JOURNAL OF GLOBAL TRADE, ETHICS AND LAW
Volume 3 Issue 1, 2025
ASSESSING THE IMPACT OF COVID-19 ON THE
CHINESE ECONOMY:
A COMPREHENSIVE ANALYSIS
Mario Arturo Ruiz Estrada,1* Evangelos Koutronas,2 Donghyun Park,3
Minsoo Lee,4
1 Business School, University of Kuala Lumpur, Kuala Lumpur, Kuala Lumpur, mario.arturo@unikl.edu.my
And Economics, Vizja University, Warsaw, Poland, m.estrada@vizja.pl
2 Department of Economics, School of Business & Economics, Westminster International University in Tashkent
Tashkent 100047, Uzbekistan, ekoutronas@wiut.uz
34 Economic Research and Regional Cooperation Department (ERCD), Asian Development Bank (ADB), Manila,
Philippines, 3dpark@adb.org. 4mlee@adb.org
*Corresponding Author
Abstract. This paper examines the macroeconomic impact of the COVID-19 shock
on the Chinese economy through a multidimensional policy-modeling framework.
The Massive Contagious Infectious Diseases Simulator (ECMCID-Simulator)
employs macroeconomic indicators to examine how asymmetric infection dynamics
propagate economic shocks across activity, labor, financial markets, investment
flows, and aggregate output within strategic economic sectors during 2019–2021.
The ECMCID-Simulator is constructed under the Omnia Mobilis assumption and
interpreted through the Dynamic Imbalanced State framework. Key findings reveal
pronounced heterogeneity in economic responses, with sharp contractions in contact-
intensive activities alongside relative resilience in essential production systems. The
ECMCID-Simulator conceptualizes pandemic-induced economic disruptions as
dynamic economic waves rather than static shocks. The ECMCID-Simulator offers
policy-relevant insights into the management of systemic economic risk and the
design of targeted stabilization strategies under conditions of extreme uncertainty.
Keywords: COVID-19, Chinese economy, ECMCID-Simulator,
Policy Modeling, Econographicology.
JEL: F1, O10, O17, O40, O47.
© 2025 Durham & Thunmann, London
Journal of Global Trade, Ethics and Law
ISSN 2977-0025 (Online). Published under Creative Common (CC) BY 4.0 license.
DOI
Durham &
Thunmann
Mario Arturo Ruiz Estrada et al., 2025
87
1. Introduction
In December 2019, a novel coronavirus disease (COVID-19) was first identified in
Wuhan, China, rapidly evolving into a global pandemic with unprecedented health and
economic consequences. As of 31 March 2024, over 774 million confirmed cases and
more than seven million deaths had been reported globally to the World Health
Organization (World Health Organization 2024). The pandemic surpassed in scale and
speed earlier major coronavirus outbreaks, such as SARS (2002–2003), which involved
8,096 probable cases and 774 deaths (World Health Organization 2003), and MERS
(since 2012), with over 2,600 confirmed cases and an approximately 37% case fatality
ratio (World Health Organization 2025). In doing so, it exposed long-standing structural
vulnerabilities and capacity limitations in health systems (Legido-Quigley et al. 2020;
World Health Organization 2020). The COVID-19 pandemic revealed the ‘Achilles' heel’
of modern health systems: structural deficits in surge capacity, fragmented decision-
making, supply-chain resilience, and crisis coordination. In principle, the health systems’
responsiveness appeared efficient under normal conditions but proved ill-prepared for a
prolonged, high-intensity shock, particularly under high-transmission scenarios (World
Health Organization 2020). The unprecedented scale and duration of COVID-19
transformed these vulnerabilities into economic stress through labor shortages, mobility
restrictions, and disruptions to essential services, reinforcing the tight interdependence
between public health capacity and macroeconomic stability.
Consequentially, governments across major regions implemented large-scale
containment measures, including lockdowns, travel restrictions, and temporary
shutdowns of economic activity, which were adversely affected by both demand
contraction and disruptions in global supply chains (Deb et al. 2022; Musella 2023;
United Nations Conference on Trade and Development 2020; Xu et al. 2020).
