Introduction
In response to the coronavirus disease 2019 (COVID-19) pandemic, numerous countries implemented a range of non-pharmaceutical interventions, including social distancing and lockdowns. While these containment measures successfully reduced incidence and mortality rates [
1], they also generated substantial negative economic impacts [
2–
5]. As a result, policymakers face the ongoing challenge of deploying containment measures efficiently to control infection spread while simultaneously minimizing adverse societal consequences [
6,
7].
Diagnostic testing plays a pivotal role in both patient management and the control of infectious disease transmission [
8–
12]. Based on diagnostic test results, governments can obtain essential data for public health decision-making and effectively implement infection control measures such as contact tracing, isolation, and treatment [
13,
14]. Accordingly, many countries worldwide actively incorporated diagnostic testing into their COVID-19 response strategies [
15–
17]. The essential components required to perform such testing are
in vitro diagnostics (IVD).
In the absence of a crisis, the IVD industry, particularly within the domain of infectious disease diagnostics, is characterized by limited innovation due to low marketability stemming from constrained demand [
8,
18,
19]. However, a major disruptive event such as a pandemic generates an urgent, large-scale problem that stimulates problem-driven innovation [
20]. Because IVD for novel infectious diseases do not preexist, governments in many countries implement policies to secure diagnostic capacity, including relaxing regulatory requirements and expediting review processes [
21–
23]. These policy actions create large-scale demand and provide powerful incentives for firms to enter the market, thereby establishing conditions conducive to radical innovation.
Following the COVID-19 outbreak, numerous studies have examined the effectiveness of containment measures as well as the pandemic’s broader impact on various industries [
24–
26]. However, research that explicitly analyzes the effectiveness of containment policies centered on diagnostic testing and their subsequent impact on the IVD industry remains limited. To address this gap, the present study developed a system dynamics (SD) model based on the Republic of Korea’s COVID-19 response. The model captures the dynamic interplay between epidemic progression and the IVD industry value chain. The Republic of Korea was selected as the study context because it successfully flattened the epidemic curve during the early stages of the outbreak by establishing a proactive diagnostic testing system, enabled by rapid public–private partnerships and the implementation of an emergency use authorization (EUA) for its domestic IVD industry [
15,
27–
29].
This model enables a systematic analysis of epidemic control policies and their effects on innovation within the IVD industry through scenario-based modeling. The findings derived from this analysis can enhance understanding of the pandemic’s impact on the IVD industry and assist policymakers in designing effective control strategies for future infectious disease crises.
The remainder of this paper is organized as follows. Section 2 outlines the research methodology. Section 3 presents the results for each modeled scenario. Section 4 discusses the key findings. Finally, Section 5 provides the conclusions of the study.
Materials and Methods
Sample and Data
The model was developed using both qualitative and quantitative data to parameterize, calibrate, and validate its dynamic behavior. The structure of the model, which represents causal relationships and feedback loops, was developed using qualitative data. First, based on a comprehensive review of the literature, 4 key factors central to IVD innovation, termed “innovation catalysts,” were identified and used to establish the model’s overall framework. In addition, interviews were conducted with government officials from the Republic of Korea involved in diagnostic testing as well as with employees from IVD manufacturers. Insights obtained from these interviews were subsequently used to refine the framework and to ensure that the final model structure accurately reflects the interactions among government infectious disease policies, regulatory processes, and the IVD industry.
The model was operationalized through parameter population, calibration, and validation using quantitative data spanning 2019 to 2021. Epidemiological data, including confirmed COVID-19 cases and polymerase chain reaction (PCR) test volumes, were obtained from the Korea Disease Control and Prevention Agency. Regulatory data on IVD product approvals were sourced from the Ministry of Food and Drug Safety. Industry performance was assessed using sales revenue data collected for 197 firms that had received IVD approvals.
