Bayesian Spatial Regression Models Research Articles

Methodological approaches to exploring the spatial variation in social impacts of protected areas: An intercomparison of Bayesian regression modeling approaches and potential implications.

Protected Areas (PAs) are widely used to conserve biodiversity by protecting and restoring ecosystems while also contributing to socio-economic priorities. An increasing number of studies aim to examine the social impacts of PAs on aspects of people's well-being, such as, quality of life, livelihoods, and connectedness to nature. Despite the increase in literature on this topic, there are still few studies that explore possible robust methodological approaches to capturing and assessing the spatial distribution of impacts in a PA. This study aims to contribute to this research gap by comparing Bayesian spatial regression models that explore links between perceived social impacts and the relative location of local residents and communities in a PA. We use primary data collected from 227 individuals, via structured questionnaires, living in or near the Peak District National Park, United Kingdom. By comparing different models we were able to show that the location of respondents influences their perception of social impacts and that neighboring communities within the national park can have similar perceptions regarding social impacts. Simulation based on existing data using the Bootstrap sub-sampling was also conducted to validate the association between social impacts and mutual proximity of residents. Our findings suggest that this type of data is better treated, in terms of accounting for potential spatial effects, using models that allow for proximity effects to be stronger between people living nearby, e.g. between neighbors in the same community and have minimum effects otherwise. Understanding the spatial clustering of perceived social impacts in and around PA, is key to understanding their causes and to managing and mitigating them. Our findings highlight therefore the need to develop new methodological approaches to assessing and predicting accurately the spatial distribution of social impacts when designating PAs. The findings in this paper will assist practitioners in this regard by proposing approaches to the consideration of the distribution of social impacts when designing the boundaries of PAs alongside typical ecological and socio-economic criteria.

Spillover effects of internal audit effectiveness among commercial banks

This study explores the multifaceted landscape of factors influencing the effectiveness of internal audit within the context of Vietnamese commercial banks. While a plethora of literature exists on the determinants of internal audit effectiveness, an unexplored dimension is the examination of spillover effects amongst companies operating within the same industry. This research addresses this gap by investigating not only the factors that impact the effectiveness of internal audit in Vietnamese commercial banks but also the inter-bank spillover effects of such effectiveness. Utilizing data obtained from 24 commercial banks listed on the Vietnam Stock Exchange and Vietnam Upcom (The Unlisted Public Company Market), we employ a Bayesian spatial regression model (a robust analytical tool) to conduct comprehensive analyses of the interrelationships and co-movements of internal audit effectiveness across these banks. Our findings offer empirical support for the positive effects of key determinants, namely, the "Competence of the IA Department," "IA Independence," and "Management Training Ground," on the effectiveness of internal audit in Vietnamese commercial banks. Notably, our results also validate the significant positive inter-bank spillover effect of internal audit effectiveness. This underscores the importance for banks to collectively prioritize measures aimed at enhancing the quality of internal audit, including the establishment of a joint internal audit association and the organization of collaborative training initiatives for internal audit personnel.

Spatial accessibility of pre-exposure prophylaxis (PrEP): different measure choices and the implications for detecting shortage areas and examining its association with social determinants of health

PurposeWe examine how various pre-exposure prophylaxis (PrEP) accessibility measures impact the detection of PrEP shortage areas and the relation of shortage areas to social determinants of health (SDOH). MethodsUsing ZIP Code Tabulation Areas (ZCTAs) in New York City as a case study, we compared 25 measures of spatial PrEP accessibility across four categories, including density, proximity, two-step floating catchment area (2SFCA), and Gaussian 2SFCA (G2SFCA). Bayesian spatial regression models were used to examine how PrEP accessibility is associated with SDOH. ResultsUsing density to measure PrEP accessibility for small areas such as ZCTAs poses challenges to statistical modeling because the measured accessibility values are highly skewed with excess zeros, leading to the necessity of using complex models such as the two-part mixture model. When G2SFCA measures are used, which account for distance decay effects and the competition from the PrEP demand side, findings on PrEP shortage area detection and the association between PrEP accessibility and SDOH were more consistent and less sensitive to spatial scales (i.e., varying from 10- to 30-minute driving). ConclusionsThis research adds to the nascent research on PrEP accessibility measurement and sheds light on selecting an appropriate measure to assess spatial disparities in PrEP accessibility and its associations with SDOH.

