Spatial Risk Mapping Research Articles

High-resolution spatio-temporal risk mapping for malaria in Namibia: a comprehensive analysis

BackgroundNamibia, a low malaria transmission country targeting elimination, has made substantial progress in reducing malaria burden through improved case management, widespread indoor residual spraying and distribution of insecticidal nets. The country's diverse landscape includes regions with varying population densities and geographical niches, with the north of the country prone to periodic outbreaks. As Namibia approaches elimination, malaria transmission has clustered into distinct foci, the identification of which is essential for deployment of targeted interventions to attain the southern Africa Elimination Eight Initiative targets by 2030. Geospatial modelling provides an effective mechanism to identify these foci, synthesizing aggregate routinely collected case counts with gridded environmental covariates to downscale case data into high-resolution risk maps.MethodsThis study introduces innovative infectious disease mapping techniques to generate high-resolution spatio-temporal risk maps for malaria in Namibia. A two-stage approach is employed to create maps using statistical Bayesian modelling to combine environmental covariates, population data, and clinical malaria case counts gathered from the routine surveillance system between 2018 and 2021.ResultsA fine-scale spatial endemicity surface was produced for annual average incidence, followed by a spatio-temporal modelling of seasonal fluctuations in weekly incidence and aggregated further to district level. A seasonal profile was inferred across most districts of the country, where cases rose from late December/early January to a peak around early April and then declined rapidly to a low level from July to December. There was a high degree of spatial heterogeneity in incidence, with much higher rates observed in the northern part and some local epidemic occurrence in specific districts sporadically.ConclusionsWhile the study acknowledges certain limitations, such as population mobility and incomplete clinical case reporting, it underscores the importance of continuously refining geostatistical techniques to provide timely and accurate support for malaria elimination efforts. The high-resolution spatial risk maps presented in this study have been instrumental in guiding the Namibian Ministry of Health and Social Services in prioritizing and targeting malaria prevention efforts. This two-stage spatio-temporal approach offers a valuable tool for identifying hotspots and monitoring malaria risk patterns, ultimately contributing to the achievement of national and sub-national elimination goals.

Evil and allies: Opportunistic gulls as both spreaders and sentinels of antibiotic‐resistant bacteria in human‐transformed landscapes

Abstract Human‐transformed residuals, especially those derived from human waste (dumps), farmland, and livestock are involved in the emergence of antibiotic‐resistant bacteria (ARB) in the environment. Wildlife can act as vectors of ARB dispersal through different environments, but also as sentinels to detect the early spread and determine ARB sources. The development of integrated monitoring programmes focused on wildlife would help to anticipate the risks of ARB to humans and livestock. We used the yellow‐legged gull (Larus michahellis) as a model species to investigate and monitor the spatial patterns of ARB dispersal across an extensive farmland region located in northeastern Spain (Lleida). By integrating GPS tracking data and ARB clinical testing for 26 individuals within a network analysis framework, we modelled the risk of spatial pathogen spread through faeces during the bacteria‐transmission latency period (16 days after sample collection). Additionally, we created a connectivity network to determine the main sources of ARB in the area, focusing on three main habitats of special risk for infection: dumps, livestock facilities, and irrigation ponds. Seven individuals were infected by Escherichia coli, with one also co‐infected with Listeria monocytogenes and Salmonella spp. Potential pathogen dispersal distances ranged from 1.13 km to 23.13 km from the breeding colony. Our network analyses revealed 54 main nodes (i.e. high‐risk habitats recurrently visited by tracked gulls) and 1182 links among them. Our findings revealed a high degree of connectivity between the breeding area, located in a shallow lake, and nearby dumps, highlighting them as significant contributors to ARB dispersal. Synthesis and applications: The integration of GPS data, pathogen testing and network analyses can shed further light on pathogen dynamics by creating spatial risk maps and identifying ARB sources. In combination with complementary molecular epidemiology techniques within a One Health framework, our approach can emerge as an important tool for monitoring ARB dynamics within highly human‐transformed ecosystems. This may empower managers for the development of targeted ARB monitoring programmes and effective mitigation strategies, ultimately improving both animal and public health.

