Predictive Risk Map Research Articles

Evidence of spatial clustering distribution patterns of malaria in Epe, Lagos, Nigeria

Investigation of the spatial distribution patterns of malaria with the goal of detecting intense transmission areas and clustering is prerequisite to planning an effective targeted malaria intervention. Parasitological sampling was done in settlements in Epe and Orimedu, Lagos, Nigeria. Blood samples were collected from 360 participants with georeferenced residences, selected through multistage systematic sampling technique. Each study area was delineated into 3 Malaria Risk Zones (MRZs), namely; Very High (V-MRZ), High (H-MRZ) and low (L-MRZ), according to distances of 500 m, 1000 m and 1500 m respectively away from perennial water bodies. Clustering and Outlier analysis was carried out to generate predictive malaria risk maps. Overall malaria prevalence was 32.2%., being significantly higher in Orimedu (52.6%) than Epe (47.4%), p < 0.05. Prevalence was 28.2%, 32.0% and 50.0% in the VH-MRZ, High-MRZ and L-MRZ respectively in Epe. H-MRZ accounted for the highest prevalence (66.7%), in Orimedu, followed by the V-MRZ (32.8%) while the L-MRZ was uninhabited. Malaria clusterings were detected in Uraka, Iberikodo and Sagidan settlements in Epe (Moran’s Index = 0.62, p < 0.05) but not in Orimedu. The multiple distinct malaria risk zones coupled with the characteristic steep topography may be responsible for forcing malaria to localize and cluster in Epe compared to Orimedu.

Climate risk maps as boundary objects for future forests

Climate change poses significant threats to ecosystems and biodiversity. Conventional management strategies often fall short, leading to uncertainties in addressing these challenges. Natural and environmental scientists play a crucial role by providing evidence-based guidance. Social science research, at the same time, highlights the complexity of transferring and applying knowledge across different social and professional groups and shows that further research is needed. Using a German case study, my research addresses this issue by examining the dynamics between predictive climate risk maps, intended as decision-support tool for forest management, the developing scientists, the receiving environmental managers and further political actors. Semi-structured qualitative interviews with representatives from these groups were conducted and analyzed, revealing that climate risk maps can function as predictive boundary objects, balancing flexibility and robustness. With their high level of visual and epistemic power, these maps generate knowledge tensions, facilitate interactions, and foster the implicit co-production of broader environmental management discourse. At the same time the maps are continuously contested, discussed, and updated through feedback, becoming themselves part of an ongoing informal co-productive process. This dual role creates ambiguity: they provide concrete answers to specific management related questions while highlighting simultaneously limitations that prompt more fundamental inquiries, driving an overall societal learning process. Hence, future efforts should enhance formal support for co-productive processes to ensure evidence-based advisory tools are scientifically robust, contextually adapted, and democratize knowledge dynamics through continuous dialogue, mutual learning, and integration of scientific as well as local knowledge.

Comprehensive Spatial-Temporal and Risk Factor Insights for Optimizing Livestock Anthrax Vaccination Strategies in Karnataka, India.

Anthrax, a zoonotic disease affecting both livestock and humans globally, is caused by Bacillus anthracis. The objectives of this study were the following: (1) to identify environmental risk factors for anthrax and use this information to develop an improved predictive risk map, and (2) to estimate spatial variation in basic reproduction number (Ro) and herd immunity threshold at the village level, which can be used to optimize vaccination policies within high-risk regions. Based on the anthrax incidences from 2000-2023 and vaccine administration figures between 2008 and 2022 in Karnataka, this study depicted spatiotemporal pattern analysis to derive a risk map employing machine learning algorithms and estimate Ro and herd immunity threshold for better vaccination coverage. Risk factors considered were key meteorological, remote sensing, soil, and geographical parameters. Spatial autocorrelation and SaTScan analysis revealed the presence of hotspots and clusters predominantly in the southern, central, and uppermost northern districts of Karnataka and temporal cluster distribution between June and September. Factors significantly associated with anthrax were air temperature, surface pressure, land surface temperature (LST), enhanced vegetation index (EVI), potential evapotranspiration (PET), soil temperature, soil moisture, pH, available potassium, sulphur, and boron, elevation, and proximity to waterbodies and waterways. Ensemble technique with random forest and classification tree models were used to improve the prediction accuracy of anthrax. High-risk areas are expected in villages in the southern, central, and extreme northern districts of Karnataka. The estimated Ro revealed 11 high-risk districts with Ro > 1.50 and respective herd immunity thresholds ranging from 11.24% to 55.47%, and the assessment of vaccination coverage at the 70%, 80%, and 90% vaccine efficacy levels, all serving for need-based strategic vaccine allocation. A comparison analysis of vaccinations administered and vaccination coverage estimated in this study is used to illustrate difference in the supply and vaccine force. The findings from the present study may support in planning preventive interventions, resource allocation, especially of vaccines, and other control strategies against anthrax across Karnataka, specifically focusing on predicted high-risk regions.

