Model-guided suggestions for targeted surveillance based on cattle shipments in the U.S.
Risk-based sampling is an essential component of livestock health surveillance because it targets resources towards sub-populations with a higher risk of infection. Risk-based surveillance in U.S. livestock is limited because the locations of high-risk herds are often unknown and data to identify hi...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Gorsich, Erin E. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018transfer abstract |
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| Schlagwörter: |
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| Umfang: |
8 |
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| Übergeordnetes Werk: |
Enthalten in: Modelling the continuous calcination of CaCO3 in a Ca-looping system - Martínez, I. ELSEVIER, 2013, an international journal on research and development in veterinary epidemiology, animal disease prevention and control, and animal health economics, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:150 ; year:2018 ; day:1 ; month:02 ; pages:52-59 ; extent:8 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.prevetmed.2017.12.004 |
|---|
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ELV041823427 |
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| 520 | |a Risk-based sampling is an essential component of livestock health surveillance because it targets resources towards sub-populations with a higher risk of infection. Risk-based surveillance in U.S. livestock is limited because the locations of high-risk herds are often unknown and data to identify high-risk herds based on shipments are often unavailable. In this study, we use a novel, data-driven network model for the shipments of cattle in the U.S. (the U.S. Animal Movement Model, USAMM) to provide surveillance suggestions for cattle imported into the U.S. from Mexico. We describe the volume and locations where cattle are imported and analyze their predicted shipment patterns to identify counties that are most likely to receive shipments of imported cattle. Our results suggest that most imported cattle are sent to relatively few counties. Surveillance at 10 counties is predicted to sample 22–34% of imported cattle while surveillance at 50 counties is predicted to sample 43%–61% of imported cattle. These findings are based on the assumption that USAMM accurately describes the shipments of imported cattle because their shipments are not tracked separately from the remainder of the U.S. herd. However, we analyze two additional datasets – Interstate Certificates of Veterinary Inspection and brand inspection data – to ensure that the characteristics of potential post-import shipments do not change on an annual scale and are not dependent on the dataset informing our analyses. Overall, these results highlight the utility of USAMM to inform targeted surveillance strategies when complete shipment information is unavailable. | ||
| 520 | |a Risk-based sampling is an essential component of livestock health surveillance because it targets resources towards sub-populations with a higher risk of infection. Risk-based surveillance in U.S. livestock is limited because the locations of high-risk herds are often unknown and data to identify high-risk herds based on shipments are often unavailable. In this study, we use a novel, data-driven network model for the shipments of cattle in the U.S. (the U.S. Animal Movement Model, USAMM) to provide surveillance suggestions for cattle imported into the U.S. from Mexico. We describe the volume and locations where cattle are imported and analyze their predicted shipment patterns to identify counties that are most likely to receive shipments of imported cattle. Our results suggest that most imported cattle are sent to relatively few counties. Surveillance at 10 counties is predicted to sample 22–34% of imported cattle while surveillance at 50 counties is predicted to sample 43%–61% of imported cattle. These findings are based on the assumption that USAMM accurately describes the shipments of imported cattle because their shipments are not tracked separately from the remainder of the U.S. herd. However, we analyze two additional datasets – Interstate Certificates of Veterinary Inspection and brand inspection data – to ensure that the characteristics of potential post-import shipments do not change on an annual scale and are not dependent on the dataset informing our analyses. Overall, these results highlight the utility of USAMM to inform targeted surveillance strategies when complete shipment information is unavailable. | ||
| 650 | 7 | |a Brand inspection |2 Elsevier | |
| 650 | 7 | |a Cattle shipment |2 Elsevier | |
| 650 | 7 | |a Surveillance |2 Elsevier | |
