When resolution does matter: Modelling indirect contacts in dairy farms at different levels of detail

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.

[1]  B Dufour,et al.  Network of contacts between cattle herds in a French area affected by bovine tuberculosis in 2010. , 2012, Transboundary and emerging diseases.

[2]  Vu Chi Cuong,et al.  Interventions for avian influenza A (H5N1) risk management in live bird market networks , 2013, Proceedings of the National Academy of Sciences.

[3]  Robert K. Colwell,et al.  Abundance‐Based Similarity Indices and Their Estimation When There Are Unseen Species in Samples , 2006, Biometrics.

[4]  S. Fanning,et al.  An investigation into the efficacy of washing trucks following the transportation of pigs--a salmonella perspective. , 2008, Foodborne pathogens and disease.

[5]  Alex Donaldson,et al.  Managing foot-and-mouth , 2001, Nature.

[6]  J. Rushton,et al.  The economics of foot and mouth disease. , 2002, Revue scientifique et technique.

[7]  Igor M. Sokolov,et al.  Infections on Temporal Networks—A Matrix-Based Approach , 2016, PloS one.

[8]  Jari Saramäki,et al.  Temporal Networks , 2011, Encyclopedia of Social Network Analysis and Mining.

[9]  Rowland R Kao,et al.  The role of mathematical modelling in the control of the 2001 FMD epidemic in the UK. , 2002, Trends in microbiology.

[10]  L. Danon,et al.  Demographic structure and pathogen dynamics on the network of livestock movements in Great Britain , 2006, Proceedings of the Royal Society B: Biological Sciences.

[11]  L. Kiemeney,et al.  Obesity, metabolic factors and risk of different histological types of lung cancer: A Mendelian randomization study , 2017, PloS one.

[12]  S. Rushton,et al.  Where diseases and networks collide: lessons to be learnt from a study of the 2001 foot-and-mouth disease epidemic , 2005, Epidemiology and Infection.

[13]  R. Kitching,et al.  Use and abuse of mathematical models: an illustration from the 2001 foot and mouth disease epidemic in the United Kingdom. , 2006, Revue scientifique et technique.

[14]  Jennifer E. Dent,et al.  Contact structures in the poultry industry in Great Britain: Exploring transmission routes for a potential avian influenza virus epidemic , 2008, BMC veterinary research.

[15]  B. Durand,et al.  Structural vulnerability of the French swine industry trade network to the spread of infectious diseases. , 2012, Animal : an international journal of animal bioscience.

[16]  M. Kendall.  A NEW MEASURE OF RANK CORRELATION , 1938 .

[17]  R. Christley,et al.  Direct and indirect contacts between cattle farms in north-west England. , 2008, Preventive veterinary medicine.

[18]  S. Alexandersen,et al.  The pathogenesis and diagnosis of foot-and-mouth disease. , 2003, Journal of comparative pathology.

[19]  Piet Van Mieghem,et al.  Epidemic processes in complex networks , 2014, ArXiv.

[20]  Ellen Brooks-Pollock,et al.  A dynamic model of bovine tuberculosis spread and control in Great Britain , 2014, Nature.

[21]  Maria Nöremark,et al.  Patterns of between-farm contacts via professionals in Sweden , 2014, Acta Veterinaria Scandinavica.

[22]  Mark C Thurmond,et al.  Description of an epidemic simulation model for use in evaluating strategies to control an outbreak of foot-and-mouth disease. , 2003, American journal of veterinary research.

[23]  Michael Greger,et al.  The long haul: risks associated with livestock transport. , 2007, Biosecurity and bioterrorism : biodefense strategy, practice, and science.

[24]  C W Revie,et al.  Analysis of Swine Movement in Four Canadian Regions: Network Structure and Implications for Disease Spread. , 2016, Transboundary and emerging diseases.

[25]  M. Nöremark,et al.  Network analysis of cattle and pig movements in Sweden: measures relevant for disease control and risk based surveillance. , 2011, Preventive veterinary medicine.

[26]  Luca Bolzoni,et al.  Epidemiological modelling for the assessment of bovine tuberculosis surveillance in the dairy farm network in Emilia-Romagna (Italy). , 2014, Epidemics.

