Gastric cancer is among the most common cancers in the world, and it has a significant negative impact on the health and economies of different countries, led by those that are developing. This study formulates a deterministic model for the transmission dynamics of gastric cancer through gastric ulcers, incorporating screening and treatment strategies. The model is thoroughly analyzed both quantitatively, qualitatively, and numerically. The key properties considered in the model analysis are positivity, invariant region, equilibria, stabilities, and bifurcation analysis. We compute the control reproduction number $ \mathcal R_{C} $ using the next-generation matrix approach. This enables us to prove that the model has a unique disease-free equilibrium (DFE) and admits a unique endemic equilibrium, which are locally and globally asymptotically stable whenever $ \mathcal R_{C} < 1 $ and $ \mathcal R_{C} > 1 $, respectively. Sensitivity analysis indicates that increasing the rate of screening decreases the control reproduction number, consequently reducing the rate of transmission of infections. Simulation results demonstrate that the combination of screening and treatment is the most effective intervention in reducing infection transmission. Furthermore, a combination of early screening and treatment proves more effective than a combination of late screening and treatment of gastric ulcers. Screening the infected population alone is identified as the least effective strategy for curtailing transmission of infection in the susceptible population. The findings of this study will guide public health officers in making decisions regarding the screening and treatment of exposed individuals with Helicobacter pylori infection and gastric ulcer patients, therefore aiding in fighting gastric ulcers and their progression to gastric cancer.
Citation: Glory Kawira Mutua, Musyoka Kinyili, Dominic Makaa Kitavi. Mathematical modeling of the effects of screening and treatment of gastric ulcers as a control strategy for gastric cancer[J]. Mathematical Biosciences and Engineering, 2026, 23(4): 1067-1095. doi: 10.3934/mbe.2026040
Gastric cancer is among the most common cancers in the world, and it has a significant negative impact on the health and economies of different countries, led by those that are developing. This study formulates a deterministic model for the transmission dynamics of gastric cancer through gastric ulcers, incorporating screening and treatment strategies. The model is thoroughly analyzed both quantitatively, qualitatively, and numerically. The key properties considered in the model analysis are positivity, invariant region, equilibria, stabilities, and bifurcation analysis. We compute the control reproduction number $ \mathcal R_{C} $ using the next-generation matrix approach. This enables us to prove that the model has a unique disease-free equilibrium (DFE) and admits a unique endemic equilibrium, which are locally and globally asymptotically stable whenever $ \mathcal R_{C} < 1 $ and $ \mathcal R_{C} > 1 $, respectively. Sensitivity analysis indicates that increasing the rate of screening decreases the control reproduction number, consequently reducing the rate of transmission of infections. Simulation results demonstrate that the combination of screening and treatment is the most effective intervention in reducing infection transmission. Furthermore, a combination of early screening and treatment proves more effective than a combination of late screening and treatment of gastric ulcers. Screening the infected population alone is identified as the least effective strategy for curtailing transmission of infection in the susceptible population. The findings of this study will guide public health officers in making decisions regarding the screening and treatment of exposed individuals with Helicobacter pylori infection and gastric ulcer patients, therefore aiding in fighting gastric ulcers and their progression to gastric cancer.
| [1] |
J. Ferlay, M. Colombet, I. Soerjomataram, D. M. Parkin, M. Pieros, A. Znaor, et al., Cancer statistics for the year 2020: An overview, Int. J. Cancer, 149 (2021), 778–789. https://doi.org/10.1002/ijc.33588 doi: 10.1002/ijc.33588
|
| [2] |