The association between the socioeconomic deprivation level and ischemic heart disease mortality in Japan: an analysis using municipality-specific data.

Geographical variation in the standardized mortality ratio (SMR) for ischemic heart disease (IHD) among municipalities has not been assessed in Japan. Additionally, associations between area-level socioeconomic deprivation indices and IHD mortality have not been identified in Japan. The present study investigated this association. Information on IHD mortality was extracted from Vital Statistics data from 2018 to 2020 for each municipality in Japan. The socioeconomic deprivation level was derived from multiple socioeconomic characteristics. We classified municipalities into quintiles based on the deprivation level and investigated the association between the deprivation level and the SMR of IHD. Additionally, a Bayesian spatial regression model was used to investigate this association, adjusting for other municipal characteristics. Geographical variation in the SMR of IHD was revealed, and municipalities with high SMRs were spatially clustered. There was a weak negative correlation between the socioeconomic deprivation level and the SMRs (correlation coefficient, -0.057 for men and -0.091 for women). In contrast, the regression analysis showed a statistically significant positive association between deprived areas and the IHD mortality rate, and the relative risks for the most deprived municipalities compared with the least deprived municipalities were 1.184 (95% credible interval [CrI], 1.110 to 1.277) and 1.138 (95% CrI, 1.048 to 1.249) for men and women, respectively. A weak negative correlation between the socioeconomic deprivation level and the SMR was observed in the descriptive analysis, while the regression analysis showed that living in deprived areas was statistically positively associated with the IHD mortality rate.

Association of environmental and socioeconomic indicators with serious mental illness diagnoses identified from general practitioner practice data in England: A spatial Bayesian modelling study.

BackgroundThe evidence is sparse regarding the associations between serious mental illnesses (SMIs) prevalence and environmental factors in adulthood as well as the geographic distribution and variability of these associations. In this study, we evaluated the association between availability and proximity of green and blue space with SMI prevalence in England as a whole and in its major conurbations (Greater London, Birmingham, Liverpool and Manchester, Leeds, and Newcastle).Methods and findingsWe carried out a retrospective analysis of routinely collected adult population (≥18 years) data at General Practitioner Practice (GPP) level. We used data from the Quality and Outcomes Framework (QOF) on the prevalence of a diagnosis of SMI (schizophrenia, bipolar affective disorder and other psychoses, and other patients on lithium therapy) at the level of GPP over the financial year April 2014 to March 2018. The number of GPPs included ranged between 7,492 (April 2017 to March 2018) to 7,997 (April 2014 to March 2015) and the number of patients ranged from 56,413,719 (April 2014 to March 2015) to 58,270,354 (April 2017 to March 2018). Data at GPP level were converted to the geographic hierarchy unit Lower Layer Super Output Area (LSOA) level for analysis. LSOAs are a geographic unit for reporting small area statistics and have an average population of around 1,500 people. We employed a Bayesian spatial regression model to explore the association of SMI prevalence in England and its major conurbations (greater London, Birmingham, Liverpool and Manchester, Leeds, and Newcastle) with environmental characteristics (green and blue space, flood risk areas, and air and noise pollution) and socioeconomic characteristics (age, ethnicity, and index of multiple deprivation (IMD)). We incorporated spatial random effects in our modelling to account for variation at multiple scales.Across England, the environmental characteristics associated with higher SMI prevalence at LSOA level were distance to public green space with a lake (prevalence ratio [95% credible interval]): 1.002 [1.001 to 1.003]), annual mean concentration of PM2.5 (1.014 [1.01 to 1.019]), and closeness to roads with noise levels above 75 dB (0.993 [0.992 to 0.995]). Higher SMI prevalence was also associated with a higher percentage of people above 24 years old (1.002 [1.002 to 1.003]), a higher percentage of ethnic minorities (1.002 [1.001 to 1.002]), and more deprived areas.Mean SMI prevalence at LSOA level in major conurbations mirrored the national associations with a few exceptions. In Birmingham, higher average SMI prevalence at LSOA level was positively associated with proximity to an urban green space with a lake (0.992 [0.99 to 0.998]). In Liverpool and Manchester, lower SMI prevalence was positively associated with road traffic noise ≥75 dB (1.012 [1.003 to 1.022]). In Birmingham, Liverpool, and Manchester, there was a positive association of SMI prevalence with distance to flood zone 3 (land within flood zone 3 has ≥1% chance of flooding annually from rivers or ≥0.5% chance of flooding annually from the sea, when flood defences are ignored): Birmingham: 1.012 [1.000 to 1.023]; Liverpool and Manchester: 1.016 [1.006 to 1.026]. In contrast, in Leeds, there was a negative association between SMI prevalence and distance to flood zone 3 (0.959 [0.944 to 0.975]). A limitation of this study was because we used a cross-sectional approach, we are unable to make causal inferences about our findings or investigate the temporal relationship between outcome and risk factors. Another limitation was that individuals who are exclusively treated under specialist mental health care and not seen in primary care at all were not included in this analysis.ConclusionsOur study provides further evidence on the significance of socioeconomic associations in patterns of SMI but emphasises the additional importance of considering environmental characteristics alongside socioeconomic variables in understanding these patterns. In this study, we did not observe a significant association between green space and SMI prevalence, but we did identify an apparent association between green spaces with a lake and SMI prevalence. Deprivation, higher concentrations of air pollution, and higher proportion of ethnic minorities were associated with higher SMI prevalence, supporting a social-ecological approach to public health prevention. It also provides evidence of the significance of spatial analysis in revealing the importance of place and context in influencing area-based patterns of SMI.