Furthermore, consumption, retail trade, and contact-intensive service industries were
particularly affected as consumer confidence deteriorated and mobility collapsed. Travel
bans and flight cancellations severely disrupted international tourism and air
transportation. In contrast, international trade was adversely affected through
synchronized demand contraction and global supply chain breakdowns centered on
China's manufacturing hubs. Unlike conventional recessionary episodes, these concurrent
demand–supply shocks generated inflationary pressures in selected sectors despite
collapsing economic activity, giving rise to what has been conceptualized as
stagpression—a novel economic condition characterized by the overlap of recessionary
dynamics with structural economic degradation and heightened uncertainty, limiting the
effectiveness of traditional monetary and fiscal stabilization tools (Ruiz Estrada et al.
2021). The transformation of the public health emergency into a systemic economic shock
triggered one of the most severe, synchronized global economic contractions in modern
history.
This paper proposes a complementary policy-modeling framework to analyze the
economic consequences of large-scale infectious disease outbreaks. The Economic Crisis
from Massive Contagious Infectious Diseases Simulator (ECMCID-Simulator)
investigates how pandemic-induced shocks affect four strategic sectors of the economy –
Mario Arturo Ruiz Estrada et al., 2025
88
tourism, international trade, air transportation, and electricity consumption – through key
macroeconomic channels, including economic activity, labor market conditions, financial
market performance, foreign direct investment, and aggregate output. The ECMCID-
Simulator analyzes the dynamic propagation of pandemic-induced shocks and their
cumulative socio-economic effects under conditions of extreme uncertainty. Developed
within a multidimensional coordinate space, it captures behavioral change, systemic risk,
and nonlinear shock transmission within a Dynamic Imbalanced State (DIS) framework
(Ruiz Estrada and Yap 2013) and under the Omnia Mobilis assumption (Ruiz Estrada
2011). On this basis, the ECMCID-Simulator generates policy-relevant insights for crisis
management strategies that aim to mitigate economic damage while preserving systemic
resilience.
The paper is organized as follows. Section 2 offers an overview of the relevant
literature. Section 3 describes the underlying model. Section 4 presents the model across
seven historical periods, providing graphical results and their corresponding
interpretations. The discussion then situates these findings within broader geopolitical
and economic trends, while the conclusion summarizes the study's contributions, outlines
policy implications, and suggests directions for future research. Section 5 summarizes
and provides policy recommendations.
2. Relevant literature
Pandemics have periodically shaped human societies, producing profound
demographic, social, and economic disruptions. Historical evidence documents a
succession of major outbreaks, including the Plague of Athens (430–426 BC), the
Antonine Plague (165–180), the Justinian Plague (541-750), the Black Death (1347-
1351), and the Spanish Influenza (1918–1920) (Huremović 2019). The aforementioned
events serve as benchmark episodes for understanding, managing, and preparing for
systemic health shocks. However, recent outbreaks have generated observable disruptions
across output, labor markets, trade, tourism, and financial systems, allowing for empirical
investigation of pandemic-induced economic shocks (Baldwin and Weder di Mauro 2020;
Lee and McKibbin 2004; McKibbin and Sidorenko 2006; World Bank 2014). During the
2003 SARS outbreak, estimates suggest that GDP declined by approximately 1 percent
in China and around 0.5 percent across affected Southeast Asian economies, while global
macroeconomic losses were estimated to range between USD 40 and 54 billion. The crisis
severely disrupted regional tourism activity, with international arrivals declining by
roughly 41 percent and an estimated 3 million jobs in tourism-related sectors affected
(Brahmbhatt and Dutta 2008; McKercher and Chon 2004). The 2009 H1N1 influenza
pandemic similarly generated substantial tourism-related revenue losses in Mexico,
where approximately 1 million fewer overseas visitors resulted in estimated losses of
about USD 2.8 billion (Rassy and Smith 2013). The 2014 Ebola epidemic led to sharp
economic contractions in Guinea, Liberia, and Sierra Leone, with estimated cumulative
GDP losses of around USD 2.2 billion by 2015, driven primarily by declines in private-
Mario Arturo Ruiz Estrada et al., 2025
89
sector activity, trade, and agricultural production (World Bank 2014). These episodes
consistently reveal sharp short-term contractions and heterogeneous medium-term
recovery patterns, particularly in highly interconnected and mobility-dependent
economies.
While earlier pandemics reshaped economic structures through population collapse and
long-term adjustments in labor and productivity, modern pandemics increasingly operate
through highly integrated global systems, amplifying their economic impact via mobility
restrictions, supply-chain interruptions, and financial market instability. The historical
record thus illustrates that pandemics are not isolated health events but systemic shocks
whose economic consequences evolve with the degree of economic integration and
institutional complexity.
