Processing, Please wait...

  • Publisher Home
  • Home
  • 🔙 Back
  • 📚 Journals
    • ⚙️ IJIEC - Industrial Engineering Computations
    • 🌐 IJDNS - Data and Network Science
    • 🧪 CCL - Current Chemistry Letters
    • 💹 AC - Accounting
    • 🎯 DSL - Decision Science Letters
    • 🚛 USCM - Uncertain Supply Chain Management
    • 🏗️ JPM - Journal of Project Management
    • 🏥 HE - Healthcare Engineering
    • 📈 SCI - Scientometrica
    • 🔩 ESM - Engineering Solid Mechanics
    • 🌿 JFS - Journal of Future Sustainability
    • 💼 MSL - Management Science Letters
  • 📝 Submit Article
  • 📊 Statistics
  • 📋 About
    • 📄 About Us
    • 📰 Blog
    • 📢 News
    • 📧 Contact
  • 📺 Tutorial
  • Search:
  • Advanced Search

Growing Science » Journal of Future Sustainability » Using mathematical models to understand and control Influenza A (H1N1) outbreaks with quarantine and treatment

⭐ Highly Cited Articles

  • Jaya Algorithm
  • Rao Algorithm
  • TLBO Algorithm
  • ChatGPT and Blended Learning

Journals

  • IJIEC (804)
  • IJDS (992)
  • DSL (722)
  • ESM (434)
  • CCL (563)
  • JPM (350)
  • AC (567)
  • JFS (101)
  • MSL (2658)
  • USCM (1104)
  • HE (51)
  • SCI (51)

JFS Volumes

    • ▼ Volume 6 (15)
      • Issue 1 (5)
      • Issue 2 (5)
      • Issue 3 (5)
    • ▼ Volume 5 (20)
      • Issue 1 (5)
      • Issue 2 (5)
      • Issue 3 (5)
      • Issue 4 (5)
    • ▼ Volume 4 (20)
      • Issue 1 (5)
      • Issue 2 (5)
      • Issue 3 (5)
      • Issue 4 (5)
    • ▼ Volume 3 (21)
      • Issue 1 (5)
      • Issue 2 (5)
      • Issue 3 (5)
      • Issue 4 (6)
    • ▼ Volume 2 (20)
      • Issue 1 (5)
      • Issue 2 (5)
      • Issue 3 (5)
      • Issue 4 (5)
    • ▼ Volume 1 (5)
      • Issue 1 (5)

🔑 Keywords

Jordan(172)
Supply chain management(169)
Vietnam(154)
Customer satisfaction(124)
Performance(117)
Supply chain(114)
Service quality(101)
Artificial intelligence(101)
Competitive advantage(98)
Tehran Stock Exchange(94)
SMEs(94)
Sustainability(93)
optimization(88)
TOPSIS(85)
Trust(84)
Financial performance(84)
Job satisfaction(81)
Knowledge Management(80)
Genetic Algorithm(80)
Organizational performance(79)


» Show all keywords

✍️ Authors

Naser Azad(82)
Zeplin Jiwa Husada Tarigan(68)
Mohammad Reza Iravani(65)
Endri Endri(45)
Muhammad Alshurideh(42)
Hotlan Siagian(41)
Dmaithan Almajali(39)
Jumadil Saputra(36)
Muhammad Turki Alshurideh(35)
Ahmad Makui(33)
Sautma Ronni Basana(32)
Barween Al Kurdi(32)
Basrowi Basrowi(31)
Mohammad Khodaei Valahzaghard(30)
Haitham M. Alzoubi(30)
Hassan Ghodrati(30)
Shankar Chakraborty(29)
Ni Nyoman Kerti Yasa(29)
Sulieman Ibraheem Shelash Al-Hawary(28)
Prasadja Ricardianto(28)


» Show all authors

🌍 Countries

1. Algeria (52)
2. Angola (2)
3. Argentina (22)
4. Armenia (2)
5. Australia (52)
6. Austria (2)
7. Bahrain (26)
8. Bangladesh (58)
9. Belarus (4)
10. Belgium (3)
11. Benin (2)
12. Benin Republic (1)
13. Bhutan (1)
14. Bosnia and Herzegovina (1)
15. Botswana (8)
16. Brazil (40)
17. Brunei (1)
18. Bulgaria (1)
19. Burkina Faso (1)
20. Cameroon (1)
Total: 121 countries

