Cholera, an acute intestinal infection caused by the bacterium Vibrio cholerae, remains a major public health problem in many parts of Africa, Asia, and Latin America. A mathematical model is developed, to assess the impact of increasing antimicrobial resistance of Vibrio cholerae on the future trends of the cholera epidemic. Equilibrium states of the model are determined and their stabilities have been examined. The impacts of increasing antimicrobial resistance of Vibrio cholerae on the future trends of cholera epidemic have been investigated through the reproductive number. Numerical results are provided to support analytical findings. 1. Introduction The global burden of cholera remains substantial. In 2005, 131,943 cases and 2,272 deaths were reported to the WHO (World Health Organization), and recently major, sustained epidemics have been reported in many parts of Africa, such as Zimbabwe and Nigeria to mention a few [1]. These statistics are gross underestimates, as many cholera-endemic countries do not report cholera to the WHO, including Zimbabwe, which has one of the highest rates of cholera in the world. More realistic estimates of the global burden of cholera mortality place the figure at 100,000–150,000 deaths per year [2]. Cholera is contracted by ingestion of food or water contaminated with the Gram-negative bacterium Vibrio cholerae. The bacteria pass through the human gastric acid barrier into the small intestine where they colonize, multiply, and begin to secrete cholera toxin. The primary treatment for cholera is rehydration with oral or intravenous fluids [3]. For severe cases, antimicrobial agents may reduce the volume and duration of diarrhea [3]. Tetracyclines (e.g., doxycycline), fluoroquinolones (e.g., ciprofloxacin), macrolides (e.g., erythromycin), and trimethoprim/sulfamethoxazole have commonly been used to treat cholera [3]. Antimicrobial drug resistance can undermine the success of antimicrobial therapy. V. cholerae becomes drug resistant by exporting drugs through efflux pumps, chromosomal mutations, or developing genetic resistance via the exchange of conjugative plasmids, conjugative transposons, and integrons [4]. Several reports have documented tetracycline- and fluoroquinolone-resistant V. cholerae, and multidrug resistance is increasing [4]. The aim of this study is to assess the impact of an increase on antimicrobial resistance of Vibrio cholerae on the future trends of the cholera epidemic, with the aid of a simple mathematical model. Mathematical models have become an important tool in describing the dynamics of
References
[1]
World Health Organization, “Cholera 2005,” Weekly Epidemiological Record, vol. 81, no. 31, pp. 297–308, 2006.
[2]
I. M. Longini, A. Nizam, M. Ali, M. Yunus, N. Shenvi, and J. D. Clemens, “Controlling endemic cholera with oral vaccines,” PLoS Medicine, vol. 4, no. 11, article e336, 2007.
[3]
M. Sjlund-Karlsson, A. Reimer, J. P. Folster, M. Walker, G. A. Dahourou, D. G. Batra, et al., “Drug resistance mechanisms in Vibrio cholerae O1 outbreak strain, Haiti, 2010,” Emerging Infectious Diseases, vol. 17, no. 11, pp. 2151–2154.
[4]
M. Kitaoka, S. T. Miyata, D. Unterweger, and S. Pukatzki, “Antibiotic resistance mechanisms of Vibrio cholerae,” Journal of Medical Microbiology, vol. 60, no. 4, pp. 397–407, 2011.
[5]
U. Ledzewicz and H. Schattler, “On optimal singular controls for a general SIR-model with vaccination and treatment,” Discrete and Continuous Dynamical Systems, supplement, pp. 981–990, 2011.
[6]
P. Das and D. Mukherjee, “Qualitative analysis of a cholera bacteriophage model,” ISRN Biomathematics, vol. 2012, Article ID 621939, 13 pages, 2012.
[7]
D. M. Hartley, J. G. Moriss, and D. L. Smith, “Hyperinfectivity: a critical element in the ability of V. cholerae to cause epidemics,” PLOS Medicine, vol. 3, no. 1, article e7, 2006.
[8]
R. L. M. Neilan, E. Schaefer, H. Gaff, K. R. Fister, and S. Lenhart, “Modeling optimal intervention strategies for cholera,” Bulletin of Mathematical Biology, vol. 72, no. 8, pp. 2004–2018, 2010.
[9]
C. Torres Code?o, “Endemic and epidemic dynamics of cholera: the role of the aquatic reservoir,” BMC Infectious Diseases, vol. 1, article 1, 2001.
[10]
D. S. Merrell, S. M. Butler, F. Qadri et al., “Host-induced epidemic spread of the cholera bacterium,” Nature, vol. 417, no. 6889, pp. 642–645, 2002.
[11]
A. A. King, E. L. Ionides, M. Pascual, and M. J. Bouma, “Inapparent infections and cholera dynamics,” Nature, vol. 454, no. 7206, pp. 877–880, 2008.
[12]
P. V. Driessche and J. Watmough, “Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission,” Mathematical Biosciences, vol. 180, pp. 29–48, 2002.
[13]
J. C. Kamgang and G. Sallet, “Computation of threshold conditions for epidemiological models and global stability of the disease-free equilibrium (DFE),” Mathematical Biosciences, vol. 213, no. 1, pp. 1–12, 2008.
[14]
A. Berman and R. J. Plemmons, “Nonnegative matrices in the mathematical sciences,” SIAM Review, vol. 35, no. 1, pp. 43–79, 1993.
[15]
S. Mushayabasa and C. P. Bhunu, “Is HIV infection associated with an increased risk for cholera? Insights from a mathematical model,” Biosystems, vol. 109, no. 2, pp. 203–213, 2012.
[16]
C. Castillo-Chavez and B. Song, “Dynamical models of tuberculosis and their applications,” Math Biosciences and Engineering, vol. 1, no. 2, pp. 361–404, 2004.
[17]
J. C. Helton, “Uncertainty and sensitivity analysis techniques for use in performance assessment for radioactive waste disposal,” Reliability Engineering & System Safety, vol. 42, no. 2-3, pp. 327–367, 1993.
[18]
A. Saltelli, K. Chan, and M. Scott, Sensitivity Analysis, John Wiley & Sons, Chichester, UK, 2000.