Performance of a Five-Layer ANN Model for Earthquake Magnitude Prediction and Spatial Risk Mapping in Turkey

Turkey faces significant seismic risks, necessitating accurate earthquake forecasting for effective disaster preparedness. This study employs advanced Artificial Neural Networks (ANN) to predict earthquake magnitudes and assess risks specific to Turkey. Focusing on a distinct segment of the USGS earthquake catalogue from January 2014 to August 2023, the research tailors ANN algorithms with five layers to Turkey's seismic challenges. Rigorous dataset cleaning and processing ensure accuracy, with the ANN model demonstrating exceptional alignment with earthquake data ( RMSE: 0.078, R2: 0.89). Comparative evaluations highlight the effectiveness of ANN models in forecasting earthquake magnitudes in Turkey. The study explores the spatial distribution of earthquake risk across Turkey through an ANN-based map, emphasizing the critical window for preventive measures in this seismically active region. The analysis ensures further enhancement of model accuracy in seismic-prone areas globally. This study advances earthquake prediction by showcasing the high accuracy of our five-layer ANN model in forecasting magnitudes and spatial risk, significantly improving disaster preparedness and risk management in regions such as Turkey.

Risk of pedestrian collision for persons with peripheral field loss: A computational analysis.

People with peripheral field loss report colliding with other pedestrians on their blind side(s). We show that, in dyadic collision scenarios between persons, one with field loss, such as homonymous hemianopia, and the other normally sighted pedestrian, collisions occur only if the persons with homonymous hemianopia are overtaking the pedestrians, and the collision risk is concentrated at farther bearing angles than previously suggested. Prior work computed the risk of collision while simulating both pedestrians as points and did not consider the ability of the other pedestrian's normal vision to avoid the collision. We extended the model to better characterize the open space collision risk posed for persons with homonymous hemianopia by normally sighted pedestrians where both have volume. We computed the risk of collision with approaching pedestrians using a model that simulates approaching pedestrians as volumetric entities without vision, volumetric entities with vision, and as points for comparison with the prior work. Collision risk of approaching pedestrians is characterized for all three conditions through spatial collision risk maps and collision risk densities as a function of bearing and radial distances. The collision risk for volumetric pedestrians is slightly different from that of point pedestrians. For volumetric pedestrians simulated with normal vision, the risk of collision was reduced substantially, as the other pedestrians could detect and avoid most impending collisions. The remaining collision risk is from pedestrians approaching at higher bearing angles (>50°) and from shorter radial distances (<2 m). Thus, collisions occurred when the pedestrians started in front of the person with homonymous hemianopia that was overtaking the pedestrian. The probability of collisions between pedestrians and the person with peripheral field loss is low and occurs only when the person with peripheral field loss is walking from behind the pedestrian at faster speed, thereby overtaking them. Such collisions occur with pedestrians at higher bearing angles, which should be monitored by assistive aids to avoid collisions. The same collision risk applies not only in homonymous hemianopia but also in other peripheral field loss such as monocular vision loss or concentric field loss, as common in retinitis pigmentosa and glaucoma.

Enhancing spatial modeling and risk mapping of six air pollutants using synthetic data integration with convolutional neural networks

Air pollution poses significant risks to human health and the environment, necessitating effective air quality management strategies. This study presents a novel approach to air quality management by integrating an autoencoder (AE) with a convolutional neural network (CNN) algorithm in Tehran city of Iran. One of the primary and vital problems in deep learning is model complexity, and the complexity of a model is affected by data distribution, data complexity, and information volume. AE provide a helpful way to denoise input data and make building deep learning models much more efficient. The proposed methodology enables spatial modeling and risk mapping of six air pollutants, namely, particulate matter 2.5 (PM2.5), particulate matter 10 (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3), and carbon monoxide (CO). For air pollution modelling, data from a spatial database containing the annual average of six pollutants from 2012 to 2022 was utilized. The model considered various parameters influencing air pollution: altitude, humidity, distance to industrial areas, NDVI (normalized difference vegetation index), population density, rainfall, distance to the street, temperature, traffic volume, wind direction, and wind speed. The risk map accuracy was assessed using the area under the receiver operating characteristic (ROC) curve for six pollutants. Among them, NO2, PM10, CO, PM2.5, O3, and SO2 exhibited the highest accuracy with values of 0.964, 0.95, 0.896, 0.878, 0.877, and 0.811, respectively, in the risk map generated by the CNN-AE model. The findings demonstrated the CNN-AE model’s impressive precision when generating the pollution risk map.

