#PAGE_PARAMS# #ADS_HEAD_SCRIPTS# #MICRODATA#

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


Autoři: Alba Bernini aff001;  Luca Bolzoni aff002;  Renato Casagrandi aff001
Působiště autorů: Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy aff001;  Risk Analysis and Genomic Epidemiology Unit, Istituto Zooprofilattico Sperimentale della Lombardia e dell’Emilia Romagna, Parma, Italy aff002
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0223652

Souhrn

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.

Klíčová slova:

Farms – Infectious disease control – Livestock – Pathogens – Transportation – Veterinary diseases – Infectious disease epidemiology – Veterinary epidemiology


Zdroje

1. James AD, Rushton J. The economics of foot and mouth disease. Rev Sci Tech—OIE. 2002;21: 637–644. doi: 10.20506/rst.21.3.1356 12523703

2. Yee WMS, Yeung RMW. An empirical examination of the role of trust in consumer and supplier relationship of little direct contact: a structural equation modeling approach. J Int Food Agribus Mark. 2010;22: 143–163. doi: 10.1080/08974430903373003

3. Knight-Jones TJD, Rushton J. The economic impacts of foot and mouth disease—What are they, how big are they and where do they occur? Prev Vet Med. 2013;112: 162–173. doi: 10.1016/j.prevetmed.2013.07.013 23958457

4. Foot Anderson I. and Disease Mouth 2001: Lessons to be Learned Inquiry Report, HC888. 2002

5. Taylor N. Review of the use of models in informing disease control policy development and adjustment. DEFRA, UK. 2003

6. Danon L, Ford AP, House T, Jewell CP, Keeling MJ, Roberts GO, et al. Networks and the epidemiology of infectious disease. Interdiscip Perspect Infect Dis. 2011;2011. doi: 10.1155/2011/284909 21437001

7. Pastor-Satorras R, Castellano C, Van Mieghem P, Vespignani A. Epidemic processes in complex networks. Rev Mod Phys. 2015;87: 925–979. doi: 10.1103/RevModPhys.87.925

8. Woolhouse M, Donaldson A. Managing foot-and-mouth. Nature. 2001;410: 515–516. doi: 10.1038/35069250 11279464

9. Gilbert M, Mitchell A, Bourn D, Mawdsley J, Clifton-Hadley R, Wint W. Cattle movements and bovine tuberculosis in Great Britain. Nature. 2005;435: 491–496. doi: 10.1038/nature03548 15917808

10. Shirley MDF, Rushton SP. Where diseases and networks collide: Lessons to be learnt from a study of the 2001 foot-and-mouth disease epidemic. Epidemiol Infect. 2005;133: 1023–1032. doi: 10.1017/S095026880500453X 16274498

11. Gibbens JC, Sharpe CE, Wilesmith JW, Mansley LM, Michalopoulou E, Ryan JBM, et al. Descriptive epidemiology of the 2001 foot-and-mouth disease epidemic in Great Britain: the first five months. 2001;149: 729–743. http://dx.doi.org/10.1136/vr.149.24.729

12. Kao RR. The role of mathematical modelling in the control of the 2001 FMD epidemic in the UK. Trends Microbiol. 2002;10: 279–286. doi: 10.1016/s0966-842x(02)02371-5 12088664

13. Bigras-Poulin M, Thompson RA, Chriel M, Mortensen S, Greiner M. Network analysis of Danish cattle industry trade patterns as an evaluation of risk potential for disease spread. Prev Vet Med. 2006;76: 11–39. doi: 10.1016/j.prevetmed.2006.04.004 16780975

14. Kao RR, Danon L, Green DM, Kiss IZ. Demographic structure and pathogen dynamics on the network of livestock movements in Great Britain. Proc R Soc B Biol Sci. 2006;273: 1999–2007. doi: 10.1098/rspb.2006.3505 16846906

15. Bajardi P, Barrat A, Natale F, Savini L, Colizza V. Dynamical patterns of cattle trade movements. PLoS One. 2011;6: e19869. doi: 10.1371/journal.pone.0019869 21625633