[27]  Luca Bolzoni,et al.  The Potential Role of Direct and Indirect Contacts on Infection Spread in Dairy Farm Networks , 2017, PLoS Comput. Biol..

[28]  E. Fèvre,et al.  Animal movements and the spread of infectious diseases , 2006, Trends in Microbiology.

[29]  Morgane Salines,et al.  Pig movements in France: Designing network models fitting the transmission route of pathogens , 2017, PloS one.

[30]  Mark F Spire,et al.  Transportation of cattle. , 2006, The Veterinary clinics of North America. Food animal practice.

[31]  M. Konschake,et al.  On the Robustness of In- and Out-Components in a Temporal Network , 2013, PloS one.

[32]  Z. Poljak,et al.  Network analysis of swine shipments in Ontario, Canada, to support disease spread modelling and risk-based disease management. , 2013, Preventive veterinary medicine.

[33]  M. Bigras-Poulin,et al.  Network analysis of Danish cattle industry trade patterns as an evaluation of risk potential for disease spread. , 2006, Preventive veterinary medicine.

[34]  Matthias Greiner,et al.  Relationship of trade patterns of the Danish swine industry animal movements network to potential disease spread. , 2007, Preventive veterinary medicine.

[35]  T E Carpenter,et al.  Direct and indirect contact rates among beef, dairy, goat, sheep, and swine herds in three California counties, with reference to control of potential foot-and-mouth disease transmission. , 2001, American journal of veterinary research.

[36]  Satoshi Otake,et al.  An experimental model to evaluate the role of transport vehicles as a source of transmission of porcine reproductive and respiratory syndrome virus to susceptible pigs. , 2004, Canadian journal of veterinary research = Revue canadienne de recherche veterinaire.

[37]  Kimberly VanderWaal,et al.  Role of animal movement and indirect contact among farms in transmission of porcine epidemic diarrhea virus , 2018, Epidemics.

[38]  Wallace M. S. Yee,et al.  An Empirical Examination of the Role of Trust in Consumer and Supplier Relationship of Little Direct Contact: A Structural Equation Modeling Approach , 2010 .

[39]  Scott Dee,et al.  An assessment of sanitation protocols for commercial transport vehicles contaminated with porcine reproductive and respiratory syndrome virus. , 2004, Canadian journal of veterinary research = Revue canadienne de recherche veterinaire.

[40]  K Owen,et al.  Foot and mouth disease model verification and 'relative validation' through a formal model comparison. , 2011, Revue scientifique et technique.

[41]  R. Christley,et al.  Exploring the role of auction markets in cattle movements within Great Britain. , 2007, Preventive veterinary medicine.

[42]  M. Gilbert,et al.  Cattle movements and bovine tuberculosis in Great Britain , 2005, Nature.

[43]  Javier Sanchez,et al.  Development of a network based model to simulate the between-farm transmission of the porcine reproductive and respiratory syndrome virus. , 2015, Veterinary microbiology.

[44]  Z. Wang,et al.  The structure and dynamics of multilayer networks , 2014, Physics Reports.

[45]  Jenny Frössling,et al.  A survey of visitors on Swedish livestock farms with reference to the spread of animal diseases , 2013, BMC Veterinary Research.

[46]  Lara Savini,et al.  Predicting Epidemic Risk from Past Temporal Contact Data , 2014, PLoS Comput. Biol..

[47]  A. Barrat,et al.  Dynamical Patterns of Cattle Trade Movements , 2011, PloS one.

[48]  Satrajit S. Ghosh,et al.  Mindboggling morphometry of human brains , 2016, bioRxiv.

[49]  Matt J. Keeling,et al.  Representing the UK's cattle herd as static and dynamic networks , 2008, Proceedings of the Royal Society B: Biological Sciences.

[50]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[51]  S Cabras,et al.  Assessing the variability in transmission of bovine tuberculosis within Spanish cattle herds. , 2018, Epidemics.

[52]  Luca Bolzoni,et al.  Modelling farm-to-farm disease transmission through personnel movements: from visits to contacts, and back , 2017, Scientific Reports.

[53]  Alain Barrat,et al.  Optimizing surveillance for livestock disease spreading through animal movements , 2012, Journal of The Royal Society Interface.