Scilight Press

Author Information

Received: 12 Oct 2022 | Accepted: 23 Nov 2022 | Published: 22 Dec 2022

Abstract

Graphical Abstract

Image Viewer

Open in new tab

Download Image

Keywords

Complex networks | Optimal Control | Brucelosis

References 

• 1.
  Paulin, L.M.; Ferreira Neto, J. S, A experiência Brasileira no combate à Brucelose bovina. Arq. Inst. Biol., 2002, 69: 105−112.
• 2.
  Pappas, G.; Papadimitriou, P.; Akritidis, N.;, et al, The new global map of human brucellosis. Lancet Infect. Dis., 2006, 6: 91−99.
• 3.
  Franc, K.A.; Krecek, R.C.; Häsler, B.N.;, et al, Brucellosis remains a neglected disease in the developing world: A call for interdisciplinary action. BMC Public Health, 2018, 18: 125.
• 4.
  de Souza, V.A.F.; Ferreira Neto, J.S.; Amaku, M.;, et al, Mathematical modeling of bovine brucellosis control using the RB51 vaccine. Semin. Ciênc. Agrár., 2016, 37: 3767−3776.
• 5.
  Borba, M.R.; Stevenson, M.A.; Gonçalves, V.S.P.;, et al, Prevalence and risk-mapping of bovine brucellosis in Maranhão state, Brazil. Prev. Vet. Med., 2013, 110: 169−176.
• 6.
  Inchaisri, C.; Prasomsri, P.; Boonserm, T.;, et al, A stochastic simulation model for brucellosis eradication in goat flocks in an area with high flock prevalence but low animal prevalence. Small Rumin. Res., 2016, 136: 227−237.
• 7.
  H ou, Q.; Zhang, F, Global dynamics of a general brucellosis model with discrete delay. J. Appl. Anal. Comput., 2016, 6: 227−241.
• 8.
  de Alencar Mota, A.L.A.; Ferreira, F.; Ferreira Neto, J.S.;, et al, Large-scale study of herd-level risk factors for bovine brucellosis in Brazil. Acta Trop., 2016, 164: 226−232.
• 9.
  Abatih, E.; Ron, L.; Speybroeck, N.;, et al, Mathematical analysis of the transmission dynamics of brucellosis among bison. Math. Methods Appl. Sci., 2015, 38: 3818−3832.
• 10.
  L i, M.T.; Pei, X.; Zhang, J.;, et al, Asymptotic analysis of endemic equilibrium to a brucellosis model. Math. Biosci. Eng., 2019, 16: 5836−5850.
• 11.
  Havas, K.A.; Boone, R.B.; Hill, A.E.;, et al, A brucellosis disease control strategy for the kakheti region of the country of Georgia: An agent-based model. Zoonoses Public Health, 2014, 61: 260−270.
• 12.
  Nepomuceno, E.G.; Barbosa, A.M.; Silva, M.X.;, et al, Individual-based modelling and control of bovine brucellosis. R. Soc. Open Sci., 2018, 5: 180200.
• 13.
  R oy, S.; McElwain, T.F.; Wan, Y, A network control theory approach to modeling and optimal control of zoonoses: Case study of brucellosis transmission in Sub-Saharan Africa. PLoS Negl. Trop. Dis., 2011, 5: e1259.
• 14.
  Cipullo, R.I.; Grisi-Filho, J.H.H.; Dias, R.A.;, et al, Cattle movement network, herd size, and bovine brucellosis in the state of Mato Grosso, Brazil. Semin. Ciênc. Agrár., 2016, 37: 3777−3792.
• 15.
  Darbon, A.; Valdano, E.; Poletto, C.;, et al, Network-based assessment of the vulnerability of Italian regions to bovine brucellosis. Prev. Vet. Med., 2018, 158: 25−34.
• 16.
  Amaku, M.; Dias, R.A.; Ferreira Neto, J.S.;, et al, Modelagem matemática do controle de Brucelose bovina por vacinação. Arq. Bras. Med. Vet. Zootec., 2009, 61: 135−141.
• 17.
  Lentz, H.H.K.; Koher, A.; Hövel, P.;, et al, Disease spread through animal movements: A static and temporal network analysis of pig trade in Germany. PLoS One, 2016, 11: e0155196.