| 650 | 7 | |a Interstate Certificate of Veterinary Inspection |2 Elsevier | |
| 650 | 7 | |a Livestock disease |2 Elsevier | |
| 650 | 7 | |a Network analysis |2 Elsevier | |
| 650 | 7 | |a U.S. cattle industry |2 Elsevier | |
| 700 | 1 | |a McKee, Clifton D. |4 oth | |
| 700 | 1 | |a Grear, Daniel A. |4 oth | |
| 700 | 1 | |a Miller, Ryan S. |4 oth | |
| 700 | 1 | |a Portacci, Katie |4 oth | |
| 700 | 1 | |a Lindström, Tom |4 oth | |
| 700 | 1 | |a Webb, Colleen T. |4 oth | |
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10.1016/j.prevetmed.2017.12.004 doi GBV00000000000255A.pica (DE-627)ELV041823427 (ELSEVIER)S0167-5877(17)30539-1 DE-627 ger DE-627 rakwb eng 630 630 DE-600 660 VZ 660 VZ 530 600 670 VZ 51.00 bkl Gorsich, Erin E. verfasserin aut Model-guided suggestions for targeted surveillance based on cattle shipments in the U.S. 2018transfer abstract 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Risk-based sampling is an essential component of livestock health surveillance because it targets resources towards sub-populations with a higher risk of infection. Risk-based surveillance in U.S. livestock is limited because the locations of high-risk herds are often unknown and data to identify high-risk herds based on shipments are often unavailable. In this study, we use a novel, data-driven network model for the shipments of cattle in the U.S. (the U.S. Animal Movement Model, USAMM) to provide surveillance suggestions for cattle imported into the U.S. from Mexico. We describe the volume and locations where cattle are imported and analyze their predicted shipment patterns to identify counties that are most likely to receive shipments of imported cattle. Our results suggest that most imported cattle are sent to relatively few counties. Surveillance at 10 counties is predicted to sample 22–34% of imported cattle while surveillance at 50 counties is predicted to sample 43%–61% of imported cattle. These findings are based on the assumption that USAMM accurately describes the shipments of imported cattle because their shipments are not tracked separately from the remainder of the U.S. herd. However, we analyze two additional datasets – Interstate Certificates of Veterinary Inspection and brand inspection data – to ensure that the characteristics of potential post-import shipments do not change on an annual scale and are not dependent on the dataset informing our analyses. Overall, these results highlight the utility of USAMM to inform targeted surveillance strategies when complete shipment information is unavailable. Risk-based sampling is an essential component of livestock health surveillance because it targets resources towards sub-populations with a higher risk of infection. Risk-based surveillance in U.S. livestock is limited because the locations of high-risk herds are often unknown and data to identify high-risk herds based on shipments are often unavailable. In this study, we use a novel, data-driven network model for the shipments of cattle in the U.S. (the U.S. Animal Movement Model, USAMM) to provide surveillance suggestions for cattle imported into the U.S. from Mexico. We describe the volume and locations where cattle are imported and analyze their predicted shipment patterns to identify counties that are most likely to receive shipments of imported cattle. Our results suggest that most imported cattle are sent to relatively few counties. Surveillance at 10 counties is predicted to sample 22–34% of imported cattle while surveillance at 50 counties is predicted to sample 43%–61% of imported cattle. These findings are based on the assumption that USAMM accurately describes the shipments of imported cattle because their shipments are not tracked separately from the remainder of the U.S. herd. However, we analyze two additional datasets – Interstate Certificates of Veterinary Inspection and brand inspection data – to ensure that the characteristics of potential post-import shipments do not change on an annual scale and are not dependent on the dataset informing our analyses. Overall, these results highlight the utility of USAMM to inform targeted surveillance strategies when complete shipment information is unavailable. Brand inspection Elsevier Cattle shipment Elsevier Surveillance Elsevier Interstate Certificate of Veterinary Inspection Elsevier Livestock disease Elsevier Network analysis Elsevier U.S. cattle industry Elsevier McKee, Clifton D. oth Grear, Daniel A. oth Miller, Ryan S. oth Portacci, Katie oth Lindström, Tom oth Webb, Colleen T. oth Enthalten in Elsevier Science Martínez, I. ELSEVIER Modelling the continuous calcination of CaCO3 in a Ca-looping system 2013 an international journal on research and development in veterinary epidemiology, animal disease prevention and control, and animal health economics Amsterdam [u.a.] (DE-627)ELV011355530 volume:150 year:2018 day:1 month:02 pages:52-59 extent:8 https://doi.org/10.1016/j.prevetmed.2017.12.004 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_21 GBV_ILN_22 GBV_ILN_24 GBV_ILN_40 GBV_ILN_70 GBV_ILN_77 GBV_ILN_121 GBV_ILN_130 GBV_ILN_666 GBV_ILN_683 51.00 Werkstoffkunde: Allgemeines VZ AR 150 2018 1 0201 52-59 8 045F 630 |
