National population mapping from sparse survey data: A hierarchical Bayesian modeling framework to account for uncertainty

Leasure, Douglas R.; Jochem, Warren C.; Weber, Eric; Seaman, Vincent; Tatem, Andrew J.

doi:10.1073/pnas.1913050117

Cited by 63 publications

(71 citation statements)

References 14 publications

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“…The collection of recent enumeration data from small sample areas, or the use of listing data from recent surveys, can provide training data for statistical models that utilize relationships between these enumeration data and geospatial covariates to estimate population numbers in unsampled areas, together with uncertainty metrics. Examples of application of such approaches have been shown recently for Nigeria [ 52 ], Zambia [ 53 ] and DRC [ 54 ], with outputs available to explore at https://apps.worldpop.org/woprVision . Better information on population denominators, demographics and mobility will improve planning and monitoring.…”

Section: Discussionmentioning

confidence: 99%

Vaccination strategies for measles control and elimination: time to strengthen local initiatives

Cutts

Ferrari

Krause

et al. 2021

BMC Med

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Background Through a combination of strong routine immunization (RI), strategic supplemental immunization activities (SIA) and robust surveillance, numerous countries have been able to approach or achieve measles elimination. The fragility of these achievements has been shown, however, by the resurgence of measles since 2016. We describe trends in routine measles vaccine coverage at national and district level, SIA performance and demographic changes in the three regions with the highest measles burden. Findings WHO-UNICEF estimates of immunization coverage show that global coverage of the first dose of measles vaccine has stabilized at 85% from 2015 to 19. In 2000, 17 countries in the WHO African and Eastern Mediterranean regions had measles vaccine coverage below 50%, and although all increased coverage by 2019, at a median of 60%, it remained far below levels needed for elimination. Geospatial estimates show many low coverage districts across Africa and much of the Eastern Mediterranean and southeast Asian regions. A large proportion of children unvaccinated for MCV live in conflict-affected areas with remote rural areas and some urban areas also at risk. Countries with low RI coverage use SIAs frequently, yet the ideal timing and target age range for SIAs vary within countries, and the impact of SIAs has often been mitigated by delays or disruptions. SIAs have not been sufficient to achieve or sustain measles elimination in the countries with weakest routine systems. Demographic changes also affect measles transmission, and their variation between and within countries should be incorporated into strategic planning. Conclusions Rebuilding services after the COVID-19 pandemic provides a need and an opportunity to increase community engagement in planning and monitoring services. A broader suite of interventions is needed beyond SIAs. Improved methods for tracking coverage at the individual and community level are needed together with enhanced surveillance. Decision-making needs to be decentralized to develop locally-driven, sustainable strategies for measles control and elimination.

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Section: Discussionmentioning

confidence: 99%

Vaccination strategies for measles control and elimination: time to strengthen local initiatives

Cutts

Ferrari

Krause

et al. 2021

BMC Med

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“…Most global gridded population producers constrain estimates to settled cells as defined with a settlement layer (e.g. LandScan [21,81], GHP-POP [16,17], HRSL [18], GRID3 [25,82], WPE [23]). Until recently, these settlement layers tended to be relatively coarse (e.g.…”

Section: Discussionmentioning

confidence: 99%

“…More work will be needed to improve building footprint datasets by distinguishing residential and nonresidential buildings to avoid population being misallocated to business districts, factories, universities, airports, and other non-residential cells. These two steps -use of building footprint covariates and finerscale training data -stand to improve cell-level accuracy of all top-down and bottom-up gridded population datasets derived from complex models, including all WorldPop datasets as well as LandScan [21,22], WPE [23], and GRID3 [25,82]. Gridded population datasets that do not vary (weight) population densities within areal units (e.g., HRSL [18], GHS-POP [16,17], GPW [14,15]) should be used cautiously within urban areas, as cell-level inaccuracies are likely to be high.…”

Section: Discussionmentioning

confidence: 99%

“…To estimate gridded population figures beyond the year of the last census; birth, migration, and death rates are used to project new population totals by areal unit [24]. "Bottom-up" gridded population estimates are derived from micro-census population counts in a sample of areas, or from assumptions about the average household size, and have only recently been developed [25,26]. Most gridded population datasets use a settlement layer to "constrain" population estimates to settled grid cells.…”

Section: Introductionmentioning

confidence: 99%

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How Accurate are WorldPop-Global Gridded Population Data at the Cell-Level?: A Simulation Analysis in Urban Namibia