J. Ferlay, M. Colombet, I. Soerjomataram, C. Mathers, D. M. Parkin, M. Pieros, et al., Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods, Int. J. Cancer, 144 (2019), 1941–1953. https://doi.org/10.1002/ijc.31937 doi: 10.1002/ijc.31937
|
| [3] |
V. E. Reyes, Helicobacter pylori and its role in gastric cancer, Microorganisms, 11 (2023), 1312. https://doi.org/10.3390/microorganisms11051312 doi: 10.3390/microorganisms11051312
|
| [4] |
M. Shen, R. Xia, Z. Luo, H. Zeng, W. Wei, G. Zhuang, et al., The long-term population impact of endoscopic screening programmes on disease burdens of gastric cancer in China: A mathematical modelling study, J. Theor. Biol., 484 (2020), 109996. https://doi.org/10.1016/j.jtbi.2019.109996 doi: 10.1016/j.jtbi.2019.109996
|
| [5] |
E. A. V. Noguera, S. C. Trujillo, E. Ibargüen-Mondragón, A within-host model on the interactions of sensitive and resistant Helicobacter pylori to antibiotic therapy considering immune response, Math. Biosci. Eng., 22 (2025), 185–224. https://doi.org/10.3934/mbe.2025009 doi: 10.3934/mbe.2025009
|
| [6] |
V. R. RaviKKumar, S. Rathi, S. Singh, B. Patel, S. Singh, K. Chaturvedi, et al., A comprehensive review on Ulcer and their treatment, Chinese J. Appl. Physiol., 39 (2023), e20230006. https://doi.org/10.62958/j.cjap.2023.006 doi: 10.62958/j.cjap.2023.006
|
| [7] | A. H. Khan, M. A. Dar, M. A. Mir, Gastric ulcer: an overview, Int. J. Current Res. Phys. Pharmacol., (2023), 1–7. Available from: https://ijcrpp.com/index.php/ijcrpp/article/view/63 |
| [8] |
J. J. Hwang, D. H. Lee, A. R. Lee, H. Yoon, C. M. Shin, Y. Park, et al., Characteristics of gastric cancer in peptic ulcer patients with Helicobacter pylori infection, World J. Gastroentero., 21 (2015), 4954. https://doi.org/10.3748/wjg.v21.i16.4954 doi: 10.3748/wjg.v21.i16.4954
|
| [9] |
M. Kinyili, J. B. Munyakazi, A. Y. Mukhtar, Modeling the impact of combined use of COVID Alert SA app and vaccination to curb COVID-19 infections in South Africa, Plos One, 18 (2023), e0264863. https://doi.org/10.1371/journal.pone.0264863 doi: 10.1371/journal.pone.0264863
|
| [10] | G. K. Mutua, C. G. Ngari, G. G. Muthuri, D. M. Kitavi, Mathematical modeling and simulating of Helicobacter pylori treatment and transmission implications on stomach cancer dynamics, Commun. Math. Biol. Neurosci., (2022). https://scik.org/index.php/cmbn/article/view/7542/0 |
| [11] |
T. Feng, Z. Zheng, J. Xu, P. Cao, S. Gao, X. Yu, Cost-effectiveness analysis of the Helico-bacter pylori screening programme in an asymptomatic population in China, Int. J. Environ. Res. Public Health, 19 (2022), 9986. https://doi.org/10.3390/ijerph19169986 doi: 10.3390/ijerph19169986
|
| [12] |
M. Cousins, J. M. Sargeant, D. Fisman, A. L. Greer, Modelling the transmission dynamics of Campylobacter in Ontario, Canada, assuming house flies, Musca domestica, are a mechanical vector of disease transmission, Royal Soc. Open Sci., 6 (2019), 181394. https://doi.org/10.1098/rsos.181394 doi: 10.1098/rsos.181394
|
| [13] | M. P. Dore, D. Y. Graham, Modern approach to the diagnosis of Helicobacter pylori infection, Aliment. Pharm. Therap., 55 (2022). https://doi.org/10.1111/apt.16566 |
| [14] | M. Wameko, P. Koya, A. Wodajo, Mathematical model for transmission dynamics of typhoid fever with optimal control strategies, Int. J. Indust. Math., 12 (2020), 283–296. |
| [15] |
G. T. Tilahun, O. D. Makinde, D. Malonza, Co-dynamics of pneumonia and typhoid fever diseases with cost effective optimal control analysis, Appl. Math. Comput., 316 (2018), 438–459. https://doi.org/10.1016/jamc.2017.07.063 doi: 10.1016/jamc.2017.07.063
|
| [16] |
G. T. Tilahun, O. D. Makinde, D. Malonza, Modelling and optimal control of typhoid fever disease with cost-effective strategies, Comput. Math. Methods Med., 2017 (2017), 2324518. https://doi.org/10.1155/2017/2324518 doi: 10.1155/2017/2324518
|
| [17] |
P. Duve, S. Charles, J. Munyakazi, R. Lühken, P. Witbooi, A mathematical model for malaria disease dynamics with vaccination and infected immigrants, Math. Biosci. Eng., 21 (2024), 1082–1109. https://doi.org/10.3934/mbe.2024045 doi: 10.3934/mbe.2024045
|
| [18] |
M. Kinyili, J. B. Munyakazi, A. Y. Mukhtar, To use face masks or not after COVID-19 vaccination? An impact analysis using mathematical modeling, Front. Appl. Math. Stat., 8 (2022), 872284. https://doi.org/10.3389/fams.2022.872284 doi: 10.3389/fams.2022.872284
