617 research outputs found
Population, vaccination coverage, and zero-dose children estimates for Mali version 1.0
No full textThese data were produced by the WorldPop Research Group at the University of Southampton. This work was part of the Reach the Unreached – Digital technologies to map zero-dose and unreached children in West and Central Africa project, funded by UNICEF — The United Nations Children’s Fund (contract No. 43387656). The project is led by UNICEF West Africa Regional Office focusing on five West African countries: Cameroon, Chad, Côte D’Ivoire, Guinea and Mali. The partners include the UNICEF Country Offices, WorldPop at the University of Southampton, MapAction and CartONG.
This data release provides gridded population, vaccination coverage and zero dose children estimates for Mali. The reference year of this data package is 2023. N.B. Please note the uncertainties resulting from the old DHS data utilised for this study.
All GIS files in this data release have the geographic coordinate system of WGS84 (World Geodetic System 1984).
Population modelling used a Random Forest (RF)-based dasymetric mapping approach of observed or projected census population counts, developed by Stevens et al. (2015) and implemented in the popRF ‘R’ package by Bondarenko et al. (2021). The modelling disaggregated the 2023 census projections. These population estimates have a spatial resolution of approximately 100-metre (0.0008333 decimal degrees grid) and also contain age and sex disaggregated results with the same spatial resolution.
Geo-statistical estimates of under-vaccinated (DPT3 antigen coverage) and zero-dose (DPT1 coverage) children under one-year-old utilised a Bayesian spatial regression model (Utazi et al. 2021; 2022; 2023), implemented by Chaudhuri et al. (2025). This application utilised the 2018-19 DHS for the modelling. The vaccination coverage estimates have a spatial resolution of approximately 1-kilometre resolution (0.008333 decimal degrees grid), but the results are also aggregated up to the various administrative levels in GIS shapefile and table formats.
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A Bayesian latent process spatiotemporal regression model for areal count data
No full textModel-based approaches for the analysis of areal count data are commonplace in spatiotemporal analysis. In Bayesian hierarchical models, a latent process is incorporated in the mean function to account for dependence in space and time. Typically, the latent process is modelled using a conditional autoregressive (CAR) prior. The aim of this paper is to offer an alternative approach to CAR-based priors for modelling the latent process. The proposed approach is based on a spatiotemporal generalization of a latent process Poisson regression model developed in a time series setting. Spatiotemporal dependence in the autoregressive model for the latent process is modelled through its transition matrix, with a structured covariance matrix specified for its error term. The proposed model and its parameterizations are fitted in a Bayesian framework implemented via MCMC techniques. Our findings based on real-life examples show that the proposed approach is at least as effective as CAR-based models.<br/
Population, vaccination coverage, and zero-dose children estimates for Chad version 1.0
No full textThese data were produced by the WorldPop Research Group at the University of Southampton. This work was part of the Reach the Unreached – Digital technologies to map zero-dose and unreached children in West and Central Africa project, funded by UNICEF — The United Nations Children’s Fund (contract No. 43387656). The project is led by UNICEF West Africa Regional Office focusing on five West African countries: Cameroon, Chad, Côte D’Ivoire, Guinea and Mali. The partners include the UNICEF Country Offices, WorldPop at the University of Southampton, MapAction and CartONG.
This data release provides gridded population, vaccination coverage and zero dose children estimates for Chad. The reference year of this data package is 2024. N.B. Please note the uncertainties resulting from the old DHS data utilised for this study.
All GIS files in this data release have the geographic coordinate system of WGS84 (World Geodetic System 1984).
Population modelling used a Random Forest (RF)-based dasymetric mapping approach of observed or projected census population counts, developed by Stevens et al. (2015) and implemented in the popRF ‘R’ package by Bondarenko et al. (2021). The modelling disaggregated the 2024 census projections. These population estimates have a spatial resolution of approximately 100-metre (0.0008333 decimal degrees grid) and also contain age and sex disaggregated results with the same spatial resolution.
