• International Journal of Technology (IJTech)
  • Vol 11, No 8 (2020)

Simulation Modelling Application for Balancing Epidemic and Economic Crisis in the Region

Simulation Modelling Application for Balancing Epidemic and Economic Crisis in the Region

Title: Simulation Modelling Application for Balancing Epidemic and Economic Crisis in the Region
Aleksey I. Borovkov, Marina V. Bolsunovskaya, Aleksei M. Gintciak, Tatiana Ju. Kudryavtseva

Corresponding email:


Cite this article as:
Borovkov, A.I., Bolsunovskaya, M.V., Gintciak, A.M., Kudryavtseva, T.J., 2020. Simulation Modelling Application for Balancing Epidemic and Economic Crisis in the Region. International Journal of Technology. Volume 11(8), pp. 1579-1588

787
Downloads
Aleksey I. Borovkov Peter the Great St. Petersburg Polytechnic University, 29 Polytechnicheskaya st., St. Petersburg 195251, Russia
Marina V. Bolsunovskaya Peter the Great St. Petersburg Polytechnic University, 29 Polytechnicheskaya st., St. Petersburg 195251, Russia
Aleksei M. Gintciak Peter the Great St. Petersburg Polytechnic University, 29 Polytechnicheskaya st., St. Petersburg 195251, Russia
Tatiana Ju. Kudryavtseva Peter the Great St. Petersburg Polytechnic University, 29 Polytechnicheskaya st., St. Petersburg 195251, Russia
Email to Corresponding Author

Abstract
Simulation Modelling Application for Balancing Epidemic and Economic Crisis in the Region

The new coronavirus pandemic has had a major impact on worldwide economic development. Many infection-countering measures have imposed restrictions on economic activities. At the same time, economic sectors contribute unequally to both the disease’s spread and to regional economic development. This article proposes a method for assessing the consequences of partial regional isolation. Simulation modelling and system dynamics are applied to assess both epidemiological and economic consequences. The classic “Susceptible – Exposed – Infected – Recovered” disease spread model has been modified with the addition of a new group: Isolated Individuals. This modified model allows the size of the receptive population and the frequency of their contact to be regulated based on scenarios of partial regional isolation in the context of economic sectors. The model is tested in St. Petersburg. Changes in the region’s infected population are forecasted as the result of the establishment and removal of partial isolation measures in the context of individual economic sectors.

Economic crisis; Epidemic impact; Pandemic; Simulation modelling; System dynamics

Introduction

The new coronavirus (COVID-19) is an acute respiratory infection caused by the SARS-CoV-2 virus (Rothan and Byrareddy, 2020). On March 11, 2020, a worldwide pandemic of the disease was declared. This state continues today, greatly impacting all spheres of human activity (Berawi, 2020). Many researchers are now predicting global changes to modern society as consequences of the pandemic (Tisdell, 2020). These estimates are based on two main approaches: extrapolation of the current dynamics of the pandemic’s impact, and translation of the effects of previous epidemics and pandemics to the current situation.

Extrapolating the current pandemic’s impact on changes to modern society may not be appropriate because of the complexity of society as a system. Public relations in different spheres are characterized by their nonlinearity, the presence of feedback loops, inaccurate parameters, and openness and delays in system response. These factors mean that any extrapolations concerning current impact can only be applied in the very short term. It is impossible to estimate the absolute or relative magnitude of influence using this approach, as the pandemic is not yet over.

In the 21st century alone, society has experienced several global disease outbreaks: the SARS-CoV pandemic in 2002–2003, the H1N1 influenza pandemic in 2009–2010, and the Ebola epidemic in 2014–2016. These cases, however, are inferior to the current COVID-19 pandemic either in their globality (as in the case of Ebola) or lethality (as in the cases of H1N1 influenza and SARS-CoV). In addition, society has changed since these events: behavioral habits adopted by some of the population, for example, affect both the spread of the virus and acceptance of the pandemic’s impact. Still, some studies have potential usefulness in assessing the impact of the current pandemic (Smith et al., 2009).

Several studies report that the global economy has historically been most influenced by epidemics and pandemics. This is confirmed by current research (Baldwin and Weder, 2020). The named key factors of a pandemic’s impact on the economy are border closures, damage to global logistics, reduced consumption of specific goods, and the downtime of production capacity. Further, these consequences affect different economy sectors unevenly.