Spatiotemporal distribution and environmental influences of severe fever with thrombocytopenia syndrome in Shandong Province, China

BackgroundSevere fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease discovered in China in 2009. The purpose of this study was to describe the spatiotemporal distribution of SFTS and to identify its environmental influencing factors and potential high-risk areas in Shandong Province, China.MethodsData on the SFTS incidence from 2010 to 2021 were collected. Spatiotemporal scan statistics were used to identify the time and area of SFTS clustering. The maximum entropy (MaxEnt) model was used to analyse environmental influences and predict high-risk areas.ResultsFrom 2010 to 2021, a total of 5705 cases of SFTS were reported in Shandong. The number of SFTS cases increased yearly, with a peak incidence from April to October each year. Spatiotemporal scan statistics showed the existence of one most likely cluster and two secondary likely clusters in Shandong. The most likely cluster was in the eastern region, from May to October 2021. The first secondary cluster was in the central region, from May to October 2021. The second secondary cluster was in the southeastern region, from May to September 2020. The MaxEnt model showed that the mean annual wind speed, NDVI, cattle density and annual cumulative precipitation were the key factors influencing the occurrence of SFTS. The predicted risk map showed that the area of high prevalence was 28,120 km2, accounting for 18.05% of the total area of the province.ConclusionsThe spatiotemporal distribution of SFTS was heterogeneous and influenced by multidimensional environmental factors. This should be considered as a basis for delineating SFTS risk areas and developing SFTS prevention and control measures.

A systematic review of the data, methods and environmental covariates used to map Aedes-borne arbovirus transmission risk

BackgroundAedes (Stegomyia)-borne diseases are an expanding global threat, but gaps in surveillance make comprehensive and comparable risk assessments challenging. Geostatistical models combine data from multiple locations and use links with environmental and socioeconomic factors to make predictive risk maps. Here we systematically review past approaches to map risk for different Aedes-borne arboviruses from local to global scales, identifying differences and similarities in the data types, covariates, and modelling approaches used.MethodsWe searched on-line databases for predictive risk mapping studies for dengue, Zika, chikungunya, and yellow fever with no geographical or date restrictions. We included studies that needed to parameterise or fit their model to real-world epidemiological data and make predictions to new spatial locations of some measure of population-level risk of viral transmission (e.g. incidence, occurrence, suitability, etc.).ResultsWe found a growing number of arbovirus risk mapping studies across all endemic regions and arboviral diseases, with a total of 176 papers published 2002–2022 with the largest increases shortly following major epidemics. Three dominant use cases emerged: (i) global maps to identify limits of transmission, estimate burden and assess impacts of future global change, (ii) regional models used to predict the spread of major epidemics between countries and (iii) national and sub-national models that use local datasets to better understand transmission dynamics to improve outbreak detection and response. Temperature and rainfall were the most popular choice of covariates (included in 50% and 40% of studies respectively) but variables such as human mobility are increasingly being included. Surprisingly, few studies (22%, 31/144) robustly tested combinations of covariates from different domains (e.g. climatic, sociodemographic, ecological, etc.) and only 49% of studies assessed predictive performance via out-of-sample validation procedures.ConclusionsHere we show that approaches to map risk for different arboviruses have diversified in response to changing use cases, epidemiology and data availability. We identify key differences in mapping approaches between different arboviral diseases, discuss future research needs and outline specific recommendations for future arbovirus mapping.

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.

Health Impact of Household Waste Burning in Khartoum State, Sudan.

