Impacts of relaxed mask policies on COVID-19 epidemics: A modeling study in South Korea

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Although many studies have examined COVID-19 policies, there is a lack of research on the impact of mask mandate relaxation in South Korea. Retrospective analyses of this topic are essential to inform optimized policy responses in future pandemics. Methods: We used a discrete-time, age-structured Susceptible–Exposed–Infectious–Vaccinated–Recovered (SEIVR) compartmental model to simulate COVID-19 transmission in South Korea and conducted counterfactual analyses to assess the impact of five major mask policy adjustment points (PAPs). The model estimated changes in confirmed cases, severe cases, and deaths under counterfactual scenarios in which mask mandates were relaxed 2 weeks earlier or later than they were in reality. Analyses were stratified by age group to evaluate differential effects. Results: Changes in Rt (effective reproduction number) following mask policy relaxations were modest across all five PAPs. While some policy shifts were followed by slight increases or decreases in Rt, none led to uncontrolled epidemic growth. Counterfactual simulations showed that advancing mask relaxation by 2 weeks could have led to significantly more confirmed cases, with increases of up to 29.5% in children and 25.2% in older adults, compared to the observed timeline. Conversely, delaying relaxation reduced case numbers across all age groups. The timing of relaxation, especially when Rt was low, appeared to play a more critical role than population immunity in determining transmission outcomes. A positive association was observed between higher Rt at the time of relaxation and increased case counts, whereas immunity levels did not show a consistent correlation. Conclusions: The timing of mask mandate relaxation substantially influenced short-term COVID-19 transmission dynamics. Real-time indicators such as Rt were more predictive of outcomes than estimated immunity levels, suggesting their utility for informing policy adjustments. Counterfactual evidence underscores that premature relaxation could disproportionately impact vulnerable populations. Policymakers should incorporate transmission dynamics, age-specific vulnerability, and timing considerations into future pandemic response strategies. Clinical trial number : Not applicable. Pandemic COVID-19 SARS-CoV-2 Mask Relaxation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 INTRODUCTION COVID-19, caused by the novel coronavirus SARS-CoV-2, has resulted in more than 760 million cases and 6.9 million deaths worldwide as of August 9, 2023 [ 1 ]. The first case of COVID-19 in South Korea was confirmed on January 20, 2020 and, since then, the country has experienced several waves of outbreaks, with about 34 million confirmed cases and 35,000 deaths as of August 31, 2023 [ 2 ]. In the absence of effective pharmaceutical treatments at the onset of the pandemic, countries implemented various non-pharmaceutical interventions (NPIs) to slow the spread of the virus and protect healthcare capacity [ 3 , 4 ]. These measures included social distancing, mask-wearing, testing, contact tracing, and isolation of confirmed cases. South Korea adopted a proactive and adaptive strategy, adjusting the intensity of NPIs based on real-time risk assessments and epidemiological trends [ 5 ]. While initial efforts focused on suppression, the prolonged nature of the pandemic, along with the rollout of mass vaccination programs, led to a gradual shift toward strategies that balanced infection control with social and economic sustainability. Determining the appropriate timing for easing public health measures is a complex and critical task [ 6 ]. Premature relaxation may lead to a resurgence in cases and overwhelm healthcare systems, whereas prolonged restrictions can result in economic burdens and mental health challenges [ 7 – 9 ]. However, assessing the impact of NPIs on the dynamics of an epidemic presents significant challenges. These challenges arise from the complex and dynamic nature of disease transmission, the diverse and interconnected nature of NPIs, and the potential confounders and biases present in observational data [ 10 , 11 ]. In this context, mathematical modeling emerges as a valuable tool. It enables the construction of counterfactual scenarios that can help quantify the potential outcomes of various NPIs under different assumptions and uncertainties [ 12 ]. Such modeling not only provides information about the direct effects of specific interventions but can also aid