16. Nöremark M, Håkansson N, Lewerin SS, Lindberg A, Jonsson A. Network analysis of cattle and pig movements in Sweden: Measures relevant for disease control and risk based surveillance. Prev Vet Med. 2011;99: 78–90. doi: 10.1016/j.prevetmed.2010.12.009 21288583

17. Rossi G, De Leo GA, Pongolini S, Natalini S, Vincenzi S, Bolzoni L. Epidemiological modelling for the assessment of bovine tuberculosis surveillance in the dairy farm network in Emilia-Romagna (Italy). Epidemics. 2015;11: 62–70. doi: 10.1016/j.epidem.2015.02.007 25979283

18. Bates TW, Thurmond MC, Carpenter TE. Description of an epidemic simulation model for use in evaluating strategies to control an outbreak of foot-and-mouth disease. Am J Vet Res. 2003;64: 195–204. doi: 10.2460/ajvr.2003.64.195 12602589

19. Bates TW, Thurmond MC, Carpenter TE. 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. Am J Vet Res. 2001;62: 1121–1129. doi: 10.2460/ajvr.2001.62.1121 11453490

20. Olofsson E, Nöremark M, Lewerin SS. Patterns of between-farm contacts via professionals in Sweden. Acta Vet Scand. 2014;56: 70. doi: 10.1186/s13028-014-0070-2 25366065

21. Nöremark M, Frössling J, Lewerin SS. A survey of visitors on Swedish livestock farms with reference to the spread of animal diseases. BMC Vet Res. 2013;9: 184. doi: 10.1186/1746-6148-9-184 24040830

22. Brennan ML, Kemp R, Christley RM. Direct and indirect contacts between cattle farms in north-west England. Prev Vet Med. 2008;84: 242–260. doi: 10.1016/j.prevetmed.2007.12.009 18222555

23. Rossi G, De Leo GA, Pongolini S, Natalini S, Zarenghi L, Ricchi M, et al. The potential role of direct and indirect contacts on infection spread in dairy farm networks. PLoS Comput Biol. 2017;13: 1–19. doi: 10.1371/journal.pcbi.1005301 28125610

24. Alexandersen S, Zhang Z, Donaldson AI, Garland AJM. The pathogenesis and diagnosis of foot-and-mouth disease. J Comp Pathol. 2003;129: 1–36. doi: 10.1016/s0021-9975(03)00041-0 12859905

25. Dee SA, Deen J, Otake S, Pijoan C. 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. Can J Vet Res. 2004;68: 128–133. 15188957

26. Fèvre EM, Bronsvoort BMDC, Hamilton KA, Cleaveland S. Animal movements and the spread of infectious diseases. Trends Microbiol. 2006;14: 125–131. doi: 10.1016/j.tim.2006.01.004 16460942

27. Salines M, Andraud M, Rose N. Pig movements in France: Designing network models fitting the transmission route of pathogens. PLoS One. 2017;12: 1–24. doi: 10.1371/journal.pone.0185858 29049305

28. Greger M. The long haul: risks associated with livestock transportt. Biosecurity Bioterrorism Biodefense Strateg Pract Sci. 2007;5: 301–312. doi: 10.1089/bsp.2007.0028 18081490

29. Rautureau S, Dufour B, Durand B. Structural vulnerability of the French swine industry trade network to the spread of infectious diseases. Animal. 2012;6: 1152–1162. doi: 10.1017/S1751731111002631 23031477

30. Thakur KK, Sanchez J, Hurnik D, Poljak Z, Opps S, Revie CW. Development of a network based model to simulate the between-farm transmission of the porcine reproductive and respiratory syndrome virus. Vet Microbiol. 2015;180: 212–222. doi: 10.1016/j.vetmic.2015.09.010 26464321

31. Thakur KK, Revie CW, Hurnik D, Poljak Z, Sanchez J. Analysis of swine movement in four Canadian regions: Network structure and implications for disease spread. Transbound Emerg Dis. 2016;63: e14–e26. doi: 10.1111/tbed.12225 24739480