• 18.
  Wa ng, Z.; Moreno, Y.; Boccaletti, S.;, et al, Vaccination and epidemics in networked populations—an introduction. Chaos Solitons Fractals, 2017, 103: 177−183.
• 19.
  Jin, Z.; Li, S.P.; Zhang, X.G.; et al. Epidemiological modeling on complex networks. In Complex Systems and Networks; Lü, J.H.; Yu, X.H.; Chen, G.R.; et al., Eds.; Springer: Berlin/Heidelberg, 2016; pp. 51–77. doi: 10.1007/978-3-662-47824-0_3
• 20.
  Biegus, T.; Kwasnicka, H. Complex networks in the epidemic modelling. In 9th Asian Conference on Intelligent Information and Database Systems, Kanazawa, Japan, April 3–5, 2017; Springer: Kanazawa, Japan, 2017; pp. 202–213. doi: 10.1007/978-3-319-54472-4_20
• 21.
  Campanharo, A.S.L.O.; Ramos, F. M, Hurst exponent estimation of self-affine time series using quantile graphs. Phys. A, 2016, 444: 43−48.
• 22.
  Liljeros, F.; Edling, C.R.; Amaral, L.A.N.;, et al, The web of human sexual contacts. Nature, 2001, 411: 907−908.
• 23.
  Kupennan, M.; Abramson, G. Small world effect in an epidemiological model. In The Structure and Dynamics of Networks; Newman, M.; Barabási, A.L.; Watts, D.J., Eds.; Princeton University Press: Princeton, 2011; pp. 489–492. doi: 10.1515/9781400841356.489
• 24.
  Ch en, S.; White, B.J.; Sanderson, M.W.;, et al, Highly dynamic animal contact network and implications on disease transmission. Sci. Rep., 2014, 4: 4472.
• 25.
  Ruget, A.S.; Rossi, G.; Pepler, P.T.;, et al, Multi-species temporal network of livestock movements for disease spread. Appl. Netw. Sci., 2021, 6: 15.
• 26.
  Chaters, G.L.; Johnson, P.C.D.; Cleaveland, S.;, et al, Analysing livestock network data for infectious disease control: An argument for routine data collection in emerging economies. Philos. Trans. R. Soc. B Biol. Sci., 2019, 374: 2180264.
• 27.
  Nepomuceno, E.G.; Peixoto, M.L.C.; Lacerda, M.J.;, et al, Application of optimal control of infectious diseases in a model-free scenario. SN Comput. Sci., 2021, 2: 405.
• 28.
  Newman, M.E.J. Networks: An Introduction. Oxford University Press: New York, 2010. doi: 10.1093/acprof:oso/9780199206650.001.0001
• 29.
  da F Costa, L.; Rodrigues, F.A.; Travieso, G.;, et al, Characterization of complex networks: A survey of measurements. Adv. Phys., 2007, 56: 167−242.
• 30.
  Pinto, E.R.; Nepomuceno, E.G.; Campanharo, A.S.L.O, Impact of network topology on the spread of infectious diseases. Trends Comput. Appl. Math., 2020, 21: 95−115.
• 31.
  Barabási, A.L.; Albert, R, Emergence of scaling in random networks. Science, 1999, 286: 509−512.
• 32.
  Bakhtiar, T, Optimal intervention strategies for cholera outbreak by education and chlorination. IOP Conf. Ser.: Earth Environ. Sci., 2016, 31: 012022.
• 33.
  Instituto Brasileiro de Geografia e Estatística. Censo Agropecuário 2017: Resultados Definitivos; IBGE: Rio de Janeiro, 2019. Available online: https://loja.ibge.gov.br/censo-agropecuario-2017-resultados-definitivos.html(accessed on 5 November 2022).
• 34.
  Bigras-Poulin, M.; Thompson, R.A.; Chriel, M.;, et al, Network analysis of Danish cattle industry trade patterns as an evaluation of risk potential for disease spread. Prev. Vet. Med., 2006, 76: 11−39.