the planning of more effective public health responses for future epidemics. In this study, we focused on mask policies as one of the most prominent NPIs during the COVID-19 pandemic [ 13 , 14 ]. Masks serve a dual role, limiting transmission from infected individuals while also shielding uninfected individuals from infection [ 15 , 16 ]. Moreover, mask-wearing conveys a sense of collective responsibility and reinforces protective behavior at the population level [ 17 ]. Understanding the epidemiological role of mask-wearing, particularly in determining when and how mandates should be lifted, is critical for effective infection control. Between May 2022 and June 2023, the South Korean government implemented a phased relaxation of mask mandates at five key time points, referred to as policy adjustment points (PAP1–PAP5), which included the stepwise lifting of outdoor and indoor mask requirements. Despite the significance of these policy changes, few studies have evaluated their epidemiological impact in the South Korean context. This study addresses that gap through a modeling-based counterfactual analysis. We conducted a counterfactual analysis of the effects of mask-wearing policies on the COVID-19 epidemic in South Korea, using a mathematical model. We compared the observed epidemic curves with simulated scenarios under different levels and timings of mask-wearing compliance and estimated the number of cases and deaths averted or delayed by these NPIs. METHODS Data We utilized data from confirmed cases from April 1, 2020, to August 31, 2023 [ 18 ]. The initial phase of COVID-19 in South Korea, which covers the period from January 20, 2020, to March 31, 2020, was excluded from the analysis. This decision was based on the assessment that, during this early stage, the local transmission of the virus was not as significant as in subsequent months. By focusing on the data from April 2020 onwards, we aimed to capture the dynamics of the pandemic during periods when various public health interventions were being actively implemented and adjusted. Table 1 Policy adjustment points Policy adjustment point Date Policy Ref PAP 1 2022. 5. 2. Outdoor mask mandates are lifted, except for outdoor events with 50 attendants or more, such as at rallies, concerts, and sports stadiums [ 19 ] PAP 2 2022. 9. 26. Outdoor mask mandates fully lifted [ 20 ] PAP 3 2023. 1. 30. Indoor mask mandates are lifted, except in hospitals, pharmacies, and on public transit systems [ 21 ] PAP 4 2023. 3. 20. Indoor mask mandates for public transit and pharmacies located within supermarkets and train stations are lifted [ 22 ] PAP 5 2023. 6. 1. Indoor mask mandates for clinics and pharmacies are lifted (*Mask mandates are maintained at hospitals and higher-level medical institutions, as well as inpatient facilities vulnerable to infections.) [ 23 ] Policy adjustment points Five PAPs were identified based on the dates when the government announced significant changes to mask-wearing policies (Table 1 ). The first major shift occurred in May 2022 (PAP 1), when the government lifted outdoor mask mandates following the end of social distancing measures and the reclassification of COVID-19 as a Class 2 infectious disease. This decision was grounded in scientific evidence that outdoor transmission posed a relatively low risk and was supported by declining case numbers and rising immunity levels across the population [ 19 ]. Building on this, the complete removal of outdoor mask requirements in September 2022 (PAP 2) followed the stabilization of the BA.5 subvariant wave and a sustained improvement in key indicators such as severe cases and deaths. At the time, international trends toward relaxing mask mandates and growing expert discussions around easing measures also influenced policy decisions [ 20 ]. In January 2023 (PAP 3), the government initiated the first phase of indoor mask relaxation as multiple indicators pointed to improved epidemiological stability. Case numbers, severe outcomes, and deaths were all on a downward trajectory, and the healthcare system maintained sufficient capacity to manage critical care needs. Furthermore, seroprevalence surveys indicated that approximately 98.6% of the population had developed antibodies through vaccination or natural infection, suggesting strong collective immunity and a lower risk of widespread resurgence. These conditions provided a supportive context for easing indoor mask requirements [ 21 ]. By March 2023 (PAP 4), new daily cases and severe outcomes had continued to decline significantly even after the initial indoor mask easing, indicating