Dr¹,

Leasure²,

Bird³

et al. 2021

Preprint

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Disaggregated population counts are needed to calculate health, economic, and development indicators in Low- and Middle-Income Countries (LMICs), especially in settings of rapid urbanisation. Censuses are often outdated and inaccurate in LMIC settings, and rarely disaggregated at fine geographic scale. Modelled gridded population datasets derived from census data have become widely used by development researchers and practitioners; however, none of these datasets have been evaluated for accuracy of population estimates at the grid cell-level. This is because the finest-scale population figures generally available to data producers are those input into gridded population models and disaggregated to smaller grid cells (e.g., 100x100m). We simulate a realistic "true" 2016 population in Khomas, Namibia, a majority urban region, and introduce realistic levels of outdatedness (over 15 years) and inaccuracy in slum, non-slum, and rural areas. We then aggregate these simulated realistic populations by census and administrative boundaries (to mimic census data), and generate 32 gridded population datasets that are typical of a LMIC setting using WorldPop-Global's gridded population approach. We evaluate the cell-level accuracy of these simulated WorldPop-Global datasets, using the original "true" population as a reference. In our simulation, we found large cell-level errors, particularly in urban cells, driven by WorldPop-Global's use of average population densities in large areal units to determine cell-level population densities. Age, accuracy, and aggregation of the input data did play a primary role in these errors. We suggest incorporating finer-scale training data into gridded population models generally, and WorldPop-Global in particular (e.g., from simulated populations, routine household surveys, or slum community profiles), and use of new building footprint datasets as a covariate to improve cell-level accuracy of gridded population data. It is important to measure cell-level accuracy of all gridded population datasets, especially if they are to be used for monitoring key development indicators.

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“…To estimate gridded population figures beyond the year of the last census; birth, migration, and death rates are used to project new population totals by areal unit [25]. "Bottom-up" gridded population estimates are derived from microcensus population counts in a sample of areas, or from assumptions about the average household size, and have only recently been developed [26,27]. Most gridded population datasets use a settlement layer to "constrain" population estimates to settled grid cells.…”

Section: Introductionmentioning

confidence: 99%

How accurate are WorldPop-Global-Unconstrained gridded population data at the cell-level?: A simulation analysis in urban Namibia

Thomson¹,

Leasure²,

Bird³

et al. 2021

Preprint

Self Cite

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Disaggregated population counts are needed to calculate health, economic, and development indicators in Low- and Middle-Income Countries (LMICs), especially in settings of rapid urbanisation. Censuses are often outdated and inaccurate in LMIC settings, and rarely disaggregated at fine geographic scale. Modelled gridded population datasets derived from census data have become widely used by development researchers and practitioners. These datasets are evaluated for accuracy at the spatial scale of the input data which is often much courser (e.g. administrative units) than the neighbourhood or cell-level scale of many applications. We simulate a realistic "true" 2016 population in Khomas, Namibia, a majority urban region, and introduce realistic levels of outdatedness (over 15 years) and inaccuracy in slum, non-slum, and rural areas. We aggregate these simulated realistic populations by census and administrative boundaries (to mimic census data), and generate 32 gridded population datasets that are typical of a LMIC setting using WorldPop-Global-Unconstrained gridded population approach. We evaluate the cell-level accuracy of these simulated datasets using the original "true" population as a reference. In our simulation, we found large cell-level errors, particularly in slum cells, driven by the use of average population densities in large areal units to determine cell-level population densities. Age, accuracy, and aggregation of the input data also played a role in these errors. We suggest incorporating finer-scale training data into gridded population models generally, and WorldPop-Global-Unconstrained in particular (e.g., from routine household surveys or slum community population counts), and use of new building footprint datasets as a covariate to improve cell-level accuracy. It is important to measure accuracy of gridded population datasets at spatial scales more consistent with how the data are being applied, especially if they are to be used for monitoring key development indicators at neighbourhood scales with relevance to small dense deprived areas within larger administrative units.

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National population mapping from sparse survey data: A hierarchical Bayesian modeling framework to account for uncertainty

Cited by 63 publications

References 14 publications

Vaccination strategies for measles control and elimination: time to strengthen local initiatives

Vaccination strategies for measles control and elimination: time to strengthen local initiatives

How Accurate are WorldPop-Global Gridded Population Data at the Cell-Level?: A Simulation Analysis in Urban Namibia

How accurate are WorldPop-Global-Unconstrained gridded population data at the cell-level?: A simulation analysis in urban Namibia

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