Exploring the Impact of Climate Variables on Livestock Anthrax Outbreaks: A Machine Learning Approach

Anthrax, a globally significant disease, poses substantial threats to both livestock and human populations. Timely identification of anthrax outbreaks is paramount to mitigate its impact on animal health, human health, and public safety. This study aims to construct a predictive model for livestock anthrax disease occurrence. By leveraging the potential of advanced Machine-Learning techniques, we projected the likelihood of anthrax outbreaks across India, through incorporating a diverse set of meteorological, and remote sensing parameters. The ultimate goal is to establish a spatial risk map that can serve as an early warning system, aiding in the anticipation and management of future anthrax outbreaks in India's livestock population. Our analysis revealed elevated risk zones for anthrax outbreaks in the southern and north-eastern regions of India, contrasting with medium to low-risk areas in the central parts. Notably, Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI), rainfall, soil moisture, and wind speed emerged as pivotal variables driving the model's predictive accuracy. Among the employed models, the random forest, adaptive boosting, and classification tree analysis approaches showcased superior performance in livestock anthrax risk assessment. The risk map was generated using significant variables by exploiting best fitted models. These findings hold profound implications for policymakers, guiding the targeted deployment of control strategies against anthrax outbreaks. The dynamic risk maps generated through this study enhance public awareness, equipping decision-makers with vital insights for informed action. By spotlighting risk management endeavours, these maps further enhance governance and risk mitigation efforts.

Forest Fire Dynamics in Nepal’s Mid-hills: Insights from Spatial Analysis and Risk Mapping of Doti District

Forest fires have significantly contributed to forest degradation and pollution all over Nepal. Despite their high severity, limited studies have been conducted on the spatial-temporal analysis and risk mapping of these fires especially in the Mid-hill region. This study aims to fill this gap by analyzing Visible Infrared Imaging Radiometer Suite (VIIRS) data from 2012 to 2022, along with satellite imagery, using remote sensing and geographic information systems (GIS) technology. Nine variables, including land class, distance to roads, proximity to settlements, temperature, wind speed, precipitation, slope, aspect, and elevation, were utilized to identify forest fire patterns and create a fire risk map. A multi-parametric hierarchical weighted index model was employed, with multicollinearity checked (VIF&lt;2) to ensure model accuracy and validated by Area Under Curve (AUC=0.787). The study revealed an increasing trend of forest fires, particularly during March to May, in broad-leaved forests near human interference. Out of the total forested area of 1447.58 km2, 42.9%, 46.03%, and 11.07% were classified under high, moderate, and low-risk zones, respectively. The study emphasizes the recurring forest fire problem in often-neglected Mid-hill districts and underscores the need to prioritize them in future strategies.

Mapping the Environmental Risk of Beech Leaf Disease in the Northeastern United States.

The recently emerged beech leaf disease (BLD) is causing the decline and death of American beech in North America. First observed in 2012 in northeast Ohio, U.S.A., BLD had been documented in 10 northeastern states and the Canadian province of Ontario as of July 2022. A foliar nematode has been implicated as the causal agent, along with some bacterial taxa. No effective treatments have been documented in the primary literature. Irrespective of potential treatments, prevention and prompt eradication (rapid responses) remain the most cost-effective approaches to the management of forest tree disease. For these approaches to be feasible, however, it is necessary to understand the factors that contribute to BLD spread and use them in estimation of risk. Here, we conducted an analysis of BLD risk across northern Ohio, western Pennsylvania, western New York, and northern West Virginia, U.S.A. In the absence of symptoms, an area cannot necessarily be deemed free of BLD (i.e., absence of BLD cannot be certain) due to its fast spread and the lag in symptom expression (latency) after infection. Therefore, we employed two widely used presence-only species distribution models (SDMs), one-class support vector machine (OCSVM), and maximum entropy (Maxent) to predict the spatial pattern of BLD risk based on BLD presence records and associated environmental variables. Our results show that both methods work well for BLD environmental risk modeling purposes, but Maxent outperforms OCSVM with respect to both the quantitative receiver operating characteristics (ROC) analysis and the qualitative evaluation of the spatial risk maps. Meanwhile, the Maxent model provides a quantification of variable contribution for different environmental factors, indicating that meteorological (isothermality and temperature seasonality) and land cover type (closed broadleaved deciduous forest) factors are likely key contributors to BLD distribution. Moreover, the future trajectories of BLD risk over our study area in the context of climate change were investigated by comparing the current and future risk maps obtained by Maxent. In addition to offering the ability to predict where the disease may spread next, our work contributes to the epidemiological characterization of BLD, providing new lines of investigation to improve ecological or silvicultural management. Furthermore, this study shows strong potential for extension of environmental risk mapping over the full American beech distribution range so that proactive management measures can be put in place. Similar approaches can be designed for other significant or emerging forest pest problems, contributing to overall management efficiency and efficacy.

