Math. Model. Nat. Phenom.
Volume 15, 2020
Mathematical Models and Methods in Epidemiology
Article Number 64
Number of page(s) 21
Published online 03 December 2020
  1. F.B. Agusto, Mathematical model of Ebola transmission dynamics with relapse and reinfection. Math. Biosci. 283 (2017) 48–59. [CrossRef] [Google Scholar]
  2. M.E. Alexander, C. Bowman, S.M. Moghadas, R. Summers, A.B. Gumel and B.M. Sahai, A vaccination model for transmission dynamics of influenza. SIAM J. Appl. Dyn. Syst. 3 (2004) 503–524. [CrossRef] [Google Scholar]
  3. M.H.A. Biswas, Optimal control of Nipah virus (NIV) infections: A Bangladesh scenario. J. Pure Appl. Math. 12 (2014) 77–104. [Google Scholar]
  4. M.H.A. Biswas, M.M. Haque and G. Duvvuru, A mathematical model for understanding the spread of Nipah fever epidemic in Bangladesh. In 2015 International Conference on Industrial Engineering and Operations Management (IEOM) (2015) 1–8. [Google Scholar]
  5. C. Castillo-Chavez and B. Song, Dynamical models of Tuberculosis and their applications. Math. Biosci. Eng. 1 (2004) 361–404. [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  6. N.S. Chong and R.J. Smith, Modeling avian influenza using Filippov systems to determine culling of infected birds and quarantine. Nonlinear Anal.: Real World Appl. 24 (2015) 196–218. [CrossRef] [Google Scholar]
  7. E.M.C. DÁgata, G.F. Webb and J. Pressley, Rapid emergence of co-colonization with community-acquired and hospital-acquired methicillin-resistant Staphylococcus aureus strains in the hospital setting. MMNP 5 (2010) 76–93. [EDP Sciences] [Google Scholar]
  8. E.M.C. D’Agata, M. Horn and G. Webb, Quantifying the impact of bacterial fitness and repeated antimicrobial exposure on the emergence of multidrug-resistant gram-negative bacilli. MMNP 2 (2007) 129–142. [EDP Sciences] [Google Scholar]
  9. S. Das, P. Das and P. Das, Dynamics and control of multidrug-resistant bacterial infection in hospital with multiple delays. Commun. Nonlinear Sci. Numer. Simul. 89 (2020) 105279. [CrossRef] [Google Scholar]
  10. P. Das, P. Das and S. Das, Effects of delayed immune-activation in the dynamics of tumor-immune interactions. MMNP 15 (2020) 45. [EDP Sciences] [Google Scholar]
  11. E. de Wit and V.J. Munster, Animal models of disease shed light on Nipah virus pathogenesis and transmission. J. Pathol. 235 (2015) 196–205. [CrossRef] [PubMed] [Google Scholar]
  12. M. Deka and N. Morshed, Mapping disease transmission risk of Nipah virus in south and Southeast Asia. Trop. Med. Infect. Disease 3 (2018) 05. [CrossRef] [Google Scholar]
  13. G. Djatcha Yaleu, S. Bowong, E. Houpa Danga and J. Kurths, Mathematical analysis of the dynamical transmission of Neisseria meningitidis serogroup A. Int. J. Comput. Math. 94 (2017) 2409–2434. [CrossRef] [Google Scholar]
  14. P.R. Epstein, Climate change and emerging infectious diseases. Microb. Infection 3 (2001) 747–754. [CrossRef] [Google Scholar]
  15. W.H. Fleming and R.W. Rishel, Deterministic and stochastic optimal control. Applications of mathematics. Springer-Verlag (1975). [CrossRef] [Google Scholar]
  16. Food and Agriculture Organization of the United Nations. Farmer’s Hand Book on Pig Production (2009). [Google Scholar]
  17. B. Gomero, Latin hypercube sampling and partial rank correlation coefficient analysis applied to an optimal control problem. Master’s thesis, University of Tennessee, Knoxville (2012). [Google Scholar]
  18. J. Guckenheimer and P. Holmes, Nonlinear Oscillations Dynamical Systems, and Bifurcations of Vector Fields. Applied Mathematical Sciences. Springer, New York (1983). [CrossRef] [Google Scholar]
  19. A.B. Gumel, Global dynamics of a two-strain Avian influenza model. Int. J. Comput. Math. 86 (2009) 85–108. [CrossRef] [Google Scholar]
  20. H. Gulbudak and M. Martcheva, Forward hysteresis and backward bifurcation caused by culling in an Avian influenza model. Math. Biosci. 246 (2013) 202–212. [CrossRef] [Google Scholar]