Show all countries
Journal of Future Sustainability
ISSN 2816-8151 (Online) - ISSN 2816-8143 (Print)
Quarterly Publication
Volume 6 Issue 3 pp. 181-192, 2026

Using mathematical models to understand and control Influenza A (H1N1) outbreaks with quarantine and treatment Pages 181-192 PDF Download PDF

Authors: Mahboubeh Molavi-Arabshahi, Bahareh Moradi

📋 Author Affiliations:
Mahboubeh Molavi-Arabshahi ORCID , Bahareh Moradi
¹ Mathematics & Computer Science, Iran University of Science and Technology, Tehran, Iran
doi 10.5267/j.jfs.2026.4.004
✏️ Editor: EditorV.K. Chawla ORCID
Crossref 1 Source: CrossRef

🔑 Keywords: Influenza A (H1N1), Infectious diseases, Mathematical modeling, Control strategies

Abstract: Mathematical modeling has become an important tool for understanding and controlling infectious diseases. It allows researchers to simulate and predict the spread of diseases, identify key transmission factors, assess the impact of interventions, and inform decision-making for disease control. Researchers can use mathematical models to explore different scenarios, evaluate the effectiveness of various interventions, and estimate the potential outcomes of different control strategies. This enables policymakers and public health professionals to make informed decisions and implement targeted measures to mitigate the impact of infectious diseases. In addition, mathematical models can be used to examine and evaluate the effectiveness of control strategies such as vaccination, social restrictions, and drug use. The results show that prevention strategies such as population vaccination and social restrictions can significantly help reduce the spread of influenza. This article presents a mathematical model for Influenza A (H1N1), as well as two other models specifically for Influenza A (H1N1) after quarantine and treatment. The purpose of the article is to provide a brief review of these models and compare them.

How to cite this paper
APA: Molavi-Arabshahi, M & Moradi, B. (2026). Using mathematical models to understand and control Influenza A (H1N1) outbreaks with quarantine and treatment. Journal of Future Sustainability, 6(3), 181-192.
Chicago/Turabian: Molavi-Arabshahi, M & Moradi, B. 2026. "Using mathematical models to understand and control Influenza A (H1N1) outbreaks with quarantine and treatment." Journal of Future Sustainability 6, no. 3 (2026): 181-192.
AMA: Molavi-Arabshahi, M & Moradi, B. Using mathematical models to understand and control Influenza A (H1N1) outbreaks with quarantine and treatment. Journal of Future Sustainability. 2026;6(3):181-192.