Waste disposal and management is a global concern affecting both high- and low-income countries. This research assessed the health impact of burning household waste in Khartoum State, Sudan. An online community-based cross-sectional study was implemented on a sample of 844 participants selected through a stratified random sampling technique across Khartoum State. The data were collected through a standardized pre tested online questionnaire. The data file was georeferenced through Google Earth Pro and analysed with SPSS 23 and ArcGIS 10.3. The data were summarized numerically and graphically. The appropriate frequency tables were used in ArcGIS to generate geographical distribution maps of household waste burning and predictive health risk maps of waste burning in Khartoum State. Statistical tests performed for association carried out were Chi-square and ANOVA. A binary regression analysis established the relationship between burning of household waste and its associated factors. All statistical tests were considered significant when p < 0.05. The practice of burning household waste was performed by 74.5% (619/831) of the participants with 50.8% (311/612) who reported burning the waste weekly. The health conditions related to household waste burning were predominately asthma (57.0%) and respiratory manifestations (38.0%). Of the ten contributing factors of health risks related to burning household waste, the two statistically significant were the frequency of waste collection (OR = 0.720, 95% [CI: 0.593-0.875], p = 0.001) and the place of waste disposal (OR = 0.791, 95% [CI: 0.651-0.961], p = 0.018). The practice of burning household waste in Khartoum State was a public health concern. Sociodemographic and managerial factors exposing residents to health risks appeal political, health authorities and communities to establish a partnership to manage household waste for public safety and good quality of life.

A predictive risk map for human leptospirosis guiding further investigations in brown rats and surface water

ABSTRACT Leptospirosis is a zoonosis caused by the spirochete Leptospira spp. It is often not clear why certain areas appear to be hotspots for human leptospirosis. Therefore, a predictive risk map for the Netherlands was developed and assessed, based on a random forest model for human leptospirosis incidence levels with various environmental factors and rat density as variables. Next, it was tested whether misclassifications of the risk map could be explained by the prevalence of Leptospira spp. in brown rats. Three recreational areas were chosen, and rats (≥25/location) were tested for Leptospira spp. Concurrently, it was investigated whether Leptospira spp. prevalence in brown rats was associated with Leptospira DNA concentration in surface water, to explore the usability of this parameter in future studies. Approximately 1 L of surface water sample was collected from 10 sites and was tested for Leptospira spp. Although the model predicted the locations of patients relatively well, this study showed that the prevalence of Leptospira spp. infection in rats may be an explaining variable that could improve the predictive model performance. Surface water samples were all negative, even if they had been taken at sites with a high Leptospira spp. prevalence in rats.

Forest Fire Prediction Based on Long- and Short-Term Time-Series Network

Modeling and prediction of forest fire occurrence play a key role in guiding forest fire prevention. From the perspective of the whole world, forest fires are a natural disaster with a great degree of hazard, and many countries have taken mountain fire prediction as an important measure for fire prevention and control, and have conducted corresponding research. In this study, a forest fire prediction model based on LSTNet is proposed to improve the accuracy of forest fire forecasts. The factors that influence forest fires are obtained through remote sensing satellites and GIS, and their correlation is estimated using Pearson correlation analysis and testing for multicollinearity. To account for the spatial aggregation of forest fires, the data set was constructed using oversampling methods and proportional stratified sampling, and the LSTNet forest fire prediction model was established based on eight influential factors. Finally, the predicted data were incorporated into the model and the predicted risk map of forest fires in Chongli, China was drawn. This paper uses metrics such as RMSE to compare with traditional machine learning methods, and the results show that the LSTNet model proposed in this paper has high accuracy (ACC 0.941). This study illustrates that the model can effectively use spatial background information and the periodicity of forest fire factors, and is a novel method for spatial prediction of forest fire susceptibility.