Identifying high-risk areas for dog-mediated rabies using Bayesian spatial regression

Despite ongoing control efforts, rabies remains an endemic zoonotic disease in many countries. Determining high-risk areas and the space-time patterns of rabies spread, as it relates to epidemiologically important factors, can support policymakers and program managers alike to develop evidence-based targeted surveillance and control programs. In this One Health approach which selected Thailand as the example site, the location-based risk of contracting dog-mediated rabies by both human and animal populations was quantified using a Bayesian spatial regression model. Specifically, a conditional autoregressive (CAR) Bayesian zero-inflated Poisson (ZIP) regression was fitted to the reported human and animal rabies case counts of each district, from the 2012–2017 period. The human population was used as an offset. The epidemiologically important factors hypothesized as risk modifiers and therefore tested as predictors included: number of dog bites/attacks, the population of dogs and cats, number of Buddhist temples, garbage dumps, animal vaccination, post-exposure prophylaxis, poverty, and shared administrative borders. Disparate sources of data were used to improve the estimated associations and predictions. Model performance was assessed using cross-validation. Results suggested that accounting for the association between human and animal rabies with number of dog bites/attacks, number of owned and un-owned dogs; shared country borders, number of Buddhist temples, poverty levels, and accounting for spatial dependence between districts, may help to predict the risk districts for dog-mediated rabies in Thailand. The fitted values of the spatial regression were mapped to illustrate the risk of dog-mediated rabies. The cross-validation indicated an adequate performance of the spatial regression model (AUC = 0.81), suggesting that had this spatial regression approach been used to identify districts at risk in 2015, the cases reported in 2016/17 would have been predicted with model sensitivity and specificity of 0.71 and 0.80, respectively. While active surveillance is ideal, this approach of using multiple data sources to improve risk estimation may inform current rabies surveillance and control efforts including determining rabies-free zones, and the roll-out of human post-exposure prophylaxis and anti-rabies vaccines for animals in determining high-risk areas.

Environmental Injustices of Leaks from Urban Natural Gas Distribution Systems: Patterns among and within 13 U.S. Metro Areas.

Natural gas leaks in local distribution systems can develop as underground pipeline infrastructure degrades over time. These leaks lead to safety, economic, and climate change burdens on society. We develop an environmental justice analysis of natural gas leaks discovered using advanced leak detection in 13 U.S. metropolitan areas. We use Bayesian spatial regression models to study the relationship between the density of leak indications and sociodemographic indicators in census tracts. Across all metro areas combined, we found that leak densities increase with increasing percent people of color and with decreasing median household income. These patterns of infrastructure injustice also existed within most metro areas, even after accounting for housing age and the spatial structure of the data. Considering the injustices described here, we identify actions available to utilities, regulators, and advocacy groups that can be taken to improve the equity of local natural gas distribution systems.

