|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|
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
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.
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.
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).
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