Some scientists (Fernandes, 2020) conclude that the damage to the economy and society from measures designed to counter the spread of a virus may exceed the damage caused by the disease itself. Consequently, there is a need to balance epidemiological and economic damage while strategies are being formed to counter regional spread.

On the one hand, government measures should sufficiently contain the incidence of a disease like COVID-19 at such a level that the healthcare system is able to cope with active patients. This capability requires sufficient medical personnel with the needed qualifications, certain quantities of specialized equipment, and the ready availability of personal protective equipment and consumable medical supplies. Stricter containment measures may also reduce the total number of cases and the number of active patients, thereby reducing the burden on the healthcare system. On the other hand, the measures countering the spread of the disease must be structured to reduce the pandemic’s negative economic (both global and regional) and societal impacts (Shirov, 2020).

Strict containment measures reduce the consumption of certain goods and services, increase the cost of production, and contribute to the forced downtime of enterprises. This makes the development and implementation of measures to counter the spread of COVID-19 an optimization task. Existing empirical approaches do not account for the complexity of the region as a system; as such, any results of management decisions based on an empirical approach are far from optimal values. This demonstrates the need for a scientific approach to assessing the epidemiological and economic implications of the various strategies designed to counter the regional spread of an infectious disease. To do so, this study proposes a model that allows regional morbidity dynamics to be predicted in relation to the results of partial isolation in the context of economic sectors.

Conclusion

This article develops a methodological apparatus of an SIR-class model for practical use in decision-making by regional leaders. The proposed modified SQEIR model allows researchers to form proposals and test hypotheses concerning the different control measures adopted in specific regions.

According to the proposed approach, various strategies for countering the spread of an infectious disease in an economic context are transformed into a modelling scenario. The series of simulation experiments produce a predictive series that can describe the dynamics of the spread of an infectious disease. Based on these data series, it is possible to estimate the potential mortality in a given region, the burden on the healthcare system, and the economic damage likely to result.

The proposed approach may be used to estimate the spread of COVID-19 infection in 10 regions of the Russian Federation, including Moscow and St. Petersburg, from March 2020 to the present (December 2020). Governments may then use these modeling results to form infection-countering strategies. It is hoped that this model will help decision-makers find a balance between the negative effects of the virus’ spread and the economic damages resultant from countermeasures.


Acknowledgement

This research is partially funded by the Ministry of Science and Higher Education of the Russian Federation as part of the World-Class Research Center Program: Advanced Digital Technologies (contract No. 075-15-2020-934 dated 17.11.2020). The research is funded by the Russian Science Foundation (project No. 20-78-10123).


References

Baldwin, R.E., Weder, B., 2020. Economics in the Time of COVID-19. Centre for Economic Policy Research. Available Online at https://voxeu.org/system/files/epublication/COVID-19.pdf, Accessed on November 15, 2020

Berawi, M.A., 2020. Empowering Healthcare, Economic, and Social Resilience during Global Pandemic Covid-19. International Journal of Technology, Volume 11(3), pp. 436–439

Currie, C.S.M., Fowler, J.W., Kotiadis, K., Monks, T., Onggo, B.S., Robertson, D.A., Tako, A.A., 2020. How Simulation Modelling Can Help Reduce the Impact of COVID-19. Journal of Simulation, Volume 14(2), pp. 83–97

Fernandes, N., 2020. Economic Effects of Coronavirus Outbreak (COVID-19) on the World Economy. IESE Business School Working Paper No. WP-1240-E, Available Online at https://ssrn.com/abstract=3557504 or http://dx.doi.org/10.2139/ssrn.3557504

Giordano, G., Blanchini, F., Bruno, R., Colaneri, P., Di Filippo, A., Di Matteo, A., Colaneri, M., 2020. Modelling the COVID-19 Epidemic and Implementation of Population-wide Interventions in Italy. Nature Medicine, Volume 26, pp. 855–860

Husin, A.E., Berawi, M.A., Dikun, S., Ilyas, T., Berawi, A.R.B., 2015. Forecasting Demand on Mega Infrastructure Projects: Increasing Financial Feasibility. International Journal of Technology, Volume 6(1), pp. 73–83