Measures of Variables
The primary drivers of the model are 4 innovation catalysts: policy, investment, demand, and network. Policy encompasses regulatory frameworks and government interventions, where adaptive mechanisms such as EUA can spur rapid innovation [
11,
30,
31], while more stringent regulatory requirements may act as barriers to market entry [
32]. Investment, particularly in research and development (R&D), serves as a fundamental driver of new product development [
33], with government funding often signaling institutional support that encourages complementary private investment [
34]. Demand, especially during a public health crisis, directly increases production incentives and stimulates innovation to address unmet clinical needs [
35]. Finally, network effects, operating through collaborations among firms, government agencies, and research institutions, facilitate knowledge spillovers and accelerate innovation processes [
36–
38].
These innovation factors were specified within the context of the medical device industry by identifying stages associated with market entry. Market entry for IVD products is influenced by a wide range of interrelated factors. Previous studies have proposed various frameworks to describe the medical device development process, frequently including stages such as regulation, R&D, manufacturing, marketing, and legal pathways [
39]. A stage-gate model has been introduced that encompasses opportunity assessment, feasibility analysis, development, regulatory approval, and post-market evaluation [
40,
41]. These frameworks were later adapted or simplified, resulting in the identification of common stages such as concept development, validation, production, regulatory approval, and commercialization [
19,
42–
44].
The relationships between parameters and variables, the time delays embedded in the equations, and assumptions regarding large-scale transmission events were established based on national data describing the progression of the COVID-19 pandemic in the Republic of Korea, supplemented by relevant literature. The population dynamics of the model were initialized using the Republic of Korea’s total population of 51,779,203 in 2019. The daily average number of births from 2019 to 2021 (
n=762) was added on a daily basis, while the daily average number of deaths from 2017 to 2019 (
n=803) was subtracted. This approach was implemented to reflect baseline mortality under non-pandemic conditions and to exclude excess mortality attributable to COVID-19, thereby enhancing model accuracy. The R&D-to-sales ratio of manufacturers was set at 2% [
45], and external R&D investment was modeled as 16.3% of internal investment [
46]. A susceptible–infected–recovered (SIR) epidemiological model was used to generate demand dynamics [
47], and transmission parameters were calibrated to match the observed pandemic trajectory in the Republic of Korea [
48–
50]. Market dynamics, including pricing pressure, competition, and new firm entry, were also incorporated [
51–
53]. The effects of vaccines were excluded from the model because of uncertainty regarding their protection levels and duration during the modeled timeframe [
54–
58]. Specific parameter values and the rationale for their calibration, including process-specific time delays, are provided in
Tables S1–
S3 [
59–
63].
Models and Data Analysis Procedure
The model was developed in multiple stages. A conceptual model grounded in innovation theory and the IVD value chain was first constructed and subsequently refined through expert interviews (
Figure 1). This conceptual framework was then translated into a quantitative simulation model consisting of 3 interconnected modules representing the stages of market entry in the IVD industry: R&D, regulatory approval, and marketing and pandemic progression.
The R&D module captures the dynamics of product development within the IVD value chain. It models how manufacturers develop prototype products through processes such as market analysis and target selection, which are driven by R&D investment and influenced by R&D costs. Network dynamics are incorporated as a central driver of innovation, affecting R&D costs, success rates, and development timelines. For example, patent-based disclosures are modeled as mechanisms that foster competition and facilitate knowledge spillovers, thereby reducing R&D expenses for subsequent innovators [
38,
64]. Investment behavior is driven by firm revenues and external funding, which in turn are influenced by demand-driven marketability and government intervention signals [
34]. The detailed structure of this module is illustrated in
Figure 2, and the corresponding mathematical formulations are provided in
Table S1.
The regulatory approval module represents the process of obtaining market access. It models the flow of products through regulatory pathways leading to market approval and incorporates policy levers such as EUA that modify the duration and complexity of the approval process. The module also accounts for post-approval procedures specific to the Republic of Korea, including new health technology assessment and insurance benefit assessment. The structure of this module is illustrated in
Figure 3, and its mathematical formulations are presented in
Table S2.
