Disease mapping and innovation: A history from wood-block prints to Web 3.0

Spatial autocorrelation mixtures in geospatial disease data: An important global epidemiologic/public health assessment ingredient?

“…Nevertheless, because they lack a coherent theme of region, spatial and temporal scales, or even etiology, and are unrepresentative and not exhaustive of disease types (contents) and locality (space), analysis of these specimen data sets falls under the heading of exploratory, with subsequent additional such studies needed to establish a firm global epidemiology or public health assessment claim. Griffith (2006b) illustratively documents a rather long tradition recognizing why PSA is relevant to assessments of diseases and their diffusion in his spatial statistical analysis of the original John Snow cholera data; publications by Koch (2017Koch ( , 2022, among others, bolsters this viewpoint. Not only is PSA conspicuous in the geographic distribution of cholera deaths (e.g., a Moran Coefficient [MC] index of 0.88 [close to its maximum of 1.01, and far from its randomness value of −0.01], and a Geary ratio [GR] index of 0.25 [close to its minimum of 0.03, and far from its randomness value of one]), for example, but it also accounts for roughly 60% or more of the geographic variation exhibited by a Poisson/negative binomial (NB) probability model description of Snow's density of death counts data, based on Moran eigenvector spatial filtering (MESF 1 ; see Griffith et al, 2019) methodology.…”

“…Interest in geospatial disease and public health data is widespread, with example freely available data sets appearing in prominent predigital era sources (Andrews & Herzberg, 1985; Hand et al, 1993), and more recently via digital databases housing hundreds of such sets of data (e.g., https://data.world/datasets/disease, last accessed on 29 October 2022). Cartographic portrayals of such data have a long history (see Koch, 2017, 2022), with Snow propelling it into the more contemporary public health arena. Commonly, georeferencing of this data type is with country, state/province, county/district, census areal unit, postal area, synthetic quadrat, and/or surface point location (e.g., global positioning system [GPS]).…”

GIScience III: Questions of time

“…Our analysis of present spatiotemporal dynamics often entertains or tilts toward desired or certain futures, whether of modeling urban growth (Wang et al, 2022) or of managing pandemics (Koch 2022). However, as Rahul Rao (2020: 25) writes in Out of Time , “futurity is no less a battleground than memory, providing the stage for struggles to ward off foreboding specters and to bring preferred futures into being.” Rao suggests that while time operates as a site of atonement and opportunity, of remembrance and mourning, these are still slippery grounds for greater justice.…”

A New Integrated Interpolation Method for High Missing Unstable Disease Surveillance Data — 12 Urban Agglomerations, China, 2009–2020

“…These factors hinder the ability to accurately delineate disease distribution temporally and spatially, and to discern patterns and anomalies. Traditional spatio-temporal interpolation methods ( 2 ) are ill-suited for addressing the volatility and gaps in disease data, particularly when integrating significant influencing factors. Conversely, the structural equation model (SEM) facilitates analysis of complex data interactions to unearth underlying relationships among variables ( 3 ), thus enabling more precise interpolation.…”