Geo-statistical estimates of under-vaccinated (DPT3 antigen coverage) and zero-dose (DPT1 coverage) children under one-year-old utilised a Bayesian spatial regression model (Utazi et al. 2021; 2022; 2023), implemented by Chaudhuri et al. (2025). This application utilised the 2014 DHS for the modelling. The vaccination coverage estimates have a spatial resolution of approximately 1-kilometre resolution (0.008333 decimal degrees grid), but the results are also aggregated up to the various administrative levels in GIS shapefile and table formats.
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Population, vaccination coverage, and zero-dose children estimates for Côte D’Ivoire version 1.0
No full textThese data were produced by the WorldPop Research Group at the University of Southampton. This work was part of the Reach the Unreached – Digital technologies to map zero-dose and unreached children in West and Central Africa project, funded by UNICEF — The United Nations Children’s Fund (contract No. 43387656). The project is led by UNICEF West Africa Regional Office focusing on five West African countries: Cameroon, Chad, Côte D’Ivoire, Guinea and Mali. The partners include the UNICEF Country Offices, WorldPop at the University of Southampton, MapAction and CartONG.
This data release provides gridded population, vaccination coverage and zero dose children estimates for Côte D’Ivoire. The reference year of this data package is 2021.
All GIS files in this data release have the geographic coordinate system of WGS84 (World Geodetic System 1984).
Population modelling used a Random Forest (RF)-based dasymetric mapping approach of observed or projected census population counts, developed by Stevens et al. (2015) and implemented in the popRF ‘R’ package by Bondarenko et al. (2021). The modelling disaggregated the 2021 census results. These population estimates have a spatial resolution of approximately 100-metre (0.0008333 decimal degrees grid) and also contain age and sex disaggregated results with the same spatial resolution.
Geo-statistical estimates of under-vaccinated (DPT3 antigen coverage) and zero-dose (DPT1 coverage) children under one-year-old utilised a Bayesian spatial regression model (Utazi et al. 2021; 2022; 2023), implemented by Chaudhuri et al. (2025). This application utilised the 2021 DHS for the modelling. The vaccination coverage estimates have a spatial resolution of approximately 1-kilometre resolution (0.008333 decimal degrees grid), but the results are also aggregated up to the various administrative levels in GIS shapefile and table formats.
The number of zero dose and under-vaccinated children for DTP1 and DTP3 was estimated by integrating the estimated number of children under the age of 1 and the vaccination coverage estimates in a GIS workflow (https://github.com/wpgp/RtU_vaccination_modelling/tree/main/Zero-dose). The zero dose children estimates have a spatial resolution of approximately 1-kilometre resolution (0.008333 decimal degrees grid), but the results are also aggregated up to the various administrative levels in GIS shapefile and table formats.
Details of the inputs, methodologies and outputs are found in the specific subfolders (Population Estimates, Vaccination Estimates, Zero-dose results).
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Population, vaccination coverage, and zero-dose children estimates for Guinea version 1.0
No full textThese data were produced by the WorldPop Research Group at the University of Southampton. This work was part of the Reach the Unreached – Digital technologies to map zero-dose and unreached children in West and Central Africa project, funded by UNICEF — The United Nations Children’s Fund (contract No. 43387656). The project is led by UNICEF West Africa Regional Office focusing on five West African countries: Cameroon, Chad, Côte D’Ivoire, Guinea and Mali. The partners include the UNICEF Country Offices, WorldPop at the University of Southampton, MapAction and CartONG.
This data release provides gridded population, vaccination coverage and zero dose children estimates for Guinea. The reference year of this data package is 2023. N.B. Please note the uncertainties resulting from the old DHS data utilised for this study. All GIS files in this data release have the geographic coordinate system of WGS84 (World Geodetic System 1984).
Population modelling used a Random Forest (RF)-based dasymetric mapping approach of observed or projected census population counts, developed by Stevens et al. (2015) and implemented in the popRF ‘R’ package by Bondarenko et al. (2021). The modelling disaggregated the 2023 census projections. These population estimates have a spatial resolution of approximately 100-metre (0.0008333 decimal degrees grid) and also contain age and sex disaggregated results with the same spatial resolution. Geo-statistical estimates of under-vaccinated (DPT3 antigen coverage) and zero-dose (DPT1 coverage) children under one-year-old utilised a Bayesian spatial regression model (Utazi et al. 2021; 2022; 2023), implemented by Chaudhuri et al. (2025). This application utilised the 2018 DHS for the modelling. The vaccination coverage estimates have a spatial resolution of approximately 1-kilometre resolution (0.008333 decimal degrees grid), but the results are also aggregated up to the various administrative levels in GIS shapefile and table formats.