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10.1016/j.prevetmed.2017.12.004 doi GBV00000000000255A.pica (DE-627)ELV041823427 (ELSEVIER)S0167-5877(17)30539-1 DE-627 ger DE-627 rakwb eng 630 630 DE-600 660 VZ 660 VZ 530 600 670 VZ 51.00 bkl Gorsich, Erin E. verfasserin aut Model-guided suggestions for targeted surveillance based on cattle shipments in the U.S. 2018transfer abstract 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Risk-based sampling is an essential component of livestock health surveillance because it targets resources towards sub-populations with a higher risk of infection. Risk-based surveillance in U.S. livestock is limited because the locations of high-risk herds are often unknown and data to identify high-risk herds based on shipments are often unavailable. In this study, we use a novel, data-driven network model for the shipments of cattle in the U.S. (the U.S. Animal Movement Model, USAMM) to provide surveillance suggestions for cattle imported into the U.S. from Mexico. We describe the volume and locations where cattle are imported and analyze their predicted shipment patterns to identify counties that are most likely to receive shipments of imported cattle. Our results suggest that most imported cattle are sent to relatively few counties. Surveillance at 10 counties is predicted to sample 22–34% of imported cattle while surveillance at 50 counties is predicted to sample 43%–61% of imported cattle. These findings are based on the assumption that USAMM accurately describes the shipments of imported cattle because their shipments are not tracked separately from the remainder of the U.S. herd. However, we analyze two additional datasets – Interstate Certificates of Veterinary Inspection and brand inspection data – to ensure that the characteristics of potential post-import shipments do not change on an annual scale and are not dependent on the dataset informing our analyses. Overall, these results highlight the utility of USAMM to inform targeted surveillance strategies when complete shipment information is unavailable. Risk-based sampling is an essential component of livestock health surveillance because it targets resources towards sub-populations with a higher risk of infection. Risk-based surveillance in U.S. livestock is limited because the locations of high-risk herds are often unknown and data to identify high-risk herds based on shipments are often unavailable. In this study, we use a novel, data-driven network model for the shipments of cattle in the U.S. (the U.S. Animal Movement Model, USAMM) to provide surveillance suggestions for cattle imported into the U.S. from Mexico. We describe the volume and locations where cattle are imported and analyze their predicted shipment patterns to identify counties that are most likely to receive shipments of imported cattle. Our results suggest that most imported cattle are sent to relatively few counties. Surveillance at 10 counties is predicted to sample 22–34% of imported cattle while surveillance at 50 counties is predicted to sample 43%–61% of imported cattle. These findings are based on the assumption that USAMM accurately describes the shipments of imported cattle because their shipments are not tracked separately from the remainder of the U.S. herd. However, we analyze two additional datasets – Interstate Certificates of Veterinary Inspection and brand inspection data – to ensure that the characteristics of potential post-import shipments do not change on an annual scale and are not dependent on the dataset informing our analyses. Overall, these results highlight the utility of USAMM to inform targeted surveillance strategies when complete shipment information is unavailable. Brand inspection Elsevier Cattle shipment Elsevier Surveillance Elsevier Interstate Certificate of Veterinary Inspection Elsevier Livestock disease Elsevier Network analysis Elsevier U.S. cattle industry Elsevier McKee, Clifton D. oth Grear, Daniel A. oth Miller, Ryan S. oth Portacci, Katie oth Lindström, Tom oth Webb, Colleen T. oth Enthalten in Elsevier Science Martínez, I. ELSEVIER Modelling the continuous calcination of CaCO3 in a Ca-looping system 2013 an international journal on research and development in veterinary epidemiology, animal disease prevention and control, and animal health economics Amsterdam [u.a.] (DE-627)ELV011355530 volume:150 year:2018 day:1 month:02 pages:52-59 extent:8 https://doi.org/10.1016/j.prevetmed.2017.12.004 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_21 GBV_ILN_22 GBV_ILN_24 GBV_ILN_40 GBV_ILN_70 GBV_ILN_77 GBV_ILN_121 GBV_ILN_130 GBV_ILN_666 GBV_ILN_683 51.00 Werkstoffkunde: Allgemeines VZ AR 150 2018 1 0201 52-59 8 045F 630 |
