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Preventing and controlling the risk of importing the coronavirus disease (COVID-19) has rapidly become a major concern. In addition to air freight, ocean-going ships play a non-negligible role in spreading COVID-19 due to frequent visits to countries with infected populations. This research introduces a method to dynamically assess the infection risk of ships based on a data-driven approach. It automatically identifies the ports and countries these ships approach based on their Automatic Identification Systems (AIS) data and a spatio-temporal density-based spatial clustering of applications with noise (ST_DBSCAN) algorithm. We derive daily and 14 day cumulative ship exposure indexes based on a series of country-based indices, such as population density, cumulative confirmed cases, and increased rate of confirmed cases. These indexes are classified into high-, middle-, and low-risk levels that are then coded as red, yellow, and green according to the health Quick Response (QR) code based on the reference exposure index of Wuhan on April 8, 2020. This method was applied to a real container ship deployed along a Eurasian route. The results showed that the proposed method can trace ship infection risk and provide a decision support mechanism to prevent and control overseas imported COVID-19 cases from international shipping.
Zhihuan Wang; Mengyuan Yao; Chenguang Meng; Christophe Claramunt. Risk Assessment of the Overseas Imported COVID-19 of Ocean-Going Ships Based on AIS and Infection Data. ISPRS International Journal of Geo-Information 2020, 9, 351 .
AMA StyleZhihuan Wang, Mengyuan Yao, Chenguang Meng, Christophe Claramunt. Risk Assessment of the Overseas Imported COVID-19 of Ocean-Going Ships Based on AIS and Infection Data. ISPRS International Journal of Geo-Information. 2020; 9 (6):351.
Chicago/Turabian StyleZhihuan Wang; Mengyuan Yao; Chenguang Meng; Christophe Claramunt. 2020. "Risk Assessment of the Overseas Imported COVID-19 of Ocean-Going Ships Based on AIS and Infection Data." ISPRS International Journal of Geo-Information 9, no. 6: 351.
The increasing availability of big Automatic Identification Systems (AIS) sensor data offers great opportunities to track ship activities and mine spatial-temporal patterns of ship traffic worldwide. This research proposes a data integration approach to construct Global Shipping Networks (GSN) from massive historical ship AIS trajectories in a completely bottom-up way. First, a DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm is applied to temporally identify relevant stop locations, such as marine terminals and their associated events. Second, the semantic meanings of these locations are obtained by mapping them to real ports as identified by the World Port Index (WPI). Stop events are leveraged to develop travel sequences of any ship between stop locations at multiple scales. Last, a GSN is constructed by considering stop locations as nodes and journeys between nodes as links. This approach generates different levels of shipping networks from the terminal, port, and country levels. It is illustrated by a case study that extracts country, port, and terminal level Global Container Shipping Networks (GCSN) from AIS trajectories of more than 4000 container ships in 2015. The main features of these GCSNs and the limitations of this work are finally discussed.
Zhihuan Wang; Christophe Claramunt; Yinhai Wang. Extracting Global Shipping Networks from Massive Historical Automatic Identification System Sensor Data: A Bottom-Up Approach. Sensors 2019, 19, 3363 .
AMA StyleZhihuan Wang, Christophe Claramunt, Yinhai Wang. Extracting Global Shipping Networks from Massive Historical Automatic Identification System Sensor Data: A Bottom-Up Approach. Sensors. 2019; 19 (15):3363.
Chicago/Turabian StyleZhihuan Wang; Christophe Claramunt; Yinhai Wang. 2019. "Extracting Global Shipping Networks from Massive Historical Automatic Identification System Sensor Data: A Bottom-Up Approach." Sensors 19, no. 15: 3363.