Data-sparse Environments Research Articles

A health decision analytical model to evaluate the cost-effectiveness of female genital schistosomiasis screening strategies: The female genital schistosomiasis SCREEN framework.

Female genital schistosomiasis is a chronic gynaecological disease caused by the waterborne parasite Schistosoma (S.) haematobium. It affects an estimated 30-56 million girls and women globally, mostly in sub-Saharan Africa where it is endemic, and negatively impacts their sexual and reproductive life. Recent studies found evidence of an association between female genital schistosomiasis and increased prevalence of HIV and cervical precancer lesions. Despite the large population at risk, the burden and impact of female genital schistosomiasis are scarcely documented, resulting in neglect and insufficient resource allocation. There is currently no standardised method for individual or population-based female genital schistosomiasis screening and diagnosis which hinders accurate assessment of disease burden in endemic countries. To optimise financial allocations for female genital schistosomiasis screening, it is necessary to explore the cost-effectiveness of different strategies by combining cost and impact estimates. Yet, no economic evaluation has explored the value for money of alternative screening methods. This paper describes a novel application of health decision analytical modelling to evaluate the cost-effectiveness of different female genital schistosomiasis screening strategies across endemic settings. The model combines a decision tree for female genital schistosomiasis screening strategies, and a Markov model for the natural history of cervical cancer to estimate the cost per disability-adjusted life-years averted for different screening strategies, stratified by HIV status. It is a starting point for discussion and for supporting priority setting in a data-sparse environment.

Climate change and coastal resiliency of Suva, Fiji: a holistic approach for measuring climate risk using the climate and ocean risk vulnerability index (CORVI).

The online version contains supplementary material available at 10.1007/s11027-022-10043-4.

Using machine learning to assess the livelihood impact of electricity access.

In many regions of the world, sparse data on key economic outcomes inhibit the development, targeting and evaluation of public policy1,2. We demonstrate how advancements in satellite imagery and machine learning (ML) can help ameliorate these data and inference challenges. In the context of an expansion of the electrical grid across Uganda, we show how a combination of satellite imagery and computer vision can be used to develop local-level livelihood measurements appropriate for inferring the causal impact of electricity access on livelihoods. We then show how ML-based inference techniques deliver more reliable estimates of the causal impact of electrification than traditional alternatives when applied to these data. We estimate that grid access improves village-level asset wealth in rural Uganda by up to 0.15 standard deviations, more than doubling the growth rate during our study period relative to untreated areas. Our results provide country-scale evidence on the impact of grid-based infrastructure investment and our methods provide a low-cost, generalizable approach to future policy evaluation in data-sparse environments.

Mapping the Flood Risk Exposure Using Open-Source Geospatial Tools and Techniques: A Case of Gampaha Divisional Secretariat Division, Sri Lanka

The use of open-source data and tools in disaster exposure mapping is presented in this paper. Disaster exposure is a collection of the element at risk to potential loss. Gampaha divisional secretariat (DS) is a study area laid on the lower part of the Attanagalu Oya river basin. As the geospatial tools, OpenStreetMap (OSM), Java OpenStreetMap (JOSM), QGIS, GPS Essentials, and Open Map Kit (OMK) are used. The elements of disaster exposure, including the number of people or types of assets, are surveyed and inventoried using the OSM platforms. Local, national, and international agencies produce and evaluate the data. The study developed spatial data for building footprints of 165,000 households, street lengths of 2300 km, hospital units of 16, and utility units of 2300. This could overcome the main challenges of exposure mapping in the area. The procedure developed in the exposure mapping can be used in a data-sparse environment. Exposure mapping is generally used to estimate the impact of hazards or disasters, which are essential in effective disaster management. How are there still remaining challenges in disaster exposure mapping such as less awareness about the mapping procedure, lack of government support, internet access, hardware, and inability to understand the value of exposure mapping?