Predicting road flooding risk with crowdsourced reports and fine-grained traffic data

The objective of this study is to predict road flooding risks based on topographic, hydrologic, and temporal precipitation features using machine learning models. Existing road inundation studies either lack empirical data for model validations or focus mainly on road inundation exposure assessment based on flood maps. This study addresses this limitation by using crowdsourced and fine-grained traffic data as an indicator of road inundation, and topographic, hydrologic, and temporal precipitation features as predictor variables. Two tree-based machine learning models (random forest and AdaBoost) were then tested and trained for predicting road inundations in the contexts of 2017 Hurricane Harvey and 2019 Tropical Storm Imelda in Harris County, Texas. The findings from Hurricane Harvey indicate that precipitation is the most important feature for predicting road inundation susceptibility, and that topographic features are more critical than hydrologic features for predicting road inundations in both storm cases. The random forest and AdaBoost models had relatively high AUC scores (0.860 and 0.810 for Harvey respectively and 0.790 and 0.720 for Imelda respectively) with the random forest model performing better in both cases. The random forest model showed stable performance for Harvey, while varying significantly for Imelda. This study advances the emerging field of smart flood resilience in terms of predictive flood risk mapping at the road level. In particular, such models could help impacted communities and emergency management agencies develop better preparedness and response strategies with improved situational awareness of road inundation likelihood as an extreme weather event unfolds.

Geospatial distribution and predictive modeling of onchocerciasis in Ogun State, Nigeria.

Onchocerciasis caused by infection with Onchocerca volvulus is a disease of public health importance and is highly associated with disability. As Nigeria is aiming at eliminating onchocerciasis by 2030, there is a need to develop newer tools to map disease prevalence and identify environmental factors driving disease prevalence, even in places that have not been previously targeted for preventive chemotherapy. This study produced predictive risk-maps of onchocerciasis in Ogun State. Georeferenced onchocerciasis infection data obtained from a cross-sectional survey at 32 locations between March and July 2015 together with remotely-sensed environmental data were analyzed using Ecological Niche Models (ENM). A total of 107 field occurrence points for O. volvulus infection were recorded. A total of 43 positive occurrence points were used for modelling. ENMs were used to estimate the current geographic distribution of O. volvulus in Ogun State. Maximum Entropy distribution modeling (MaxEnt) was used for predicting the potential suitable habitats, using a portion of the occurrence records. A total of 19 environmental variables were used to model the potential geographical distribution area under current climatic conditions. Empirical prevalence of 9.3% was recorded in this study. The geospatial distribution of infection revealed that all communities in Odeda Local Government Area (a peri-urban LGA) showed remarkably high prevalence compared with other LGAs. The predicted high-risk areas (probability > 0.8) of O. volvulus infection were all parts of Odeda, Abeokuta South, and Abeokuta North, southern part of Imeko-Afon, a large part of Yewa North, some parts of Ewekoro and Obafemi-Owode LGAs. The estimated prevalence for these regions were >60% (between 61% and 100%). As predicted, O. volvulus occurrence showed a positive association with variables reflecting precipitation in Ogun State. Our predictive risk-maps has provided useful information for the elimination of onchocerciais, by identifying priority areas for delivery of intervention in Ogun State, Nigeria.

Elevation determines the spatial risk of Anthrax outbreaks in Karnataka, India

Anthrax is an economically important livestock disease affecting subsistence farmers and it is of zoonotic importance. Anthrax is endemic in many states of India including Karnataka. Identification of spatial risk factors for occurrence of anthrax and development of predictive risk maps are required for planning adequate vaccination in high-risk areas as well as targeted surveillance activities in animals, humans and environment. In this study, village level anthrax outbreak locations from Karnataka (1997-2016) were geo-referenced and predictive risk map was developed using temporally Fourier Processed remotely sensed variables. A non-linear discriminant analysis approach was used to develop the risk map for Karnataka. Elevation was identified as top predictor variable in the 10 variables selected. The predicted risk map showed good accuracy and validation statistics when evaluated using different metrics (Kappa, sensitivity, specificity, AUC). The predicted risk map also showed good correspondence with past outbreaks. Further, we used Bayesian Penalised spline method to estimate species response curves for top 10 variables selected. The validated risk map can be used in planning vaccination strategy and surveillance in high-risk areas.