Bayesian Spatial Binary Regression for Label Fusion in Structural Neuroimaging

Alzheimer’s disease is a neurodegenerative condition that accelerates cognitive decline relative to normal aging. It is of critical scientific importance to gain a better understanding of early disease mechanisms in the brain to facilitate effective, targeted therapies. The volume of the hippocampus is often used in diagnosis and monitoring of the disease. Measuring this volume via neuroimaging is difficult since each hippocampus must either be manually identified or automatically delineated, a task referred to as segmentation. Automatic hippocampal segmentation often involves mapping a previously manually segmented image to a new brain image and propagating the labels to obtain an estimate of where each hippocampus is located in the new image. A more recent approach to this problem is to propagate labels from multiple manually segmented atlases and combine the results using a process known as label fusion. To date, most label fusion algorithms employ voting procedures with voting weights assigned directly or estimated via optimization. We propose using a fully Bayesian spatial regression model for label fusion that facilitates direct incorporation of covariate information while making accessible the entire posterior distribution. Our results suggest that incorporating tissue classification (e.g., gray matter) into the label fusion procedure can greatly improve segmentation when relatively homogeneous, healthy brains are used as atlases for diseased brains. The fully Bayesian approach also produces meaningful uncertainty measures about hippocampal volumes, information which can be leveraged to detect significant, scientifically meaningful differences between healthy and diseased populations, improving the potential for early detection and tracking of the disease. Supplementary materials for this article are available online.

An ecological study of chronic kidney disease in five Mesoamerican countries: associations with crop and heat

BackgroundMesoamerica is severely affected by an epidemic of Chronic Kidney Disease of non-traditional origin (CKDnt), an epidemic with a marked variation within countries. We sought to describe the spatial distribution of CKDnt in Mesoamerica and examine area-level crop and climate risk factors.MethodsCKD mortality or hospital admissions data was available for five countries: Mexico, Guatemala, El Salvador, Nicaragua and Costa Rica and linked to demographic, crop and climate data. Maps were developed using Bayesian spatial regression models. Regression models were used to analyze the association between area-level CKD burden and heat and cultivation of four crops: sugarcane, banana, rice and coffee.ResultsThere are regions within each of the five countries with elevated CKD burden. Municipalities in hot areas and much sugarcane cultivation had higher CKD burden, both compared to equally hot municipalities with lower intensity of sugarcane cultivation and to less hot areas with equally intense sugarcane cultivation, but associations with other crops at different intensity and heat levels were not consistent across countries.ConclusionMapping routinely collected, already available data could be a first step to identify areas with high CKD burden. The finding of higher CKD burden in hot regions with intense sugarcane cultivation which was repeated in all five countries agree with individual-level studies identifying heavy physical labor in heat as a key CKDnt risk factor. In contrast, no associations between CKD burden and other crops were observed.

Extended Generalized Skew Laplace Random Field: Spatial Autoregressive and Moving Average Model for Prediction of Missing Data in Skew and Heavy Tailed Data

In this paper, we define a spatial skew and heavy-tailed random field by an extended version of multivariate generalized skew Laplace distribution. The Bayesian spatial regression model is developed to explain the spatial data. A simulation study is then carried out to validate and evaluate the performance of the proposed model. The application of this model is also demonstrated in an analysis of a geological real data set.

Spatial Relationships between Urban Structures and Air Pollution in Korea

Urban structures facilitate human activities and interactions but are also a main source of air pollutants; hence, investigating the relationship between urban structures and air pollution is crucial. The lack of an acceptable general model poses significant challenges to investigations on the underlying mechanisms, and this gap fuels our motivation to analyze the relationships between urban structures and the emissions of four air pollutants, including nitrogen oxides, sulfur oxides, and two types of particulate matter, in Korea. We first conduct exploratory data analysis to detect the global and local spatial dependencies of air pollutants and apply Bayesian spatial regression models to examine the spatial relationship between each air pollutant and urban structure covariates. In particular, we use population, commercial area, industrial area, park area, road length, total land surface, and gross regional domestic product per person as spatial covariates of interest. Except for park area and road length, most covariates have significant positive relationships with air pollutants ranging from 0 to 1, which indicates that urbanization does not result in a one-to-one negative influence on air pollution. Findings suggest that the government should consider the degree of urban structures and air pollutants by region to achieve sustainable development.

Validation of a Google Street View-Based Neighborhood Disorder Observational Scale.