3. The Economic Crisis from Massive Contagious Infection
Diseases Simulator (ECMCID-Simulator)
Consider a multidimensional, interlinked coordinate space constructed as the Cartesian
product of the individual n-dimensional strategy spaces of n players. The formation of
this interlinked coordinate system is based on Ruiz Estrada's (2017) Econographicology
framework. An n-dimensional state vector captures the cumulative effects of prior
strategic interactions in each dimension. Within this framework, n-dimensional
equilibrium points represent both symmetric and asymmetric viral behaviors occurring
simultaneously across space and time. The projection of these n-dimensional state vectors
onto a multidimensional Euclidean n-sphere manifold embedded in a Euclidean (n+1)-
dimensional space provides a richer geometric representation of system dynamics. This
multidimensional formulation jointly represents endogenous and exogenous variables
and captures the full complexity of simultaneous strategic interactions and economic
dynamics across varying spatial and temporal configurations. Conventional two-
dimensional Euclidean representations cannot adequately capture these dynamics.
The ECMCID-Simulator consists of four strategic economic sectors, denoted ,
which collectively represent the principal transmission channels of pandemic-induced
economic shocks. Each strategic sector is modeled as a general axis originating from a
common epicenter and structured by multiple interconnected sub-axes corresponding to
sector-specific economic components. Along each general axis, the model incorporates
multiple refraction windows (or quadrants), each defined by an X-axis representing time
(years) and a Y-axis representing the primary variable(s) under consideration. These sub-
axes are linearly linked along the general axis, allowing the simulator to capture the
cumulative and sequential escalation of pandemic shocks within and across sectors.
Mario Arturo Ruiz Estrada et al., 2025
90
Figure 1: The ECMCID-Simulator Coordinate System
Source: Authors' elaboration
First-order partial differentiation equations calculate the behavior of each sub-axis,
in real time (☼), allowing the simulator to capture instantaneous movements and
adjustments within the same graphical and analytical space. All sub-axes are connected
to their corresponding general axis through an interlinking sub-axis system, "╬", ensuring
coherence across different layers of the model. The model then integrates all general axes
and sub-axes at a common level of analysis using four multidimensional vectors, each of
which simultaneously incorporates three first-order partial derivatives within the same
time period. This configuration enables the model to represent endogenous and
exogenous interactions dynamically rather than sequentially, and to avoid the restrictive
assumptions imposed by static or ceteris-paribus frameworks.
The model operates under the Omnia Mobilis assumption (Ruiz Estrada 2011; Ruiz
Estrada and Park 2018), which posits the continuous, simultaneous observation of all
variables in real time. This assumption allows the ECMCID-Simulator to generate a large,
evolving multidimensional surface that represents the aggregate economic response to a
contagious disease shock. The surface originates at the epicenter of the multidimensional
coordinate system and propagates outward along successive sub-axes within each general
axis. The terminal sub-axis assesses the economic significance of simulating each general
axis, as the cumulative impact of pandemic-driven disruptions fully materializes.
Analytical results can be derived either axis-by-axis, to assess sector-specific dynamics,
or by examining the entire multidimensional surface, which captures the simultaneous
and interconnected effects of the pandemic across the economic system.
Mario Arturo Ruiz Estrada et al., 2025
91
The empirical application focuses on the four strategic sectors of the Chinese economy
that were directly and indirectly affected by COVID-19 containment measures: tourism
growth rate, , air transportation growth rate, , international trade growth rate, ,
and electricity consumption growth rate, . These four strategic sectors were selected
based on their economic relevance, exposure to mobility restrictions to be examined in
connection with COVID-19 cases growth rate, . The former is the model's epicenter
and it is subject to dramatic, uncontrolled, and unpredictable fluctuations, including
periods of expansion, contraction, or stagnation (Ruiz Estrada and Yap 2013). A sharp
acceleration in the infection growth rate in China can trigger widespread, simultaneous
disruptions across all four sectors, with demand contraction constituting the dominant
transmission channel.
☼
☼
☼
☼
☼
☼
☼
☼
☼
☼
☼
☼
The model assumes that pandemic is materialized by economic waves. These waves
are represented by a large surface plotted within the same graphical space. Each general
axis, corresponding to a strategic economic sector, is decomposed into five sub-axes
representing key macroeconomic dimensions: the demand growth rate, ,
unemployment growth rate, , stock market performance growth rate, , foreign
direct investment growth rate, , and the contribution of each strategic sector to
China's GDP growth rate, . The calculation of follows Expression (2).