that the overall epidemic situation remained stable. Based on this continued downward trend and absence of emerging high-risk variants, the government proceeded to lift mandates in public transportation while maintaining protections in settings with vulnerable populations [ 22 ]. Finally, in June 2023 (PAP 5), Korea marked its formal transition to endemic management by lowering the national COVID-19 crisis alert level from “serious” to “alert”. This shift accompanied the removal of most remaining mask mandates and signaled a full transition from government-enforced measures to voluntary, individual-level practices [ 23 ]. These five PAPs represent pivotal policy shifts that occurred under distinct epidemiological and institutional conditions. To assess their short-term impacts systematically, we defined standardized observational windows around each policy change. Descriptive analysis To evaluate the effects of relaxation policies, we analyzed the variation in the reproduction number (Rt) before and after the introduction of such policies. This value was estimated using the EpiEstim package in R, assuming a parametric serial interval with a mean of 2.9 days and standard deviation of 1.6 days, based on the characteristics of the Omicron variant [ 24 ]. We computed the average Rt values for 2 weeks preceding and succeeding the implementation of a relaxation policy. Subsequently, we assessed the rates of change. Counterfactual scenarios We conducted a counterfactual analysis using a mathematical model to assess the impact of mask-wearing policy relaxations on the COVID-19 epidemic in South Korea. This approach allowed us to estimate the potential outcomes of alternative policy timings and gauge the effectiveness of mask mandates in mitigating transmission. The counterfactual analysis focused on the 14-day periods preceding and following each PAP, approximately amounting to 1 month. This approach was designed to capture the immediate and short-term effects of policy changes on the pandemic’s trajectory, based on the premise that policy interventions typically exhibit their impacts in the weeks following their implementation. To assess the effectiveness of these policy adjustments, we compared the actual observed data with the scenarios generated by the model. The primary metrics for this comparison were the differences in the rates of confirmed cases, severe cases, and deaths between the actual and counterfactual scenarios. The numbers of severe cases and deaths were derived from age- and period-specific severity and fatality rates, estimated in a national cohort study conducted in South Korea for different age groups: 0–17 years, 18–59 years, and 60 years and above [ 25 ] (Table 2 ). Table 2 Period/age group-specific severity and fatality rate * Case severity rate Age group Omicron (2022) Omicron (2023) 0–17 years 0.00010 0.00017 18–59 years 0.00023 0.00027 60 years and above 0.00651 0.00511 * Case fatality rate Age group Omicron (2022) Omicron (2023) 0–17 years 0.00010 0.00009 18–59 years 0.00013 0.00011 60 years and above 0.00442 0.00207 Model description We developed a discrete-time, age-structured, Susceptible–Exposed–Infectious–Vaccinated–Recovered (SEIVR) compartmental model to simulate the transmission dynamics of COVID-19 in South Korea. The model stratified the population into the three age groups defined above, reflecting differences in contact patterns, susceptibility, and vaccine coverage. Transitions between compartments were based on established epidemiological assumptions, including defined durations for latent and infectious periods, vaccine-induced protection, and waning of immunity. Age-specific vaccination data and coverage rates were incorporated, and immunity levels were updated dynamically. The model considered two distinct periods during the Omicron-dominant phase of the epidemic: January 16 to December 31, 2022 (Omicron 2022) and January 1 to August 31, 2023 (Omicron 2023). Each period was modeled and calibrated separately to account for differences in circulating subvariants, public health policies, and behavioral changes. Daily age-specific case counts and vaccination records were used to fit the model. Initial compartment sizes for each simulation period were obtained from the final state of a prior calibrated simulation (Delta period for 2022; early Omicron period for 2023), ensuring consistency with the epidemic dynamics prior to the evaluation period. The model also allowed time-varying contact rates both within and between age groups. Within-group contact rates were estimated weekly, and between-group contact