Skip to main content
Log in

A novel attempt to describe the impact of infectious disease on the nation’s economy: an illustration through the Econo-epidemics model

  • Regular Article
  • Published:
The European Physical Journal Plus Aims and scope Submit manuscript

Abstract

The impacts of pandemic situation on the health and economy of any nation are indeed significant. Several paradigms are present, where the nations’s economy has stumbled due to impact of infectious diseases. Hence, we formulate an econo-epidemics model to study economic impact of infectious disease. The structure is amalgamated with Solow growth functions to describe effect of epidemiological factors on economy. Numerous numerical experiments, viz. bifurcation analysis, production of capital growth profile, etc., is conducted to provide valuable insights into the potential outcomes and dynamics of the economy. Additionally, we consider the impact of demographic stochasticity on the economy of third-tier countries, Bangladesh and Tanzania, as these nations face unique challenges and vulnerabilities. Enumeration of probability of economic losses in upcoming years and stationary distributions on the capital growth data of those countries provide an outline of strengthening the economy after a period of depreciation caused by an infectious disease outbreak. Overall, the statistical experiments and analysis of capital data provide valuable information about the potential economic consequences of infectious diseases. This knowledge can guide the formulation of policies and schemes to mitigate economic disasters in the future and promote a resilient economy that is better prepared to handle pandemics.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Data availability statement

No Data are associated in the manuscript.

References

  1. F. Karim, S. Chauhan, J. Dhar, Analyzing an epidemic-economic model in the presence of novel corona virus infection: capital stabilization, media effect, and the role of vaccine. The European Physical Journal Special Topics. 1–18 (2022)

  2. R. Blundell, L. Pistaferri, I. Saporta-Eksten, Consumption inequality and family labor supply. Am. Econ. Rev. 106(2), 387–435 (2016)

    Article  Google Scholar 

  3. A.K. Dutt, Aggregate demand, aggregate supply and economic growth. Int. Rev. Appl. Econ. 20(3), 319–336 (2006)

    Article  Google Scholar 

  4. D.J. Gubler, The economic burden of dengue. Am. J. Trop. Med. Hyg. 86(5), 743 (2012)

    Article  Google Scholar 

  5. M.A. Mikheeva, I.V. Mikheeva, Ranking dynamics of economic burden of infectious diseases as a criterion of effectiveness of epidemiologic control. J. Microbiol. Epidemiol. Immunobiol. 97(2), 174–181 (2020)

    Article  Google Scholar 

  6. J.M. Kirigia, L.G. Sambo, A. Yokouide, E. Soumbey-Alley, L.K. Muthuri, D.G. Kirigia, Economic burden of cholera in the WHO African region. BMC Int. Health Human Rights 9, 1–14 (2009)

    Google Scholar 

  7. A. Paul, S. Reja, S. Kundu, S. Bhattacharya, COVID-19 pandemic models revisited with a new proposal: Plenty of epidemiological models outcast the simple population dynamics solution. Chaos, Solitons & Fractals. 144, 110697 (2021)

    Article  MathSciNet  Google Scholar 

  8. W.H. Organization et al., Guidelines for cholera control (World Health Organization, 1993)

    Google Scholar 

  9. S. Hosek, A. Pettifor, HIV prevention interventions for adolescents. Current HIV/AIDS Reports. 16, 120–128 (2019)

    Article  Google Scholar 

  10. M. Nicola, Z. Alsafi, C. Sohrabi, A. Kerwan, A. Al-Jabir, C. Iosifidis et al., The socio-economic implications of the coronavirus pandemic (COVID-19): a review. Int. J. Surg. 78, 185–193 (2020)

    Article  Google Scholar 

  11. L. Thunström, S.C. Newbold, D. Finnoff, M. Ashworth, J.F. Shogren, The benefits and costs of using social distancing to flatten the curve for COVID-19. J. Benefit-Cost Anal. 11(2), 179–195 (2020)