|
| [19] |
A. Omame, S. A. Iyaniwura, Q. Han, A. Ebenezer, N. L. Bragazzi, et al., Dynamics of Mpox in an HIV endemic community: A mathematical modelling approach, Math. Biosci. Eng., 22 (2025), 225–259. https://doi.org/10.3934/mbe.2025010 doi: 10.3934/mbe.2025010
|
| [20] |
M. Kinyili, J. B. Munyakazi, A. Y. Mukhtar, Mathematical modeling and impact analysis of the use of COVID Alert SA app, AIMS Public Health, 9 (2022), 106–128. https://doi.org/10.3934/publichealth.2022009 doi: 10.3934/publichealth.2022009
|
| [21] |
L. Liu, X. Wang, Y. Li, Mathematical analysis and optimal control of an epidemic model with vaccination and different infectivity, Math. Biosci. Eng., 20 (2023), 20914–20938. https://doi.org/10.3934/mbe.2023925 doi: 10.3934/mbe.2023925
|
| [22] | M. Kinyili, J. M-S. Lubuma, J. B. Munyakazi, A. Y. A. Mukhtar, Analyzing the role of comorbidity on COVID-19 infections by mathematical modeling, J. Math. Comput. Sci., 14 (2024). https://scik.org/index.php/jmcs/article/view/7582 |
| [23] |
P. Van den Driessche, J. Watmough, Reproduction numbers and sub-threshold endemic Equilibria for compartmental models of disease transmission, Math. Biosci., 180 (2004), 29–48. https://doi.org/10.1016/S0025-5564(02)00108-6 doi: 10.1016/S0025-5564(02)00108-6
|
| [24] |
C. Castillo-Chavez, B. Song, Dynamical models of tuberculosis and their applications, Math. Biosci. Eng., 1 (2004), 361–404. https://doi.org/10.3934/mbe.2004.1.361 doi: 10.3934/mbe.2004.1.361
|
| [25] |
T. J. Tsafack, C. K. Kum, A. J. O. Tassé, B. Tsanou, Mathematical modelling of the dynamics of typhoid fever and two modes of treatment in a Health District in Cameroon, Math. Biosci. Eng., 22 (2025), 477–510. https://doi.org/10.3934/mbe.2025018 doi: 10.3934/mbe.2025018
|
| [26] |
M. F. T. Rupnow, R. D. Shachter, D. K. Owens, J. Parsonnet, A dynamic transmission model for predicting trends in Helicobacter pylori and associated diseases in the United States, Emerg. Infect. Diseases, 6 (2000), 228–237. https://doi.org/10.3201/eid0603.000302 doi: 10.3201/eid0603.000302
|
| [27] |
Q. Chen, X. Liang, X. Long, L. Yu, W. Liu, H. Lu, Cost effectiveness analysis of screen and treat strategy in asymptomatic Chinese for preventing Helicobacter pylori associated diseases, Helicobacter, 24 (2019), e12563. https://doi.org/10.1111/hel.12563 doi: 10.1111/hel.12563
|
| [28] |
Y. C. Lee, T. H. Chiang, H. M. Chiu, W. W. Su, K. C. Chou, S. L. S. Chen, et al., Screening for Helicobacter pylori to prevent gastric cancer: A pragmatic randomized clinical trial, JAMA, 332 (2024), 1642–1651. https://doi.org/10.1001/jama.2024.14887 doi: 10.1001/jama.2024.14887
|
| [29] |
K. F. Pan, L. Zhang, M. Gerhard, J. L. Ma, W. D. Liu, K. Ulm, et al., A large randomised controlled intervention trial to prevent gastric cancer by eradication of Helicobacter pylori in Linqu County, China: Baseline results and factors affecting the eradication, Gut, 65 (2016), 9–18. https://doi.org/10.1136/gutjnl-2015-309197 doi: 10.1136/gutjnl-2015-309197
|
| [30] |
S. Take, M. Mizuno, K. Ishiki, C. Kusumoto, T. Imada, F. Hamada, et al., Correction to: Risk of gastric cancer in the second decade of follow-up after Helicobacter pylori eradication, J. Gastroenterol., 55 (2019), 289. https://doi.org/10.1007/s00535-019-01654-x doi: 10.1007/s00535-019-01654-x
|
| [31] |
M. Ghosh, P. Chandra, P. Sinha, J. B. Shukla, Modelling the spread of bacterial infectious disease with environmental effect in a logistically growing human population, Nonlinear Anal. Real World Appl., 7 (2006), 341–363. https://doi.org/10.1016/j.nonrwa.2005.03.005 doi: 10.1016/j.nonrwa.2005.03.005
|
| [32] |
G. T. Tilahun, O. D. Makinde, D. Malonza, Modelling and optimal control of pneumonia disease with cost-effective strategies, J. Biol. Dynam., 11 (2017), suppl. 2,400–426. https://doi.org/10.1080/17513758.2017.1337245 doi: 10.1080/17513758.2017.1337245
|
| [33] | V. N. Njenga, C. G. Ngari, W. M. Nduku, L. S. Luboobi, Modelling the impact of hygiene and treatment on the dynamics of childhood diarrhea in Nairobi County, Kenya, Int. J. Math. Math. Sci., (2024), 3336826. https://doi.org/10.1155/2024/3336826 |