High resolution age-structured mapping of childhood vaccination coverage in low and middle income countries

Utazi, C. Edson; Thorley, Julia; Alegana, Victor A.; Ferrari, Matthew J.; Takahashi, Saki; Metcalf, C. Jessica E.; Lessler, Justin; Tatem, Andrew J.

doi:10.1016/j.vaccine.2018.02.020

Cited by 95 publications

(129 citation statements)

References 26 publications

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“…If the spatial locations of the clusters are known, the locations can be used within a model-based approach with a 'pixel level' model. This endeavour is popular, with outcomes including malaria (Gething et al, 2016), U5MR (Golding et al, 2017) and vaccination (Utazi et al, 2018). These analyses depend on household survey data, but the survey design is not explicitly acknowledged and we are currently investigating the implications of this (Paige et al, 2018).…”

Section: Geir-arne Fuglstad and Andrea Riebler (Norwegian University mentioning

confidence: 99%

From Start to Finish: A Framework for the Production of Small Area Official Statistics

Tzavidis

Zhang

Luna

et al. 2018

Journal of the Royal Statistical Society Series A: Statistics in Society

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Summary Small area estimation is a research area in official and survey statistics of great practical relevance for national statistical institutes and related organizations. Despite rapid developments in methodology and software, researchers and users would benefit from having practical guidelines for the process of small area estimation. We propose a general framework for the production of small area statistics that is governed by the principle of parsimony and is based on three broadly defined stages, namely specification, analysis and adaptation, and evaluation. Emphasis is given to the interaction between a user of small area statistics and the statistician in specifying the target geography and parameters in the light of the available data. Model‐free and model‐dependent methods are described with a focus on model selection and testing, model diagnostics and adaptations such as use of data transformations. Uncertainty measures and the use of model and design‐based simulations for method evaluation are also at the centre of the paper. We illustrate the application of the proposed framework by using real data for the estimation of non‐linear deprivation indicators. Linear statistics, e.g. averages, are included as special cases of the general framework.

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Section: Geir-arne Fuglstad and Andrea Riebler (Norwegian University mentioning

confidence: 99%

From Start to Finish: A Framework for the Production of Small Area Official Statistics

Tzavidis

Zhang

Luna

et al. 2018

Journal of the Royal Statistical Society Series A: Statistics in Society

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“…Finally, model extensions that directly incorporate data on housing types from censuses or other sources could be considered. Including percentages of different housing types as an areal covariate similar to C. Utazi et al (2018) is a potential first step in this direction.…”

Section: Discussionmentioning

confidence: 99%

Evaluating the INLA-SPDE approach for Bayesian modeling of earthquake damages from geolocated cluster data

Wilson¹

2020

Preprint

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Modeled damage estimates are an important source of information in the hours to weeks following major earthquake disasters, but often lack sufficient spatial resolution for highlighting specific areas of need. Using damage assessment data from the 2015 Gorkha, Nepal Earthquake, this paper evaluates a Bayesian spatial model (INLA-SPDE) for interpolating geolocated damage survey data onto 1 km2 grid cells. The proposed approach uses a combination of geospatial covariate data and Gaussian spatial process random effects modeling to estimate the percentage of structures attaining complete damage states from sparse survey clusters. Model performance is evaluated across fifty iterations of 100, 250, and 1000 simulated survey clusters and compared to observed damage assessments and model predictions using more traditional fragility-based methods. Results show strong model fit to observed values, with mean absolute errors of .17, .13, and .11 and correlation coefficients of .75, .82, and .85 for increasing numbers of survey clusters. These results show improvements over traditional damage estimation methods with a small percentage of the damage surveys that were available within several weeks after the Gorkha event. Thus, with sufficient rapid damage assessment mobilization, the proposed model is able to provide improved damage estimates in the time frame required to deliver a Post Disaster Needs Assessment even in cases where no additional damage data is available.

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“…The 2030 Agenda for Sustainable Development and its SDGs, have reinforced the importance of data to being able to account for "all people everywhere (8). Differences in the dynamic spatial distributions of hazards (100,101), the spatial variation of the effects of climate change (5,102,103), spatially allocating services to ensure sufficient coverage (104,105), and targeting interventions and planning (106,107) However, the BSGM is neither without error nor a total replacement for urban feature extractions. Given that the BSGM is interpolative, its predictions are limited by the accuracy, the spatial quality, and the temporal quality of its input urban feature dataset, the arc second resolution due to the MERIS and PROBA V imagery not being available (75).…”

Section: Discussionmentioning

confidence: 99%

Modelling Built-Settlements between Remotely-Sensed Observations

Nieves¹,

Sorichetta²,

Linard³

et al. 2019

Preprint

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Mapping settlement extents at the annual time step has a wide variety of applications in demography, public health, sustainable development, and many other fields. Recently, while more multitemporal urban feature or human settlement datasets have become available, issues still exist in remotely-sensed imagery due to coverage, adverse atmospheric conditions, and expenses involved in producing such feature sets. These challenges make it difficult to increase temporal coverage while maintaining high fidelity in the spatial resolution. Here we demonstrate an interpolative and flexible modeling framework for producing annual builtsettlement extents. We use a combined technique of random forest and spatio-temporal dasymetric modeling with open source subnational data to produce annual 100m x 100m resolution binary settlement maps in four test countries of varying environmental and developmental contexts for test periods of five-year gaps. We find that in the majority of years, across all study areas, the model correctly identified between 85-99% of pixels that transition to built-settlement. Additionally, with few exceptions, the model substantially out performed a model that gave every pixel equal chance of transitioning to the category "built" in each year. This modelling framework shows strong promise for filling gaps in crosssectional urban feature datasets derived from remotely-sensed imagery, provide a base upon which to create future built/settlement extent projections, and further explore the relationships between built area and population dynamics.As a result, many studies have turned to a definition based upon the remotely-sensed physical features of urban areas, i.e. the built-environment. However, even reducing the definitional scope of urban to its physical dimension, the form of built-environment can widely vary across space and time due to materials used, differences in urban morphology, and the surrounding environmental context (18,31,32). Initially, remotely sensed urban definitions were optically-based thematic classifications of land cover, typically captured the "built-environment," including buildings, roads, runways, and, sometimes erroneously, bare soil (18,(33)(34)(35). Later improvements using supporting information about the surrounding environment and vegetation during post-processing helped discern the true built-environment from the surrounding land covers (18). Other notable advances include the use of high resolution orthographic imagery to detect subtle short-term built-environment change in China (36) and the use of Landsat imagery to create multi-temporal thematic representations of the built environment across the globe (37).Coinciding with advances in imagery, statistical methods, and computational resource availability, high-resolution datasets with global extent have been created either through combining multi-source optical imagery with contrast detection methods (38,39) or utilizing Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted:

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High resolution age-structured mapping of childhood vaccination coverage in low and middle income countries

Cited by 95 publications

References 26 publications

From Start to Finish: A Framework for the Production of Small Area Official Statistics

From Start to Finish: A Framework for the Production of Small Area Official Statistics

Evaluating the INLA-SPDE approach for Bayesian modeling of earthquake damages from geolocated cluster data

Modelling Built-Settlements between Remotely-Sensed Observations

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