Using satellite data and deep learning to estimate educational outcomes in data-sparse environments

ABSTRACT The lack of systematic measurements of socioeconomic factors on a worldwide scale remains a significant challenge to our understanding of human well-being. A growing body of literature suggests that some of these measurement gaps can be filled using remote sensing, imputing human conditions on the ground based on the ways in which social groups have modified – or, not – their physical environment. In this article, we contribute to this growing body of literature by presenting a case study estimating school test scores based solely on publicly available imagery in both the Philippines (2010, 2014) and Brazil (2016). We contrast single image convolutional neural network (CNN) approaches to multi-source ensembles and find predictive accuracy for individual schools across years and regions ranging from 76% to 80%. Finally, we discuss broader considerations related to the operational use of CNN-based approaches for measuring socioeconomic factors, and provide open source computer code for community use.

Cumulative Effects of Uncertainty on Simulated Streamflow in a Hydrologic Modeling Environment

It is common in the literature to not consider all sources of uncertainty simultaneously: input, structural, parameter, and observed calibration data uncertainty, particularly in data-sparse environments due to data limitations and the complexities that arise from data limitations when propagating uncertainty downstream in a modelling chain. This paper presents results for the propagation of multiple sources of uncertainty towards the estimation of streamflow uncertainty in a data-sparse environment. Uncertainty sources are separated to ensure low likelihood uncertainty distribution tails are not rejected to examine the interaction of sources of uncertainty. Three daily resolution hydrologic models (HYPE, WATFLOOD, and HEC-HMS), forced with three precipitation ensemble realizations, generated from five gridded climate datasets, for the 1981–2010 period were used to examine the effects of cumulative propagation of uncertainty in the Lower Nelson River Basin as part of the BaySys project. Selected behavioral models produced an average range of Kling-Gupta Efficiency scores of 0.79–0.68. Two alternative methods for behavioral model selection were also considered that ingest streamflow uncertainty. Structural and parameter uncertainty was found to be insufficient, individually, by producing some uncertainty envelopes narrower than observed streamflow uncertainty. Combined structural and parameter uncertainty, propagated to simulated streamflow, often enveloped nearly 100% of observed streamflow values, however, high and low flow years were generally a source for lower reliabilities in simulated results. Including all sources of uncertainty generated simulated uncertainty bounds that enveloped most of the observed flow uncertainty bounds including improvement for high and low flow years across all gauges although the uncertainty bounds generated were of low likelihood. Overall, accounting for each source of uncertainty added value to the simulated uncertainty bounds when compared to hydrometric uncertainty; the inclusion of hydrometric uncertainty was key for identifying the improvements to simulated ensembles.

Assessing the soil quality of Bansloi river basin, eastern India using soil-quality indices (SQIs) and Random Forest machine learning technique

In order to address agronomic sustainability issues for the densely populated country like India tools for assessing soil quality are the prime requisite. In the present study with the fundamental goal to assess soil quality we have intended to assemble a range of soil quality indicators and validate the result with respect to a Bansloi river basin (Area-1860 km2) of eastern India. In aim to approximate soil quality, field survey data of 296 surface samples (0–20 cm) are tested in laboratory settings and 12 soil quality indicators (namely- N, P, K, pH, EC, OC, Fe, Cu, Mn, Zn, B, and S) are integrated into the Soil Quality Indices (SQIs). Simple additive (SQISA), PCA based (SQIPCA) and Correlation (SQIr) based weighted index methods are used for the SQI estimation. SQIs results are then co-related with major crops (Oryza sativa, Triticum aestivum, Brassica juncea, Zea mays etc.) yield to verify their functional relationships. Spatial prediction and mapping of soil quality in data sparse environment is another aim of the study. In this regard Random Forest (RF) method is employed to calculate the predicted soil quality indices (PSOIs). Three error criteria (namely, R2; ME and RMSE) are used to appraise their relative credibility. PCA results show that, micro-nutrients Fe, Mn apart from N, P, K have significant control towards soil quality. Results also demonstrate that, SQIr and PSQIr are relatively more consistent in their correlation with the crops yield. Among the PSQIs, PSQIr counts the smallest error in the prediction of spatial soil quality. Overall results also confirm optimal performance of RF for SQI prediction and PSQIr is found to be the most competent tool to predict the soil quality at un-sampled locations. However, before generalizing, the authors recommend thorough validation of the PSQIr in other regions and long-term field experiments.

