Risk Assessment of the Overseas Imported COVID-19 of Ocean-Going Ships Based on AIS and Infection Data
1
Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, China
2
Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China
3
Naval Academy Research Institute, 29240 Brest, France
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(6), 351; https://doi.org/10.3390/ijgi9060351
Received: 26 April 2020 / Revised: 20 May 2020 / Accepted: 25 May 2020 / Published: 27 May 2020
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.
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Keywords:
COVID-19; international shipping; overseas imported cases; risk assessment; automatic identification systems; ST-DBSCAN; health QR code
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Wang, Z.; Yao, M.; Meng, C.; Claramunt, C. Risk Assessment of the Overseas Imported COVID-19 of Ocean-Going Ships Based on AIS and Infection Data. ISPRS Int. J. Geo-Inf. 2020, 9, 351.
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