Background <p>Identifying 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.</p> Methods <p>We 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 − <i>α</i>)% confidence regions in multivariate analysis.</p> Results <p>We 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.</p> Conclusions <p>The 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.</p>