Generation of swine movement network and analysis of efficient mitigation strategies for African swine fever virus
Autoři:
Tanvir Ferdousi aff001; Sifat Afroj Moon aff001; Adrian Self aff002; Caterina Scoglio aff001
Působiště autorů:
Department of Electrical and Computer Engineering, Kansas State University, Manhattan, Kansas, United States of America
aff001; National Agricultural Biosecurity Center, Kansas State University, Manhattan, Kansas, United States of America
aff002
Vyšlo v časopise:
PLoS ONE 14(12)
Kategorie:
Research Article
prolekare.web.journal.doi_sk:
https://doi.org/10.1371/journal.pone.0225785
Souhrn
Animal movement networks are essential in understanding and containing the spread of infectious diseases in farming industries. Due to its confidential nature, movement data for the US swine farming population is not readily available. Hence, we propose a method to generate such networks from limited data available in the public domain. As a potentially devastating candidate, we simulate the spread of African swine fever virus (ASFV) in our generated network and analyze how the network structure affects the disease spread. We find that high in-degree farm operations (i.e., markets) play critical roles in the disease spread. We also find that high in-degree based targeted isolation and hypothetical vaccinations are more effective for disease control compared to other centrality-based mitigation strategies. The generated networks can be made more robust by validation with more data whenever more movement data will be available.
Klíčová slova:
Network analysis – Vaccination and immunization – Infectious disease control – Fevers – Livestock – Epidemiology – Centrality – Swine
Zdroje
1. Burdett CL, Kraus BR, Garza SJ, Miller RS, Bjork KE. Simulating the distribution of individual livestock farms and their populations in the United States: An example using domestic swine (Sus scrofa domesticus) farms. PLoS One. 2015;10(11):e0140338. doi: 10.1371/journal.pone.0140338 26571497
2. Valdes-Donoso P, VanderWaal K, Jarvis LS, Wayne SR, Perez AM. Using machine learning to predict swine movements within a regional program to improve control of infectious diseases in the US. Frontiers in Veterinary Science. 2017;4:2. doi: 10.3389/fvets.2017.00002 28154817
3. Moon SA, Ferdousi T, Self A, Scoglio CM. Estimation of swine movement network at farm level in the US from the Census of Agriculture data. Scientific Reports. 2019;9(1):6237. doi: 10.1038/s41598-019-42616-w 30996237
4. Erdös P, Rényi A. On random graphs I. Publ Math Debrecen. 1959;6:290–297.
5. Newman ME, Strogatz SH, Watts DJ. Random graphs with arbitrary degree distributions and their applications. Physical Review E. 2001;64(2):026118. doi: 10.1103/PhysRevE.64.026118
6. Milo R, Kashtan N, Itzkovitz S, Newman ME, Alon U. On the uniform generation of random graphs with prescribed degree sequences. arXiv preprint cond-mat/0312028. 2003;.
7. Rao AR, Jana R, Bandyopadhyay S. A Markov chain Monte Carlo method for generating random (0, 1)-matrices with given marginals. Sankhyā: The Indian Journal of Statistics, Series A. 1996; p.225–242.
8. Roberts JM Jr. Simple methods for simulating sociomatrices with given marginal totals. Social Networks. 2000;22(3):273–283. doi: 10.1016/S0378-8733(00)00026-5
9. Molloy M, Reed B. A critical point for random graphs with a given degree sequence. Random Structures & Algorithms. 1995;6(2-3):161–180. doi: 10.1002/rsa.3240060204
10. Britton T, Deijfen M, Martin-Löf A. Generating simple random graphs with prescribed degree distribution. Journal of Statistical Physics. 2006;124(6):1377–1397. doi: 10.1007/s10955-006-9168-x
11. Ferdousi T, Cohnstaedt LW, McVey DS, Scoglio CM. Understanding the survival of Zika virus in a vector interconnected sexual contact network. Scientific reports. 2019;9(1):7253. doi: 10.1038/s41598-019-43651-3 31076660
12. Shahtori NM, Ferdousi T, Scoglio C, Sahneh FD. Quantifying the impact of early-stage contact tracing on controlling Ebola diffusion. Mathematical biosciences and engineering: MBE. 2018;15(5):1165–1180. doi: 10.3934/mbe.2018053 30380305
13. Lentz HH, Koher A, Hövel P, Gethmann J, Sauter-Louis C, Selhorst T, et al. Disease spread through animal movements: a static and temporal network analysis of pig trade in Germany. PloS one. 2016;11(5):e0155196. doi: 10.1371/journal.pone.0155196 27152712
14. Pastor-Satorras R, Vespignani A. Immunization of complex networks. Physical Review E. 2002;65(3):036104. doi: 10.1103/PhysRevE.65.036104
15. Scoglio C, Schumm W, Schumm P, Easton T, Chowdhury SR, Sydney A, et al. Efficient mitigation strategies for epidemics in rural regions. PLoS One. 2010;5(7):e11569. doi: 10.1371/journal.pone.0011569 20644715
16. Costard S, Mur L, Lubroth J, Sanchez-Vizcaino J, Pfeiffer D. Epidemiology of African swine fever virus. Virus Research. 2013;173(1):191–197. doi: 10.1016/j.virusres.2012.10.030 23123296
17. Tsolova T. Bulgaria reports its first outbreak of African swine fever; 2018. Available from: https://reut.rs/2NzcCM9.