rates were estimated monthly. These contact matrices were dynamically constructed based on fitted transmission parameters and empirical age contact proportions. Parameter estimation was performed using maximum likelihood estimation with 100,000 simulation iterations. Figure 1 shows a schematic of the meta-population SEIVR model used in this study. Each age group had separate compartments for different vaccination statuses (V1, V2, V3), exposure (E), infectiousness (I), and recovery (R), allowing for realistic simulation of immunity waning and reinfection. The mathematical formulation of the SEIVR model, including all compartment transitions and force of infection equations, is provided in Supplementary Material 1. The parameters used in this study and their values are summarized in Table 3 . Table 3 Parameters used in this study Symbols Parameters Value Ref vac1 Vaccination rate for first dose 0.428 [ 26 ] vac2 Vaccination rate for second dose 0.655 [ 26 ] vac3 Vaccination rate for third dose 0.672 [ 26 ] β Effective contact rate calibrated - µ Probability of being detected 3.13 + 2 [ 27 ] θ Rate of becoming infectious (1/incubation period) 3.5-2 [ 28 ] γ Recovery rate (1/infectious period) calibrated - γ _und Recovery rate for undetected infections 3.13 + 10 Assumed ω Immune waning period V1, V2: 138 days; R: 480 days [ 29 , 30 ] k Relative infectiousness (I₂ vs. I₁) 0.1 Assumed β _min Lower bound of effective contact rate 0.1 Assumed To assess the validity of the model, we conducted a model fitting process using observed age-specific daily case data during the Omicron-dominant periods in 2022 and 2023. The fitting results demonstrated that the model closely captured the temporal dynamics across all age groups. Additionally, we estimated time-varying effective contact rates by age group using a calibration procedure. The model fitting results and estimated contact rates are shown in Fig. 2 . RESULTS Figure 3 shows the estimated Rt over the Omicron-dominant period, with vertical lines marking the five PAPs (PAP1–PAP5). The value showed notable fluctuations over time, corresponding to changes in transmission patterns following policy adjustments. A sharp increase was observed prior to PAP1, followed by elevated levels after the policy change. Similarly, Rt rose around PAP2 and PAP4. In contrast, it remained relatively stable or declined around PAP3 and PAP5. Table 4 summarizes the changes in the 2-week mean Rt before and after each PAP during the Omicron variant-dominant period. Overall, most policy relaxations were associated with a slight increase in Rt, although the degree of change varied across different periods. Notably, Rt increased after the relaxation of mask mandates at PAP1 (May 2, 2022), PAP2 (September 26, 2022), and PAP4 (March 20, 2023), with percentage increases of + 8.62%, + 2.73%, and + 3.35%, respectively. In contrast, PAP3 (January 30, 2023) and PAP5 (June 1, 2023) were followed by slight decreases in Rt, by − 0.77% and − 0.82%, respectively. Table 4 Changes in 2-week mean Rt before vs. after the policy adjustment point Policy adjustment point 2-week mean Rt before relaxation (A) 2-week mean Rt after relaxation (B) Percentage change ( \(\:\frac{\varvec{B}-\varvec{A}}{\varvec{A}}\varvec{*}100\) ) PAP1 (2022. 5. 2.) 0.858 0.932 + 8.62% PAP2 (2022. 9. 26.) 0.916 0.941 + 2.73% PAP3 (2023. 1. 30.) 0.910 0.903 -0.77% PAP4 (2023. 3. 20.) 0.984 1.017 + 3.35% PAP5 (2023. 6. 1.) 0.981 0.973 -0.82% *Rt during the dominance period of the Omicron variant among all infections (from 2022-02-19 onwards), assuming a serial interval of 2.9 days (std 1.6 days) [ 24 ] Figure 4 compares the change in COVID-19 cases under hypothetical scenarios where policy relaxations were implemented either 2 weeks earlier (“advance”) or 2 weeks later (“delay”) than the actual date, across the three age groups. In all groups, the advance scenarios generally resulted in greater increases in case numbers, while the “delay” scenarios showed either smaller increases or actual decreases. For instance, in the child group, advancing the policy relaxation led to up to a 29.51% increase in cases, whereas delaying it resulted in a 28.79% decrease. Model-estimated total immunity was 61.0% in May 2022, dropped to 54.7% by September 2022, and further declined to 43.1% by June 2023. Across all points, immunity was consistently higher in children and lower in older adults (Supplementary Table S1 ). Figure 5 presents scatter plots showing the relationship between both immunity levels and Rt and the increase in confirmed COVID-19 cases. Pearson correlation analysis showed that