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

ABSTRACT

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.

KEYWORDS:

• Respiratory infectious diseases
• ring-shaped hotspots
• outbreak source areas
• road network space

Data and codes availability statement

The data and codes that support the findings of this study are available in figshare.com with the identifiers at 10.6084/m9.figshare.14904858.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary material

Supplemental data for this article can be accessed here.

Additional information

Funding

This work was supported by the National Key Research and Development Program of China, [No. 2018YFB1004603]; the National Natural Science Foundation of China (NSFC), [No. 42071452, 41771492 and 41730105]; the Natural Science Foundation of Hunan Province, China, [No. 2020JJ4696]; the Open Foundation of the Key Laboratory of Spatial Data Mining and Information Sharing, Ministry of Education (Fuzhou University), [No. 2019LSDMIS05].

Notes on contributors

Yan Shi

Yan Shi is currently an associate professor in the school of Geosciences and Info-physics, Central South University, Changsha, Hunan, China. He works in the area of spatio-temporal clustering, anomaly detection and association rule mining.

Yuanfang Chen

Yuanfang Chen is currently a Ph.D candidate in the school of Geosciences and Info-physics, Central South University, Changsha, Hunan, China. She majors in spatio-temporal data mining for epidemics.

Min Deng

Min Deng is currently a professor in the school of Geosciences and Info-physics, Central South University, Changsha, Hunan, China. He works in the area of geographical big data mining.

Liang Xu

Liang Xu received master degree from the school of Geosciences and Info-physics, Central South University, Changsha, Hunan, China. He majors in trajectory data mining.

Jiaqin Xia

Jiaqin Xia is currently a postgraduate in the school of Geosciences and Info-physics, Central South University, Changsha, Hunan, China. She majors in trajectory data mining.