Gaussian Process Regression for Arctic Coastal Erosion Forecasting

Arctic coastal morphology is governed by multiple factors, many of which are affected by climatological changes. As the season length for shore-fast ice decreases and temperatures warm permafrost soils, coastlines are more susceptible to erosion from storm waves. Such coastal erosion is a concern since a large proportion of the population centers and infrastructure in the Arctic are located near the coasts. Stakeholders and decision makers increasingly need models capable of scenario-based predictions to assess and mitigate the effects of coastal morphology on infrastructure and land use. Our research uses Gaussian process (GP) models to forecast Arctic coastal erosion along the Beaufort Sea near Drew Point, AK, USA. GP regression is a data-driven modeling methodology capable of extracting patterns and trends from data-sparse environments such as remote Arctic coastlines. To train our model, we use annual coastline positions and near-shore summer temperature averages from existing data sets and extend these data by extracting additional coastlines from satellite imagery. We combine our calibrated models with future climate models to generate a range of plausible future erosion scenarios. Our results show that the GP methodology substantially improves yearly predictions compared to linear and nonlinear least squares methods and is capable of generating detailed forecasts suitable for the use by decision makers.

Estimating the Population Size of Female Sex Workers in Three South African Cities: Results and Recommendations From the 2013-2014 South Africa Health Monitoring Survey and Stakeholder Consensus.

BackgroundRobust population size estimates of female sex workers and other key populations in South Africa face multiple methodological limitations, including inconsistencies in surveillance and programmatic indicators. This has, consequently, challenged the appropriate allocation of resources and benchmark-setting necessary to an effective HIV response. A 2013-2014 integrated biological and behavioral surveillance (IBBS) survey from South Africa showed alarmingly high HIV prevalence among female sex workers in South Africa’s three largest cities of Johannesburg (71.8%), Cape Town (39.7%), and eThekwini (53.5%). The survey also included several multiplier-based population size estimation methods.ObjectiveThe objective of our study was to present the selected population size estimation methods used in an IBBS survey and the subsequent participatory process used to estimate the number of female sex workers in three South African cities.MethodsIn 2013-2014, we used respondent-driven sampling to recruit independent samples of female sex workers for IBBS surveys in Johannesburg, Cape Town, and eThekwini. We embedded multiple multiplier-based population size estimation methods into the survey, from which investigators calculated weighted estimates and ranges of population size estimates for each city’s female sex worker population. Following data analysis, investigators consulted civil society stakeholders to present survey results and size estimates and facilitated stakeholder vetting of individual estimates to arrive at consensus point estimates with upper and lower plausibility bounds.ResultsIn total, 764, 650, and 766 female sex workers participated in the survey in Johannesburg, Cape Town, and eThekwini, respectively. For size estimation, investigators calculated preliminary point estimates as the median of the multiple estimation methods embedded in the IBBS survey and presented these to a civil society-convened stakeholder group. Stakeholders vetted all estimates in light of other data points, including programmatic experience, ensuring inclusion only of plausible point estimates in median calculation. After vetting, stakeholders adopted three consensus point estimates with plausible ranges: Johannesburg 7697 (5000-10,895); Cape Town 6500 (4579-9000); eThekwini 9323 (4000-10,000).ConclusionsUsing several population size estimates methods embedded in an IBBS survey and a participatory stakeholder consensus process, the South Africa Health Monitoring Survey produced female sex worker size estimates representing approximately 0.48%, 0.49%, and 0.77% of the adult female population in Johannesburg, Cape Town, and eThekwini, respectively. In data-sparse environments, stakeholder engagement and consensus is critical to vetting of multiple empirically based size estimates procedures to ensure adoption and utilization of data-informed size estimates for coordinated national and subnational benchmarking. It also has the potential to increase coherence in national and key population-specific HIV responses and to decrease the likelihood of duplicative and wasteful resource allocation. We recommend building cooperative and productive academic-civil society partnerships around estimates and other strategic information dissemination and sharing to facilitate the incorporation of additional data as it becomes available, as these additional data points may minimize the impact of the known and unknown biases inherent in any single, investigator-calculated method.