immunity levels were not significantly correlated with the rate of increase in confirmed cases (r = − 0.44, p = 0.46), while Rt was positively correlated with the rate of increase (r = 0.88, p = 0.05). DISCUSSION This study evaluated the short-term epidemiological impact of five sequential mask mandate relaxations in South Korea during the Omicron period, by mathematically modeling counterfactual scenarios. Although some increases in Rt were detected following specific policy adjustments, these changes did not translate into substantial epidemic growth. The counterfactual scenarios suggested that earlier relaxation of mask mandates could have led to substantial increases in COVID-19 cases, particularly among children and older adults, whereas delaying the policy changes by 2 weeks consistently reduced the projected incidence. These findings suggest that the timing of South Korea’s mask policy relaxations was relatively effective in minimizing transmission risk, as advance relaxation scenarios consistently led to higher case projections, particularly among vulnerable groups. This implies that the observed policy schedule may have helped avoid potential resurgences while facilitating gradual social recovery. Across the five PAPs, changes in the Rt value were modest overall. It increased slightly after some policy changes, such as in May and September 2022 (PAP1 and PAP2), and March 2023 (PAP4), but the increases remained within a limited range (2.7–8.6%) and did not lead to uncontrolled epidemic growth. In other instances, it remained stable or even decreased after the policy relaxation in January and June 2023 (PAP3 and PAP5). These limited shifts in transmission may be partly explained by the fact that policy relaxations were implemented when a certain degree of population immunity had already been established, and when real-time surveillance indicators, such as Rt and case trends, suggested relatively stable epidemic conditions [ 7 , 31 ]. This finding aligns with modeling studies showing that well-timed policy relaxation, guided by real-time indicators such as Rt, can mitigate epidemic resurgence [ 31 ]. Although population immunity may have influenced transmission at certain points, it was not a consistent predictor of policy impact. For example, some increases in Rt occurred despite relatively high immunity levels, while stable trends followed periods of declining immunity. This aligns with our Pearson correlation results, which showed no significant association between immunity rate and changes in case numbers (r = − 0.44, p = 0.46). This may reflect limitations in how immunity was estimated, such as the use of fixed waning assumptions and the exclusion of hybrid or age-specific immune responses as well as external factors including real-time transmission dynamics [ 32 ], behavioral adaptation [ 33 ], and seasonal variation in virus spread [ 34 ]. In contrast, Rt showed a strong positive correlation with case increases (r = 0.88, p = 0.05), supporting its use as a more reliable indicator for policy timing. In contrast, Rt at the time of policy adjustment showed a clearer and more consistent association with subsequent transmission patterns. When policy relaxation occurred during periods of low Rt, case numbers remained stable or declined; in contrast, relaxation during high Rt was often followed by increased transmission. This suggests that real-time transmission intensity, as captured by Rt, may serve as a more reliable and actionable indicator than population immunity when determining the timing of non-pharmaceutical intervention withdrawal. Estimating population immunity presents considerable challenges due to waning vaccine protection, hybrid immunity, and emerging variants [ 35 ]. Given these uncertainties, real-time indicators such as Rt may provide a more practical and timely basis for policy decisions [ 31 ]. Age-specific findings further highlight the importance of policy timing. Our counterfactual analysis revealed that advancing mask relaxation by 2 weeks could have led to significant increases in case numbers, particularly among children (up to 29.5%) and older adults (up to 25.2%). These groups, characterized by lower vaccine coverage and higher risk of severe illness, respectively, appeared more sensitive to premature relaxation [ 36 , 37 ]. In contrast, a 2-week delay in policy shifts was consistently associated with reductions in projected incidence, especially among working-age adults. These findings emphasize that even small shifts in timing can disproportionately affect vulnerable populations, and that age-specific dynamics