32. VanderWaal K, Perez A, Torremorrell M, Morrison RM, Craft M. Role of animal movement and indirect contact among farms in transmission of porcine epidemic diarrhea virus. Epidemics. 2018;24: 67–75 doi: 10.1016/j.epidem.2018.04.001 29673815

33. Robinson SE, Christley RM. Exploring the role of auction markets in cattle movements within Great Britain. Prev Vet Med. 2007;81: 21–37. doi: 10.1016/j.prevetmed.2007.04.011 17482296

34. Dent JE, Kao RR, Kiss IZ, Hyder K, Arnold M. Contact structures in the poultry industry in Great Britain: Exploring transmission routes for a potential avian influenza virus epidemic. BMC Vet Res. 2008;4: 27. doi: 10.1186/1746-6148-4-27 18651959

35. Fournie G, Guitian J, Desvaux S, Cuong VC, Dung DH, Pfeiffer DU, et al. Interventions for avian influenza A (H5N1) risk management in live bird market networks. Proc Natl Acad Sci. 2013;110: 9177–9182. doi: 10.1073/pnas.1220815110 23650388

36. Boccaletti S, Bianconi G, Criado R, del Genio CI, Gómez-Gardeñes J, Romance M, et al. The structure and dynamics of multilayer networks. Phys Rep. 2014;544: 1–122. doi: 10.1016/j.physrep.2014.07.001

37. Holme P, Saramäki J. Temporal networks. Phys Rep. 2012;519: 97–125. doi: 10.1016/j.physrep.2012.03.001

38. Rossi G, Smith RL, Pongolini S, Bolzoni L. Modelling farm-to-farm disease transmission through personnel movements: From visits to contacts, and back. Sci Rep. 2017;7: 2375. doi: 10.1038/s41598-017-02567-6 28539663

39. Valdano E, Poletto C, Giovannini A, Palma D, Savini L, Colizza V. Predicting epidemic risk from past temporal contact data. PLoS Comput Biol. 2015;11: e1004152. doi: 10.1371/journal.pcbi.1004152 25763816

40. Konschake M, Lentz HHK, Conraths FJ, Hövel P, Selhorst T. On the robustness of in-and out-components in a temporal network. PLoS One. 2013;8: e55223. doi: 10.1371/journal.pone.0055223 23405124

41. Vernon MC, Keeling MJ. Representing the UK’s cattle herd as static and dynamic networks. Proc R Soc B Biol Sci. 2008;276: 469–476. doi: 10.1098/rspb.2008.1009 18854300

42. Bajardi P, Barrat A, Savini L, Colizza V. Optimizing surveillance for livestock disease spreading through animal movements. J R Soc Interface. 2012;9: 2814–2825. doi: 10.1098/rsif.2012.0289 22728387

43. Dorjee S, Revie CW, Poljak Z, McNab WB, Sanchez J. Network analysis of swine shipments in Ontario, Canada, to support disease spread modelling and risk-based disease management. Prev Vet Med. 2013;112: 118–127. doi: 10.1016/j.prevetmed.2013.06.008 23896577

44. Fike K, Spire MF. Transportation of cattle. Vet Clin North Am—Food Anim Pract. 2006;22: 305–320. doi: 10.1016/j.cvfa.2006.03.012 16814019

45. Dee S, Deen J, Burns D, Douthit G, Pijoan C. An assessment of sanitation protocols for commercial transport vehicles contaminated with porcine reproductive and respiratory syndrome virus. Can J Vet Res. 2004;68: 208–214. 15352546

46. Koher A, Lentz HHK, Hövel P, Sokolov IM. Infections on temporal networks—A matrix-based approach. PLoS One. 2016;11: e0151209. doi: 10.1371/journal.pone.0151209 27035128

47. Kendall MG. A new measure of rank correlation. Biometrika. 1938;30: 81–93.

48. Jaccard P. Distribution de la flore alpine dans le Bassin des Drouces et dans quelques regions voisines. Bull la Soc Vaudoise des Sci Nat. 1901;37: 241–272. doi: 10.5169/seals-266440