Spatial modeling of two mosquito vectors of West Nile virus using integrated nested Laplace approximations

AbstractThe abundance of Culex restuans and Culex pipiens in relation to ecological predictors is poorly understood in regions of the United States where their ranges overlap. It is suspected that these species play different roles in spreading West Nile virus (WNV) in these regions, but few studies have modeled these species separately or accounted for spatial correlation using Bayesian methods. We used mosquito surveillance data collected by the Pennsylvania Department of Environmental Protection from 2002 to 2016 and integrated nested Laplace approximations with the stochastic partial differential equation approach to predict C. restuans and C. pipiens abundance in relation to several ecological predictors. We then made a predictive risk surface of abundance for each species at locations that were not sampled. Explanatory variables in the models included ecological variables previously described to be important predictors of the abundance of these mosquito species. Developed habitat, temperature, and precipitation were important predictor variables for the abundance of C. restuans, whereas developed habitat, snow water equivalent, and normalized difference water index were important predictor variables for the abundance of C. pipiens. The abundance of C. restuans had a negative relationship with developed habitat in contrast to C. pipiens abundance, which had a positive relationship with developed habitat. Julian date was modeled as a temporal trend for both species and showed C. restuans to be more abundant from late April through late June and C. pipiens to be more abundant from July through September. A seasonal crossover was observed between these two species on Julian day 185, 4 July. We observed different spatial patterns of abundance in the predictive risk maps of each of the species. Our results indicate that modeling the abundance of these species spatially and separately in regions where these two mosquito vectors coexist can help gain further insight into understanding the epidemiology of WNV in human and susceptible animal populations.

Spatiotemporal modeling of traffic risk mapping: A study of urban road networks in Barcelona, Spain

Accidents on the road have always been a major concern in modern society. According to the World Health Organization, globally road traffic collisions are one of the leading and fastest growing causes of disability and death. The present research work is conducted on ten years of traffic accident data in an urban environment to explore and analyze spatial and temporal variation in the accidents and related injuries. The proposed spatiotemporal model can make predictions regarding the number of injuries incurred on individual road segments. Bayesian methodology using Integrated Nested Laplace Approximation (INLA) with Stochastic Partial Differential Equations (SPDE) has been applied to generate a predicted risk map for the entire road network. The current study introduces INLA- SPDE modeling to perform spatiotemporal predictive analysis on selected areas, precisely on road networks instead of traditional continuous regions. Additionally, the result risk maps act as a baseline to identify the safe routes in a spatiotemporal context. The methodology can be adapted and applied to enhanced INLA-SPDE modeling of spatial point processes precisely on road networks.

Spatiotemporal variation and environmental sensitivity of childhood enteric pathogen infection risk: a Planetary Health approach to predictive modelling and risk mapping

BackgroundDiarrhoeal disease is a major cause of childhood illness caused by many ecologically sensitive enteropathogens. The bacterium Shigella causes around 64 000 child deaths annually and thrives in warm, moist conditions. Spatiotemporally varying estimates of disease burden are crucial for targeting interventions like the forthcoming phase 2 trials of the Shigella vaccine. We aimed to model and map paediatric Shigella infection risk across low and middle-income countries (LMICs) using covariates with quasi-global coverage. MethodsNational Aeronautics and Space Administration (NASA), Johns Hopkins University, and University of Virginia have been collaborating to compile diagnostic data from multiple sources into a repository of unparalleled size, scope, and diversity. Data were combined from 20 studies that used PCR to diagnose Shigella in stool samples from children younger than 5 years in LMICs, resulting in a pooled database of more than 66 000 samples from 22 000 people in 23 countries. These were matched spatiotemporally to time-varying hydrometeorological variables extracted from historical daily Earth observation-based and model-based estimates derived from re-analysis, environmental and demographic spatial covariates, and household-level and subject-level data. Variable selection was carried out and a predictive projection method was implemented, making predictions separately for three age groups: 0–11 months, 12–23 months, and 24–59 months. FindingsAir temperature was the most influential variable, with infection risk peaking at temperatures of around 34°C (close to that of the human body). The interaction of precipitation with soil moisture also had a strong effect. Accessibility to cities was an influential non-hydrometeorological covariate. The model predicted wide belts of elevated Shigella risk in tropical sub-Saharan Africa, India, and Brazil, and smaller pockets of high prevalence in, for example, New Guinea, Ethiopia, the Sahel, coastal Central America, and Colombia, and others. InterpretationAdvances in differential pathogen diagnosis, climatological modelling, and geostatistical methods now make it possible to generate quasi-global maps of enteric pathogen transmission risk. These predictions will be made publicly available for use in decision making, such as identifying populations in hotspots of Shigella transmission risk that can be prioritised when vaccines become available. FundingNASA's Group on Earth Observations Work Programme (16-GEO16-0047).

