10.1007/s40096-023-00517-0

Deterministic modelling of optimal control strategies for dengue fever transmission in two interconnected patches

  1. Department of Mathematical Sciences, Federal University of Technology Akure, Akure, Ondo State, NG
  2. Department of Mathematical Sciences, Universiti Teknologi Malaysia, Johor Bahru, Johor, 81310, MY
  3. Department of Mathematics and Applied Mathematics, University of Pretoria, Pretoria, ZA

Published 2023-05-29

How to Cite

Abidemi, A., Aziz, N. A. B., & Pindza, E. (2023). Deterministic modelling of optimal control strategies for dengue fever transmission in two interconnected patches. Mathematical Sciences, 18(4 (December 2024). https://doi.org/10.1007/s40096-023-00517-0

Abstract

Abstract This paper presents a two-patch model that governs a non-autonomous system of ordinary differential equations including patch-specific insecticides (larvicide and adulticide) vector and human personal protection controls and effect of human movement to describe dengue fever transmission dynamics between the interacting human and mosquito populations of two connected patches. Next generation matrix method is used to compute the basic reproduction number ( R0\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathcal {R}}_0$$\end{document} ) associated with the autonomous version of the model. Analysis of the model reveals that the model has exactly two disease-free equilibrium, namely, trivial equilibrium and biologically realistic disease-free equilibrium (BRDFE). It is shown that BRDFE is both locally and globally asymptotically stable when R0<1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathcal {R}}_0<1$$\end{document} , and unstable otherwise. The model is validated, with the aid of single-patch version of the model, using the real epidemic data related to the 2011–2012 dengue outbreaks in Selangor and Johor, Malaysia. Using optimal control approach, the non-autonomous system is analysed to estimate the effects of patch-specific time-dependent larvicide, adulticide and personal protection controls through simulations. By patch-specific time-dependent controls, we mean time-dependent controls implemented with a view to reducing dengue prevalence at the targeted patch (or location). It is shown that the implementation of combined efforts of the three control strategies simultaneously helps in curtailing dengue fever spread in both patches.

Keywords

  • Basic reproduction number,
  • Dengue fever,
  • Optimal control,
  • Stability analysis,
  • Two-patch dengue model

