ArticleFrontiers in public health2026
Combinatorial (bio-geo-temporal) and non-combinatorial analysis of the COVID-19 dissemination that affected Georgia (the country) in 2021.
Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Introduction: To extract more information from the same data and support decision-making, we explored whether bio-geo-temporal (BGT) variables could distinguish municipalities where BGT variables differed and (if prioritized in subsequent interventions) could lead to cost-effective decision-making. This study was conducted investigating COVID-19 outbreaks reported in Georgia in 2021. Methods: Using both commercial and non-commercial proprietary software packages, we explored the test positivity rate or TP % (percentage of test-positive results among tested individuals), the date and the georeferenced location of municipalities where tests were conducted, as well as the associated population and road density (municipal road length / municipal area). Analyses included spatial (Getis-Ord and Moran's I) and non-spatial statistical tests. Results: While the TP% was not linearly related with any one variable, combinations of BGT variables displayed distinct data patterns that separated two groups of municipalities (one composed of just two municipalities). The two-municipality group showed a statistically significantly greater median road density than the remaining municipalities. The same two municipalities also possessed a greater ability to detect asymptomatic cases: they expressed 20.3 times larger TP%/km Discussion: Findings supported the view that methods exploring dynamic combinations of BGT relationships may identify highly connected municipalities (those likely to behave as network nodes during disease dissemination processes) when tested as TP%/km
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