  21. E.V. Grigorieva and E.N. Khailov, Optimal vaccination, treatment, and preventive campaigns in regard to the SIR epidemic model. MMNP 9 (2014) 105–121. [CrossRef] [EDP Sciences] [Google Scholar]
  22. E.V. Grigorieva, E.N. Khailov and A. Korobeinikov, Optimal control for a SIR epidemic model with nonlinear incidence rate. MMNP 11 (2016) 89–104. [Google Scholar]
  23. J.M. Hassell, M. Begon, M.J. Ward and E.M. Fèver, Urbanization and disease emergence: Dynamics at the wildlife-livestock-human interface. Trends Ecol. Evol. 32 (2017) 55–67. [CrossRef] [Google Scholar]
  24. J.M. Hughes, M.E. Wilson, E.S. Gurley, M. Jahangir Hossain and S.P. Luby, Transmission of Human Infection with Nipah Virus. Clin. Infect. Dis. 49 (2009) 1743–1748. [CrossRef] [PubMed] [Google Scholar]
  25. T.T.T. Huynh, A. Aarnink, A. Drucker and M. Verstegen, Pig production in Cambodia, Laos, Philippines, and Vietnam: a review. Asian J. Agric. Dev. 4 (2007). [Google Scholar]
  26. Q. Hu and X. Zou, Optimal vaccination strategies for an influenza epidemic model. J. Biol. Syst. 21 (2013) 1340006. [CrossRef] [Google Scholar]
  27. A.B. Jamaluddin and A.B. Adzhar, Nipah virus infection-Malaysia experience, 2011. Accessed on March (2019). [Google Scholar]
  28. S. Lenhart and J.T. Workman, Optimal Control Applied to Biological Models. CRC Press (2007). [Google Scholar]
  29. B. Levy, C. Edholm, O. Gaoue, R. Kaondera-Shava, M. Kgosimore, S. Lenhart, B. Lephodisa, E. Lungu, T. Marijani and F. Nyabadza, Modeling the role of public health education in Ebola virus disease outbreaks in Sudan. Infect. Dis. Model. 2 (2017) 323–340. [PubMed] [Google Scholar]
  30. L.-M. Looi and K.-B. Chua, Lessons from the Nipah virus outbreak in Malaysia. Malaysian J. Pathol. 29 (2007) 63–67. [Google Scholar]
  31. S. Marino, I. Hogue, C. Ray and D. Kirschner, A methodology for performing global uncertainty and sensitivity analysis in systems biology. J. Theor. Biol. 254 (2008) 178–196. [CrossRef] [PubMed] [Google Scholar]
  32. M.K. Mondal, M. Hanif and Md. Haider Ali Biswas, A mathematical analysis for controlling the spread of Nipah virus infection. Int. J. Model. Simul. 37 (2017) 185–197. [CrossRef] [Google Scholar]
  33. V. Pitzer, R. Aguas, S. Riley, W. Loeffen, J. Wood and B.T. Grenfell, High turnover drives prolonged persistence of influenza in managed pig herds. J. Royal Soc. Interface 13 (2016) 20160138. [CrossRef] [Google Scholar]
  34. L.S. Pontryagin, V.G. Boltyanskii, R.V. Gamkrelidze and E.F. Mishchenko, The Mathematical Theory of Optimal Processes. John Wiley & Sons (1962). [Google Scholar]
  35. B.A. Satterfield, B.E. Dawes and G.N. Milligan, Status of vaccine research and development of vaccines for Nipah virus. Vaccine 34 (2016) 2971–2975. [CrossRef] [PubMed] [Google Scholar]
  36. H.J. Shimozako, J. Wu and E. Massad, Mathematical modelling for zoonotic Visceral Leishmaniasis dynamics: a new analysis considering updated parameters and notified human Brazilian data. Infectious Disease Model 2 (2017) 143–160. [CrossRef] [Google Scholar]
  37. P. van den Driessche and J. Watmough, Further Notes on the Basic Reproduction Number. Springer Berlin Heidelberg, Berlin, Heidelberg (2008). [Google Scholar]
  38. L. Wang and G.S. Crameri, Emerging zoonotic viral diseases. Rev. Sci. Tech. 33 (2014) 569–581. [CrossRef] [Google Scholar]
  39. H. Weingartl, Hendra and Nipah viruses: pathogenesis, animal models and recent breakthroughs in vaccination. Vaccine: Dev. Therapy 59 (2015) 09. [Google Scholar]
  40. A.K. Wiethoelter, D. Beltrán-Alcrudo, R. Kock and S.M. Mor, Global trends in infectious diseases at the wildlife-livestock interface. Proc. Natl. Acad. Sci. 112 (2015) 9662–9667. [CrossRef] [Google Scholar]
  41. A. Wiratsudakul, P. Suparit and C. Modchang, Dynamics of Zika virus outbreaks: an overview of mathematical modeling approaches. PeerJ 6 (2018) e4526. [CrossRef] [PubMed] [Google Scholar]

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