ArticleInternational journal of health geographics2012
Effects of georeferencing effort on mapping monkeypox case distributions and transmission risk.
Article 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 15 papers.
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
15 citing papers in PubMed, 29 citations in OpenAlex.
- Serological differentiation between Mpox infection and vaccine-induced immunity: a mini-review.Frontiers in immunology · 2026Review
- Identifying the Most Probable Mammal Reservoir Hosts for Monkeypox Virus Based on Ecological Niche Comparisons.Viruses · 2023Article
- Human monkeypox: history, presentations, transmission, epidemiology, diagnosis, treatment, and prevention.Frontiers in medicine · 2023Review
- Ecological Niche Modeling: An Introduction for Veterinarians and Epidemiologists.Frontiers in veterinary science · 2020Review
- Spatial analyzes of HLA data in Rio Grande do Sul, south Brazil: genetic structure and possible correlation with autoimmune diseases.International journal of health geographics · 2018Article
- Novel Methods in Disease Biogeography: A Case Study with Heterosporosis.Frontiers in veterinary science · 2017Article
- Ecological approaches in veterinary epidemiology: mapping the risk of bat-borne rabies using vegetation indices and night-time light satellite imagery.Veterinary research · 2015Article
- Atlas of Mexican Triatominae (Reduviidae: Hemiptera) and vector transmission of Chagas disease.Memorias do Instituto Oswaldo Cruz · 2015Article
- A phylogeographic investigation of African monkeypox.Viruses · 2015Article
- Validation of three geolocation strategies for health-facility attendees for research and public health surveillance in a rural setting in western Kenya.Epidemiology and infection · 2014Article
- A review of data quality assessment methods for public health information systems.International journal of environmental research and public health · 2014Review
- Mapping transmission risk of Lassa fever in West Africa: the importance of quality control, sampling bias, and error weighting.PloS one · 2014Article
- Human monkeypox.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2014Review
- Phylogenetic and ecologic perspectives of a monkeypox outbreak, southern Sudan, 2005.Emerging infectious diseases · 2013Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMaps of disease occurrences and GIS-based models of disease transmission risk are increasingly common, and both rely on georeferenced diseases data. Automated methods for georeferencing disease data have been widely studied for developed countries with rich sources of geographic referenced data. However, the transferability of these methods to countries without comparable geographic reference data, particularly when working with historical disease data, has not been as widely studied. Historically, precise geographic information about where individual cases occur has been collected and stored verbally, identifying specific locations using place names. Georeferencing historic data is challenging however, because it is difficult to find appropriate geographic reference data to match the place names to. Here, we assess the degree of care and research invested in converting textual descriptions of disease occurrence locations to numerical grid coordinates (latitude and longitude). Specifically, we develop three datasets from the same, original monkeypox disease occurrence data, with varying levels of care and effort: the first based on an automated web-service, the second improving on the first by reference to additional maps and digital gazetteers, and the third improving still more based on extensive consultation of legacy surveillance records that provided considerable additional information about each case. To illustrate the implications of these seemingly subtle improvements in data quality, we develop ecological niche models and predictive maps of monkeypox transmission risk based on each of the three occurrence data sets.
resultsWe found macrogeographic variations in ecological niche models depending on the type of georeferencing method used. Less-careful georeferencing identified much smaller areas as having potential for monkeypox transmission in the Sahel region, as well as around the rim of the Congo Basin. These results have implications for mapping efforts, as each higher level of georeferencing precision required considerably greater time investment.
conclusionsThe importance of careful georeferencing cannot be overlooked, despite it being a time- and labor-intensive process. Investment in archival storage of primary disease-occurrence data is merited, and improved digital gazetteers are needed to support public health mapping activities, particularly in developing countries, where maps and geographic information may be sparse.
Indexed as
Identifiers
What OpenQuestion holds
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.