References
Arino, J., Brauer, F., van den Driessche, P., Watmough, J., & Wu, J. (2006). Simple models for containment of a pan-demic. Journal of the Royal Society Interface, 3(8), 453–457. https://doi.org/10.1098/rsif.2006.0112
Boëlle, P. Y., Bernillon, P., & Desenclos, J. C. (2009). A preliminary estimation of the reproduction ratio for the new in-fluenza A (H1N1) from the outbreak in Mexico, March–April 2009. Eurosurveillance, 14(19), Article 19205, 1–4. https://doi.org/10.2807/ese.14.19.19205-en
Brauer, F., & Castillo-Chavez, C. (2012). Mathematical models in population biology and epidemiology (2nd ed.). Springer.
Brauer, F., Castillo-Chavez, C., & Feng, Z. (2019). Mathematical models in epidemiology. Springer.
Cauchemez, S., Donnelly, C. A., Reed, C., Ghani, A. C., Fraser, C., Kent, C. K., Finelli, L., & Ferguson, N. M. (2009). Household transmission of 2009 pandemic influenza A (H1N1) virus in the United States. New England Journal of Medicine, 361(27), 2619–2627. https://doi.org/10.1056/NEJMoa0905498
Centers for Disease Control and Prevention. (2010). CDC estimates of 2009 H1N1 influenza cases, hospitalizations and deaths in the United States, April 2009 – January 16, 2010. https://www.cdc.gov/h1n1flu/estimates_2009_h1n1.htm
Coen, P. G., Cartwright, K., & Stuart, J. (2000). Mathematical modelling of infection and disease due to Neisseria men-ingitidis and Neisseria lactamica. International Journal of Epidemiology, 29(1), 180–188. https://doi.org/10.1093/ije/29.1.180
Daley, D. J., & Gani, J. (2005). Epidemic modelling: An introduction. Cambridge University Press.
Diekmann, O., Heesterbeek, J. A. P., & Roberts, M. G. (2010). The construction of next-generation matrices for com-partmental epidemic models. Journal of the Royal Society Interface, 7(47), 873–885. https://doi.org/10.1098/rsif.2009.0386
Flahault, A., Vergu, E., & Boëlle, P. Y. (2009). Potential for a global dynamic of influenza A (H1N1). BMC Infectious Diseases, 9, Article 129, 1–4. https://doi.org/10.1186/1471-2334-9-129
Fraser, C., Donnelly, C. A., Cauchemez, S., Hanage, W. P., Van Kerkhove, M. D., Hollingsworth, T. D., Griffin, J. T., Baggaley, R. F., Jenkins, H. E., Lyons, E. J., Jombart, T., Hinsley, W. R., Grassly, N. C., Balloux, F., Ghani, A. C., Ferguson, N. M., Rambaut, A., Pybus, O. G., Lopez-Gatell, H., … Riley, S. (2009). Pandemic potential of a strain of influenza A (H1N1): Early findings. Science, 324(5934), 1557–1561. https://doi.org/10.1126/science.1176062
Gillespie, D. T. (1977). Exact stochastic simulation of coupled chemical reactions. The Journal of Physical Chemis-try, 81(25), 2340–2361. https://doi.org/10.1021/j100540a008
Hanselman, D., & Littlefield, B. (2005). Mastering MATLAB 7. Prentice Hall.
Harrison, L. H., Dwyer, D. M., Maples, C. T., & Billmann, L. (1999). Risk of meningococcal infection in college stu-dents. JAMA, 281(20), 1906–1910. https://doi.org/10.1001/jama.281.20.1906
Kenah, E., Lipsitch, M., & Robins, J. M. (2008). Generation interval contraction and epidemic data analy-sis. Mathematical Biosciences, 213(1), 71–79. https://doi.org/10.1016/j.mbs.2008.02.009
Longini, I. M., Jr., Halloran, M. E., Nizam, A., & Yang, Y. (2004). Containing pandemic influenza with antiviral agents. American Journal of Epidemiology, 159(7), 623–633. https://doi.org/10.1093/aje/kwh092
Longini, I. M., Jr., Nizam, A., Xu, S., Ungchusak, K., Hanshaoworakul, W., Cummings, D. A. T., & Halloran, M. E. (2005). Containing pandemic influenza at the source. Science, 309(5737), 1083–1087. https://doi.org/10.1126/science.1115717
Rhodes, C. J., & Anderson, R. M. (2008). Contact rate calculation for a basic epidemic model. Mathematical Bioscienc-es, 216(1), 56–62. https://doi.org/10.1016/j.mbs.2008.08.007
Trottier, H., & Philippe, P. (2001). Deterministic modeling of infectious diseases: Theory and methods. The Internet Journal of Infectious Diseases, 1(2). https://print.ispub.com/api/0/ispub-article/11215
Weinstein, M. C., O'Brien, B., Hornberger, J., Jackson, J., Johannesson, M., McCabe, C., & Luce, B. R. (2003). Princi-ples of good practice for decision analytic modeling in health-care evaluation: Report of the ISPOR Task Force on Good Research Practices—Modeling Studies. Value in Health, 6(1), 9–17. https://doi.org/10.1046/j.1524-4733.2003.00234.x
  • 0
  • 1
  • 2
  • 3
  • 4
  • 5

📚 Journal: Journal of Future Sustainability | 📅 Year: 2026 | 📖 Volume: 6 | 📄 Issue: 3 | 👁️ Views: 249 | 📊 Crossref: 1

Related Articles:
  • Extending the forecasting horizon of daily new COVID-19 cases using non-pharmaceutical measures and the effective reproduction number (Rt): A deep learning-based framework
  • A novel COVID-19 infection-forecasting model based on artificial neural networks
  • Multiple endemic disease risk modeling using a Bayesian spatiotemporal shared components model
  • Predicting the weekly COVID-19 new cases using multilayer perceptron: An evidence from west Java, Indonesia
  • Fight against COVID-19: A global outbreak response management performance view

📝 Ready to share your research?

Journal of Future Sustainability is accepting new submissions for upcoming issues. Join our community of authors and publish your work with us.

✓ Open access
✓ Rigorous peer review
✓ Fast publication
📤 Submit Your Manuscript →

📖 Author Guidelines

® 2010-2026 GrowingScience.Com