    Article  Google Scholar 

  12. V. Mogasale, V.V. Mogasale, A. Hsiao, Economic burden of cholera in Asia. Vaccine 38, A160–A166 (2020)

    Article  Google Scholar 

  13. D. Ganesan, S.S. Gupta, D. Legros, Cholera surveillance and estimation of burden of cholera. Vaccine 38, A13–A17 (2020)

    Article  Google Scholar 

  14. C.B. Barrett, J.G. McPeak, Poverty traps and safety nets. In: Poverty, inequality and development. Springer; pp. 131–154 (2006)

  15. M.M. Pluciński, C.N. Ngonghala, M.H. Bonds, Health safety nets can break cycles of poverty and disease: a stochastic ecological model. J. Royal Soc. Interf. 8(65), 1796–1803 (2011)

    Article  Google Scholar 

  16. M.H. Bonds, D.C. Keenan, P. Rohani, J.D. Sachs, Poverty trap formed by the ecology of infectious diseases. Proc. Royal Soc. B Biol. Sci. 277(1685), 1185–1192 (2010)

    Article  Google Scholar 

  17. M.H. Bonds, A.P. Dobson, D.C. Keenan, Disease ecology, biodiversity, and the latitudinal gradient in income. PLoS Biol. 10(12), e1001456 (2012)

    Article  Google Scholar 

  18. C. Perrings, C. Castillo-Chavez, G. Chowell, P. Daszak, E.P. Fenichel, D. Finnoff et al., Merging economics and epidemiology to improve the prediction and management of infectious disease. EcoHealth 11, 464–475 (2014)

    Article  Google Scholar 

  19. S. Chen, K. Prettner, M. Kuhn, D.E. Bloom, The economic burden of COVID-19 in the United States: estimates and projections under an infection-based herd immunity approach. J. Econ. Age. 20, 100328 (2021)

    Article  Google Scholar 

  20. U. Goldsztejn, D. Schwartzman, A. Nehorai, Public policy and economic dynamics of COVID-19 spread: a mathematical modeling study. PloS One 15(12), e0244174 (2020)

    Article  Google Scholar 

  21. G. Rasul, A. Nepal, A. Hussain, A. Maharjan, S. Joshi, A. Lama, et al., Socio-economic implications of COVID-19 pandemic in South Asia: emerging risks and growing challenges. Front. Sociol. 6

  22. X. Liu, Y. Takeuchi, S. Iwami, SVIR epidemic models with vaccination strategies. J. Theor. Biol. 253(1), 1–11 (2008)

    Article  ADS  MathSciNet  Google Scholar 

  23. R.M. Solow, A contribution to the theory of economic growth. Q. J. Econ. 70(1), 65–94 (1956)

    Article  Google Scholar 

  24. C.W. Cobb, P.H. Douglas, A theory of production. (1928)

  25. J. Robinson, The production function and the theory of capital. Rev. Econ. Stud. 21(2), 81–106 (1953)

    Article  Google Scholar 

  26. R.C. Griffin, J.M. Montgomery, M.E. Rister, Selecting functional form in production function analysis. Western J. Agric. Econ.. 216–227 (1987)

  27. D.J. Haw, C. Morgenstern, G. Forchini, R. Johnson, P. Doohan, P.C. Smith et al., Data needs for integrated economic-epidemiological models of pandemic mitigation policies. Epidemics 41, 100644 (2022)

    Article  Google Scholar 

  28. B. Sandelin, On the origin of the Cobb-Douglas production function. Econ. History 19(2), 117–123 (1976)

    Article  Google Scholar 

  29. S. Djilali, S. Bentout, Global dynamics of SVIR epidemic model with distributed delay and imperfect vaccine. Res. Phys. 25, 104245 (2021)

    Google Scholar 

  30. C. Giannitsarou, S. Kissler, F. Toxvaerd, Waning immunity and the second wave: Some projections for SARS-CoV-2. Am. Econ. Rev. Insights 3(3), 321–38 (2021)