Mario Arturo Ruiz Estrada et al., 2025
92
Each variable is operationalized as a percentage deviation from its pre-pandemic
baseline, allowing for comparability across sectors and time. Each strategic-sector
variable corresponds to an observable macroeconomic indicator. Tourism growth is
measured using year-on-year changes in tourism revenues and passenger volumes. Air
transportation growth reflects annual changes in passenger traffic and flight activity.
International trade growth is based on the annual growth rate of total exports and imports
in value terms. Electricity consumption growth is measured using aggregate electricity
generation and usage statistics. Labor-market effects are proxied by unemployment
growth rates, financial-market performance by changes in major Chinese stock indices,
and investment dynamics by foreign direct investment inflows derived from balance-of-
payments statistics. All variables are expressed as percentage deviations from their pre-
pandemic baseline to ensure comparability across sectors and time.
Endemic infectious diseases trigger complex and nonlinear economic responses that
unfold across multiple channels and evolve over space and time. Outbreaks and epidemics
constitute rare, high-impact events whose economic effects are highly heterogeneous,
volatile, and context-dependent, varying across sectors, regions, and crisis phases. The
magnitude and duration of the health shock, the structural characteristics of the affected
economy, population density, geographic exposure, and timing all shape how economic
variables respond. While direct medical and hospitalization costs can be measured with
relative precision, the indirect economic effects—transmitted through trade, labor
markets, mobility, and investment—are inherently more difficult to quantify due to their
nonlinear interactions and overlapping transmission mechanisms. Partial differentiation
is therefore employed to isolate the marginal impact of changes in the infectious disease
shock on each economic dimension, allowing the ECMCID-Simulator to capture sector-
specific sensitivities and disentangle complex interdependencies without imposing
restrictive equilibrium assumptions.
When these marginal effects aggregate across dimensions and sectors, they generate
asymmetric fluctuations of differing periodicity that manifest as dynamic economic
waves. Such waves amplify throughout the economy and may produce a smash effect, a
sudden and systemic disruption triggered by low-probability, high-impact events (Ruiz
Estrada 2013). While Kondratieff wave theory (Kondratieff 1922; Schumpeter 1954)
characterizes long-run business cycle phases – expansion, peak, contraction, trough, and
Mario Arturo Ruiz Estrada et al., 2025
93
recovery – the smash effect captures crisis dynamics arising from improbable and
unpredictable events such as epidemics, natural disasters, or terrorist incidents. In
parallel, Black Swan theory (Taleb 2007) situates such events within a broader historical
and theoretical context, emphasizing their unpredictability and disproportionate
consequences. Despite its wide acceptance among finance professionals, Black Swan
theory offers limited analytical insight into the full spectrum of social, economic, cultural,
and environmental impacts of such shocks due to its lack of formal modeling structure.
The primary objective of the ECMCID-Simulator is not to forecast precise equilibrium
outcomes but to assess the relative magnitude, direction, and persistence of economic
disruptions under alternative pandemic scenarios, thereby supporting policy evaluation
and crisis management. Unlike equilibrium-based macroeconomic models, the ECMCID-
Simulator applies the Dynamic Imbalanced State (DIS) framework to represent the
economy as a system characterized by continuous instability, nonlinear adjustments, and
evolving behavioral responses. Consistent with the Omnia Mobilis assumption, all
variables in the system are allowed to adjust simultaneously over time, reflecting the
highly uncertain and rapidly changing conditions observed during pandemic episodes.
Within this framework, pandemic shocks are interpreted not as one-time static
disturbances but as dynamic economic waves that propagate across sectors and
macroeconomic channels.
The numerical values reported below do not represent observed sectoral outcomes in
China during 2020–2021. Instead, they correspond to stress-test scenarios generated by
the ECMCID-Simulator under hypothetical high-intensity pandemic waves, designed to
evaluate directional responses, relative magnitudes, and sectoral asymmetries.
