ABSTRACT We consider the extinction regime in the spatial stochastic logistic model in (a.k.a. Bolker–Pacala–Dieckmann–Law model of spatial populations) using the first-order perturbation beyond the mean-field equation. In space homogeneous case (i.e. when the density is non-spatial and the covariance is translation invariant), we show that the perturbation converges as time tends to infinity; that yields the first-order approximation for the stationary density. Next, we study the critical mortality – the smallest constant death rate which ensures the extinction of the population – as a function of the mean-field scaling parameter . We find the leading term of the asymptotic expansion (as ) of the critical mortality which is apparently different for the cases , d = 2, and d = 1.

River network alteration of C-N-P dynamics in a mesoscale agricultural catchment

The majority of freshwater ecosystems worldwide suffer from eutrophication, particularly because of agriculture-derived nutrient sources. In the European Union, a discrepancy exists between the scale of regulatory assessment and the size of research catchments. The Water Framework Directive sets water quality objectives at the mesoscale (50–500 km2), a scale at which both hillslope and in-stream processes influence carbon (C), nitrogen (N) and phosphorus (P) dynamics. Conversely, research catchments focus on headwaters to investigate hillslope processes while minimising the influence of river processes on C-N-P dynamics. Because hillslope and river processes have common hydro-climatic drivers, the relative influence of each on C-N-P dynamics is difficult to disentangle at the mesoscale. In the present study, we used repeated synoptic sampling throughout the river network of a 300 km2 intensively farmed catchment, spatial stochastic modelling and mass balance calculations to analyse this mesoscale conundrum. The main objective was to quantify how river processes altered C-N-P hydrochemical dynamics in different flow, concentration and temperature conditions. Our results show that flow was the main control of alterations of C-N-P dynamics in the river network, while temperature and source concentration had little or no influence. The influence of river processes peaked during low flow, with up to 50% of dissolved organic carbon (DOC) production, up to 100% of nitrate (NO3) retention and up to 50% of total phosphorus (TP) retention. Despite high percentages of river processes at low flow, their influence on annual loads was low for NO3 (median of −10%) and DOC (median of +25%) but too variable to draw conclusions for TP. Because of the differing river alteration rates among carbon and nutrients, stoichiometric ratios varied greatly from headwaters to the outlet, especially during the eutrophication-sensitive low-flow season.