The marketing and pandemic progression module links the IVD market to the progression of the pandemic. The marketing process includes manufacturing, sales, and the execution of diagnostic tests. To maintain analytical focus on IVD manufacturers, the model simplifies the purchaser landscape (e.g., wholesalers) and the provider landscape (e.g., hospitals and physicians) and excludes payer-side cost flows that generate revenue for providers rather than manufacturers. Policy variables are incorporated to capture government-mandated changes in testing intensity and social distancing measures. Pandemic progression is structured using an SIR framework, linking epidemic-driven demand to the supply of diagnostic products. This structure creates a feedback loop in which the number of infected individuals influences demand, which subsequently affects industry revenue, R&D investment, and product output. The module also incorporates market dynamics such as competition and pricing pressure [
51–
53]. The model structure is shown in
Figure 4, and mathematical formulations are provided in
Table S3.
The model was validated prior to its use in scenario analysis. Validation involved comparing baseline simulation outputs with empirical data for several key indicators, including COVID-19 infections, deaths, testing volume, and industry revenue. The model was found to reproduce the overall trends observed in the empirical data (
Figure S1). The close alignment between simulated and observed data indicates that the model adequately captures the fundamental dynamics of the system, supporting its suitability for scenario-based policy analysis.
To examine the effects of government diagnostic testing policies on patient incidence and mortality, as well as on the IVD industry, 6 policy scenarios were constructed. These scenarios were developed by deriving key dimensions from the innovation catalysts (policy, investment, demand, and network) and from the Republic of Korea’s primary diagnostic testing measures during the COVID-19 pandemic. The first scenario evaluates the effect of varying R&D investment intensity. The second examines the impact of public–private cooperation. The third assesses the effectiveness of EUA by comparing outcomes associated with differing levels of regulatory difficulty and approval time. The fourth investigates the influence of diagnostic test performance. The fifth compares outcomes under different levels of diagnostic testing intensity, and the final scenario evaluates the effects of varying degrees of social distancing stringency.
The outcomes of these 6 scenarios were analyzed by simulating the model with systematic modifications to their respective key parameters. For each scenario, key parameters were varied across approximately 10 distinct levels, and the resulting outcomes were compared. This sensitivity analysis was designed to assess the robustness of the system’s response to alternative policy interventions.
Ethics Approval
The requirement for informed consent was waived because of the retrospective nature of this study.
Results
Scenario 1 simulated outcomes based on the intensity of investment. Higher levels of R&D investment were associated with a faster time to market for new diagnostic products (
Figure 5A,
B). Conversely, as investment intensity increased, overall revenue within the IVD industry declined. This decrease was attributable to price reductions driven by intensified competition resulting from a greater number of products entering the market, as well as to a reduction in the number of patients (
Figure 5C,
D).
Scenario 2 simulated the effects of collaboration between the government and the private sector. As shown in
Figure 6, enhanced networking between the government and the IVD industry reduced the number of infected individuals and deaths (
Figure 6A,
B). Stronger network linkages improved R&D success rates and shortened development timelines, thereby accelerating product market entry (
Figure 6C). However, the resulting increase in competition led to a reduction in overall IVD industry revenue (
Figure 6D).
In Scenario 3, the effects of regulatory difficulty and approval time on both epidemic outcomes and market dynamics were simulated. As shown in
Figure 7, reductions in regulatory difficulty, achieved through easing IVD authorization requirements and shortening approval timelines, led to fewer infections. This effect was particularly pronounced during the early stages of the pandemic (
Figure 7A,
B). In contrast, increasing approval difficulty resulted in approximately 67,000 additional infections, while extending approval time led to roughly 61,000 additional cases. Lower regulatory difficulty also increased the number of approved products entering the market (
Figure 7C,
D). However, overall industry revenue declined as a result of price reductions driven by increased competition and a smaller patient population (
Figure 7E,
F).