| allfields_unstemmed |
10.1016/j.prevetmed.2017.12.004 doi GBV00000000000255A.pica (DE-627)ELV041823427 (ELSEVIER)S0167-5877(17)30539-1 DE-627 ger DE-627 rakwb eng 630 630 DE-600 660 VZ 660 VZ 530 600 670 VZ 51.00 bkl Gorsich, Erin E. verfasserin aut Model-guided suggestions for targeted surveillance based on cattle shipments in the U.S. 2018transfer abstract 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Risk-based sampling is an essential component of livestock health surveillance because it targets resources towards sub-populations with a higher risk of infection. Risk-based surveillance in U.S. livestock is limited because the locations of high-risk herds are often unknown and data to identify high-risk herds based on shipments are often unavailable. In this study, we use a novel, data-driven network model for the shipments of cattle in the U.S. (the U.S. Animal Movement Model, USAMM) to provide surveillance suggestions for cattle imported into the U.S. from Mexico. We describe the volume and locations where cattle are imported and analyze their predicted shipment patterns to identify counties that are most likely to receive shipments of imported cattle. Our results suggest that most imported cattle are sent to relatively few counties. Surveillance at 10 counties is predicted to sample 22–34% of imported cattle while surveillance at 50 counties is predicted to sample 43%–61% of imported cattle. These findings are based on the assumption that USAMM accurately describes the shipments of imported cattle because their shipments are not tracked separately from the remainder of the U.S. herd. However, we analyze two additional datasets – Interstate Certificates of Veterinary Inspection and brand inspection data – to ensure that the characteristics of potential post-import shipments do not change on an annual scale and are not dependent on the dataset informing our analyses. Overall, these results highlight the utility of USAMM to inform targeted surveillance strategies when complete shipment information is unavailable. Risk-based sampling is an essential component of livestock health surveillance because it targets resources towards sub-populations with a higher risk of infection. Risk-based surveillance in U.S. livestock is limited because the locations of high-risk herds are often unknown and data to identify high-risk herds based on shipments are often unavailable. In this study, we use a novel, data-driven network model for the shipments of cattle in the U.S. (the U.S. Animal Movement Model, USAMM) to provide surveillance suggestions for cattle imported into the U.S. from Mexico. We describe the volume and locations where cattle are imported and analyze their predicted shipment patterns to identify counties that are most likely to receive shipments of imported cattle. Our results suggest that most imported cattle are sent to relatively few counties. Surveillance at 10 counties is predicted to sample 22–34% of imported cattle while surveillance at 50 counties is predicted to sample 43%–61% of imported cattle. These findings are based on the assumption that USAMM accurately describes the shipments of imported cattle because their shipments are not tracked separately from the remainder of the U.S. herd. However, we analyze two additional datasets – Interstate Certificates of Veterinary Inspection and brand inspection data – to ensure that the characteristics of potential post-import shipments do not change on an annual scale and are not dependent on the dataset informing our analyses. Overall, these results highlight the utility of USAMM to inform targeted surveillance strategies when complete shipment information is unavailable. Brand inspection Elsevier Cattle shipment Elsevier Surveillance Elsevier Interstate Certificate of Veterinary Inspection Elsevier Livestock disease Elsevier Network analysis Elsevier U.S. cattle industry Elsevier McKee, Clifton D. oth Grear, Daniel A. oth Miller, Ryan S. oth Portacci, Katie oth Lindström, Tom oth Webb, Colleen T. oth Enthalten in Elsevier Science Martínez, I. ELSEVIER Modelling the continuous calcination of CaCO3 in a Ca-looping system 2013 an international journal on research and development in veterinary epidemiology, animal disease prevention and control, and animal health economics Amsterdam [u.a.] (DE-627)ELV011355530 volume:150 year:2018 day:1 month:02 pages:52-59 extent:8 https://doi.org/10.1016/j.prevetmed.2017.12.004 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_21 GBV_ILN_22 GBV_ILN_24 GBV_ILN_40 GBV_ILN_70 GBV_ILN_77 GBV_ILN_121 GBV_ILN_130 GBV_ILN_666 GBV_ILN_683 51.00 Werkstoffkunde: Allgemeines VZ AR 150 2018 1 0201 52-59 8 045F 630 |