4. Assessing the Impact of COVID-19 on 4 Strategic Sectors of the
Chinese Economy
This paper examines the macroeconomic impact of the COVID-19 shock on the
Chinese economic activity, labor, financial markets, investment flows, and aggregate
output, drawing on the empirical studies by Baldwin and Weder di Mauro 2020,
Eichenbaum, Rebelo, and Trabandt 2021, and Gereffi 2020. The analysis covers the
period 2019–2021, capturing pre-pandemic conditions, the initial infection shock, and the
early recovery phase. Data are compiled from multiple official and verifiable sources,
including the National Bureau of Statistics of China for macroeconomic indicators
(National Bureau of Statistics of China n.d.); the China Statistical Yearbook for
comprehensive annual data across sectors (National Bureau of Statistics of China 2024);
and the Civil Aviation Administration of China for air transport statistics (Civil Aviation
Administration of China n.d.). International economic indicators are drawn from the
World Bank Open Data Platform (World Bank 2025) and the International Monetary
Fund's statistical databases (International Monetary Fund 2025). Epidemiological data are
derived from WHO situation reports and the WHO COVID-19 dashboard, which provide
Mario Arturo Ruiz Estrada et al., 2025
94
confirmed case and mortality counts reported by Member States (World Health
Organization n.d.).
Tourism indicators include tourism revenues and passenger volumes; air transportation
data consist of passenger traffic and flight activity; international trade variables are based
on export and import volumes; and electricity consumption is measured using aggregate
electricity generation and usage statistics. Financial market performance is proxied by
major Chinese stock indices, while FDI flows are derived from official balance-of-
payments statistics. All data series are standardized and aligned temporally to ensure
internal consistency. Where high-frequency data are unavailable, annual or quarterly
averages are employed, and interpolation is avoided to preserve data integrity.
Computation is implemented in Wolfram Language (Mathematica 10) via a standardized
pipeline.
Conventional macro-stress tests rely on detailed sectoral balance sheets, bank-level
exposures, or high-frequency financial data. The proposed stress-test approach instead
focuses on macro-level transmission channels observable across countries and crises,
emphasizing robustness under data constraints. This framework aligns with earlier
pandemic-related macroeconomic analyses that prioritize scenario consistency and
structural plausibility over granular calibration (McKibbin and Sidorenko 2006; World
Bank 2014). The omission of institution-specific balance-sheet effects avoids overfitting
and maintains comparability across heterogeneous economic structures, particularly in
emerging and data-scarce contexts.
Study contribution lies in integrating epidemiological shocks, supply-chain
disruptions, and demand contractions within a unified macroeconomic simulation
environment. Prior studies typically isolate individual channels, such as trade disruptions
(United Nations Conference on Trade and Development 2020), tourism collapse
(McKercher and Chon 2004), or health-system pressure (World Health Organization
2020). The present model evaluates their joint amplification effects under adverse
scenarios. This holistic treatment enables the identification of nonlinear outcomes that are
not apparent in single-channel analyses and provides a transparent benchmark for policy
evaluation. The stress-test results should therefore be interpreted not as point forecasts,
but as structured counterfactuals that clarify the relative vulnerability of economic
systems to compound pandemic-type shocks, complementing rather than substituting
existing macro-financial stress-testing methodologies.
Figure 2 displays pandemic-induced economic wave dynamics affecting final output
contributions across four strategic sectors of the Chinese economy during 2020–2021.
The model algorithm aggregates 1,500 simulated values, produced by selecting five core
macro-sectoral variables, each decomposed into 300 sub-variables, yielding one
parameter per sub-variable. Each parameter is encoded using a binary activation rule (0/1)
that indicates whether a given sectoral or institutional condition is inactive or activated
under a specific stress configuration. These parameters are evaluated iteratively across 16
refraction windows, each representing a distinct temporal–structural configuration of the
shock environment. Within each refraction window, the model simultaneously applies
sector-specific percentage deviations from pre-pandemic baselines and computes
multidimensional interactions across variables to generate a vector of stress-test
Mario Arturo Ruiz Estrada et al., 2025
95
outcomes. Repeating this process across all refraction windows yields the full set of 1,500
simulated values. Consistent with prior applications of the inter-linkage coordinate space
framework (Ruiz Estrada and Koutronas 2016; Ruiz Estrada et al. 2021), all equations
are transformed into a single algorithmic structure and executed using deterministic and
fuzzy simulation routines, allowing visualization of extreme, yet internally consistent,
stress scenarios rather than observed outcomes.