Spatial analysis and risk mapping of Fasciola hepatica infection in dairy cattle at the Peruvian central highlands

This study aimed to develop maps for Fasciola hepatica infection occurrence in dairy cattle in the districts of Matahuasi and Baños in the Peruvian central highlands. For this, a model based on the correlation between environmental variables and the prevalence of infection was constructed. Flukefinder® coprological test were performed in samples from dairy cattle from 8 herds, during both the rainy and wet season. Grazing plots were geo-referenced to obtain information on environmental variables. Monthly temperature, monthly rainfall, elevation, slope, normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), normalized difference water index (NDWI), distance to rivers, urban areas and roads were obtained by using remote sensor images and ArcGIS®. Multilayer perceptron Artificial Neural Networks modeling were applied to construct a predictive model for the occurrence of fasciolosis, based on the relationship between environmental variables and level of infection. Kappa coefficient (k > 0.6) was used to evaluate concordance between observed and forecasted risk by the model. Coprological results demonstrated an average prevalence from 20% to 100%, in Matahuasi, and between 0 and 87.5%, in Baños. A model with a high level of concordance between predicted and observed infection risk (k = 0.77) was obtained, having as major predicting variables: slope, NDWI, NDVI and EVI. Fasciolosis risk was categorized as low (p < 20%), medium (20% < p < 50%) and high (p ≥ 50%) level. Using ArcGIS 10.4.1, risk maps were developed for each risk level of fasciolosis. Maps of fasciolosis occurrence showed that 87.2% of Matahuasi area presented a high risk for bovine fasciolosis during the dry season, and 76.6% in the wet season. In contrast, 21.9% of Baños area had a high risk of infection during the dry season and 12.1% during the wet season. In conclusion, our model showed areas with high risk for fasciolosis occurrence in both districts during both dry and rainy periods. Slope, NDWI, NDVI and EVI were the major predictors for fasciolosis occurrence.

Climate-Induced Risk Assessment of Rural and Urban Agroforestry Managers of Aizawl District, Northeast India

Climate change exerts a substantial influence on global livelihood security. This research aims to elucidate the risk faced by agroforestry managers of urban and rural areas. Adhering to the IPCC risk framework, we structured the experimental design and adopted an indicator-based methodology to delineate the risk dimensions. Altogether, 105 households from 7 villages in Aizawl district, Mizoram, India, were considered for the study. For indicator identification, we conducted a comprehensive literature review and subsequently employed principal component analysis to select relevant indicators. Finally, risk was determined using the index value of hazard, exposure, and vulnerability. Additionally, we also developed a regression model and integrated it into ArcGIS to generate a spatial risk map. Out of 69 indicators identified, 52 were selected for final assessment after PCA analysis. Our findings underscore the higher susceptibility of urban agroforestry managers to climate change which was in agreement to our hypothesis that the risk index of agroforestry households increases with altitude while it decreases with the distance from Aizawl headquarter. Furthermore, we observed that households residing at higher altitudes exhibit greater vulnerability. Key determinants contributing to elevated risk in the region encompass land ownership constraints, diminished yields, traditional farming practices with no institutional help, and a dearth of available labour resources. The study advocates the implementation of climate smart agroforestry practices integrated with agricultural credit schemes and an educational policy designed to enrol dropout youths.

Nitrate prediction in groundwater of data scarce regions: The futuristic fresh-water management outlook

