Effects of georeferencing effort on mapping monkeypox case distributions and transmission risk

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.

A Review of Data Quality Assessment Methods for Public Health Information Systems

“…This was followed by accuracy, in 5 institutional and 16 research publications [15,30,34,40,42,46,48,49,50,51,52,53,56,57,58,63,64,65,69,72,74]. The third most-used attribute, timeliness, was measured in 5 institutional and 4 research publications [22,30,40,42,44,45,64,69,73].…”

“…To avoid bias, age and sex proportions were extracted from available records, the proportions compared to National Census data.Examine data quality using a reference syndrome, thus making it possible to provide informed recommendations. Descriptive data analysis provides grounded and useful information for decision makersNo evaluation of data collection methodsHills et al 2012 [73]Timeliness: the number of days between Service Date and Entry Date of submission of data to the system (three categories: ≤7 days, =8–30 days, and ≥31 days).Completeness: the complete recording of data elements by calculating the proportion of complete fields over total number of fieldsQuantitative method to audit data setUse a de-identified 757,476 demographic records and 2,634,101 vaccination records from the systemDescriptive statistics on attributesLarge dataset provides a statistically significant associationNot able to examine two highly relevant components of data quality: vaccination record coverage completeness and accuracyLash et al 2012 [74]Completeness: the number of locations matching to latitude and longitude coordinates.Positional accuracy: spatial resolution of the dataset. Concordance: the number of localities falling within the boundary.…”

Phylogenetic and Ecologic Perspectives of a Monkeypox Outbreak, Southern Sudan, 2005

“…Details of these procedures are provided in greater depth in a separate publication ( 26 ). The geographic coordinates of exposure locations for each case and its associated uncertainty were summarized in a database from which we selected all unique localities with the highest geographic confidence (small spatial uncertainty).…”

Mapping Monkeypox Transmission Risk through Time and Space in the Congo Basin

“…We used the original human case reports from the WHO surveillance efforts in the DRC in 1970–1986, which were the object of extensive and intensive analyses by our research group [7], [27]. Confirmed human cases were georeferenced at the patient's residence location (representing the place where infection most likely occurred) using digital versions of 1∶250,000 Joint Operational Graphic (JOG) topographic maps for the DRC, and GEOnet Names Server (GNS; http://earth-info.nga.mil/gns.html/), in tandem with detailed case information from the original reports, and following the georeferencing procedures from MaNIS [28]; a detailed analysis of implications of different georeferencing protocols for MPX geography is provided in a separate publication [27]. Given our interest in modeling environmental conditions required for the disease to be transmitted from its wildlife reservoir to humans, cases most likely to have resulted from secondary transmission (i.e., the disease reported as acquired by contact with a sick person in the WHO case records) were eliminated from the study.…”