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Population, vaccination coverage, and zero-dose children estimates for Cameroon version 1.0
No full textThese data were produced by the WorldPop Research Group at the University of Southampton. This work was part of the Reach the Unreached – Digital technologies to map zero-dose and unreached children in West and Central Africa project, funded by UNICEF — The United Nations Children’s Fund (contract No. 43387656). The project is led by UNICEF West Africa Regional Office focusing on five West African countries: Cameroon, Chad, Côte D’Ivoire, Guinea and Mali. The partners include the UNICEF Country Offices, WorldPop at the University of Southampton, MapAction and CartONG.
This data release provides gridded population, vaccination coverage and zero dose children estimates for Cameroon. The reference year of this data package is 2021.
All GIS files in this data release have the geographic coordinate system of WGS84 (World Geodetic System 1984).
The population modelling builds upon earlier GRID3 Bayesian population model developed for Cameroon by Nnanatu et al. (2022), extending its spatial coverage to national boundaries as defined by Le Programme Elargi de Vaccination of Cameroon (PEV). This earlier application combined multiple nationally representative household listing datasets received from the Cameroon National Statistical Office (NIS) with satellite-based settlement data and geospatial covariates (Woods et al. 2024) to train geospatial statistical model parameters which were used to estimate population numbers and number of households at high-resolution grid cells using advanced Bayesian hierarchical statistical modelling frameworks. These population estimates have a spatial resolution of approximately 100-metre (0.0008333 decimal degrees grid) and also contain age and sex disaggregated results with the same spatial resolution.
Geo-statistical estimates of under-vaccinated (DPT3 antigen coverage) and zero-dose (DPT1 coverage) children under one-year-old utilised a Bayesian spatial regression model (Utazi et al. 2021; 2022; 2023), implemented by Chaudhuri et al (2025). This application utilised the ECVRC 2023 national vaccination survey for the modelling. The vaccination coverage estimates have a spatial resolution of approximately 1-kilometre resolution (0.008333 decimal degrees grid), but the results are also aggregated up to various administrative levels in GIS shapefile and table formats.
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High-resolution, modelled estimates of vaccination coverage for the Democratic Republic of Congo, including estimates of zero-dose- and under-vaccinated children, version 1.0
No full textThis data release provides gridded estimates (at a spatial resolution of 30 arc-seconds, approximately 1 km grid cells) of DTP1-3 and MCV1 vaccination coverage rates and numbers of zero-dose and under-vaccinated children for the Democratic Republic of Congo (DRC). The project team utilized the 2023 Enquête de Couverture Vaccinale (ECV) survey dataset, conducted by the Kinshasa School of Public Health (KSPH), along with settlement extents and geospatial covariates, to model and estimate vaccination coverage rates for children aged 12–23 months (at the time of the survey). Estimates were calculated for each grid cell within a Bayesian statistical modelling framework. The approach facilitated simultaneous accounting for the multiple levels of variability within the data. It also allowed the quantification of uncertainties in parameter estimates. These model-based coverage estimates can be considered as most accurately representing the year 2023. Although the methods were robust enough to explicitly account for key random biases within the datasets, it is noted that systematic biases, which may arise from sources other than random errors within the observed data collection process, are most likely to remain. The un- and under-vaccinated children estimation combined the new vaccination coverage estimates with existing high-resolution population estimates of children aged under one-year-old. The reference year of the un- and under-vaccinated children estimates is 2024.