| allfieldsGer |
10.1016/j.prevetmed.2017.12.004 doi GBV00000000000255A.pica (DE-627)ELV041823427 (ELSEVIER)S0167-5877(17)30539-1 DE-627 ger DE-627 rakwb eng 630 630 DE-600 660 VZ 660 VZ 530 600 670 VZ 51.00 bkl Gorsich, Erin E. verfasserin aut Model-guided suggestions for targeted surveillance based on cattle shipments in the U.S. 2018transfer abstract 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Risk-based sampling is an essential component of livestock health surveillance because it targets resources towards sub-populations with a higher risk of infection. Risk-based surveillance in U.S. livestock is limited because the locations of high-risk herds are often unknown and data to identify high-risk herds based on shipments are often unavailable. In this study, we use a novel, data-driven network model for the shipments of cattle in the U.S. (the U.S. Animal Movement Model, USAMM) to provide surveillance suggestions for cattle imported into the U.S. from Mexico. We describe the volume and locations where cattle are imported and analyze their predicted shipment patterns to identify counties that are most likely to receive shipments of imported cattle. Our results suggest that most imported cattle are sent to relatively few counties. Surveillance at 10 counties is predicted to sample 22–34% of imported cattle while surveillance at 50 counties is predicted to sample 43%–61% of imported cattle. These findings are based on the assumption that USAMM accurately describes the shipments of imported cattle because their shipments are not tracked separately from the remainder of the U.S. herd. However, we analyze two additional datasets – Interstate Certificates of Veterinary Inspection and brand inspection data – to ensure that the characteristics of potential post-import shipments do not change on an annual scale and are not dependent on the dataset informing our analyses. Overall, these results highlight the utility of USAMM to inform targeted surveillance strategies when complete shipment information is unavailable. Risk-based sampling is an essential component of livestock health surveillance because it targets resources towards sub-populations with a higher risk of infection. Risk-based surveillance in U.S. livestock is limited because the locations of high-risk herds are often unknown and data to identify high-risk herds based on shipments are often unavailable. In this study, we use a novel, data-driven network model for the shipments of cattle in the U.S. (the U.S. Animal Movement Model, USAMM) to provide surveillance suggestions for cattle imported into the U.S. from Mexico. We describe the volume and locations where cattle are imported and analyze their predicted shipment patterns to identify counties that are most likely to receive shipments of imported cattle. Our results suggest that most imported cattle are sent to relatively few counties. Surveillance at 10 counties is predicted to sample 22–34% of imported cattle while surveillance at 50 counties is predicted to sample 43%–61% of imported cattle. These findings are based on the assumption that USAMM accurately describes the shipments of imported cattle because their shipments are not tracked separately from the remainder of the U.S. herd. However, we analyze two additional datasets – Interstate Certificates of Veterinary Inspection and brand inspection data – to ensure that the characteristics of potential post-import shipments do not change on an annual scale and are not dependent on the dataset informing our analyses. Overall, these results highlight the utility of USAMM to inform targeted surveillance strategies when complete shipment information is unavailable. Brand inspection Elsevier Cattle shipment Elsevier Surveillance Elsevier Interstate Certificate of Veterinary Inspection Elsevier Livestock disease Elsevier Network analysis Elsevier U.S. cattle industry Elsevier McKee, Clifton D. oth Grear, Daniel A. oth Miller, Ryan S. oth Portacci, Katie oth Lindström, Tom oth Webb, Colleen T. oth Enthalten in Elsevier Science Martínez, I. ELSEVIER Modelling the continuous calcination of CaCO3 in a Ca-looping system 2013 an international journal on research and development in veterinary epidemiology, animal disease prevention and control, and animal health economics Amsterdam [u.a.] (DE-627)ELV011355530 volume:150 year:2018 day:1 month:02 pages:52-59 extent:8 https://doi.org/10.1016/j.prevetmed.2017.12.004 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_21 GBV_ILN_22 GBV_ILN_24 GBV_ILN_40 GBV_ILN_70 GBV_ILN_77 GBV_ILN_121 GBV_ILN_130 GBV_ILN_666 GBV_ILN_683 51.00 Werkstoffkunde: Allgemeines VZ AR 150 2018 1 0201 52-59 8 045F 630 |