Recently, there has been a growing interest in developing new tools to measure neighborhood features using the benefits of emerging technologies. This study aimed to assess the psychometric properties of a neighborhood disorder observational scale using Google Street View (GSV). Two groups of raters conducted virtual audits of neighborhood disorder on all census block groups (N = 92) in a district of the city of Valencia (Spain). Four different analyses were conducted to validate the instrument. First, inter-rater reliability was assessed through intraclass correlation coefficients, indicating moderated levels of agreement among raters. Second, confirmatory factor analyses were performed to test the latent structure of the scale. A bifactor solution was proposed, comprising a general factor (general neighborhood disorder) and two specific factors (physical disorder and physical decay). Third, the virtual audit scores were assessed with the physical audit scores, showing a positive relationship between both audit methods. In addition, correlations between the factor scores and socioeconomic and criminality indicators were assessed. Finally, we analyzed the spatial autocorrelation of the scale factors, and two fully Bayesian spatial regression models were run to study the influence of these factors on drug-related police interventions and interventions with young offenders. All these indicators showed an association with the general neighborhood disorder. Taking together, results suggest that the GSV-based neighborhood disorder scale is a reliable, concise, and valid instrument to assess neighborhood disorder using new technologies.

The Influence of On-Premise and Off-Premise Alcohol Outlets on Reported Violent Crime in the Region of Waterloo, Ontario: Applying Bayesian Spatial Modeling to Inform Land Use Planning and Policy

This research analyzes the association between alcohol outlets and reported violent crime at the small-area level in the Region of Waterloo, Ontario, Canada. Alcohol outlets are hypothesized to be associated with high violent crime, primarily because they bring together large numbers of potential offenders and targets, and from a policy perspective, alcohol outlets are a modifiable land use risk factor that can be addressed through land use planning and policy. Results show that both on- and off-premise alcohol outlets are positively associated with reported violent crime after accounting for socioeconomic characteristics. Using small-area risk estimates resulting from Bayesian spatial regression models, map decomposition visualizes the geographic pattern of violent crime risk and the contribution of specific risk factors at the small-area scale, and violent crime hotspots are identified based on posterior probability of relative risk. Applying results to land use planning and policy, we identify small-areas that may be preferred for proposed alcohol outlets based on specific criteria such as low violent crime risk and low risk due to alcohol outlets. We also discuss how the results of this research inform law enforcement initiatives.

Cyanobacteria blooms and non-alcoholic liver disease: evidence from a county level ecological study in the United States.

BackgroundHarmful cyanobacterial blooms present a global threat to human health. There is evidence suggesting that cyanobacterial toxins can cause liver damage and cancer. However, because there is little epidemiologic research on the effects of these toxins in humans, the excess risk of liver disease remains uncertain. The purpose of this study is to estimate the spatial distribution of cyanobacterial blooms in the United States and to conduct a Bayesian statistical analysis to test the hypothesis that contamination from cyanobacterial blooms is a potential risk factor for non-alcoholic liver disease.MethodsAn ecological study design was employed, in which county-specific gender and age standardized mortality rates (SMR) of non-alcoholic liver disease in the United States were computed between 1999 and 2010. Bloom coverage maps were produced based on estimated phycocyanin levels from MERIS (Medium Resolution Imaging Spectrometer) water color imageries from 08/01/2005 to 09/30/2005. A scan statistical tool was used to identify significant clusters of death from non-alcoholic liver disease. A map of local indicator of spatial association (LISA) clusters and a Bayesian spatial regression model were used to analyze the relationship between cyanobacterial bloom coverage and death from non-alcoholic liver disease.ResultsCyanobacterial blooms were found to be widely spread in the United States, including coastal areas; 62% of the counties (1949 out of 3109) showed signs of cyanobacterial blooms measured with MERIS. Significant clusters of deaths attributable to non-alcoholic liver disease were identified in the coastal areas impacted by cyanobacterial blooms. Bayesian regression analysis showed that bloom coverage was significantly related to the risk of non-alcoholic liver disease death. The risk from non-alcoholic liver disease increased by 0.3% (95% CI, 0.1% to 0.5%) with each 1% increase in bloom coverage in the affected county after adjusting for age, gender, educational level, and race.ConclusionsAt the population level, there is a statistically significant association between cyanobacterial blooms and non-alcoholic liver disease in the contiguous United States. Remote sensing-based water monitoring provides a useful tool for assessing health hazards, but additional studies are needed to establish a specific association between cyanobacterial blooms and liver disease.