Lalwani, S., Sahni, G., Mewara, B., Kumar, R., 2020. Predicting Optimal Lockdown Period with Parametric Approach using Three-phase Maturation SIRD Model for COVID-19 Pandemic. Chaos, Solitons & Fractals, Volume 138, pp. 1–8

Lauer, S.A., Grantz, K.H., Bi, Q., Jones, F.K., Zheng, Q., Meredith, H.R., Azman, A.S., Reich, N.G., Lessler, J., 2020. The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application. Annals of Internal Medicine, Volume 172(9), pp. 577–582

Li, D., Liu, S., Cui, J., 2017. Threshold Dynamics and Ergodicity of an SIRS Epidemic Model with Markovian Switching. Journal of Differential Equations, Volume 263(12), pp. 8873–8915

Li, M.Y., Graef, J.R., Wang, L., Karsai, J., 1999. Global Dynamics of a SEIR Model with Varying Total Population Size. Mathematical Biosciences, Volume 160(2), pp. 191–213

Ma, W., Song, M., Takeuchi, Y., 2004. Global Stability of an SIR Epidemic Model with Time Delay. Applied Mathematics Letters, Volume 17(10), pp. 1141–1145

Mecoli, M., De Angelis, V., Brailsford, S.C., 2013. Using System Dynamics to Evaluate Control Strategies for Mosquito-Borne Diseases Spread by Human Travel. Computers & Operations Research, Volume 40(9), pp. 2219–2228

Osthus, D., Hickmann, K.S., Caragea, P.C., Higdon, D., Del Valle, S.Y., 2017. Forecasting Seasonal Influenza with a State-Space SIR Model. The Annals of Applied Statistics, Volume 11(1), pp. 202–224

Qi, H., Liu, L., Meng, X., 2017. Dynamics of a Nonautonomous Stochastic SIS Epidemic Model with Double Epidemic Hypothesis. Complexity, Volume 2017, pp. 1–14

Redko, S.G., Tsvetkova, N.A., Seledtsova, I.A., Golubev, S.A., 2020. Systematic Approach to Education of Specialists for a New Technological Paradigm. In: Arseniev, D.G., Overmeyer, L., Kälviäinen, H., Katalini?, B., (Eds.), Cyber-Physical Systems and Control, Springer International Publishing, Cham, pp. 643–650

Rodi?, B., 2017. Industry 4.0 and the New Simulation Modelling Paradigm. Organizacija, Volume 50(3), pp. 193–207

Rothan, H.A., Byrareddy, S.N., 2020. The Epidemiology and Pathogenesis of Coronavirus Disease (COVID-19) Outbreak. Journal of Autoimmunity, Volume 109, pp. 1–4

Shirov, A.A., 2020. Statistics for the Benefit of Economics and Society. Studies on Russian Economic Development, Volume 31, pp. 3–6

Silva, P.C.L., Batista, P.V.C., Lima, H.S., Alves, M.A., Guimarães, F.G., Silva, R.C.P., 2020. COVID-ABS: An Agent-Based Model of COVID-19 Epidemic to Simulate Health and Economic Effects of Social Distancing Interventions. Chaos, Solitons & Fractals, Volume 139, pp. 1–15

Smith, R.D., Keogh-Brown, M.R., Barnett, T., Tait, J., 2009. The Economy-Wide Impact of Pandemic Influenza on the UK: A Computable General Equilibrium Modelling Experiment. BMJ, Volume 339, pp. b4571–b4571

Tisdell, C.A., 2020. Economic, Social and Political Issues Raised by the COVID-19 Pandemic. Economic Analysis and Policy, Volume 68, pp. 17–28

Tsvetkova, N.A., Tukkel, I.L., Ablyazov, V.I., 2017. Simulation Modeling the Spread of Innovations. In: 2017 XX IEEE International Conference on Soft Computing and Measurements (SCM), St. Petersburg, pp. 675–677

Upachaban, T., Khongsatit, K., Radpukdee, T., 2016. Mathematical Model and Simulation Study of a Closed-Poultry House Environment. International Journal of Technology, Volume 7(7), pp. 1246–1252

Upadhyay, R.K., Roy, P., 2014. Spread of a Disease and its Effect on Population Dynamics in an Eco-Epidemiological System. Communications in Nonlinear Science and Numerical Simulation, Volume 19(12), pp. 4170–4184