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10.1016/j.prevetmed.2017.12.004 doi GBV00000000000255A.pica (DE-627)ELV041823427 (ELSEVIER)S0167-5877(17)30539-1 DE-627 ger DE-627 rakwb eng 630 630 DE-600 660 VZ 660 VZ 530 600 670 VZ 51.00 bkl Gorsich, Erin E. verfasserin aut Model-guided suggestions for targeted surveillance based on cattle shipments in the U.S. 2018transfer abstract 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Risk-based sampling is an essential component of livestock health surveillance because it targets resources towards sub-populations with a higher risk of infection. Risk-based surveillance in U.S. livestock is limited because the locations of high-risk herds are often unknown and data to identify high-risk herds based on shipments are often unavailable. In this study, we use a novel, data-driven network model for the shipments of cattle in the U.S. (the U.S. Animal Movement Model, USAMM) to provide surveillance suggestions for cattle imported into the U.S. from Mexico. We describe the volume and locations where cattle are imported and analyze their predicted shipment patterns to identify counties that are most likely to receive shipments of imported cattle. Our results suggest that most imported cattle are sent to relatively few counties. Surveillance at 10 counties is predicted to sample 22–34% of imported cattle while surveillance at 50 counties is predicted to sample 43%–61% of imported cattle. These findings are based on the assumption that USAMM accurately describes the shipments of imported cattle because their shipments are not tracked separately from the remainder of the U.S. herd. However, we analyze two additional datasets – Interstate Certificates of Veterinary Inspection and brand inspection data – to ensure that the characteristics of potential post-import shipments do not change on an annual scale and are not dependent on the dataset informing our analyses. Overall, these results highlight the utility of USAMM to inform targeted surveillance strategies when complete shipment information is unavailable. Risk-based sampling is an essential component of livestock health surveillance because it targets resources towards sub-populations with a higher risk of infection. Risk-based surveillance in U.S. livestock is limited because the locations of high-risk herds are often unknown and data to identify high-risk herds based on shipments are often unavailable. In this study, we use a novel, data-driven network model for the shipments of cattle in the U.S. (the U.S. Animal Movement Model, USAMM) to provide surveillance suggestions for cattle imported into the U.S. from Mexico. We describe the volume and locations where cattle are imported and analyze their predicted shipment patterns to identify counties that are most likely to receive shipments of imported cattle. Our results suggest that most imported cattle are sent to relatively few counties. Surveillance at 10 counties is predicted to sample 22–34% of imported cattle while surveillance at 50 counties is predicted to sample 43%–61% of imported cattle. These findings are based on the assumption that USAMM accurately describes the shipments of imported cattle because their shipments are not tracked separately from the remainder of the U.S. herd. However, we analyze two additional datasets – Interstate Certificates of Veterinary Inspection and brand inspection data – to ensure that the characteristics of potential post-import shipments do not change on an annual scale and are not dependent on the dataset informing our analyses. Overall, these results highlight the utility of USAMM to inform targeted surveillance strategies when complete shipment information is unavailable. Brand inspection Elsevier Cattle shipment Elsevier Surveillance Elsevier Interstate Certificate of Veterinary Inspection Elsevier Livestock disease Elsevier Network analysis Elsevier U.S. cattle industry Elsevier McKee, Clifton D. oth Grear, Daniel A. oth Miller, Ryan S. oth Portacci, Katie oth Lindström, Tom oth Webb, Colleen T. oth Enthalten in Elsevier Science Martínez, I. ELSEVIER Modelling the continuous calcination of CaCO3 in a Ca-looping system 2013 an international journal on research and development in veterinary epidemiology, animal disease prevention and control, and animal health economics Amsterdam [u.a.] (DE-627)ELV011355530 volume:150 year:2018 day:1 month:02 pages:52-59 extent:8 https://doi.org/10.1016/j.prevetmed.2017.12.004 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_21 GBV_ILN_22 GBV_ILN_24 GBV_ILN_40 GBV_ILN_70 GBV_ILN_77 GBV_ILN_121 GBV_ILN_130 GBV_ILN_666 GBV_ILN_683 51.00 Werkstoffkunde: Allgemeines VZ AR 150 2018 1 0201 52-59 8 045F 630 |