Comparison of statistical linear interpolation models for monthly precipitation in South Korea

It is well recognized that statistical linear interpolation models are computationally inexpensive and applicable to any climate data compared to the dynamic simulation method at regional scales. Using five different statistical linear interpolation models, we characterized each model’s performance to predict a climate variable of interest. General linear model, generalized additive model, spatial linear model, and bayesian spatial regression model (BSM) were analyzed. The climate variable of interest was the monthly precipitation, where the spatial variability was explained using terrain information: latitude, longitude, elevation, topographic aspect, and costal proximity. We used the root mean squared error, the mean absolute error and correlation coefficient as the performance. The BSM showed better performance in reflecting the spatial pattern of monthly precipitation compared to the other models. The monthly precipitation and its 95 % prediction interval on a 1 × 1 km grid spacing were generated through a spatial interpolation of 441 point observations.

An application of Bayesian spatial statistical methods to the study of racial and poverty segregation and infant mortality rates in the US

The infant mortality rate is a fundamental measure of population health used internationally. In the United States, the infant mortality rate is higher than what would be expected for a country of its affluence. We present an analysis of US county infant mortality rates using modern Bayesian spatial statistical methodologies. Our key predictors in our statistical analysis are residential racial and poverty segregation, measured by the dissimilarity, interaction and spatial proximity indexes. We use both Exploratory Spatial Data Analysis methods and Hierarchical Bayesian spatial regression models to examine the influences of these segregation measures on the infant mortality rate for each county, net of income inequality, degree of rurality and relative socioeconomic deprivation. The spatial measures of racial segregation suggest that when blacks live in close proximity to each other, this tends to increase the infant mortality rate. The results for poverty segregation suggest the same pattern, when poor populations live in close proximity to one another this is generally detrimental to the county infant mortality rate. However, interaction between blacks and whites and poor and non-poor residents of an area is protective for infant mortality.

Bayesian spatial regression models with closed skew normal correlated errors and missing observations

This paper is concerned with Bayesian estimation of a spatial regression model with skew non-Gaussian errors. The regression parameters are estimated by using a closed skew normal (CSN) distribution, which is closed under conditioning and linear combination. The proposed model captures skewness in the response variable. Sometimes, we may encounter missing observations in the response variable, accordingly we model and predict the missing observations by a Bayesian approach using Gibbs sampling methods. Next, a simulation study is performed to asses our model validity. Also, the proposed model in this work is applied to CO data from Tehran, the capital city of Iran. Then, the accuracy of the CSN and Gaussian models is compared by cross validation criterion.

Spatial Modelling of Car Ownership Data: A Case Study from the United Kingdom

In this paper a model is formulated to estimate the strength of the relationship between household car ownership and income using cross-sectional data. Whilst reports of such studies are not uncommon in the transport literature, this study is different in that it takes explicit account of the spatial distribution of the data. By incorporating this spatial element in the model formulation, the residual errors in the model are uncorrelated and hence allows for the estimation of parameters that are, in a statistical sense, the best available. These spatial models are fitted to a large data set provided by the United Kingdom Office for National Statistics, covering the area of England and Wales. The recommended model form is a Hierarchical Bayesian spatial regression model with the parameters in the model estimated using the technique of Markov Chain Monte Carlo (MCMC). A common feature of all the spatial models is that the estimate of the elasticity of car ownership with respect to income is seen to be larger than that from a non-spatial model.

Community-Level Correlates of Hospitalizations for Persons With Schizophrenia

Community-Level Correlates of Hospitalizations for Persons With Schizophrenia

Spatial Risk Assessment of Rift Valley Fever in Senegal

Rift Valley fever (RVF) is broadening its geographic range and is increasingly becoming a disease of global importance with potentially severe consequences for human and animal health. We conducted a spatial risk assessment of RVF in Senegal using serologic data from 16,738 animals in 211 locations. Bayesian spatial regression models were developed with interpolated seasonal rainfall, land surface temperature, distance to perennial water bodies, and time of year entered as fixed-effect variables. Average total monthly rainfall during December-February was the most important spatial predictor of risk of positive RVF serologic status. Maps derived from the models highlighted the lower Senegal River basin and the southern border regions of Senegal as high-risk areas. These risk maps are suitable for use in planning improved sentinel surveillance systems in Senegal, although further data collection is required in large areas of Senegal to better define the spatial distribution of RVF.