Scenario 4 simulated the impact of diagnostic test performance, with a specific focus on sensitivity, defined as the ability to correctly identify infected individuals as positive. As illustrated in
Figure 8, reductions in test performance led to increases in both infections and deaths. Specifically, the use of an IVD with 50% sensitivity resulted in approximately 280,000 additional infections and 3,500 additional deaths compared with the use of an IVD with 95% sensitivity (
Figure 8A,
B). Conversely, the use of lower-sensitivity IVD was associated with higher overall industry revenue, driven by the larger number of patients requiring testing (
Figure 8C).
In Scenario 5, the impact of diagnostic testing intensity was simulated. As shown in
Figure 9, more aggressive testing policies led to reductions in infections and deaths. Specifically, a 20% decrease in testing intensity relative to the current level resulted in approximately 850,000 additional infections and 10,000 additional deaths over the same period. In contrast, doubling testing intensity compared with the current level resulted in approximately 40,000 fewer infections and 500 fewer deaths.
In Scenario 6, the impact of social distancing intensity was simulated. The results indicate that higher levels of contact suppression substantially reduced infections and deaths, as shown in
Figure 10. If contact suppression measures had been twice as strong as those observed in the actual situation, infections would have been reduced by approximately 60,000 and deaths by about 640. Conversely, if these measures had been implemented more weakly, reducing contact suppression effectiveness to 40% of the observed level, infections increased by approximately 700,000 and deaths by about 8,500 (
Table 1).
Discussion
Intervention policies centered on diagnostic testing were a cornerstone of the COVID-19 response in many nations. The implementation of these policies has had profound implications for both infectious disease control and the IVD industry. However, despite their widespread use, there has been limited research systematically evaluating the effectiveness of these governmental policies and their subsequent impact on the IVD industry’s response. Accordingly, this study employed an SD approach to address this gap by examining the effectiveness of government pandemic control policies alongside the corresponding dynamics within the IVD industry.
The SD simulation conducted in this study underscores the critical importance of the entire IVD industry value chain, including development, market entry, and end use, in supporting an effective pandemic response. A central finding is the pivotal role of investment as a catalyst for innovation. Similar to the drug discovery industry, the IVD sector is highly technology-dependent and requires continuous R&D investment to maintain competitiveness and comply with stringent regulatory standards [
65–
67]. The results demonstrate that higher levels of R&D investment are associated with faster market entry of IVD during a pandemic, thereby reducing overall public health damage (Scenario 1).
Policies designed to stimulate the market, such as increased government investment and strengthened network collaboration, were shown to be highly effective in achieving their intended objectives [
68,
69]. Among various forms of networking, collaboration between governments and IVD manufacturers is particularly critical in responding to infectious diseases, as large-scale outbreak responses are inherently government-led. As demonstrated in the results (Scenario 2), transparent government sharing of pathogen genetic information and policy direction enabled IVD manufacturers to rapidly develop diagnostics, which played a crucial role in limiting the spread of infection [
27,
28].
Taken together, these findings confirm that both tangible support, such as financial investment, and intangible support, such as network formation, play essential roles in securing critical IVD capacity during the early stages of an infectious disease crisis.
Once prototype products are prepared for market entry through sustained investment and collaboration, regulatory policies become necessary to enable rapid deployment. The model demonstrates that accelerating diagnostic availability by easing regulatory hurdles and shortening approval timelines is a powerful mechanism for reducing infections and deaths (Scenario 3). Procedural and regulatory innovations, particularly the EUA framework, similarly played a crucial role in the drug discovery field by substantially shortening approval timelines and enabling the rapid deployment of COVID-19 vaccines [
31,
70]. These findings suggest that strategically relaxing regulatory requirements to expand testing capacity is a key component of an effective pandemic response.