A hypothetical 65% increase in aggregate consumption demand in China leads to
sectoral contributions to output growth across tourism, international trade, air
transportation, and electricity consumption. The magnitude of this shock is intentionally
simulated as an upper-bound stress scenario rather than a realistic forecast, allowing the
simulator to reveal nonlinear sectoral asymmetries and transmission mechanisms under
extreme but analytically informative conditions. The impact on the 4 sectors and their
aggregate impact on GDP will be also significant:
(i) In the tourism sector, demand is projected to contract by 75%, the
unemployment rate to rise to 10%, the stock market index to decline by 35%,
and foreign direct investment to fall by 25%. Consequently, this sector's
contribution to China's GDP growth remains relatively limited, at 0.3%.
(ii) In the international trade sector, demand falls by 40%, unemployment rises to
15%, stock market index drops by 25%, and FDI shrinks by 35%. The sector's
contribution to China's GDP growth is about 2.5%.
(iii) In the air transportation sector, demand is projected to decline sharply by 85%,
the unemployment rate to rise to 20%, the stock market index to contract by
45%, and foreign direct investment to fall by 40%. As a result, this sector's
contribution to China's GDP growth rate is minimal, at approximately 0.2%.
(iv) Finally, under the stress-test scenario, the electricity consumption sector
exhibits a simulated increase of 65%, with no increase in unemployment, a
55% gain in the stock market index, and a 50% rise in foreign direct
investment. This sector contributes approximately 1.5% to China's GDP
growth rate. Elevated electricity demand is partly attributable to extensive
quarantine measures and the surge in demand for health and related essential
services across China.
Mario Arturo Ruiz Estrada et al., 2025
96
Figure 2: The Application of ECMCID-Simulator in the Case of COVID-19
Source: World Development Indicators, World Bank (2025), International Financial Statistics, International
Monetary Fund (2025)
5. Comments and Remarks
This paper introduces a multidimensional policy-modeling framework to assess the
macroeconomic consequences of pandemic shocks under conditions of extreme
uncertainty. The ECMCID-Simulator enables the systematic exploration of alternative
scenarios and impact intensities, allowing simulations to evaluate the economic effects of
epidemic outbreaks at the national, regional, and global levels.
Simulation findings reveal pronounced sectoral heterogeneity in economic responses:
contact-intensive sectors, such as tourism and air transportation, experience sharp,
immediate contractions, while electricity consumption and other essential activities
display relative resilience. These asymmetric outcomes underscore the critical role of
sector-specific exposure to containment measures and mobility restrictions in shaping the
aggregate economic impact of pandemics.
The simulated sectoral responses are benchmarked against established empirical
estimates to ensure consistency with the literature on pandemic economics. For the
tourism sector, the stress-test contraction of approximately 75% exceeds observed
declines of roughly 50% during the COVID-19 pandemic in China and about 41% during
the SARS outbreak, positioning the simulation as an upper-tail scenario consistent with
extreme mobility restrictions (Brahmbhatt and Dutta 2008; McKercher and Chon 2004).
In air transportation, the 85% demand contraction aligns with the upper end of global
aviation shutdowns observed during early COVID-19 waves, when passenger volumes
declined by 60–90% across major markets. For international trade, the simulated 40%
contraction does not reflect China’s realized 2020 performance but represents a
Mario Arturo Ruiz Estrada et al., 2025
97
counterfactual synchronized global collapse scenario, consistent with stress-test
approaches used in earlier pandemic simulations (McKibbin and Sidorenko 2006; World
Bank 2014).
In contrast to contact-intensive sectors, electricity consumption exhibits relative
resilience in the simulation, reflecting the prioritization of essential services and critical
infrastructure during lockdowns. This pattern is consistent with official statistics showing
limited volatility in China’s aggregate electricity demand during COVID-19, despite
sharp sectoral reallocation between industrial and residential use (McKibbin and
Sidorenko 2006; National Bureau of Statistics of China 2024; World Bank 2025)
The ECMCID-Simulator contributes to the literature by offering a transparent, non-
equilibrium approach that emphasizes nonlinear dynamics, directional validity, and
robustness rather than econometric parameter estimation. By mapping sectoral shocks
into a multidimensional coordinate space, the framework enables intuitive visualization
of economic waves and facilitates systematic comparison across scenarios. Qualitative
validation against macro-epidemiological models and empirical studies confirms the
consistency of the simulator's results in terms of both sign and relative magnitude.
Detailed documentation of data sources, variable definitions, and computational steps
further ensures replicability and adaptability, while supporting the analysis of crisis
management and stabilization strategies.
