ReviewInternational journal of health geographics2012
Large-scale spatial population databases in infectious disease research.
Review in International journal of health geographics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
40 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Visualization and analytics tools for infectious disease epidemiology: a systematic review.Journal of biomedical informatics · 2014Pooled it
- Global mapping of infectious disease.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2013Pooled it
- Population-based denominators matter: Bias in U.S. Virgin Islands COVID-19 vaccination coverage under changing population counts.Annals of epidemiology · 2024Article
- A 31-year (1990-2020) global gridded population dataset generated by cluster analysis and statistical learning.Scientific data · 2024Article
- Modifiable Areal Unit Problems for Infectious Disease Cases Described in Medicare and Medicaid Claims, 2016-2019.Journal of bacteriology & parasitology · 2024Article
- A data-driven framework to assess population dynamics during novel coronavirus outbreaks: A case study on Xiamen Island, China.PloS one · 2023Article
- Temporal Geospatial Analysis of COVID-19 Pre-Infection Determinants of Risk in South Carolina.International journal of environmental research and public health · 2021Article
- COVID-19 Deaths in the United States: Shifts in Hot Spots over the Three Phases of the Pandemic and the Spatiotemporally Varying Impact of Pandemic Vulnerability.International journal of environmental research and public health · 2021Article
- Impact of an accelerated melting of Greenland on malaria distribution over Africa.Nature communications · 2021Article
- Generalized Vertical Components of built-up areas from global Digital Elevation Models by multi-scale linear regression modelling.PloS one · 2021Article
- Evaluating the Effects of Climate and Environmental Factors on Under-5 Children Malaria Spatial Distribution Using Generalized Additive Models (GAMs).Journal of epidemiology and global health · 2020Article
- Space-Time Conditional Autoregressive Modeling to Estimate Neighborhood-Level Risks for Dengue Fever in Cali, Colombia.The American journal of tropical medicine and hygiene · 2020Article
- Routine data for malaria morbidity estimation in Africa: challenges and prospects.BMC medicine · 2020Article
- An Introductory Framework for Choosing Spatiotemporal Analytical Tools in Population-Level Eco-Epidemiological Research.Frontiers in veterinary science · 2020Review
- Article
- Population allocation at the housing unit level: estimates around underground natural gas storage wells in PA, OH, NY, WV, MI, and CA.Environmental health : a global access science source · 2019Article
- How accurate are modelled birth and pregnancy estimates? Comparison of four models using high resolution maternal health census data in southern Mozambique.BMJ global health · 2019Article
- Article
- Unlocking pandemic potential: prevalence and spatial patterns of key substitutions in avian influenza H5N1 in Egyptian isolates.BMC infectious diseases · 2018Article
- Spatially disaggregated population estimates in the absence of national population and housing census data.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Modelling studies on the spatial distribution and spread of infectious diseases are becoming increasingly detailed and sophisticated, with global risk mapping and epidemic modelling studies now popular. Yet, in deriving populations at risk of disease estimates, these spatial models must rely on existing global and regional datasets on population distribution, which are often based on outdated and coarse resolution data. Moreover, a variety of different methods have been used to model population distribution at large spatial scales. In this review we describe the main global gridded population datasets that are freely available for health researchers and compare their construction methods, and highlight the uncertainties inherent in these population datasets. We review their application in past studies on disease risk and dynamics, and discuss how the choice of dataset can affect results. Moreover, we highlight how the lack of contemporary, detailed and reliable data on human population distribution in low income countries is proving a barrier to obtaining accurate large-scale estimates of population at risk and constructing reliable models of disease spread, and suggest research directions required to further reduce these barriers.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.