    Article  Google Scholar 

  31. D. Acemoglu, V. Chernozhukov, I. Werning, M.D. Whinston et al., A multi-risk SIR model with optimally targeted lockdown, vol. 2020 (National Bureau of Economic Research Cambridge, MA, 2020)

    Book  Google Scholar 

  32. K. Prettner, A note on the implications of automation for economic growth and the labor share. Macroecon. Dyn. 23(3), 1294–1301 (2019)

    Article  Google Scholar 

  33. M. Martcheva, An introduction to mathematical epidemiology, vol. 61 (Springer, 2015)

    Book  Google Scholar 

  34. C.W. Castillo-Garsow, C. Castillo-Chavez, A tour of the basic reproductive number and the next generation of researchers. In: An Introduction to Undergraduate Research in Computational and Mathematical Biology. Springer; pp. 87–124 (2020)

  35. S. Marino, I.B. Hogue, C.J. Ray, D.E. Kirschner, A methodology for performing global uncertainty and sensitivity analysis in systems biology. J. Theor. Biol. 254(1), 178–196 (2008)

    Article  ADS  MathSciNet  Google Scholar 

  36. S. Reja, S. Ghosh, I. Ghosh, A. Paul, S. Bhattacharya, Investigation and control strategy for canine distemper disease on endangered wild dog species: a model-based approach. SN Appl. Sci. 4(6), 1–20 (2022)

    Article  Google Scholar 

  37. B.N. Ashraf, J.W. Goodell, COVID-19 social distancing measures and economic growth: distinguishing short-and long-term effects. Fin. Res. Lett. 47, 102639 (2022)

    Article  Google Scholar 

  38. R. Zhang, Y. Li, A.L. Zhang, Y. Wang, M.J. Molina, Identifying airborne transmission as the dominant route for the spread of COVID-19. Proc. Nat. Acad. Sci. 117(26), 14857–14863 (2020)

    Article  ADS  Google Scholar 

  39. E. Blum, The Mathematical Theory of Optimal Processes. JSTOR

  40. S. Lenhart, J.T. Workman, Optimal control applied to biological models (Chapman and Hall/CRC, 2007)

    Book  Google Scholar 

  41. P. Panja, Optimal control analysis of a cholera epidemic model. Biophys. Rev. Lett. 14(01), 27–48 (2019)

    Article  Google Scholar 

  42. B. Saha, A.R. Bhowmick, J. Chattopadhyay, S. Bhattacharya, On the evidence of an Allee effect in herring populations and consequences for population survival: a model-based study. Ecol. Model. 250, 72–80 (2013)

    Article  Google Scholar 

  43. A. Sau, B. Saha, S. Bhattacharya, An extended stochastic Allee model with harvesting and the risk of extinction of the herring population. J. Theor. Biol. 503, 110375 (2020)

    Article  MathSciNet  Google Scholar 

  44. S. Engen, R. Lande, B.E. Sæther, H. Weimerskirch, Extinction in relation to demographic and environmental stochasticity in age-structured models. Math. Biosci. 195(2), 210–227 (2005)

    Article  MathSciNet  Google Scholar 

  45. B.S. Ho, K.M. Chao, On the influenza vaccination policy through mathematical modeling. Int. J. Inf. Dis. 98, 71–79 (2020)

    Article  Google Scholar 

  46. T. Alamo, P. Millán, D.G. Reina, V.M. Preciado, G. Giordano, Challenges and future directions in pandemic control. IEEE Control Syst. Lett. 6, 722–727 (2021)

    Article  Google Scholar 

  47. C. Modi, V. Böhm, S. Ferraro, G. Stein, U. Seljak, Estimating COVID-19 mortality in Italy early in the COVID-19 pandemic. Nat. Commun. 12(1), 2729 (2021)

    Article  ADS  Google Scholar 

  48. W. Zhang, X. Shi, A. Huang, G. Hua, R.H. Teunter, Optimal stock and capital reserve policies for emergency medical supplies against epidemic outbreaks. Eur. J. Operat. Res. 304(1), 183–191 (2023)