Finally, these simulations support the design of policy responses aligned with specific
scenarios and positions along the economic wave cycle, ranging from broad
macroeconomic interventions to targeted, sector-specific measures for tourism,
international trade, air transportation, and electricity consumption. Ultimately, the
formulation and effectiveness of such policies depend on the institutional structure and
governance capacity of public management systems. While not intended as a closed-form
forecasting model, the ECMCID-Simulator provides policy-relevant insights into crisis
management and offers a flexible foundation for extending pandemic-impact analysis to
other countries and future systemic shocks.
Mario Arturo Ruiz Estrada et al., 2025
98
Funding
The research received no specific grant from any funding agency in the public, commercial, or
not-for-profit sectors.
Conflicts of interest/Competing interests
The authors state that there is no conflict of interests. The funders had no role in the design of
the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or
in the decision to publish the results.
Bibliography
Baldwin, Richard, and Beatrice Weder di Mauro, eds. 2020. Economics in the Time of COVID-19. CEPR
Press. https://cepr.org/publications/books-and-reports/economics-time-covid-19.
Brahmbhatt, Milan, and Arindam Dutta. 2008. On SARS Type Economic Effects during Infectious Disease
Outbreaks. Policy Research Working Paper no. 4466. World Bank.
https://openknowledge.worldbank.org/handle/10986/6451.
Civil Aviation Administration of China. n.d. “Statistics of Key Performance Indicators for China’s Civil
Aviation Industry (KPIs).” . https://www.caac.gov.cn/English/Research/Data/KPIS/.
Deb, P., D. Furceri, J. Ostry, and N. Tawk. 2022. “The Economic Effects of COVID-19 Containment
Measures.” Open Economies Review 33 (1): 1–32. https://doi.org/10.1007/s11079-021-09638-2.
Eichenbaum, Martin S., Sergio Rebelo, and Mathias Trabandt. 2021. “The Macroeconomics of Epidemics.”
Review of Financial Studies 34 (11): 5149–5187. https://doi.org/10.1093/rfs/hhab040.
Gereffi, Gary. 2020. “What Does the COVID-19 Pandemic Teach Us about Global Value Chains? The
Case of Medical Supplies.” Journal of International Business Policy 3 (3): 287–301.
https://doi.org/10.1057/s42214-020-00062-w.
Huremović, Damir. 2019. “Brief History of Pandemics (Pandemics throughout History).” In Psychiatry of
Pandemics: A Mental Health Response to Infection Outbreak, edited by Damir Huremović.
Springer. https://doi.org/10.1007/978-3-030-15346-5_2.
International Monetary Fund. 2025. International Financial Statistics (IFS). . https://data.imf.org/IFS.
Kondratieff, Nikolai D. 1922. The World Economy and Its Conjunctures during and after the War. Regional
Branch of the State Publishing House.
Lee, Jong-Wha, and Warwick J. McKibbin. 2004. “Globalization and Disease: The Case of SARS.” Asian
Economic Papers 3 (1): 113–131. https://doi.org/10.1162/1535351041747932.
Legido-Quigley, Helena, Nasim Asgari, Yi Yi Teo, et al. 2020. “Are High-Performing Health Systems
Resilient against the COVID-19 Epidemic?” The Lancet 395 (10227): 848–850.
https://doi.org/10.1016/S0140-6736(20)30551-1.
McKercher, Bob, and Kaye Chon. 2004. “The Over-Reaction to SARS and the Collapse of Asian Tourism.”
Annals of Tourism Research 31 (3): 716–719. https://doi.org/10.1016/j.annals.2003.11.002.
McKibbin, Warwick, and Alexandra Sidorenko. 2006. Global Macroeconomic Consequences of Pandemic
Influenza. Brookings Institution. https://www.brookings.edu/articles/global-macroeconomic-
consequences-of-pandemic-influenza/.
Musella, L. 2023. “The Impact of COVID-19 on Global Supply Chains.” Master’s thesis, Stockholm
University. https://www.diva-portal.org/smash/record.jsf?pid=diva2:1751221.
National Bureau of Statistics of China. 2024. China Statistical Yearbook 2024. China Statistics Press.
National Bureau of Statistics of China. n.d. National Data. . https://data.stats.gov.cn/english/.
Mario Arturo Ruiz Estrada et al., 2025
99
Rassy, D., and R. D. Smith. 2013. “The Economic Impact of H1N1 on Mexico’s Tourist and Pork Sectors.”