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model-guided suggestions for targeted surveillance based on cattle shipments in the u.s. |
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Model-guided suggestions for targeted surveillance based on cattle shipments in the U.S. |
| abstract |
Risk-based sampling is an essential component of livestock health surveillance because it targets resources towards sub-populations with a higher risk of infection. Risk-based surveillance in U.S. livestock is limited because the locations of high-risk herds are often unknown and data to identify high-risk herds based on shipments are often unavailable. In this study, we use a novel, data-driven network model for the shipments of cattle in the U.S. (the U.S. Animal Movement Model, USAMM) to provide surveillance suggestions for cattle imported into the U.S. from Mexico. We describe the volume and locations where cattle are imported and analyze their predicted shipment patterns to identify counties that are most likely to receive shipments of imported cattle. Our results suggest that most imported cattle are sent to relatively few counties. Surveillance at 10 counties is predicted to sample 22–34% of imported cattle while surveillance at 50 counties is predicted to sample 43%–61% of imported cattle. These findings are based on the assumption that USAMM accurately describes the shipments of imported cattle because their shipments are not tracked separately from the remainder of the U.S. herd. However, we analyze two additional datasets – Interstate Certificates of Veterinary Inspection and brand inspection data – to ensure that the characteristics of potential post-import shipments do not change on an annual scale and are not dependent on the dataset informing our analyses. Overall, these results highlight the utility of USAMM to inform targeted surveillance strategies when complete shipment information is unavailable. |
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Risk-based sampling is an essential component of livestock health surveillance because it targets resources towards sub-populations with a higher risk of infection. Risk-based surveillance in U.S. livestock is limited because the locations of high-risk herds are often unknown and data to identify high-risk herds based on shipments are often unavailable. In this study, we use a novel, data-driven network model for the shipments of cattle in the U.S. (the U.S. Animal Movement Model, USAMM) to provide surveillance suggestions for cattle imported into the U.S. from Mexico. We describe the volume and locations where cattle are imported and analyze their predicted shipment patterns to identify counties that are most likely to receive shipments of imported cattle. Our results suggest that most imported cattle are sent to relatively few counties. Surveillance at 10 counties is predicted to sample 22–34% of imported cattle while surveillance at 50 counties is predicted to sample 43%–61% of imported cattle. These findings are based on the assumption that USAMM accurately describes the shipments of imported cattle because their shipments are not tracked separately from the remainder of the U.S. herd. However, we analyze two additional datasets – Interstate Certificates of Veterinary Inspection and brand inspection data – to ensure that the characteristics of potential post-import shipments do not change on an annual scale and are not dependent on the dataset informing our analyses. Overall, these results highlight the utility of USAMM to inform targeted surveillance strategies when complete shipment information is unavailable. |
| abstract_unstemmed |
Risk-based sampling is an essential component of livestock health surveillance because it targets resources towards sub-populations with a higher risk of infection. Risk-based surveillance in U.S. livestock is limited because the locations of high-risk herds are often unknown and data to identify high-risk herds based on shipments are often unavailable. In this study, we use a novel, data-driven network model for the shipments of cattle in the U.S. (the U.S. Animal Movement Model, USAMM) to provide surveillance suggestions for cattle imported into the U.S. from Mexico. We describe the volume and locations where cattle are imported and analyze their predicted shipment patterns to identify counties that are most likely to receive shipments of imported cattle. Our results suggest that most imported cattle are sent to relatively few counties. Surveillance at 10 counties is predicted to sample 22–34% of imported cattle while surveillance at 50 counties is predicted to sample 43%–61% of imported cattle. These findings are based on the assumption that USAMM accurately describes the shipments of imported cattle because their shipments are not tracked separately from the remainder of the U.S. herd. However, we analyze two additional datasets – Interstate Certificates of Veterinary Inspection and brand inspection data – to ensure that the characteristics of potential post-import shipments do not change on an annual scale and are not dependent on the dataset informing our analyses. Overall, these results highlight the utility of USAMM to inform targeted surveillance strategies when complete shipment information is unavailable. |
