Geospatial modeling of pre-intervention prevalence of Onchocerca volvulus infection in Ethiopia as an aid to onchocerciasis elimination

Background Onchocerciasis is a neglected tropical and filarial disease transmitted by the bites of blackflies, causing blindness and severe skin lesions. The change in focus for onchocerciasis management from control to elimination requires thorough mapping of pre-control endemicity to identify areas requiring interventions and to monitor progress. Onchocerca volvulus infection prevalence in sub-Saharan Africa is spatially continuous and heterogeneous, and highly endemic areas may contribute to transmission in areas of low endemicity or vice-versa. Ethiopia is one such onchocerciasis-endemic country with heterogeneous O. volvulus infection prevalence, and many districts are still unmapped despite their potential for O. volvulus infection transmission. Methodology/Principle findings A Bayesian geostatistical model was fitted for retrospective pre-intervention nodule prevalence data collected from 916 unique sites and 35,077 people across Ethiopia. We used multiple environmental, socio-demographic, and climate variables to estimate the pre-intervention prevalence of O. volvulus infection across Ethiopia and to explore their relationship with prevalence. Prevalence was high in southern and northwestern Ethiopia and low in Ethiopia's central and eastern parts. Distance to the nearest river (-0.015, 95% BCI: -0.025 - -0.005), precipitation seasonality (-0.017, 95% BCI: -0.032 - -0.001), and flow accumulation (-0.042, 95% BCI: -0.07 - -0.019) were negatively associated with O. volvulus infection prevalence, while soil moisture (0.0216, 95% BCI: 0.014 - 0.03) was positively associated. Conclusions/Significance Infection distribution was correlated with habitat suitability for vector breeding and associated biting behavior. The modeled pre-intervention prevalence can be used as a guide for determining priority for intervention in regions of Ethiopia that are currently unmapped, most of which have comparatively low infection prevalence.

Organic carbon in atmospheric precipitation in the urbanized territory of the South of Western Siberia, Russia

This paper presents the results of studying the contents of total (TOC) and dissolved (DOC) organic carbon in atmospheric precipitation and their deposition fluxes on the territory of the city of Barnaul. Samples of atmospheric precipitation (rain and snow) were collected from May 2016 to December 2020 in the city center, additionally at the end of winter 2018–2019 samples of snow cover were taken in the territory of the city and its environs. The studies showed a significant content of organic carbon (OC) in atmospheric precipitation: the weighted average concentrations for the study period were 7.2 ± 0.6 and 4.2 ± 0.4 mg/L for TOC and DOC, respectively. The annual flux of OC deposition with atmospheric precipitation on the territory of Barnaul over the past three years has varied within 2.4–3.9 t/km2 for TOC and 1.4–2.1 t/km2 for DOC. To visualize the spatial distribution of organic matter over the territory of Barnaul, simple kriging was used, implemented in the Geostatistical Analyst module (ArcGIS® Desktop). The flow of organic carbon input into the snow cover during the winter period was used as data for the geostatistical model. According to the model, the deposition of OC from the atmosphere occurs unevenly throughout the urban area and depends on the location and intensity of pollution sources.

Spatial distribution and risk factors for human cysticercosis in Colombia

Background Cysticercosis is a zoonotic neglected tropical disease (NTD) that affects humans and pigs following the ingestion of Taenia solium eggs. Human cysticercosis poses a substantial public health burden in endemic countries. The World Health Organization (WHO) aims to target high-endemicity settings with enhanced interventions in 17 countries by 2030. Between 2008 and 2010, Colombia undertook a national baseline serosurvey of unprecedented scale, which led to an estimated seroprevalence of T. solium cysticercus antibodies among the general population of 8.6%. Here, we use contemporary geostatistical approaches to analyse this unique dataset with the aim of understanding the spatial distribution and risk factors associated with human cysticercosis in Colombia to inform how best to target intervention strategies. Methods We used a geostatistical model to estimate individual and household risk factors associated with seropositivity to T. solium cysticercus antibodies from 29,253 people from 133 municipalities in Colombia. We used both independent and spatially structured random effects at neighbourhood/village and municipality levels to account for potential clustering of exposure to T. solium. We present estimates of the distribution and residual correlation of seropositivity at the municipality level. Results High seroprevalence was identified in municipalities located in the north and south of Colombia, with spatial correlation in seropositivity estimated up to approximately 140 km. Statistically significant risk factors associated with seropositivity to T. solium cysticercus were related to age, sex, educational level, socioeconomic status, use of rainwater, consumption of partially cooked/raw pork meat and possession of dogs. Conclusions In Colombia, the distribution of human cysticercosis is influenced by socioeconomic considerations, education and environmental factors related to the spread of T. solium eggs. This information can be used to tailor national intervention strategies, such as targeting spatial hotspots and more highly exposed groups, including displaced people and women. Large-scale seroprevalence surveys accompanied by geospatial mapping are an essential step towards reaching the WHO’s 2021‒2030 NTD roadmap targets. Graphical Abstract

