Abstract
Animal exchanges are considered the major pathway for between-farm transmission of many livestock infectious diseases. Yet, vehicles and operators visiting several farms during routine activities can also contribute to disease spread. Indeed, if contaminated, they can act as mechanical vectors of fomites, generating indirect contacts between visited farms. While data on animal exchanges is often available in national databases, information about the daily itineraries of trucks and operators is rare because difficult to obtain. Thus, some unavoidable approximations have been frequently introduced in the description of indirect contacts in epidemic models. Here, we showed that the level of detail in such description can significantly affect the predictions on disease dynamics. Our analyses focused on the potential spread of a disease in a dairy farm system subject of a comprehensive data collection campaign on calf transportations. We developed two temporal multilayer networks to model between-farm contacts generated by either animal exchanges (direct contacts) and connections operated by trucks moving calves (indirect contacts). The complete model used the full knowledge of the daily trucks’ itineraries, while the partial informed one used only a subset of such available information. To account for various conditions of pathogen survival ability and effectiveness of cleaning operations, we performed a sensitivity analysis on trucks’ contamination period. An accurate description of indirect contacts was crucial both to correctly predict the final size of epidemics and to identify the seed farms responsible for generating the most severe outbreaks. The importance of detailed information emerged even more clearly in the case of short contamination periods. Our conclusions could be extended to between-farm contacts generated by other vehicles and operators. Overcoming these information gaps would be decisive for a deeper understanding of epidemic spread in livestock and to develop effective control plans.
Highlights
The spread of infectious diseases in livestock can cause serious negative impacts both from economic and social points of view, due to animals culling, reduced production, costs of the implemented control measures, and loss of consumer trust in the food supply chain [1,2,3]
The simulations performed using Contractor Model (CCM) led to higher values of total epidemic size with respect to the ones obtained with Transit Model (TM)
The total epidemic sizes predicted using the CCM ranged between a minimum of 1 to a maximum of 404 infected farms
Summary
The spread of infectious diseases in livestock can cause serious negative impacts both from economic and social points of view, due to animals culling, reduced production, costs of the implemented control measures, and loss of consumer trust in the food supply chain [1,2,3]. In the early phase of the 2001 FMD outbreak, the exchange of infected animals (mainly sheep) before the imposition of national movement controls was claimed to be directly responsible of the introduction of infection into several disease-free geographical areas [10,11,12] For this reason, as prescribed by EU regulations and directives (European Parliament and European Council Regulation 1760/2000/EC; Council Regulation 21/2004/EC; Council Directive 2008/71/EC), extensive databases were developed in different European countries to track the movements of farmed animals and the patterns of such movements have been largely investigated [13,14,15,16,17]
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