49. Chao A, Chazdon RL, Colwell RK, Shen TJ. Abundance-based similarity indices and their estimation when there are unseen species in samples. Biometrics. 2006;62: 361–371. doi: 10.1111/j.1541-0420.2005.00489.x 16918900

50. Fawcett T. An introduction to ROC analysis. Pattern Recognit Lett. 2006;27: 861–874. doi: 10.1016/j.patrec.2005.10.010

51. Shah N, Shah H, Malensek M, Pallickara SL, Pallickara S. Network analysis for identifying and characterizing disease outbreak influence from voluminous epidemiology data. Proc IEEE Int Conf Big Data. 2016; 1222–1231. doi: 10.1109/BigData.2016.7840726

52. Dommergues L, Rautureau S, Petit E, Dufour B. Network of contacts between cattle herds in a French area affected by bovine tuberculosis in 2010. Transbound Emerg Dis. 2012;59: 292–302. doi: 10.1111/j.1865-1682.2011.01269.x 22099740

53. Bigras-Poulin M, Barfod K, Mortensen S, Greiner M. Relationship of trade patterns of the Danish swine industry animal movements network to potential disease spread. Prev Vet Med. 2007;80: 143–165. doi: 10.1016/j.prevetmed.2007.02.004 17383759

54. Mannion C, Egan J, Lynch BP, Fanning S, Leonard N. An investigation into the efficacy of washing trucks following the transportation of pigs—a salmonella perspective. Foodborne Pathog Dis. 2008;5: 261–271. doi: 10.1089/fpd.2007.0069 18767976

55. Sanson RL, Harvey N, Garner MG, Stevenson MA, Davies TM, Hazelton ML, et al. Foot and mouth disease model verification and ‘relative validation’ through a formal model comparison. Rev Sci Tech—OIE. 2011;30: 527–540. doi: 10.20506/rst.30.2.2051 21961223

56. Ciaravino G, García-Saenz A, Cabras S, Allepuz A, Casal J, García-Bocanegra I, et al. Assessing the variability in transmission of bovine tuberculosis within Spanish cattle herds. Epidemics. 2018;23: 110–120. doi: 10.1016/j.epidem.2018.01.003 29415865

57. Kitching RP, Thrusfield MV, Taylor NM. Use and abuse of mathematical models: an illustration from the 2001 foot and mouth disease epidemic in the United Kingdom. Rev Sci Tech—OIE. 2006;25: 293–311. doi: 10.20506/rst.25.1.1665 16796055

58. Brooks-Pollock E, Roberts GO, Keeling MJ. A dynamic model of bovine tuberculosis spread and control in Great Britain. Nature. 2014;511: 228–231. doi: 10.1038/nature13529 25008532


Článek vyšel v časopise

PLOS One


2019 Číslo 10
Nejčtenější tento týden
Nejčtenější v tomto čísle
Kurzy

Zvyšte si kvalifikaci online z pohodlí domova

KOST
Koncepce osteologické péče pro gynekology a praktické lékaře
nový kurz
Autoři: MUDr. František Šenk

Sekvenční léčba schizofrenie
Autoři: MUDr. Jana Hořínková

Hypertenze a hypercholesterolémie – synergický efekt léčby
Autoři: prof. MUDr. Hana Rosolová, DrSc.

Svět praktické medicíny 5/2023 (znalostní test z časopisu)

Imunopatologie? … a co my s tím???
Autoři: doc. MUDr. Helena Lahoda Brodská, Ph.D.

Všechny kurzy
Kurzy Podcasty Doporučená témata Časopisy
Přihlášení
Zapomenuté heslo

Zadejte e-mailovou adresu, se kterou jste vytvářel(a) účet, budou Vám na ni zaslány informace k nastavení nového hesla.

Přihlášení

Nemáte účet?  Registrujte se

#ADS_BOTTOM_SCRIPTS#