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The accurate discovery of disease-outbreak source areas plays a critical role in the effective containment of epidemics at an early stage. Existing relevant methods are mostly implemented by tracing the source of disease outbreaks directly from the distribution of confirmed cases in the Euclidean space. In reality, in most respiratory infectious diseases, crowd gathering caused by resident trips significantly increases the risk of exposure to potentially infected persons, making it the driving force behind the outbreak of the disease. In light of this, this study proposes a network-constrained ring-shaped hotspot detection method based on the classical spatial scan statistic model. This new method can be used to determine the sizes of the scanning window in an adaptive manner by using the information of the trip distance distributions. Considering the centered traffic analysis zone in each hotspot as a candidate outbreak source area, a multi-factor coupling model was designed by characterizing both the areal vibrancy and resident trip distributions to further identify the potential disease-outbreak source areas. A case study on the outbreaks of COVID-19 in Wenzhou, China, was carried out to evaluate the practicability and effectiveness of the proposed method. The comparison results demonstrate the effectiveness of the proposed method for detecting areas and sources of outbreaks.

Discovering source areas of disease outbreaks based on ring-shaped hotspot detection in road network space