must be considered in future policy planning [ 37 , 38 ]. The observed stability in Rt values and the absence of marked increases in case numbers suggest that the timing of policy changes may have contributed to preventing a rapid escalation in transmission compared to advance implementation scenarios [ 39 , 40 ]. By relaxing mask mandates gradually and in response to a combination of declining transmission trends, manageable healthcare burden, and behavioral readiness, policymakers were able to minimize epidemiological risk while facilitating a transition to social normalcy. Although this study focused only on COVID-19, mandatory mask wearing should affect other infectious diseases whose transmission routes are via droplets or direct contact similarly. Previous studies have reported decreased incidence of other diseases during the COVID-19 epidemic period; these include respiratory syncytial virus (RSV) infection [ 41 ], and influenza [ 42 ]. Other diseases including hand, foot, and mouth diseases, varicella, mumps, pertussis, hepatitis B, and tuberculosis showed similar trends in multiple countries [ 43 – 45 ]. Therefore, the public health benefits of mask wearing policy should include its universal impact on other diseases as well. Potential adverse effects from mandatory mask wearing should also be considered. Previous studies have suggested a concept of immunity debt [ 46 ], referring to a lack of immunity to various pathogens. Strict NPIs lead to lower exposure rates to viruses and bacteria, leading to expansion of the naïve population. Resurgences of respiratory infections, such as influenza-like illness [ 47 ] or RSV [ 48 ], could support the phenomenon of immunity debt. In addition, wearing masks could have other adverse effects such as cardiopulmonary stress [ 49 , 50 ] or dermatitis [ 51 ], although the effects should be short-term [ 52 ]. Population-level assessment of the public health costs should also be conducted to support optimal decision making. Several limitations of this study should be considered. First, the impact of mask wearing was assessed based on assumed efficacy rather than direct measurement. Because we evaluated relaxation effects by increasing effective contact rates within the models, the resulting increase in cases in each scenario does not fully represent the increased burden. Therefore, the interpretation of findings should focus on the comparative effects between periods rather than the specific estimates themselves. Second, the effective contact rate was determined through data calibration rather than empirical measurements or prior research findings. While calibrating contact rates or transmission coefficients is a standard approach in mathematical modeling, the inherent uncertainty of this method should be acknowledged. Further studies can improve our approach by reducing uncertainty using Bayesian methods or incorporating empirical data on the number of people who wear masks or the mobility of the population into the models. Third, we did not account for the social and economic costs associated with the implementation or relaxation of mask mandates. While such considerations are critical for comprehensive policy assessment, our analyses were constrained to epidemiological outcomes due to limitations in data availability and modeling scope. Future research should incorporate these broader societal impacts. Nonetheless, our findings indicate that the timing of mandatory mask mandate removal plays a crucial role in shaping the resulting effects. The primary obstacle to evidence-based policymaking during the relaxation period was the extremely limited time available for decision-making. Conducting post-hoc analyses, such as this study, is essential for generating relevant evidence and refining optimal approaches and methodologies. These discussions are critical for enhancing preparedness for future pandemics. CONCLUSION This study highlights the critical role of timing in the relaxation of mask mandates during a pandemic. The findings show that even a slight shift in policy implementation, such as a 2-week difference, can result in significant variation in infection rates, particularly among vulnerable populations such as children and older adults. Real-time indicators such as Rt offer a practical foundation for policy decisions, especially when population immunity levels remain uncertain. Future public health responses should integrate dynamic, evidence-based modeling to fine-tune the timing and scope of NPIs. These insights contribute to pandemic preparedness strategies and emphasize the need for real-time