Nitrate contamination in groundwater poses a significant threat to water quality and public health, especially in regions with limited data availability. This study addresses this challenge by employing machine learning (ML) techniques to predict nitrate (NO3−-N) concentrations in Mexico's groundwater. Four ML algorithms—Extreme Gradient Boosting (XGB), Boosted Regression Trees (BRT), Random Forest (RF), and Support Vector Machines (SVM)—were executed to model NO3−-N concentrations across the country. Despite data limitations, the ML models achieved robust predictive performances. XGB and BRT algorithms demonstrated superior accuracy (0.80 and 0.78, respectively). Notably, this was achieved using ∼10 times less information than previous large-scale assessments. The novelty lies in the first-ever implementation of the ‘Support Points-based Split Approach’ during data pre-processing. The models considered initially 68 covariates and identified 13–19 significant predictors of NO3−-N concentration spanning from climate, geomorphology, soil, hydrogeology, and human factors. Rainfall, elevation, and slope emerged as key predictors. A validation incorporated nationwide waste disposal sites, yielding an encouraging correlation. Spatial risk mapping unveiled significant pollution hotspots across Mexico. Regions with elevated NO3−-N concentrations (>10 mg/L) were identified, particularly in the north-central and northeast parts of the country, associated with agricultural and industrial activities. Approximately 21 million people, accounting for 10 % of Mexico's population, are potentially exposed to elevated NO3−-N levels in groundwater. Moreover, the NO3−-N hotspots align with reported NO3−-N health implications such as gastric and colorectal cancer. This study not only demonstrates the potential of ML in data-scarce regions but also offers actionable insights for policy and management strategies. Our research underscores the urgency of implementing sustainable agricultural practices and comprehensive domestic waste management measures to mitigate NO3−-N contamination. Moreover, it advocates for the establishment of effective policies based on real-time monitoring and collaboration among stakeholders.

A Spatial Dengue Risk Map for the Jaffna District of Northern Sri Lanka

Dengue, a mosquito-borne disease, is a major public health concern in Sri Lanka including the northern Jaffna district. Epidemiology of dengue is associated with variations in land-use patterns and socio-economic factors. Jaffna district is one of the administrative locations with high incidence of dengue over the last two decades. The objective of the study was to identify potential dengue risk areas based on land-use patterns and socio-economic and demographic factors in Jaffna district at the Medical Officer for Health (MoH) administrative level applying spatial multi-criteria analysis using Geographic Information System Platform. The analysis also incorporated dengue cases and dengue serotypes identified in 2019 in the Jaffna district. The study reveals that the Nallur MoH division is the very high-risk area for dengue transmission. The identified risk map is expected to be useful for health authorities to prioritize control measures to identified risk areas. A detailed study considering land-use pattern, socio-economy, demography and serotypes over the years is warranted to understand the spatio-temporal patterns in dengue transmission in the Jaffna district.

Quantifying the influence of climate, host and change in land-use patterns on occurrence of Crimean Congo Hemorrhagic Fever (CCHF) and development of spatial risk map for India

Crimean Congo Hemorrhagic Fever (CCHF), is an emerging zoonosis globally and in India. The present study focused on identifying the risk factors for occurrence of CCHF in the Indian state of Gujarat and development of risk map for India. The past CCHF outbreaks in India were collated for the analyses. Influence of land use change and climatic factors in determining the occurrence of CCHF in Gujarat was assessed using Bayesian spatial models. Change in maximum temperature in affected districts was analysed to identify the significant change points over 110 years. Risk map was developed for Gujarat using Bayesian Additive Regression Trees (BART) model with remotely sensed environmental variables and host (livestock and human) factors. We found the change in land use patterns and maximum temperature in affected districts to be contributing to the occurrence of CCHF in Gujarat. Spatial risk map developed using CCHF occurrence data for Gujarat identified density of buffalo, minimum land surface temperature and elevation as risk determinants. Further, spatial risk map for the occurrence of CCHF in India was developed using selected variables. Overall, we found that combination of factors such as change in land-use patterns, maximum temperature, buffalo density, day time minimum land surface temperature and elevation led to the emergence and further spread of the disease in India. Mitigation measures for CCHF in India could be designed considering disease epidemiology and initiation of surveillance strategies based on the risk map developed in this study.

Spatial predictive risk mapping of lymphatic filariasis residual hotspots in American Samoa using demographic and environmental factors.