These data were produced by the WorldPop Research Group at the University of Southampton. This work was part of the GRID3 – DRC-GAVI-EAF project, with funding by the Zero Dose Child Vaccination Project of the Equity Acceleration Fund (EAF) of Gavi, the Vaccine Alliance [grant number: FAE/GRID3/001/2024]. Project partners included United Nations Office for Project Services (UNOPS), GRID3 Inc, the Center for Integrated Earth System Information (CIESIN) within the Columbia Climate School at Columbia University, and WorldPop at the University of Southampton. The final statistical modelling was designed and developed by C.E. Utazi and implemented by K.S. Krishnaveni. H.R. Chamberlain led on the geospatial data processing of the survey, with support from A. Cunningham, who led the geospatial covariate processing and map production. Project oversight was provided by Attila Lazar and Heather Chamberlain. The 2023 ECV data were collected, processed, anonymised and shared by the KSPH and its implementing partners. The settlement extent data was prepared and shared by CIESIN (2024). </span
An Application of Extreme Value Theory for Measuring Financial Risk in BRICS Economies
No full textCharacterization and quantification of the tail behaviour of rare events is an important issue in financial risk management. In this paper, the extreme behaviour of stock market returns from BRICS over the period 1995–2015 is described using five parametric distributions based on extreme value theory, including two mixture distributions based on the student’s t distribution. The distributions are fitted to the data using the method of maximum likelihood. The generalized extreme value (GEV) distribution is found to give the best fit. Based on the GEV distribution, estimates of value at risk, VaRp(X) and expected shortfall, ESp(X) from the five countries are computed. In addition, the correlation structure and tail dependence of these markets are characterized using several copula models. The Gumbel copula gives the best fit with evidence of significant relationships for all the pairs of the markets. To account for the possibility that due to sampling variability, a different model might be selected as the preferred model in a new sample from the same population, a short bootstrapping exercise was performed
A probabilistic predictive Bayesian approach for determining the representativeness of health and demographic surveillance networks
No full textHealth and demographic surveillance systems, formed into networks of sites, are increasingly being established to circumvent unreliable national civil registration systems for estimates of mortality and its determinants in low income countries. Health outcomes, as measured by morbidity and mortality, generally correlate strongly with socioeconomic and environmental characteristics. Therefore, to enable comparison between sites, understand which sites can be grouped and where additional sites would aid understanding of rates and determinants, determining the environmental and socioeconomic representativeness of networks becomes important. This paper proposes a full Bayesian methodology for assessing current representativeness and consequently, identification of future sites, focusing on the INDEPTH network in sub-Saharan Africa as an example. Using socioeconomic and environmental data from the current network of 39 sites, we develop a multi-dimensional finite Gaussian mixture model for clustering the existing sites. Using the fitted model we obtain the posterior predictive probability distribution for cluster membership of each 1×1 km grid cell in Africa. The maximum of the posterior predictive probability distribution for each grid cell is proposed as the criterion for representativeness of the network for that particular grid cell. We demonstrate the conceptual superiority and practical appeal of the proposed Bayesian probabilistic method over previously applied deterministic clustering methods. As an example of the potential utility and application of the method, we also suggest optimal site selection methods for possible additions to the network
District‐level estimation of vaccination coverage: discrete vs continuous spatial models
No full textHealth and development indicators (HDIs) such as vaccination coverage are regularly measured in many low‐ and middle‐income countries using household surveys, often due to the unreliability or incompleteness of routine data collection systems. Recently, the development of model‐based approaches for producing subnational estimates of HDIs using survey data, particularly cluster‐level data, has been an active area of research. This is mostly driven by the increasing demand for estimates at certain administrative levels, for example, districts, at which many development goals are set and evaluated. In this study, we explore spatial modeling approaches for producing district‐level estimates of vaccination coverage. Specifically, we compare discrete spatial smoothing models which directly model district‐level data with continuous Gaussian process (GP) models that utilize geolocated cluster‐level data. We adopt a fully Bayesian framework, implemented using the INLA and SPDE approaches. We compare the predictive performance of the models by analyzing vaccination coverage using data from two Demographic and Health Surveys (DHS), namely the 2014 Kenya DHS and the 2015‐16 Malawi DHS. We find that the continuous GP models performed well, offering a credible alternative to traditional discrete spatial smoothing models. Our analysis also revealed that accounting for between‐cluster variation in the continuous GP models did not have any real effect on the district‐level estimates. Our results provide guidance to practitioners on the reliability of these model‐based approaches for producing estimates of vaccination coverage and other HDIs
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