References

  1. WHO, Dengue: guidelines for diagnosis, treatment, prevention and control, New Edition, World Health Organization, (2009)
  2. WHO, Vector-borne diseases: Dengue, Fact Sheet 387 (March 2014)
  3. Abdelrazec et al. (2016) Modeling the spread and control of dengue with limited public health resources (pp. 136-145)
  4. Phaijoo and Gurung (2017) Modeling impact of temperature and human movement on the persistence of dengue disease https://doi.org/10.1155/2017/1747134
  5. WHO, Dengue haemorrhagic fever: diagnosis, treatment, prevention and control, 2nd Edition, World Health Organization, (1997)
  6. Esteva and Vargas (2003) Coexistence of different serotypes of dengue virus (pp. 31-47)
  7. Ndii (2022) The effects of vaccination, vector controls and media on dengue transmission dynamics with a seasonally varying mosquito population https://doi.org/10.1016/j.rinp.2022.105298
  8. WHO, Report of the meeting of the WHO/VMI workshop on dengue modeling: 25–26 August 2010, Geneva, Switzerland, Tech. rep., Geneva: World Health Organization (2011)
  9. WHO, Dengue and severe dengue, Tech. rep., World Health Organization. Regional Office for the Eastern Mediterranean (2014)
  10. Mustafa et al. (2015) Discovery of fifth serotype of dengue virus (DENV-5): a new public health dilemma in dengue control 71(1) (pp. 67-70)
  11. Mallhi et al. (2015) Clinico-laboratory spectrum of dengue viral infection and risk factors associated with dengue hemorrhagic fever: a retrospective study 15(1)
  12. Cucunawangsih and Lugito (2017) Trends of dengue disease epidemiology (pp. 1-6)
  13. Wijayanti et al. (2016) Dengue in Java, Indonesia: relevance of mosquito indices as risk predictors 10(3)
  14. Bichara et al. (2016) On the dynamics of dengue virus type 2 with residence times and vertical transmission 3(1) (pp. 140-160)
  15. Mishra and Gakkhar (2018) Non-linear dynamics of two-patch model incorporating secondary dengue infection 4(1)
  16. WHO, Dengue and severe dengue, Accessed 20th August (2019).
  17. http://www.who.int/mediacentre/factsheets/fs117/en/
  18. Aldila and Seno (2019) A population dynamics model of mosquito-borne disease transmission, focusing on mosquitoes’ biased distribution and mosquito repellent use 81(12) (pp. 4977-5008)
  19. Romero-Leiton et al. (2018) An optimal control problem applied to malaria disease in Colombia 12(6) (pp. 279-292)
  20. Xiao and Zou (2014) Transmission dynamics for vector-borne diseases in a patchy environment 69(1) (pp. 113-146)
  21. Cosner et al. (2009) The effects of human movement on the persistence of vector-borne diseases 258(4) (pp. 550-560)
  22. Cosner (2015) Models for the effects of host movement in vector-borne disease systems (pp. 192-197)
  23. Bichara and Castillo-Chavez (2016) Vector-borne diseases models with residence times-A Lagrangian perspective (pp. 128-138)
  24. Esteva and Vargas (1998) Analysis of a dengue disease transmission model 150(2) (pp. 131-151)
  25. Esteva and Vargas (1999) A model for dengue disease with variable human population 38(3) (pp. 220-240)
  26. Abidemi et al. (2020) Mathematical modelling of coexistence of two dengue virus serotypes with seasonality effect 17(2–3) (pp. 783-794)
  27. Rodrigues et al. (2012) Dengue disease, basic reproduction number and control 89(3) (pp. 334-346)
  28. Srivastav and Ghosh (2019) Assessing the impact of treatment on the dynamics of dengue fever: a case study of India
  29. Aldila et al. (2013) An optimal control problem arising from a dengue disease transmission model 242(1) (pp. 9-16)
  30. Dorsett et al. (2016) Optimal repellent usage to combat dengue fever 78(5) (pp. 916-922)
  31. Rodrigues et al. (2015) Dengue in Madeira Island (pp. 593-605) Springer, Games and Science
  32. Abidemi et al. (2019) The impact of vaccination, individual protection, treatment and vector controls on dengue 27(3) (pp. 613-622)
  33. Ndii (2020) Modelling the use of vaccine and wolbachia on dengue transmission dynamics 5(2) https://doi.org/10.3390/tropicalmed5020078
  34. Rodrigues et al. (2013) Bioeconomic perspectives to an optimal control dengue model 90(10) (pp. 2126-2136)
  35. Sepulveda-Salcedo et al. (2020) Optimal control of dengue epidemic outbreaks under limited resources 144(2) (pp. 185-212)
  36. Ndii et al. (2021) Estimating the reproduction number and designing the integrated strategies against dengue https://doi.org/10.1016/j.rinp.2021.104473
  37. Ndii et al. (2020) Optimal vaccination strategy for dengue transmission in Kupang city, Indonesia 6(11) https://doi.org/10.1016/j.heliyon.2020.e05345
  38. Abidemi and Aziz (2020) Optimal control strategies for dengue fever spread in Johor, Malaysia
  39. Falcón-Lezama et al. (2016) Day-to-day population movement and the management of dengue epidemics 78(10) (pp. 2011-2033)
  40. Arino (2017) Spatio-temporal spread of infectious pathogens of humans 2(2) (pp. 218-228)
  41. Abidemi et al. (2021) Assessing the roles of human movement and vector vertical transmission on dengue fever spread and control in connected patches: from modelling to simulation 136(11) https://doi.org/10.1140/epjp/s13360-021-02195-0
  42. Zhu et al. (2019) Effects of human mobility, temperature and mosquito control on the spatiotemporal transmission of dengue (pp. 969-978) https://doi.org/10.1016/j.scitotenv.2018.09.182
  43. Zhu et al. (2018) The spatiotemporal transmission of dengue and its driving mechanism: a case study on the 2014 dengue outbreak in Guangdong (pp. 252-259) https://doi.org/10.1016/j.scitotenv.2017.11.314
  44. Bock and Jayathunga (2018) Optimal control and basic reproduction numbers for a compartmental spatial multipatch dengue model 41(9) (pp. 3231-3245)
  45. Kim et al. (2017) Assessment of optimal strategies in a two-patch dengue transmission model with seasonality 12(3)
  46. Lee and Castillo-Chavez (2015) The role of residence times in two-patch dengue transmission dynamics and optimal strategies (pp. 152-164)
  47. Abidemi and Aziz (2022) Analysis of deterministic models for dengue disease transmission dynamics with vaccination perspective in Johor, Malaysia https://doi.org/10.1007/s40819-022-01250-3
  48. Hsieh et al. (2007) Impact of travel between patches for spatial spread of disease 69(4) (pp. 1355-1375)
  49. Arino and Van den Driessche (2006) Disease spread in metapopulations 48(1) (pp. 1-12)
  50. van den Driessche and Watmough (2002) Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission 180(1) (pp. 29-48)
  51. Castillo-Chavez et al. (2002) On the computation of R0documentclass[12pt]{minimal}
  52. usepackage{amsmath}
  53. usepackage{wasysym}
  54. usepackage{amsfonts}
  55. usepackage{amssymb}
  56. usepackage{amsbsy}
  57. usepackage{mathrsfs}
  58. usepackage{upgreek}
  59. setlength{oddsidemargin}{-69pt}
  60. begin{document}$${cal{R} }_0$$end{document} and its role on global stability (pp. 229-250) Springer
  61. Pontryagin et al. (1962) Wiley
  62. Abidemi et al. (2019) Global stability and optimal control of dengue with two coexisting virus serotypes, MATEMATIKA: Malaysian 35(4) (pp. 149-170)
  63. Asamoah et al. (2020) Global stability and cost-effectiveness analysis of COVID-19 considering the impact of the environment: using data from Ghana
  64. Okyere et al. (2020) Analysis of Zika virus dynamics with sexual transmission route using multiple optimal controls
  65. Abidemi (2022) Optimal cost-effective control of drug abuse by students: insight from mathematical modeling https://doi.org/10.1007/s40808-022-01534-z
  66. Pantha et al. (2016) Optimal control applied in an anthrax epizootic model 23(4) (pp. 1-23)
  67. Fleming and Rishel (1975) Springer
  68. Ministry of Health Malaysia, MOH denggue mortality 2010–2015,
  69. http://www.data.gov.my
  70. , Accessed 12 February 2020
  71. Ministry of Health Malaysia, Health facts 2013,
  72. http://www.moh.gov.my
  73. , Accessed 15 March 2020
  74. Abidemi et al. (2020) Vaccination and vector control effect on dengue virus transmission dynamics: modelling and simulation
  75. Lenhart and Workman (2007) CRC Press
  76. Rector et al. (2005) Narosa Publishing House
  77. Abidemi et al. (2022) Assessing the dynamics of Lassa fever with impact of environmental sanitation: optimal control and cost-effectiveness analysis https://doi.org/10.1007/s40808-022-01624-y
  78. Abidemi et al. (2022) An explicit note on the existence theorem of optimal control problem