Comprehensive evaluation of soil quality in a desert steppe influenced by industrial activities in northern China

Desert steppe soil security issues have been the focus of attention. Therefore, to understand the impact of industrial activities on the soil quality of desert grasslands, this experiment investigated the Gaoshawo Industrial Concentration Zone in Yanchi County. Based on the distance and direction from the industrial park, sample plots were established at intervals of 1–2 km. A total of 82 surface soil samples (0–20 cm) representing different pollution sources were collected. The samples were analysed for pH, total nitrogen, total phosphorus, available phosphorus, available potassium, organic matter, copper (Cu), cadmium (Cd), chromium (Cr), lead (Pb), and zinc (Zn). The desert steppe soil quality was analysed based on the integrated fertility index (IFI) and the Nemerow pollution index (PN), followed by the calculation of the comprehensive soil quality index (SQI), which considers the most suitable soil quality indicators through a geostatistical model. The results showed that the IFI was 0.393, indicating that the soil fertility was relatively poor. Excluding the available potassium, the nugget coefficients of the fertility indicators were less than 25% and showed strong spatial autocorrelation. The average values of Cu, Cd, Cr, Pb and Zn were 21.64 ± 3.26, 0.18 ± 0.02, 44.99 ± 21.23, 87.18 ± 25.84, and 86.63 ± 24.98 mg·kg−1, respectively; the nugget coefficients of Cr, Pb and Zn were 30.79–47.35%. Pb was the main element causing heavy metal pollution in the study area. Higher PN values were concentrated north of the highway in the study area, resulting in lower soil quality in the northern region and a trend of decreasing soil quality from south to north. The results of this research showed that the average SQI was 0.351 and the soil quality was extremely low. Thus, industrial activities and transportation activities in the Gaoshawo Industrial Zone significantly impact the desert steppe soil quality index.

Spatio-temporal modelling for non-stationary point referenced data

<p><b>Spatial and spatio-temporal phenomena are commonly modelled as Gaussian processes via the geostatistical model (Gelfand & Banerjee, 2017). In the geostatistical model the spatial dependence structure is modelled using covariance functions. Most commonly, the covariance functions impose an assumption of spatial stationarity on the process. That means the covariance between observations at particular locations depends only on the distance between the locations (Banerjee et al., 2014). It has been widely recognized that most, if not all, processes manifest spatially nonstationary covariance structure Sampson (2014). If the study domain is small in area or there is not enough data to justify more complicated nonstationary approaches, then stationarity may be assumed for the sake of mathematical convenience (Fouedjio, 2017). However, relationships between variables can vary significantly over space, and a ‘global’ estimate of the relationships may obscure interesting geographical phenomena (Brunsdon et al., 1996; Fouedjio, 2017; Sampson & Guttorp, 1992). </b></p> <p>In this thesis, we considered three non-parametric approaches to flexibly account for non-stationarity in both spatial and spatio-temporal processes. First, we proposed partitioning the spatial domain into sub-regions using the K-means clustering algorithm based on a set of appropriate geographic features. This allowed for fitting separate stationary covariance functions to the smaller sub-regions to account for local differences in covariance across the study region. Secondly, we extended the concept of covariance network regression to model the covariance matrix of both spatial and spatio-temporal processes. The resulting covariance estimates were found to be more flexible in accounting for spatial autocorrelation than standard stationary approaches. The third approach involved geographic random forest methodology using a neighbourhood structure for each location constructed through clustering. We found that clustering based on geographic measures such as longitude and latitude ensured that observations that were too far away to have any influence on the observations near the locations where a local random forest was fitted were not selected to form the neighbourhood. </p> <p>In addition to developing flexible methods to account for non-stationarity, we developed a pivotal discrepancy measure approach for goodness-of-fit testing of spatio-temporal geostatistical models. We found that partitioning the pivotal discrepancy measures increased the power of the test.</p>

Spatio-temporal modelling for non-stationary point referenced data

<p><b>Spatial and spatio-temporal phenomena are commonly modelled as Gaussian processes via the geostatistical model (Gelfand & Banerjee, 2017). In the geostatistical model the spatial dependence structure is modelled using covariance functions. Most commonly, the covariance functions impose an assumption of spatial stationarity on the process. That means the covariance between observations at particular locations depends only on the distance between the locations (Banerjee et al., 2014). It has been widely recognized that most, if not all, processes manifest spatially nonstationary covariance structure Sampson (2014). If the study domain is small in area or there is not enough data to justify more complicated nonstationary approaches, then stationarity may be assumed for the sake of mathematical convenience (Fouedjio, 2017). However, relationships between variables can vary significantly over space, and a ‘global’ estimate of the relationships may obscure interesting geographical phenomena (Brunsdon et al., 1996; Fouedjio, 2017; Sampson & Guttorp, 1992). </b></p> <p>In this thesis, we considered three non-parametric approaches to flexibly account for non-stationarity in both spatial and spatio-temporal processes. First, we proposed partitioning the spatial domain into sub-regions using the K-means clustering algorithm based on a set of appropriate geographic features. This allowed for fitting separate stationary covariance functions to the smaller sub-regions to account for local differences in covariance across the study region. Secondly, we extended the concept of covariance network regression to model the covariance matrix of both spatial and spatio-temporal processes. The resulting covariance estimates were found to be more flexible in accounting for spatial autocorrelation than standard stationary approaches. The third approach involved geographic random forest methodology using a neighbourhood structure for each location constructed through clustering. We found that clustering based on geographic measures such as longitude and latitude ensured that observations that were too far away to have any influence on the observations near the locations where a local random forest was fitted were not selected to form the neighbourhood. </p> <p>In addition to developing flexible methods to account for non-stationarity, we developed a pivotal discrepancy measure approach for goodness-of-fit testing of spatio-temporal geostatistical models. We found that partitioning the pivotal discrepancy measures increased the power of the test.</p>