monitoring for adaptive, data-driven policymaking in future outbreaks. Abbreviations COVID-19: Coronavirus disease 2019 NPIs: Non-pharmaceutical interventions PAP: Policy adjustment point Rt: Effective Reproduction Number SARS-CoV-2: Severe acute respiratory syndrome coronavirus 2 SEIVR: Susceptible–exposed–infectious–vaccinated–recovered Declarations Ethics approval and consent to participate Not applicable. Clinical Trial Not applicable. Consent for publication Not applicable. Availability of data and material The datasets used and analyzed during the current study are publicly available from the Korea Disease Control and Prevention Agency (KDCA) website at https://ncov.kdca.go.kr/pot/cv/trend/dmstc/selectMntrgSttus.do. All relevant details are described in the Methods section. Competing interests The authors have no competing interests to declare. Funding This study was supported by grants from the Korea Disease Control and Prevention Agency (2023-03-007) and the National Research Foundation of Korea (RS-2023-00227944), awarded to Dr. Asaph Young Chun of the Seoul National University Institute for Pandemic Sciences AI.celerator. The National Research Foundation is funded by the Ministry of Science, Technology, and Telecommunication of South Korea. Authors' contributions J.P. and K.D.M. conceptualized the study and developed the methodology. J.P., J.H., and S.C. curated the data; J.P. conducted the formal analysis and visualization. J.P. drafted the original manuscript. J.H., S.C., A.Y.C., and K.D.M. reviewed and edited the manuscript. K.D.M. validated the results and supervised the project. A.Y.C. and K.D.M. acquired funding. All authors reviewed and approved the final manuscript. 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Front public health. 2022;10:874693. https://doi.org/10.3389/fpubh.2022.874693 . Zhang Y, Zhang L, Gao W, Li M, Luo Q, Xiang Y, Bao K. The impact of COVID-19 pandemic on reported tuberculosis incidence and mortality in China: An interrupted time series analysis. J Global Health. 2023;13:06043. https://doi.org/10.7189/jogh.13.06043 . Sun X, Xu Y, Zhu Y, Tang F. Impact of non-pharmaceutical interventions on the incidences of vaccine-preventable diseases during the COVID-19 pandemic in the eastern of China. Hum Vaccines Immunotherapeutics. 2021;17(11):4083–9. https://doi.org/10.1080/21645515.2021.1956227 . Kim E-Y, Park C, Lee G, Jeong S, Song J, Lee D-H. Epidemiological characteristics of varicella outbreaks in the Republic of Korea, 2016–2020. Osong Public Health Res Perspect. 2022;13(2):133. https://doi.org/10.24171/j.phrp.2022.0087 . Cohen R, Levy C, Rybak A, Angoulvant F, Ouldali N, Grimprel E. Immune debt: recrudescence of disease and confirmation of a contested concept. Infect Dis Now. 2023;53(2):104638. https://doi.org/10.1016/j.idnow.2022.12.003 . Choi YJ, Sohn JW, Choi WS, Wie S-H, Lee J, Lee J-S, Jeong HW, Eom JS, Nham E, Seong H. Interim estimates of 2023–2024 seasonal influenza vaccine effectiveness among adults in Korea. J Korean Med Sci. 2024;39(15):e146. https://doi.org/10.3346/jkms.2024.39.e146 . Korsun N, Trifonova I, Madzharova I, Alexiev I, Uzunova I, Ivanov I, Velikov P, Tcherveniakova T, Christova I. Resurgence of respiratory syncytial virus with dominance of RSV-B during the 2022–2023 season. Front Microbiol. 2024;15:1376389. https://doi.org/10.3389/fmicb.2024.1376389 . Bao R, Ning G, Sun Y, Pan S, Wang W. Evaluation of mask-induced cardiopulmonary stress: A randomized crossover trial. JAMA Netw Open. 2023;6(6):e2317023–2317023. https://doi.org/10.1001/jamanetworkopen.2023.17023 . Zhang G, Li M, Zheng M, Cai X, Yang J, Zhang S, Yilifate A, Zheng Y, Lin Q, Liang J. Effect of surgical masks on cardiopulmonary function in healthy young subjects: a crossover study. Front Physiol. 2021;12:710573. https://doi.org/10.3389/fphys.2021.710573 . Thatiparthi A, Liu J, Martin A, Wu JJ. Adverse effects of COVID-19 and face masks: a systematic review. The Journal of clinical and aesthetic dermatology. 