However, this emphasis on speed introduces a significant risk. The simulation results indicate that infectious disease outcomes worsen when IVD quality is compromised (Scenario 4). False negative results, which are frequently associated with low test sensitivity, pose challenges at both policy and individual levels. At the policy level, false negatives can lead to missed opportunities for isolation and contact tracing, resulting in ineffective control strategies based on inaccurate epidemiological data [
71–
73]. At the individual level, false negatives may create a false sense of security that increases transmission risk. Conversely, false positives can result in unnecessary quarantines and personal restrictions [
74].
This situation highlights a central dilemma faced by policymakers during infectious disease crises. On one hand, there is strong pressure to maximize regulatory speed in order to ensure widespread access to diagnostic testing. On the other hand, rapid approval processes increase the likelihood that low-performance tests may enter the market, potentially undermining public health outcomes. The core policy challenge therefore lies in managing the trade-off between regulatory speed and product quality. Addressing this challenge requires adaptive regulatory frameworks that can expedite diagnostic review processes while maintaining safeguards to prevent the approval of unreliable products.
Once an adequate supply of high-performance IVD has been secured, ensuring sufficient testing volume becomes critical. Consistent with previous studies [
15,
23,
29], the simulation results confirm that proactive diagnostic testing plays a central role in reducing infection transmission (Scenario 5). Diagnostic testing forms the foundation of preventive measures, including epidemiological investigation, isolation, and treatment, which explains why governments implemented large-scale testing strategies during the COVID-19 pandemic [
71,
75,
76].
As demonstrated in numerous studies, contact reduction measures such as social distancing and lockdowns effectively limit the spread of infectious diseases, with higher-intensity interventions yielding greater effects [
72,
77,
78]. Consistent with this evidence, the present study found that increases in the intensity of contact reduction policies were associated with greater reductions in infections and deaths (Scenario 6).
In summary, contact reduction measures, including social distancing and isolation, are effective tools for breaking transmission chains when they are informed by accurate diagnostic testing data.
As demonstrated across the study scenarios, policy measures such as increased investment, network enhancement, and regulatory relaxation were associated with reductions in expected revenue for the IVD industry (Scenarios 1–4). This outcome arises from 2 primary factors: intensified price competition resulting from increased product entry and a decline in patient numbers due to effective infection control. Reduced profitability driven by heightened competition is a well-documented phenomenon across industries. This pattern is particularly pronounced in the IVD sector, which, while structurally similar to the drug discovery industry, is characterized by lower technological entry barriers and faster development cycles, yet faces intense post-launch competition [
79]. Importantly, this reduction in expected revenue is relative and may be progressively offset by broader market expansion driven by sustained public health investment, a pattern consistent with established theories of industrial development and innovation diffusion [
80,
81].
Conclusion
In this study, the effectiveness of government epidemic control policies and the corresponding response of the IVD industry were examined using an SD model. The model provides a robust analytical framework for forecasting the impacts of different policy levers and for assessing how a pandemic influences the complex system of industrial innovation. By simulating multiple scenarios grounded in the Republic of Korea’s COVID-19 experience, this research offers strategic insights that can inform responses to future public health challenges.
Theoretical Implications
As defined by Schumpeter, innovation may be radical, emerging as a response to new environments, or incremental, driven by factors such as sustained R&D investment [
32]. Historically, the infectious disease IVD industry has faced weaker market incentives for innovation than structurally similar sectors such as drug discovery, in part because treatment decisions can often be made based on symptoms without laboratory confirmation [
8,
19]. However, a pandemic creates an urgent and large-scale need that stimulates what has been described as “crisis-driven innovation” [
20].
