Risk Assessment of the Overseas Imported COVID-19 of Ocean-Going Ships Based on AIS and Infection Data
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 introduc...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-05-01
|
| Series: | ISPRS International Journal of Geo-Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2220-9964/9/6/351 |
| _version_ | 1797566926360150016 |
|---|---|
| author | Zhihuan Wang Mengyuan Yao Chenguang Meng Christophe Claramunt |
| author_facet | Zhihuan Wang Mengyuan Yao Chenguang Meng Christophe Claramunt |
| author_sort | Zhihuan Wang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T19:34:20Z |
| format | Article |
| id | doaj.art-0acd66ae5a2740ba846d2deeefd4f1eb |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-10T19:34:20Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-0acd66ae5a2740ba846d2deeefd4f1eb2023-11-20T01:51:39ZengMDPI AGISPRS International Journal of Geo-Information2220-99642020-05-019635110.3390/ijgi9060351Risk Assessment of the Overseas Imported COVID-19 of Ocean-Going Ships Based on AIS and Infection DataZhihuan Wang0Mengyuan Yao1Chenguang Meng2Christophe Claramunt3Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, ChinaInstitute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, ChinaInstitute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, ChinaLogistics Engineering College, Shanghai Maritime University, Shanghai 201306, ChinaPreventing 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.https://www.mdpi.com/2220-9964/9/6/351COVID-19international shippingoverseas imported casesrisk assessmentautomatic identification systemsST-DBSCAN |
| spellingShingle | 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 COVID-19 international shipping overseas imported cases risk assessment automatic identification systems ST-DBSCAN |
| title | Risk Assessment of the Overseas Imported COVID-19 of Ocean-Going Ships Based on AIS and Infection Data |
| title_full | Risk Assessment of the Overseas Imported COVID-19 of Ocean-Going Ships Based on AIS and Infection Data |
| title_fullStr | Risk Assessment of the Overseas Imported COVID-19 of Ocean-Going Ships Based on AIS and Infection Data |
| title_full_unstemmed | Risk Assessment of the Overseas Imported COVID-19 of Ocean-Going Ships Based on AIS and Infection Data |
| title_short | Risk Assessment of the Overseas Imported COVID-19 of Ocean-Going Ships Based on AIS and Infection Data |
| title_sort | risk assessment of the overseas imported covid 19 of ocean going ships based on ais and infection data |
| topic | COVID-19 international shipping overseas imported cases risk assessment automatic identification systems ST-DBSCAN |
| url | https://www.mdpi.com/2220-9964/9/6/351 |
| work_keys_str_mv | AT zhihuanwang riskassessmentoftheoverseasimportedcovid19ofoceangoingshipsbasedonaisandinfectiondata AT mengyuanyao riskassessmentoftheoverseasimportedcovid19ofoceangoingshipsbasedonaisandinfectiondata AT chenguangmeng riskassessmentoftheoverseasimportedcovid19ofoceangoingshipsbasedonaisandinfectiondata AT christopheclaramunt riskassessmentoftheoverseasimportedcovid19ofoceangoingshipsbasedonaisandinfectiondata |