Health Economics 22 (7): 824–834. https://doi.org/10.1002/hec.2862.
Ruiz Estrada, Mario Arturo. 2011. “Multi-Dimensional Coordinate Spaces.” International Journal of
Physical Sciences 6 (3): 340–357. https://www.researchgate.net/publication/228300293_Multi-
Dimensional_Coordinate_Spaces.
Ruiz Estrada, Mario Arturo. 2013. “The Global Economic Crisis Smash Effect Simulator (GECSE-
Simulator).” International Journal of Economic Research 10 (2): 257–263.
https://serialsjournals.com/abstract/97314_4.pdf.
Ruiz Estrada, Mario Arturo. 2017. “An Alternative Graphical Modeling for Economics:
Econographicology.” Quality & Quantity 51 (5): 2115–2139. https://doi.org/10.1007/s11135-015-
0280-3.
Ruiz Estrada, Mario Arturo, V. G. R. Chandran, and M. Tahir. 2016. “An Introduction to the
Multidimensional Real-Time Economic Modeling.” Contemporary Economics 10 (1): 55–70.
https://doi.org/10.5709/ce.1897-9254.198.
Ruiz Estrada, Mario Arturo, and Evangelos Koutronas. 2016. “Terrorist Attack Assessment: Paris
November 2015 and Brussels March 2016.” Journal of Policy Modeling 38 (3): 553–571.
https://doi.org/10.1016/j.jpolmod.2016.04.001.
Ruiz Estrada, Mario Arturo, Evangelos Koutronas, and Minsoo Lee. 2021. “Stagpression: The Economic
and Financial Impact of the COVID-19 Pandemic.” Contemporary Economics 15 (1): 19–33.
https://doi.org/10.5709/ce.1897-9254.433.
Ruiz Estrada, Mario Arturo, and Donghyun Park. 2018. “The Past, Present and Future of Policy Modeling.”
Journal of Policy Modeling 40 (1): 1–15. https://doi.org/10.1016/j.jpolmod.2018.01.003.
Ruiz Estrada, Mario Arturo, and Su-Fei Yap. 2013. “The Origins and Evolution of Policy Modeling.”
Journal of Policy Modeling 35 (1): 170–182. https://doi.org/10.1016/j.jpolmod.2011.12.003.
Schumpeter, Joseph A. 1954. History of Economic Analysis. Oxford University Press.
Taleb, Nassim Nicholas. 2007. The Black Swan: The Impact of the Highly Improbable. Random House.
United Nations Conference on Trade and Development. 2020. “Coronavirus Outbreak Disrupts Global
Trade and Value Chains.” March 4. https://unctad.org/news/coronavirus-outbreak-disrupts-
global-trade-and-value-chains.
World Bank. 2014. The Economic Impact of the 2014 Ebola Epidemic: Short- and Medium-Term Estimates
for West Africa. World Bank Group. https://documents.worldbank.org/en/publication/documents-
reports/documentdetail/524521468141287875/the-economic-impact-of-the-2014-ebola-
epidemic-short-and-medium-term-estimates-for-west-africa.
World Bank. 2025. World Development Indicators (WDI). . https://data.worldbank.org/.
World Health Organization. 2003. “Summary of Probable SARS Cases with Onset of Illness from 1
November 2002 to 31 July 2003.” https://www.who.int/publications/m/item/summary-of-
probable-sars-cases-with-onset-of-illness-from-1-november-2002-to-31-july-2003.
World Health Organization. 2020. Critical Preparedness, Readiness and Response Actions for COVID-19:
2020 Action Points. WHO Regional Office for Europe.
https://www.who.int/europe/publications/i/item/critical-preparedness-readiness-and-response-
actions-for-covid-19-2020-action-points.
World Health Organization. n.d. “Coronavirus Disease (COVID-2019) Situation Reports.” .
https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports.
World Health Organization. 2024. “COVID-19 Epidemiological Update—Edition 166.” April 12.
https://www.who.int/publications/m/item/covid-19-epidemiological-update---edition-166.
World Health Organization. 2025. “Middle East Respiratory Syndrome Coronavirus (MERS-CoV).”
December 11. https://www.who.int/news-room/fact-sheets/detail/middle-east-respiratory-
syndrome-coronavirus-(mers-cov).
World Health Organization. n.d. “WHO Coronavirus (COVID-19) Dashboard.” . https://covid19.who.int/.