Drawing on existing literature and expert interviews, this study identifies 4 key factors driving innovation in the IVD industry: policy, investment, demand, and networks. The primary theoretical contribution of this study is the explicit modeling of the IVD value chain to demonstrate how these 4 factors are interconnected and mutually reinforcing. The model illustrates how these elements interact to accelerate the development and deployment of new diagnostics, influence disease control outcomes, and subsequently shape the next cycle of product development.
Policy Implications
As demonstrated in this study, IVD serve as a critical tool for governments during infectious disease outbreaks. Because IVD can be developed relatively rapidly, they function as an essential bridging intervention to mitigate an epidemic while longer-term solutions, such as vaccines and therapeutics, are still under development. Policy efforts to accelerate the adoption of IVD can be categorized according to the innovation factors proposed in this study: investment, networks, regulation, and the infrastructure required to meet demand. The policy implications associated with each of these factors are discussed below.
First, policy support and investment should be sustained during non-crisis periods. Just as continuous military investment is essential for national defense, sustained investment in diagnostic preparedness is fundamental to health security. The IVD industry’s ability to rapidly develop diagnostics for a novel infectious disease depends on maintaining sufficient R&D capacity prior to a crisis. Accordingly, policy should be designed to preserve crisis-driven innovative momentum not only during emergencies but also in the intervals between them. To ensure preparedness for future public health emergencies, policymakers should establish mechanisms that promote long-term stability and growth, including strategic government stockpiling contracts, R&D tax incentives, and sustained public–private partnerships.
A continuously maintained public–private network that facilitates rapid and transparent information sharing, including regulatory guidance and epidemic control strategies, during non-crisis periods can substantially accelerate the development of next-generation diagnostic solutions [
20,
82,
83]. Such investments ensure that when a pandemic emerges, the diagnostic industry can scale up immediately rather than initiating development from the outset.
From the perspective of individual manufacturers, maintaining sustained investment in IVD development is critical for both financial performance and long-term viability. Because the timing and scale of infectious disease outbreaks are inherently unpredictable, manufacturers face considerable challenges in consistently investing in R&D for infectious disease diagnostics [
8]. Nevertheless, firms must prepare for technology development, regulatory approval processes, the rapid expansion of production capacity to meet sudden demand surges, supply-chain adjustments, funding acquisition strategies, and market positioning decisions [
25]. Entering new markets during the early stages of an outbreak allows firms to capture early market dominance; therefore, continued investment despite uncertainty remains strategically important [
84,
85]. Moreover, manufacturers must closely monitor outbreak severity, government responses, and international developments to anticipate demand and to develop prudent investment strategies for securing raw materials and short-term labor in preparation for abrupt market fluctuations.
Second, quality must accompany quantity. As demonstrated in this study, rapidly securing IVD to enable aggressive diagnostic testing during the early stages of a pandemic is essential for effective epidemic control. In this context, the use of EUA, which reduced approval barriers to facilitate swift diagnostic deployment, contributed positively to containment efforts. However, the findings also indicate that low-sensitivity tests can generate inaccurate results that undermine containment strategies and ultimately exacerbate disease transmission.
In particular, lateral flow immunoassays, which were widely deployed during the COVID-19 response, contributed substantially to epidemic control by providing rapid, low-cost, point-of-care results without requiring complex equipment [
86]. Nevertheless, these assays have a critical limitation in that their diagnostic performance is substantially inferior to that of PCR-based tests [
87]. Consequently, as recommended by the World Health Organization and supported by prior studies [
11], governments must carefully evaluate epidemiological data at each stage of a pandemic to balance testing throughput, speed, and accuracy in accordance with the characteristics of the diagnostic tools being implemented.
One practical approach to lowering entry barriers for the rapid introduction of IVD while simultaneously maintaining high performance is to closely monitor pathogenic changes and analyze them in conjunction with the performance of IVD already on the market. A clear distinction exists between analytical performance, which is evaluated in controlled laboratory environments, and clinical performance, which is assessed in real-world clinical settings and is influenced by multiple contextual factors [
74].