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Model-guided suggestions for targeted surveillance based on cattle shipments in the U.S. |
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Risk-based surveillance in U.S. livestock is limited because the locations of high-risk herds are often unknown and data to identify high-risk herds based on shipments are often unavailable. In this study, we use a novel, data-driven network model for the shipments of cattle in the U.S. (the U.S. Animal Movement Model, USAMM) to provide surveillance suggestions for cattle imported into the U.S. from Mexico. We describe the volume and locations where cattle are imported and analyze their predicted shipment patterns to identify counties that are most likely to receive shipments of imported cattle. Our results suggest that most imported cattle are sent to relatively few counties. Surveillance at 10 counties is predicted to sample 22–34% of imported cattle while surveillance at 50 counties is predicted to sample 43%–61% of imported cattle. These findings are based on the assumption that USAMM accurately describes the shipments of imported cattle because their shipments are not tracked separately from the remainder of the U.S. herd. However, we analyze two additional datasets – Interstate Certificates of Veterinary Inspection and brand inspection data – to ensure that the characteristics of potential post-import shipments do not change on an annual scale and are not dependent on the dataset informing our analyses. Overall, these results highlight the utility of USAMM to inform targeted surveillance strategies when complete shipment information is unavailable.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Brand inspection</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Cattle shipment</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Surveillance</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Interstate Certificate of Veterinary Inspection</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Livestock disease</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Network analysis</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">U.S. cattle industry</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">McKee, Clifton D.</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Grear, Daniel A.</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Miller, Ryan S.</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Portacci, Katie</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Lindström, Tom</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Webb, Colleen T.</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science</subfield><subfield code="a">Martínez, I. ELSEVIER</subfield><subfield code="t">Modelling the continuous calcination of CaCO3 in a Ca-looping system</subfield><subfield code="d">2013</subfield><subfield code="d">an international journal on research and development in veterinary epidemiology, animal disease prevention and control, and animal health economics</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV011355530</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:150</subfield><subfield code="g">year:2018</subfield><subfield code="g">day:1</subfield><subfield code="g">month:02</subfield><subfield code="g">pages:52-59</subfield><subfield code="g">extent:8</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.prevetmed.2017.12.004</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_21</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_77</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_121</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_130</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_666</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_683</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">51.00</subfield><subfield code="j">Werkstoffkunde: Allgemeines</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">150</subfield><subfield code="j">2018</subfield><subfield code="b">1</subfield><subfield code="c">0201</subfield><subfield code="h">52-59</subfield><subfield code="g">8</subfield></datafield><datafield tag="953" ind1=" " ind2=" "><subfield code="2">045F</subfield><subfield code="a">630</subfield></datafield></record></collection>
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