Research Summary - 3
What is the impact of incomplete animal movement networks in understanding disease transmission dynamics?
| Date/Time: |
9/14/2024
08:30
|
| Author: |
Sara Sequeira |
| Clinic: |
The Ohio State University |
| City, State, ZIP: |
Columbus, OH 43220 |
A. G. Arruda, DVM, MSC, PHD
1
;
G. Habing, DVM, MS, PHD, DACPVM
1
;
1Department of Veterinary Preventive Medicine, The Ohio State University, Columbus, Ohio, 43210, United States
Introduction:
The rise of infectious diseases in animal and human populations underscores the need for a comprehensive examination of their origins and transmission pathways. Network analysis methodologies have been increasingly applied in veterinary preventive medicine to understand how animal movements shape disease spread. Dairy calves, in particular, stand as major contributors to the crisis of antimicrobial resistance. While these animals travel through a complex network of stakeholders, the absence of comprehensive data limits our understanding of disease spread dynamics. Presently, interstate animal movements require an Interstate Certificates of Veterinary Inspection (ICVIs), yet certain agreements, such as Owner Shipper Statements (OSSs), can exempt this requirement and are often overlooked in research. This study aimed to use Ohio-based movement records to describe calf networks in the US and explore how OSSs impact the structure of calf networks built using ICVIs. We hypothesized that networks built exclusively using ICVIs will differ from those combined with OSSs.
Materials and methods:
Calf movement records to and from Ohio were collected in collaboration with the Ohio Department of Agriculture. Data included Interstate Certificates of Veterinary Inspection (ICVIs) and Owner Shipper Statements (OSSs), documented from June 2021 to June 2022. Animal imports and exports were analyzed along with shipment descriptive information using R software. To explore and compare movement patterns, network analysis was performed individually for an ICVI-based network and a network combining both document types, using the R packages igraph and visNetwork. Zip codes were considered nodes and calf movements links. Whole-network (e.g., density, component ratio, etc.) and node-level (e.g., degree, eigenvector centrality, etc.) parameters were calculated for each network. Wilcoxon sign-rank tests were performed to evaluate whether parameters differed statistically (P<0.05, 95% CI) by network type.
Results:
A total of 1,475 shipments were obtained, including 50.2% ICVIs (n=772) and 49.8% OSSs (n=766). Most shipments included mixed sex loads (60.0%), dairy breeds (81.6%) and animals up to one week old (74.1%). Animal imports represented the majority (77.3%) of the state’s recorded movements. Movements recorded through OSSs showed a larger median number of animals per movement (60; IQR 23-105) compared to ICVIs (49; IQR 16-80), reaching up to 696 calves per load. Failing to consider OSSs resulted in the absence of 40.3% of zipcodes in the study. ICVI-based networks involved fewer zip codes across states, whereas combined networks exhibited a larger and denser network. The two analyzed networks revealed contrasting results regarding degree centrality, especially for out-going geographical regions or zip codes (P< 0.01), suggesting a discrepancy in their potential to influence dynamics of disease transmission. Lower closeness centrality scores in the combined networks indicated a more integrated structure, in which surveillance measures would likely be harder to implement (P=0.01).
Significance:
Results suggest heterogeneous patterns of calf movements, depending on the source of records. OSSs are a representative proportion of the calf network in the state of Ohio. There were major differences in the movement structure when incorporating OSSs compared to when using ICVIs exclusively. This study emphasizes the importance of incorporating multiple sources of movement data for the development of targeted disease surveillance strategies.
