ArticleInternational journal of health geographics2026
Characterizing spatiotemporal spread of infectious diseases using ellipse-shaped transmission hotspots: application to dengue virus outbreaks.
Article in International journal of health geographics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIdentifying transmission hotspots associated with micro-clustering patterns at the early stages of epidemics is helpful to characterize spatiotemporal spread of infectious diseases. However, standard methods with statistical validation to establish a dynamic warning system for emerging infectious diseases are lacking. We therefore aimed to integrate a geographic information system-based surveillance system and data-driven methods to identify transmission hotspots, thereby assisting decision-makers to implement appropriate policies at the early stages of epidemics.
methodsWe propose a method to identify micro-clusters and transform them into ellipse-shaped transmission hotspots to characterize disease propagation. The ellipse-shaped transmission hotspots built by the machine-learning method and mathematical models agree with 100(1 - α)% confidence regions in multivariate analysis.
resultsWe provided a flowchart for the construction of ellipse-shaped transmission hotspots. Import parameters, such as cluster range and orientation, were determined by statistical models as validation. The elliptical transmission hotspots reveal the expansion pattern for the dengue virus infection. Compared with density-based spatial clustering of applications with noise, the ellipse-shaped transmission hotspots more effectively characterize the orientations of the spread patterns at the early stage of epidemics.
conclusionsThe geographic information system-based surveillance system used here to characterize ellipse-shaped transmission hotspots of dengue fever can also be applied to other infectious diseases to visualize the evolution of their dispersion areas and conduct early impact assessments. The mapped information could be aggregated to continuously update the propagation patterns, thereby helping public health departments to implement appropriate control measures for infectious diseases.
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.