Moving horizon estimation for assimilating H-SAF remote sensing data into the HBV hydrological model

Remote sensing information has been extensively developed over the past few years including spatially distributed data for hydrological applications at high resolution. The implementation of these products in operational flow forecasting systems is still an active field of research, wherein data assimilation plays a vital role on the improvement of initial conditions of streamflow forecasts. We present a novel implementation of a variational method based on Moving Horizon Estimation (MHE), in application to the conceptual rainfall-runoff model HBV, to simultaneously assimilate remotely sensed snow covered area (SCA), snow water equivalent (SWE), soil moisture (SM) and in situ measurements of streamflow data using large assimilation windows of up to one year. This innovative application of the MHE approach allows to simultaneously update precipitation, temperature, soil moisture as well as upper and lower zones water storages of the conceptual model, within the assimilation window, without an explicit formulation of error covariance matrixes and it enables a highly flexible formulation of distance metrics for the agreement of simulated and observed variables.The framework is tested in two data-dense sites in Germany and one data-sparse environment in Turkey. Results show a potential improvement of the lead time performance of streamflow forecasts by using perfect time series of state variables generated by the simulation of the conceptual rainfall-runoff model itself. The framework is also tested using new operational data products from the Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF) of EUMETSAT. This study is the first application of H-SAF products to hydrological forecasting systems and it verifies their added value. Results from assimilating H-SAF observations lead to a slight reduction of the streamflow forecast skill in all three cases compared to the assimilation of streamflow data only. On the other hand, the forecast skill of soil moisture shows a significant improvement.

Spatial multi-criteria decision analysis to predict suitability for African swine fever endemicity in Africa

BackgroundAfrican swine fever (ASF) is endemic in several countries of Africa and may pose a risk to all pig producing areas on the continent. Official ASF reporting is often rare and there remains limited awareness of the continent-wide distribution of the disease.In the absence of accurate ASF outbreak data and few quantitative studies on the epidemiology of the disease in Africa, we used spatial multi-criteria decision analysis (MCDA) to derive predictions of the continental distribution of suitability for ASF persistence in domestic pig populations as part of sylvatic or domestic transmission cycles. In order to incorporate the uncertainty in the relative importance of different criteria in defining suitability, we modelled decisions within the MCDA framework using a stochastic approach. The predictive performance of suitability estimates was assessed via a partial ROC analysis using ASF outbreak data reported to the OIE since 2005.ResultsOutputs from the spatial MCDA indicate that large areas of sub-Saharan Africa may be suitable for ASF persistence as part of either domestic or sylvatic transmission cycles. Areas with high suitability for pig to pig transmission (‘domestic cycles’) were estimated to occur throughout sub-Saharan Africa, whilst areas with high suitability for introduction from wildlife reservoirs (‘sylvatic cycles’) were found predominantly in East, Central and Southern Africa. Based on average AUC ratios from the partial ROC analysis, the predictive ability of suitability estimates for domestic cycles alone was considerably higher than suitability estimates for sylvatic cycles alone, or domestic and sylvatic cycles in combination.ConclusionsThis study provides the first standardised estimates of the distribution of suitability for ASF transmission associated with domestic and sylvatic cycles in Africa. We provide further evidence for the utility of knowledge-driven risk mapping in animal health, particularly in data-sparse environments.

Modelling catchment management impact on in-stream phosphorus loads in northern Victoria

Phosphorus pollution severely impairs the water quality of rivers in Australia and worldwide. Conceptual models have proved useful to assess management impact on phosphorus loads, particularly in data-sparse environments. This paper develops and evaluates the coupling of a point-scale model (HowLeaky2008) to a catchment scale model (CatchMODS) to enhance modelling of farm management impacts on in-stream phosphorus loads. The model was tested in two adjacent catchments in northern Victoria (Avon-Richardson and Avoca), Australia. After calibration of the in-stream attenuation parameter against measurements at gauging stations, the model simulated specific annual phosphorus loads across the catchments well (Nash–Sutcliffe model efficiency of 0.52 in the Avon-Richardson and 0.83 for the Avoca catchment). Phosphorus loads at both catchment outlets under current conditions were estimated at 7 t y−1 and were dominated by field exports. Changes to farm management practices, i.e. the use of perennial pastures in grazing systems and zero-tillage in cropping systems were estimated to reduce phosphorus load by 31% in the Avon-Richardson catchment and 19% in the Avoca catchment, relative to current practices (annual pasture and minimum tillage). The model afforded a major improvement in conceptual modelling by explicit simulation of the impacts of soil and climatic conditions on field-scale exports and by placing them in the context of landscape processes.