Evaluating the Spatial Risk of Bacterial Foodborne Diseases Using Vulnerability Assessment and Geographically Weighted Logistic Regression

Foodborne diseases are an increasing concern to public health; climate and socioeconomic factors influence bacterial foodborne disease outbreaks. We developed an “exposure–sensitivity–adaptability” vulnerability assessment framework to explore the spatial characteristics of multiple climatic and socioeconomic environments, and analyzed the risk of foodborne disease outbreaks in different vulnerable environments of Zhejiang Province, China. Global logistic regression (GLR) and geographically weighted logistic regression (GWLR) models were combined to quantify the influence of selected variables on regional bacterial foodborne diseases and evaluate the potential risk. GLR results suggested that temperature, total precipitation, road density, construction area proportions, and gross domestic product (GDP) were positively correlated with foodborne diseases. GWLR results indicated that the strength and significance of these relationships varied locally, and the predicted risk map revealed that the risk of foodborne diseases caused by Vibrio parahaemolyticus was higher in urban areas (60.6%) than rural areas (20.1%). Finally, distance from the coastline was negatively correlated with predicted regional risks. This study provides a spatial perspective for the relevant departments to prevent and control foodborne diseases.

Smart flood resilience: harnessing community-scale big data for predictive flood risk monitoring, rapid impact assessment, and situational awareness

Smart resilience is the beneficial result of the collision course of the fields of data science and urban resilience to flooding. The objective of this study is to propose and demonstrate a smart flood resilience framework that leverages heterogeneous community-scale big data and infrastructure sensor data to enhance predictive risk monitoring and situational awareness. The smart flood resilience framework focuses on four core capabilities that could be augmented by the use of heterogeneous community-scale big data and analytics techniques: (1) predictive flood risk mapping; (2) automated rapid impact assessment; (3) predictive infrastructure failure prediction and monitoring; and (4) smart situational awareness capabilities. We demonstrate the components of these core capabilities of the smart flood resilience framework in the context of the 2017 Hurricane Harvey in Harris County, Texas. First, we present the use of flood sensors for the prediction of floodwater overflow in channel networks and inundation of co-located road networks. Second, we discuss the use of social media and machine learning techniques for assessing the impacts of floods on communities and sensing emotion signals to examine societal impacts. Third, we describe the use of high-resolution traffic data in network-theoretic models for nowcasting of flood propagation on road networks and the disrupted access to critical facilities, such as hospitals. Fourth, we introduce how location-based and credit card transaction data were used in spatial analyses to proactively evaluate the recovery of communities and the impacts of floods on businesses. These analyses show that the significance of core capabilities of the smart flood resilience framework in helping emergency managers, city planners, public officials, responders, and volunteers to better cope with the impacts of catastrophic flooding events.

Mapping Risk of Nipah Virus Transmission from Bats to Humans in Thailand.

Nipah virus (NiV) is a zoonotic virus that can pose a serious threat to human and livestock health. Old-world fruit bats (Pteropus spp.) are the natural reservoir hosts for NiV, and Pteropus lylei, Lyle's flying fox, is an important host of NiV in mainland Southeast Asia. NiV can be transmitted from bats to humans directly via bat-contaminated foods (i.e., date palm sap or fruit) or indirectly via livestock or other intermediate animal hosts. Here we construct risk maps for NiV spillover and transmission by combining ecological niche models for the P. lylei bat reservoir with other spatial data related to direct or indirect NiV transmission (livestock density, foodborne sources including fruit production, and human population). We predict the current and future (2050 and 2070) distribution of P. lylei across Thailand, Cambodia, and Vietnam. Our best-fit model predicted that central and western regions of Thailand and small areas in Cambodia are currently the most suitable habitats for P. lylei. However, due to climate change, the species range is predicted to expand to include lower northern, northeastern, eastern, and upper southern Thailand and almost all of Cambodia and lower southern Vietnam. This expansion will create additional risk areas for human infection from P. lylei in Thailand. Our combined predictive risk maps showed that central Thailand, inhabited by 2.3 million people, is considered highly suitable for the zoonotic transmission of NiV from P. lylei. These current and future NiV transmission risk maps can be used to prioritize sites for active virus surveillance and developing awareness and prevention programs to reduce the risk of NiV spillover and spread in Thailand.