    Article  MathSciNet  Google Scholar 

  49. X. Chen, W.F. Chong, R. Feng, L. Zhang, Pandemic risk management: resources contingency planning and allocation. Insurance: Math. Econ. 101, 359–383 (2021)

    MathSciNet  Google Scholar 

  50. M. Barnett, G. Buchak, C. Yannelis, Epidemic responses under uncertainty. Proc. Nat. Acad. Sci. 120(2), e2208111120 (2023)

    Article  Google Scholar 

  51. M. Chinazzi, J.T. Davis, M. Ajelli, C. Gioannini, M. Litvinova, S. Merler et al., The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak. Science 368(6489), 395–400 (2020)

    Article  ADS  Google Scholar 

  52. S. Devi, Travel restrictions hampering COVID-19 response. The Lancet 395(10233), 1331–1332 (2020)

    Article  Google Scholar 

  53. A. Paul, N. Ghosh, S. Bhattacharya, Estimation of the present status of the species based on the theoretical bounds of environmental noise intensity: an illustration through a big abundance data and simulation. Theor. Ecol. 15(3), 245–266 (2022)

    Article  Google Scholar 

  54. R Core Team.: R: A Language and Environment for Statistical Computing. Vienna, Austria. Available from: https://www.R-project.org/

  55. O.K. Jackob, O. Akinyi, F. Tireito et al., A mathematical model on the dynamics of in-host infection cholera disease with vaccination. discrete Dynamics in Nature and Society. 2023; (2023)

  56. G. Brankston, L. Gitterman, Z. Hirji, C. Lemieux, M. Gardam, Transmission of influenza A in human beings. The Lancet Infect. Dis. 7(4), 257–265 (2007)

    Article  Google Scholar 

  57. M. Shamim, IT skills development project and economic development in Bangladesh. Acad. J. Digital Econ. Stab.. 19(7), 13–21 (2022)

    Google Scholar 

  58. M. Epaphra, J. Massawe, Investment and economic growth: an empirical analysis for Tanzania. Turkish Econ. Rev. 3(4), 578–609 (2016)

    Google Scholar 

  59. G.I. Bischi, F. Grassetti, E.J.S. Carrera, On the economic growth equilibria during the Covid-19 pandemic. Commun. Nonlinear Sci. Numer. Simul. 112, 106573 (2022)

    Article  MathSciNet  Google Scholar 

  60. F. Grassetti, G. Hunanyan, C. Mammana, E. Michetti, A note on the influence of saving behaviors on economic growth. Metroeconomica 70(3), 442–457 (2019)

    Article  Google Scholar 

  61. A. Paul, N. Chatterjee, S. Bhattacharya, Revisiting and redefining return rate for determination of the precise growth status of a species. J. Biol. Phys. 49(2), 195–234 (2023)

    Article  Google Scholar 

Download references

Acknowledgements

The author Sudipta Panda is thankful to the University Grant Commission (UGC), India (Grant Number: 201610153745) Department of Science and Technology, Government of India, for supporting the fellowship to develop this work. The author Ayan Paul is also thankful to the Department of Science and Technology, Government of India, i.e., DST INSPIRE (Grant Number: IF180793), for supporting the fellowship throughout this work. However, we are also grateful to Dr. Prabir Panja from the Haldia Institute of Technology for giving his valuable insight on developing the section 2.3. We are also thankful to both of our learned reviewers for their comments to improve the quality of the manuscript.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ayan Paul.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file 1 (pdf 778 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Panda, S., Paul, A., Chattopadhyay, A. et al. A novel attempt to describe the impact of infectious disease on the nation’s economy: an illustration through the Econo-epidemics model. Eur. Phys. J. Plus 139, 286 (2024). https://doi.org/10.1140/epjp/s13360-024-05066-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1140/epjp/s13360-024-05066-6

Navigation