Joint climate–hydrology modeling: an impact study for the data-sparse environment of the Volta Basin in West Africa

The Volta Basin in West Africa is a region sensitive to water shortage. Future climate conditions therefore may put additional stress on the competition for the scarce water resources between industry, agriculture, and households. For an investigation of the sensitivity of the hydrological regime to global climate change in the data-sparse and poorly gauged region of the Volta Basin, joint regional climate–hydrology simulations were performed. MM5 was used as a regional climate model to downscale two time slices of a global ECHAM4 simulation to a resolution of 9 km. These regional climate simulations were used to drive a physically based, distributed hydrological model at 1 km resolution. The performance of the model components and the joint model system was evaluated for different historical periods. Results show that discharge in the Volta Basin reacts highly sensitively to precipitation differences. The pronounced rainfall decrease at the beginning of the rainy season is not transferred to discharge changes. During the rainy season most of the surplus rainfall evaporates due to a strong increase in evaporation as a consequence of higher near-surface air temperatures. The average change signal in precipitation, as well as surface and subsurface hydrology variables, lies in most variables within the range of inter-annual variability, but regionally stronger signals are also observed.

Gaussian Sum Filters for Space Surveillance: Theory and Simulations

While standard Kalman-based filters, Gaussian assumptions, and covariance-weighted metrics are very effective in data-rich tracking environments, their use in the data-sparse environment of space surveillance ismore limited. To properly characterize non-Gaussian density functions arising in the problem of long-term propagation of state uncertainties, a Gaussian sum filter adapted to the two-body problem in space surveillance is proposed and demonstrated to achieve uncertainty consistency. The proposed filter is made efficient by using only a onedimensional Gaussian sum in equinoctial orbital elements, thereby avoiding the expensive representation of a full six-dimensional mixture and hence the “curse of dimensionality.” Additionally, an alternate set of equinoctial elements is proposed and is shown to provide enhanced uncertainty consistently over the traditional element set. Simulation studies illustrate the improvements in theGaussian sumapproach over the traditional unscentedKalman filter and the impact of correct uncertainty representation in the problems of data association (correlation) and anomaly (maneuver) detection.

Geophysical and remote sensing-based approach to model regolith thickness in a data-sparse environment

Abstract Mapping regolith thickness to bedrock is important for environmental modeling in general and for seismic hazard assessment in particular. However, regolith thickness is often ignored in such studies because of its presumed difficulty of mapping in many terrains. To overcome this limitation we developed a generic remote sensing and geophysics based approach to model regolith thickness for areas with limited possibility of direct field observations. The approach was tested in a seismically-active and depositional landscape in northern Pakistan. Regolith thickness was sampled at exposed bedrock outcrops along the river bed and scarps. At unexposed sites the regolith-bedrock interface was identified through electrical resistivity survey, thus providing an indirect measurement of regolith thickness. A geomorphic classification of landforms and topographic attributes were derived from a remote sensing-based (ASTER) digital elevation model and SPOT-5 satellite imagery. Landform was the most important predictor (adjusted R2 = 72.1%), showing the importance of geomorphic interpretation. A multivariate linear model based on landform, elevation and distance to stream was able to predict the regolith thickness (adjusted R2 = 91.7), including field-observed abrupt changes at landform boundaries.

Methodological Issues in Bridging Ideal Points in Disparate Institutions in a Data Sparse Environment

In earlier work, we created Congressional common space scores for multiple state legislatures using bridge actors who served in both institutions. Here, we employ simulations to explore the general issues involved in bridging institutions in data-sparse environments, where only a few bridge actors exist to allow inter-institutional comparisons. We find that only a few such bridges are necessary to improve ideal point estimates of rescaled legislative chambers.