Because pathogens, particularly viruses, continuously undergo mutation, alterations in genetic sequences and antigenic structures may occur, potentially reducing the performance of existing diagnostic reagents [
88]. Accordingly, it is necessary to establish a robust post-market surveillance system through measures such as information-sharing platforms, including Global Initiative on Sharing All Influenza Data, which was actively utilized during the COVID-19 pandemic [
82], as well as systematic data collection based on public–private collaboration.
Furthermore, the nature of a crisis renders traditional innovation pathways infeasible, shifting priorities toward the rapid deployment of existing technologies [
89]. This observation suggests that pre-crisis efforts to establish tiered or expedited regulatory review pathways, linked to transparent and predefined performance benchmarks, represent a practical and scalable response strategy for future pandemics.
Third, comprehensive public health infrastructure, including large-scale diagnostic testing, epidemiological investigation, quarantine, and treatment, must be strengthened. During the COVID-19 pandemic, laboratories in the majority of countries were unable to conduct required testing in a timely manner because of shortages in critical resources such as diagnostic reagents and trained personnel [
14]. Large-scale containment policies implemented in response to pandemics also generate substantial socioeconomic costs due to reductions in social and economic activity [
2,
3,
5]. By proactively establishing robust diagnostic testing infrastructure, policymakers can design response strategies that mitigate such socioeconomic harm during future crises [
90]. Clinical laboratories should therefore no longer be viewed merely as ancillary support units but rather as frontline components of the public health system. This represents a critical domain that must be reinforced in preparation for future pandemics. Furthermore, diagnostic testing constitutes only 1 component of a broader response system. While diagnostic tests provide essential data to guide interventions, complementary measures, including effective contact tracing systems, supported isolation, and accessible treatment, must also be in place and adequately resourced.
Lastly, efforts aimed at cost reduction can ultimately result in greater overall expenditure. Although establishing the tangible and intangible infrastructure discussed above requires substantial upfront investment, countries that invested more heavily in their health sectors prior to the pandemic experienced significantly lower COVID-19 mortality rates. Specifically, a 1% increase in per capita health expenditure was associated with a 0.74% reduction in COVID-19 mortality [
91]. Suppressing infection incidence is inherently more cost-effective than managing cases on a per-test basis [
92]. Consequently, by maintaining higher levels of investment in the diagnostics sector during non-crisis periods, governments can respond to emerging health crises in a more cost-effective and resilient manner.
Ideas for Future Research
This study has several limitations. First, modeling-based studies inherently reflect conditions at specific points in time and may not fully capture dynamic real-world changes, including viral mutations, evolving government policies, and shifts in population behavior [
93]. To address this challenge, this study employs an SD model to examine the relationship between pandemic progression and industrial innovation in the IVD sector. This methodology is well-suited to analyzing complex systems characterized by feedback loops, time delays, and non-linear interactions, and it has been widely applied in studies of pandemic responses [
94–
97] and industrial innovation systems [
98–
102]. Despite these strengths, the present model necessarily simplifies certain complex processes; for example, it abstracts regulatory pathways and streamlines innovation trajectories, thereby omitting detailed approval sequences and specific market mechanisms.
Second, the model is grounded in the specific context of the Republic of Korea’s COVID-19 response. As a result, the findings may not be directly generalizable to countries with differing healthcare systems, regulatory environments, and industrial structures.
Lastly, the scope of this study focuses primarily on immediate crisis response and does not explicitly account for operational constraints within public health infrastructure or the longer-term phases of preparedness and recovery [
93].
Future research should build upon these limitations. Comparative analyses could assess the applicability of the model across diverse national contexts. In addition, insights derived from the present simulation should be complemented by quantitative studies using large-scale empirical datasets to further validate the findings. Finally, extending the model to incorporate more granular representations of regulatory processes, innovation systems, and public health infrastructure capacity would enable a more comprehensive understanding to inform global health policy.