Models for predicting bulinids species habitats in southwestern Nigeria

BackgroundSchistosomiasis prevalence is high in southwestern Nigeria and planorbids of the genus Bulinus had been implicated in the transmission of the disease in the area. The knowledge of species distribution in relation to environmental variables will be auspicious in planning control strategies. MethodsSatellite imagery and geographic information system (GIS) were used to develop models for predicting the habitats suitable for bulinid species. Monthly snail sample collection was done in twenty-three randomly selected water contact sites using the standard method for a period of two years. Remotely sensed variables such as Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI) were extracted from Landsat TM, ETM+; Slope and Elevation were obtained from digital elevation model (DEM) while Rainfall was retrieved from European Meteorology Research Program. These environmental factors and snail species were integrated into QGIS to predict the potential habitats of different bulinid species using an exploratory regression model. ResultsThe following environmental variables: flat-moderate slope (0.01–15.83), LST (21.1 °C-23.4 °C), NDVI (0.19–0.52), rainfall (> 1569.34 mm) and elevation (1–278 m) contributed to the model used in predicting habitat suitable for bulinids snail intermediate hosts. Exploratory regression models showed that LST, NDVI and slope were predictors of Bulinus globosus and Bulinus jousseaumei; elevation, LST, rainfall and slope were predictors of Bulinus camerunensis; rainfall, NDVI and slope were predictors of B. senegalensis while NDVI and slope were predictors of Bulinus forskalii in the area. Bulinids in the forskalii group showed clustering in middle belt and south. The predictive risk map of B. jousseaumei was similar to the pattern described for B. globosus, but with a high R-square value of 81%. ConclusionThe predictive risk models of bulinid species in this study provided a robust output for the study area which could be used as base-line for other areas in that ecological zone. It will be useful in appropriate allocation of scarces resources in the control of schistosomiasis in that environment.

Estimating the population at risk with soil transmitted helminthiasis and annual drug requirements for preventive chemotherapy in Ogun State, Nigeria

Soil transmitted helminth (STH) infections are among the most common human infections worldwide with over 1 billion people affected. Many estimates of STH infection are often based on school-aged children (SAC). This study produced predictive risk-maps of STH on a more finite scale, estimated the number of people infected, and the amount of drug required for preventive chemotherapy (PC) in Ogun state, Nigeria. Georeferenced STH infection data obtained from a cross-sectional survey at 33 locations between July 2016 and November 2018, together with remotely-sensed environmental and socio-economic data were analyzed using Bayesian geostatistical modelling. Stepwise variable selection procedure was employed to select a parsimonious set of predictors to predict risk and spatial distribution of STH infections. The number of persons (pre-school ages children, SAC and adults) infected with STH were estimated, with the amount of tablets needed for preventive chemotherapy. An overall prevalence of 17.2% (95% CI 14.9, 19.5) was recorded for any STH infection. Ascaris lumbricoides infections was the most predominant, with an overall prevalence of 13.6% (95% CI 11.5, 15.7), while Hookworm and Trichuris trichiura had overall prevalence of 4.6% (95% CI 3.3, 5.9) and 1.7% (95% CI 0.9, 2.4), respectively. The model-based prevalence predictions ranged from 5.0 to 23.8% for Ascaris lumbricoides, from 2.0 to 14.5% for hookworms, and from 0.1 to 5.7% for Trichuris trichiura across the implementation units. The predictive maps revealed a spatial pattern of high risk in the central, western and on the border of Republic of Benin. The model identified soil pH, soil moisture and elevation as the main predictors of infection for A. lumbricoides, Hookworms and T. trichiura respectively. About 50% (10/20) of the implementation units require biannual rounds of mass drug administration. Approximately, a total of 1.1 million persons were infected and require 7.8 million doses. However, a sub-total of 375,374 SAC were estimated to be infected, requiring 2.7 million doses. Our predictive risk maps and estimated PC needs provide useful information for the elimination of STH, either for resource acquisition or identifying priority areas for delivery of interventions in Ogun State, Nigeria.