Mathematical Analysis of Cryptosporidium Outbreak
Main Article Content
Abstract
Cryptosporidium is a waterborne pathogen that transmits through various routes, including contact with the feces of infected individuals, contaminated environments, unsafe water, unsanitized food, raw or unpasteurized milk, animal exposure, and recreational water bodies. This study formulates and analyzes five compartmental models to propose effective strategies for controlling the spread of cryptosporidiosis. The models were assessed for biological and mathematical validity using the theory of positivity and were confirmed to be epidemiologically well-posed. The basic reproduction number was derived using the next generation matrix method and found to be less than unity, suggesting that the infection has the potential to be eliminated from the population. Stability analysis of the disease-free equilibrium was conducted using the Jacobian matrix method and confirmed local asymptotic stability. Sensitivity analysis identified the contact rate between susceptible and infected individuals as the most influential parameter affecting the basic reproduction number. This highlights the importance of reducing contact rates as a key intervention strategy. Numerical simulations performed using Maple 22 provided supportive insights and interpretations of the model dynamics, reinforcing the analytical findings.

Citation Metrics:
Downloads
Article Details

Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
References
2. Checkley, W., White, A. C. Jr., Jaganath, D., Arrowood, M. J., Chalmers, R. M., Chen, X. M., et al. (2015). A review of the global burden, novel diagnostics, therapeutics, and vaccine targets for Cryptosporidium. The Lancet Infectious Diseases, 15(1), 85–94.
3. Khalil, I. A., Troeger, C., Rao, P. C., et al. (2018). Morbidity, mortality, and long-term consequences associated with diarrhea from Cryptosporidium infection in children younger than 5 years: A meta-analysis study. The Lancet Global Health, 6(7), e758–e768.
4. Centers for Disease Control and Prevention. (2019). Nationally notifiable infectious diseases and conditions, United States: Annual tables. https://wonder.cdc.gov/nndsss/nndss_annual_tables_menu.asp
5. Tzipori, S., & Griffiths, J. (1998). Natural history and biology of Cryptosporidium parvum. Advances in Parasitology, 40, 5–36. https://doi.org/10.1016/S0065-308X(08)60116-5
6. Brookhart, M. A., Hubbard, A. E., van der Laan, M. J., et al. (2002). Statistical estimation of parameters in a disease transmission model: Analysis of a Cryptosporidium outbreak. Statistics in Medicine, 21, 3627–3638.
7. Boehmer, T. K., Alden, N. B., Ghosh, T. S., & Vogt, R. L. (2009). Cryptosporidiosis from a community swimming pool: Outbreak investigation and follow-up study. Epidemiology and Infection, 137(11), 1651–1654.
8. Black, M., & McAnulty, J. (2006). The investigation of an outbreak of cryptosporidiosis in New South Wales in 2005. New South Wales Public Health Bulletin, 17(5–6), 76–79.
9. Centers for Disease Control and Prevention. (2007). Cryptosporidiosis outbreaks associated with recreational water use—Five states, 2006. Morbidity and Mortality Weekly Report, 56(29), 729–732.
10. Stanford University. (n.d.). Cryptosporidiosis. https://web.stanford.edu/class/Cryptosporidiosis
11. Food Safety News. (2024, May). Cryptosporidium outbreak affects dozens in England. https://www.foodsafetynews.com/2024/05/cryptosporidium-outbreak-affects-dozens-in-england/
12. NHS Direct. (n.d.). Retrieved from http://www.nhsdirect.nhs.uk or http://www.nhsdirect.wales.nhs.uk
13. Heymann, D. L. (Ed.). (2008). Control of communicable diseases manual (19th ed.). American Public Health Association.
14. Insulander, M., Lebbad, M., Stenström, T. A., & Svenungsson, B. (2005). An outbreak of cryptosporidiosis associated with exposure to swimming pool water. Scandinavian Journal of Infectious Diseases, 37(5), 354–360.
15. Sponseller, J. K., Griffiths, J. K., & Tzipori, S. (2014). The evolution of respiratory cryptosporidiosis: Evidence for transmission by inhalation. Clinical Microbiology Reviews, 27(3), 575–586. https://doi.org/10.1128/CMR.00115-13
16. Lemmon, J. M., McAnulty, J. M., & Bawden-Smith, J. (1996). Outbreak of cryptosporidiosis linked to an indoor swimming pool. Medical Journal of Australia, 165(11–12), 613–616.
17. McAnulty, J. M., Fleming, D. W., & Gonzalez, A. H. (1994). A community-wide outbreak of cryptosporidiosis associated with swimming at a wave pool. Journal of the American Medical Association, 272(20), 1597–1600.
18. Puech, M. C., McAnulty, J. M., Lesjak, M., Shaw, N., Heron, L., & Watson, J. M. (2001). A statewide outbreak of cryptosporidiosis in New South Wales associated with swimming at public pools. Epidemiology and Infection, 126(3), 389–396.
19. Anderson, R. M., & May, R. M. (1991). Infectious diseases of humans: Dynamics and control. Oxford University Press.
20. Brookhart, M. A., Hubbard, A. E., van der Laan, M. J., et al. (2002). Statistical estimation of parameters in a disease transmission model: Analysis of a Cryptosporidium outbreak. Statistics in Medicine, 21, 3627–3638. https://doi.org/10.1002/sim.1258
21. Bonyah, E., Zakari, A., & Bakari, L. (2016). Qualitative analysis of malaria dynamics with nonlinear incidence function. Asia Pacific Journal of Computer Engineering, 3(2). https://doi.org/10.1186/s40540-016-0018-2
22. Wikipedia contributors. (n.d.). Cryptosporidium life cycle. Wikipedia. https://www.en.wikipedia.org/wiki/Cryptosporidiosis
23. Stanford University. (n.d.). Cryptosporidiosis. https://web.stanford.edu/class/Cryptosporidiosis
24. Hampson, K., Dushoff, J., Bingham, J., Bruckner, G., Ali, Y. H., & Dobson, A. (2007). Synchronous cycles of domestic dog rabies in sub-Saharan Africa and the impact of control efforts. Proceedings of the National Academy of Sciences, 104(18), 7717–7722.
25. Olaniyi, S., & Obaniyi, O. S. (2014). Qualitative analysis of malaria dynamics with nonlinear incidence function. Applied Mathematical Sciences, 8(78), 3889–3904.
26. Olusola, A. O., Oladejo, J. K., Salahu, W. O., Taiwo, A. A., & Ayanrinola, O. W. (2025). Stability and sensitivity analysis of HIV/AIDS model with saturated incidence rate. Transpublika International Research in Exact Sciences, 4(2). https://doi.org/10.55047/tires.v4i2.1650