Quantitative Approaches to Fiscal Sustainability Analysis: A Case Study of Turkey since the Crisis of 2001

This case study of fiscal sustainability in Turkey after the crisis in 2001 reviews and extends quantitative approaches to fiscal sustainability analysis and brings them together in a user-friendly tool applicable in a data-sparse environment. It combines a dynamic simulations approach with a steady-state consistency approach. It also incorporates user-defined stress tests and stochastic simulations to deal with uncertainty. And it derives the future distribution of debt-output ratios, evaluating the fiscal adjustment required to stabilize them. Value at Risk analysis shows that considerable risks remain unless explicit feedback rules from debt surprises to the primary surplus are implemented.

Modeling Urban Growth in Data-Sparse Environments: A New Approach

Although there exist numerous urban growth models, most have significant data input requirements, limiting their utility in a developing-world context. Yet, it is precisely in the developing world where there is an urgent need for urban growth models and scenarios since most expected urban growth in the next two decades will occur in such countries. This paper describes a physical urban growth model that requires few, but widely available, spatially explicit data. Utilizing binary urban/nonurban maps generated by satellite imaging, our model can inform urban planners and policy makers about the most probable locations and periods of future urban land-use change. Using a discrete choice framework, the model employs a spatially explicit logistic regression analysis to evaluate probabilities of urban growth for a baseline period. It calibrates parameters, validates results, predicts urban land-use change and examines future growth scenarios. Future growth scenarios can be generated through the inclusion of land prohibited from development, transportation routes, or new planned urban developments. A novel and important element of the model is the incorporation of an explicit policy-making framework that captures and reduces model uncertainty (theory and specification uncertainties), effectively addressing problems of predictive bias; this framework also allows the user or policy maker to associate predictions with a loss function. The model is applied to three cities in southern China that have experienced dramatic urban land growth in the last two decades. From 1988 to 1999, urban land in the region increased by 451.6% or at an annual rate of approximately 16.5%. Results show that the model achieves 73% – 77% accuracy for different cities at 30 m and 60 m resolutions. Aggregating the predictions to the county/administrative district shows that prediction through thresholding underperforms in comparison to the technique of sample enumeration.

Adapting modelling systems for salinity management of farms and catchments in Australia and Thailand

The Spatial Optimisation Model for Analysing Catchment Management (SMAC) model was chosen as the catchment-modelling tool in an ACIAR project on salinisation to be carried out by a research team headed by the University of Technology, Sydney and, in Thailand, the project will involve Khon Kaen University, The Royal Forest Department and the Land Development Department of the Ministry of Agriculture and Cooperatives. The SMAC has been developed to quantify the inter-relationships between socio-economic conditions, land-use and the biophysical condition of the catchment. SMAC will be applied for hydrological, economic and social modelling, at catchment and other scales to evaluate the impact of revegetation in Thailand (Lam Pao and Khorat and Australia (Liverpool Plains and Upper Macquarie Valley). This paper describes the SMAC model and discusses the methodological and other challenges of applying it in other regions with different land-use systems, economic conditions and hydrology, and a data-sparse environment.

Beyond Our Original Horizons: the Tropicalization of Beverton and Holt

The extension into tropical areas of Beverton and Holt's yield per recruit approach for stock assessment represents a straightforward case of ‘normal science’, the common Kuhnian counterpart to his much rarer ‘paradigm shifts’. It is shown that the normal science which, in recent decades, has led to new methods for estimating growth, mortality and other statistics required for yield per recruit analyses in data-sparse environments, has not only enriched fisheries science and aquatic biology as a whole, but has also contributed to identify the limitations of the single-species research programme originally defined by Beverton and Holt. The most likely prospect for that programme, in the tropics and elsewhere, is to become a component of the ‘multispecies’, or rather ‘ecosystem’ approach that is emerging, and to which Beverton and Holt will have contributed many of the concepts, and much of the rigour.