3D Geological Model Creates Potential for Increased Production in Libyan Field

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 201417, “Reservoir Characterization and Geostatistical Model of the Cretaceous and Cambrian-Ordovician Reservoir Intervals, Meghil Field, Sirte Basin, Libya,” by Mohamed Masoud, Sirte Oil Company; W. Scott Meddaugh, SPE, Midwestern State University; and Masud Eljaroshi Masud, Sirte Oil Company, prepared for the 2020 SPE Annual Technical Conference and Exhibition, originally scheduled to be held in Denver, Colorado, 5–7 October. The paper has not been peer reviewed. The study outlined in the complete paper focuses on developing models of the Upper Cretaceous Waha carbonate and Bahi sandstone reservoirs and the Cambrian-Ordovician Gargaf sandstone reservoir in the Meghil field, Sirte Basin, Libya. The objective of this study is to develop a representative geostatistically based 3D model that preserves geological elements and eliminates uncertainty of reservoir properties and volumetric estimates. This study demonstrates the potential for significant additional hydrocarbon production from the Meghil field and the effect of heterogeneity on well placement and spacing. Introduction The reservoir of interest consists of three stratigraphic layers of different ages: the Waha and Bahi Formations and the Gargaf Group intersecting the Meghil field. The Waha reservoir is a porous limestone that forms a single reservoir with underlying Upper Cretaceous Bahi sandstone and Cambro-Ordovician Gargaf Group quartzitic sandstone. The Waha provides excel-lent reservoir characteristics. The Bahi has fair to good reservoir characteristics, while the Gargaf Group has very poor reservoir quality. The Waha and Bahi contain significant amounts of hydrocarbons. The Bahi is composed of erratically distributed detritus from the eroded Gargaf Group. The characteristic of the Gargaf sediments is quartzitic sandstones indurate to a quartzite with low reservoir quality.

Median Polish Kriging and Sequential Gaussian Simulation for the Spatial Analysis of Source Rock Data

In this technical note, a geostatistical model was applied to explore the spatial distribution of source rock data in terms of total organic carbon weight concentration. The median polish kriging method was used to approximate the “row and column effect” in the generated array data, in order for the ordinary kriging methodology to be applied by means of the residuals. Moreover, the sequential Gaussian simulation was employed to quantify the uncertainty of the estimates. The modified Box–Cox technique was applied to normalize the residuals and a cross-validation analysis was performed to evaluate the efficiency of the method. A map of the spatial distribution of total organic carbon weight concentration was constructed along with the 5% and 95% confidence intervals. This work encourages the use of the median polish kriging method for similar applications.

A geostatistical spatially varying coefficient model for mean annual runoff that incorporates process-based simulations and short records

We present a Bayesian geostatistical model for mean annual runoff that incorporates simulations from a process-based hydrological model by treating the simulations as a covariate in the statistical model. The regression coefficient of the covariate is modeled as a spatial field such that the relationship between the covariate (simulations from a hydrological model) and the response variable (observed mean annual runoff) is allowed to vary within the study area. Hence, it is a spatially varying coefficient. A preprocessing step for including short records in the modeling is also suggested and we obtain a model that can exploit several data sources by using state of the art statistical methods. The geostatistical model is evaluated by predicting mean annual runoff for 1981–2010 for 127 catchments in Norway based on observations from 411 catchments. Simulations from the process-based HBV model on a 1 km × 1 km grid are used as input. We found that on average the proposed approach outperformed a purely process-based approach (HBV) when predicting runoff for ungauged and partially gauged catchments: The reduction in RMSE compared to the HBV model was 20 % for ungauged catchments and 58 % for partially gauged catchments, where the latter is due to the preprocessing step. For ungauged catchments the proposed framework also outperformed a purely geostatistical method with a 10 % reduction in RMSE compared to the geostatistical method. For partially gauged catchments however, purely geostatistical methods performed equally well or slightly better than the proposed combination approach. It is not surprising that purely geostatistical methods perform well in areas where we have data. In general, we expect the proposed approach to outperform geostatistics in areas where the data availability is low to moderate.