18. Driver A. 4,000 pigs to be culled to control African swine fever in Belgium; 2018. Available from: http://www.pig-world.co.uk/news/4000-pigs-to-be-culled-to-control-african-swine-fever-in-belgium.html.
19. ASF situation in Asia update; 2019. Available from: http://www.fao.org/ag/againfo/programmes/en/empres/ASF/situation_update.html.
20. Gu H, Patton D. China’s top pig farmers see sharp fall in profits amid disease epidemic; 2019. Available from: https://reut.rs/2Vz8TCD.
21. Wang T, Sun Y, Qiu HJ. African swine fever: an unprecedented disaster and challenge to China. Infectious Diseases of Poverty. 2018;7(1):111. doi: 10.1186/s40249-018-0495-3 30367672
22. Herrera-Ibatá DM, Martínez-López B, Quijada D, Burton K, Mur L. Quantitative approach for the risk assessment of African swine fever and Classical swine fever introduction into the United States through legal imports of pigs and swine products. PLoS One. 2017;12(8):e0182850. doi: 10.1371/journal.pone.0182850 28797058
23. Barongo MB, Bishop RP, Fèvre EM, Knobel DL, Ssematimba A. A mathematical model that simulates control options for African swine fever virus (ASFV). PloS one. 2016;11(7):e0158658. doi: 10.1371/journal.pone.0158658 27391689
24. Halasa T, Boklund A, Bøtner A, Toft N, Thulke HH. Simulation of spread of African swine fever, including the effects of residues from dead animals. Frontiers in veterinary science. 2016;3:6. doi: 10.3389/fvets.2016.00006 26870740
25. Guinat C, Gubbins S, Vergne T, Gonzales J, Dixon L, Pfeiffer D. Experimental pig-to-pig transmission dynamics for African swine fever virus, Georgia 2007/1 strain–CORRIGENDUM. Epidemiology & Infection. 2016;144(16):3564–3566. doi: 10.1017/S0950268816001667
26. Gulenkin V, Korennoy F, Karaulov A, Dudnikov S. Cartographical analysis of African swine fever outbreaks in the territory of the Russian Federation and computer modeling of the basic reproduction ratio. Preventive Veterinary Medicine. 2011;102(3):167–174. doi: 10.1016/j.prevetmed.2011.07.004 21840611
27. Barongo MB, Ståhl K, Bett B, Bishop RP, Fèvre EM, Aliro T, et al. Estimating the basic reproductive number (R0) for African swine fever virus (ASFV) transmission between pig herds in Uganda. PloS one. 2015;10(5):e0125842. doi: 10.1371/journal.pone.0125842 25938429
28. Guinat C, Porphyre T, Gogin A, Dixon L, Pfeiffer D, Gubbins S. Inferring within-herd transmission parameters for African swine fever virus using mortality data from outbreaks in the Russian Federation. Transboundary and Emerging Diseases. 2018;65(2):e264–e271. doi: 10.1111/tbed.12748 29120101
29. Hu B, Gonzales JL, Gubbins S. Bayesian inference of epidemiological parameters from transmission experiments. Scientific Reports. 2017;7(1):16774. doi: 10.1038/s41598-017-17174-8 29196741
30. Motroni R, Neilan J, Rasmussen M, Chung C, Puckette M, Brake D, et al. Development of next-generation vaccines and diagnostics for transboundary animal disease preparedness; 2017. Available from: http://www.oie.int/eng/BIOTHREAT2017/posters/22_MOTRONI-poster.pdf.
31. 2012 Census of Agriculture; 2014. Available from: https://www.nass.usda.gov/Publications/AgCensus/2012/Full_Report/Volume_1,_Chapter_2_US_State_Level/st99_2_012_012.pdf.
32. Wayne SR. Assessment of the demographics and network structure of swine populations in relation to regional disease transmission and control. 2011;.
33. Sahneh FD, Vajdi A, Shakeri H, Fan F, Scoglio C. GEMFsim: a stochastic simulator for the generalized epidemic modeling framework. Journal of Computational Science. 2017;22:36–44. doi: 10.1016/j.jocs.2017.08.014
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