American Samoa successfully completed seven rounds of mass drug administration (MDA) for lymphatic filariasis (LF) from 2000-2006. The territory passed the school-based transmission assessment surveys in 2011 and 2015 but failed in 2016. One of the key challenges after the implementation of MDA is the identification of any residual hotspots of transmission. Based on data collected in a 2016 community survey in persons aged ≥8 years, Bayesian geostatistical models were developed for LF antigen (Ag), and Wb123, Bm14, Bm33 antibodies (Abs) to predict spatial variation in infection markers using demographic and environmental factors (including land cover, elevation, rainfall, distance to the coastline and distance to streams). In the Ag model, females had a 26.8% (95% CrI: 11.0-39.8%) lower risk of being Ag-positive than males. There was a 2.4% (95% CrI: 1.8-3.0%) increase in the odds of Ag positivity for every year of age. Also, the odds of Ag-positivity increased by 0.4% (95% CrI: 0.1-0.7%) for each 1% increase in tree cover. The models for Wb123, Bm14 and Bm33 Abs showed similar significant associations as the Ag model for sex, age and tree coverage. After accounting for the effect of covariates, the radii of the clusters were larger for Bm14 and Bm33 Abs compared to Ag and Wb123 Ab. The predictive maps showed that Ab-positivity was more widespread across the territory, while Ag-positivity was more confined to villages in the north-west of the main island. The findings may facilitate more specific targeting of post-MDA surveillance activities by prioritising those areas at higher risk of ongoing transmission.

Ensuring Access to Opioid Treatment Program Services Among Delawareans Vulnerable to Flooding.

To quantify potential flood-related access disruptions to medication-assisted treatment for opioid use disorder (OUD) among Delawareans. Spatial flood risk maps and infrastructure, services, and hazard risk, transportation networks, opioid treatment programs (OTPs) for the State of Delaware were integrated to visually display the relationship between these layers. A complex network theory-based simulation model was used to assess both direct (e.g., inundation with flood water) and indirect (e.g., isolation) impacts of floods. Delaware is at increasing risk from flooding associated with storms and sea-level rise, which can lead to sunny day flooding during high tides. Of the 18 OTPs in Delaware, 4 are expected to be flooded in a 100-year flood and 7 are expected to be severely disrupted, increasing to 9 by 2035 and to 10 by 2050, with service reachability less than 15 square miles due to flood-induced isolation. Individuals utilizing OTPs for OUDs must be able to access treatment programs regardless of external disruptors like floods. Because these programs require consistent treatment adherence and in-person oversight by clinicians, timely restoration of services and continuity of operations for treatment facilities in post-disaster settings is critical for treatment compliance. The State of Delaware has the third highest rate of drug overdose mortality in the U.S., with three-quarters of all drug-related deaths involving opioids. Impeded access to opioid treatment during a flood disaster can lead to relapse, overdose, and death. Hazard planning must develop policies and practices to address these risks.

Susceptibility Risk Index Mapping of Population at Tuberculosis Epidemic Risk

This paper presents the spatial susceptibility risk mapping of tuberculosis (TB) using a geographical information system (GIS) index model and satellite remote sensing (RS) imagery. GIS and RS-based index approach is proposed as an alternative method in identifying potentially high-risk areas in Klang, Selangor. The level of risk for the selected socio-spatial factors and the risk map was classified into five-scale from level 1, which is no risk to level 5, which indicates high risk by applying an overlay analysis and a weighted linear combination. The risk index map shows that a high concentration of TB cases is located in the district's urban and crowded areas.

Spatial risk for a superspreading environment: Insights from six urban facilities in six global cities across four continents.

This study sets out to provide scientific evidence on the spatial risk for the formation of a superspreading environment. Focusing on six common types of urban facilities (bars, cinemas, gyms and fitness centers, places of worship, public libraries and shopping malls), it first tests whether visitors' mobility characteristics differ systematically for different types of facility and at different locations. The study collects detailed human mobility and other locational data in Chicago, Hong Kong, London, São Paulo, Seoul and Zurich. Then, considering facility agglomeration, visitors' profile and the density of the population, facilities are classified into four potential spatial risk (PSR) classes. Finally, a kernel density function is employed to derive the risk surface in each city based on the spatial risk class and nature of activities. Results of the human mobility analysis reflect the geographical and cultural context of various facilities, transport characteristics and people's lifestyle across cities. Consistent across the six global cities, geographical agglomeration is a risk factor for bars. For other urban facilities, the lack of agglomeration is a risk factor. Based on the spatial risk maps, some high-risk areas of superspreading are identified and discussed in each city. Integrating activity-travel patterns in risk models can help identify areas that attract highly mobile visitors and are conducive to superspreading. Based on the findings, this study proposes a place-based strategy of non-pharmaceutical interventions that balance the control of the pandemic and the daily life of the urban population.

Flood risk assessment by using an interpretative structural modeling based Bayesian network approach (ISM-BN): An urban-level analysis of Shenzhen, China

With increasingly uncertain environmental conditions under global change, it is rather important for water security management to evaluate the flood risk, which is influenced by the compound effect of severe weather events and strong anthropogenic activities. In this paper, a risk assessment model in the framework of Bayesian network (BN) was proposed through incorporating with the Interpretative Structural Modeling method (ISM), which would produce an integrated ISM-BN model for reliable flood assessments. The ISM is employed to identify the relations among multiple risk factors, and then helps to configure the BN structure to conduct a risk inference. The established model was further demonstrated in Shenzhen city of China to perform an urban-level risk analysis of the flood disaster, and the Enhanced Water Index (EWI) was introduced to derive model parameters for training and verification. The obtained results of risk assessment lead to an accuracy of 76% with the Area Under ROC Curve (AUC) of 0.82, and spatial distribution of risk levels also showed a satisfactory performance. In addition, it was found that the maximum daily rainfall among ten risk factors play a key part in flood occurrence, while the elevation and storm frequency are also sensitive indicators for the study area. Besides, the spatial flood risk map generated under various design rainfall scenarios would contribute to identifying potential areas that are worth paying particular attention. Thus, the developed assessment model would be a useful tool for supporting flood risk governance to achieve reliable urban water security.

Spatiotemporal Variation and Predictors of Unsuppressed Viral Load among HIV-Positive Men and Women in Rural and Peri-Urban KwaZulu-Natal, South Africa.

Unsuppressed HIV viral load is an important marker of sustained HIV transmission. We investigated the prevalence, predictors, and high-risk areas of unsuppressed HIV viral load among HIV-positive men and women. Unsuppressed HIV viral load was defined as viral load of ≥400 copies/mL. Data from the HIV Incidence District Surveillance System (HIPSS), a longitudinal study undertaken between June 2014 to June 2016 among men and women aged 15–49 years in rural and peri-urban KwaZulu-Natal, South Africa, were analysed. A Bayesian geoadditive regression model which includes a spatial effect for a small enumeration area was applied using an integrated nested Laplace approximation (INLA) function while accounting for unobserved factors, non-linear effects of selected continuous variables, and spatial autocorrelation. The prevalence of unsuppressed HIV viral load was 46.1% [95% CI: 44.3–47.8]. Predictors of unsuppressed HIV viral load were incomplete high school education, being away from home for more than a month, alcohol consumption, no prior knowledge of HIV status, not ever tested for HIV, not on antiretroviral therapy (ART), on tuberculosis (TB) medication, having two or more sexual partners in the last 12 months, and having a CD4 cell count of <350 cells/μL. A positive non-linear effect of age, household size, and the number of lifetime HIV tests was identified. The higher-risk pattern of unsuppressed HIV viral load occurred in the northwest and northeast of the study area. Identifying predictors of unsuppressed viral load in a localized geographic area and information from spatial risk maps are important for targeted prevention and treatment programs to reduce the transmission of HIV.

Coupling spatial statistics with social network analysis to estimate distinct risk areas of disease circulation to improve risk-based surveillance.

Most animal disease surveillance systems concentrate efforts in blocking transmission pathways and tracing back infected contacts while not considering the risk of transporting animals into areas with elevated disease risk. Here, we use a suite of spatial statistics and social network analysis to characterize animal movement among areas with an estimated distinct risk of disease circulation to ultimately enhance surveillance activities. Our model utilized equine infectious anemia virus (EIAV) outbreaks, between-farm horse movements, and spatial landscape data from 2015 through 2017. We related EIAV occurrence and the movement of horses between farms with climate variables that foster conditions for local disease propagation. We then constructed a spatially explicit model that allows the effect of the climate variables on EIAV occurrence to vary through space (i.e., non-stationary). Our results identified important areas in which in-going movements were more likely to result in EIAV infections and disease propagation. Municipalities were then classified as having high 56 (11.3%), medium 48 (9.66%), and low 393 (79.1%) spatial risk. The majority of the movements were between low-risk areas, altogether representing 68.68% of all animal movements. Meanwhile, 9.48% were within high-risk areas, and 6.20% were within medium-risk areas. Only 5.37% of the animals entering low-risk areas came from high-risk areas. On the other hand, 4.91% of the animals in the high-risk areas came from low- and medium-risk areas. Our results demonstrate that animal movements and spatial risk mapping could be used to make informed decisions before issuing animal movement permits, thus potentially reducing the chances of reintroducing infection into areas of low risk.