2021;14(9 Suppl 1):S39-S45. PMID: 34980966. Marek E-M, van Kampen V, Jettkant B, Kendzia B, Strauß B, Sucker K, Ulbrich M, Deckert A, Berresheim H, Eisenhawer C. Effects of wearing different face masks on cardiopulmonary performance at rest and exercise in a partially double-blinded randomized cross-over study. Sci Rep. 2023;13(1):6950. https://doi.org/10.1038/s41598-023-32180-9 . Additional Declarations No competing interests reported. Supplementary Files MaskpolicySupplementmaterial0609submit.docx Cite Share Download PDF Status: Published Journal Publication published 25 Apr, 2026 Read the published version in BMC Infectious Diseases → Version 1 posted Editorial decision: Revision requested 29 Jul, 2025 Reviews received at journal 27 Jul, 2025 Reviews received at journal 01 Jul, 2025 Reviewers agreed at journal 26 Jun, 2025 Reviewers agreed at journal 25 Jun, 2025 Reviewers agreed at journal 24 Jun, 2025 Reviewers agreed at journal 24 Jun, 2025 Reviewers invited by journal 24 Jun, 2025 Editor invited by journal 12 Jun, 2025 Editor assigned by journal 10 Jun, 2025 Submission checks completed at journal 10 Jun, 2025 First submitted to journal 09 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6852152","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":476119946,"identity":"4700fa16-dc28-43be-92b9-68ee4a222c56","order_by":0,"name":"Jungmi Park","email":"","orcid":"","institution":"Seoul National University","correspondingAuthor":false,"prefix":"","firstName":"Jungmi","middleName":"","lastName":"Park","suffix":""},{"id":476119947,"identity":"76379380-6813-4569-b330-6b398e0da891","order_by":1,"name":"Jaeyoung Ha","email":"","orcid":"","institution":"Seoul National University","correspondingAuthor":false,"prefix":"","firstName":"Jaeyoung","middleName":"","lastName":"Ha","suffix":""},{"id":476119948,"identity":"daa0be0a-3926-45c0-b6b4-d0cedd410def","order_by":2,"name":"Soyeon Chu","email":"","orcid":"","institution":"Seoul National University","correspondingAuthor":false,"prefix":"","firstName":"Soyeon","middleName":"","lastName":"Chu","suffix":""},{"id":476119949,"identity":"ecc9f003-55fd-4e55-a98d-1e229c05d64c","order_by":3,"name":"Asaph Young Chu","email":"","orcid":"","institution":"Seoul National University AI Institute","correspondingAuthor":false,"prefix":"","firstName":"Asaph","middleName":"Young","lastName":"Chu","suffix":""},{"id":476119950,"identity":"011187ec-061f-4401-9c33-1a2e9ab59f70","order_by":4,"name":"Kyung-Duk Min","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYBACCYYEhgMMFUCSgYGNAciE0IS1nCFVCwNjGylaJNuzEw8XzrPLM7jdfu0xzxkGef4GtrQP+LRI87zdcHjmtuRigztnyo15bjAYzjjAdngGPi1yErkbDvNuO5C44UZOmjTPBwbGDQzszXgdBtEyB6HFnqAWabCWBpCW9GPSQIclbmBgO4xXi2QP0C88x5ITZ97IYTecc0YiecZhtmS8WiSO527+zFNjl9h3I/3ZgzfHbGz729uM8WpBAjwGICMYGJiJ1cDAwP6AeLWjYBSMglEwogAAS3pPRZAnDKYAAAAASUVORK5CYII=","orcid":"","institution":"Chungbuk National University","correspondingAuthor":true,"prefix":"","firstName":"Kyung-Duk","middleName":"","lastName":"Min","suffix":""}],"badges":[],"createdAt":"2025-06-09 08:08:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6852152/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6852152/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12879-026-13395-3","type":"published","date":"2026-04-25T15:58:44+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":85647971,"identity":"09f74eba-f7d0-412e-9d8a-90afb909a9f8","added_by":"auto","created_at":"2025-06-30 08:52:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":73594,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic diagram of the meta-population compartment model\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e*S: Susceptible, E: Exposed, I: Infectious, V: Vaccinated, R: Recovered\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6852152/v1/b9d86f58960919926f1a4f3f.png"},{"id":85650060,"identity":"66b736e9-e30e-4779-9ff4-0137dee91397","added_by":"auto","created_at":"2025-06-30 09:08:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":235872,"visible":true,"origin":"","legend":"\u003cp\u003eModel fitting and time-varying effective contact rate during the Omicron periods in 2022 and 2023\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e*Upper panel shows the results from January–December 2022; lower panel shows January–August 2023.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e*Left: Daily observed (black dots) vs. model-estimated (red lines) cases.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e*Right: Weekly/monthly calibrated effective contact rates by age group.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6852152/v1/beb663e8635409b172c68d04.png"},{"id":85649551,"identity":"2407249d-abc8-4ec3-9267-57fbf1210e3d","added_by":"auto","created_at":"2025-06-30 09:00:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":26223,"visible":true,"origin":"","legend":"\u003cp\u003eEstimated Rt during the Omicron period\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6852152/v1/d0da2fb27201b4ce62197cfb.png"},{"id":85649554,"identity":"5b59dc69-14dd-47c1-82cf-f8096e44edcb","added_by":"auto","created_at":"2025-06-30 09:00:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":15018,"visible":true,"origin":"","legend":"\u003cp\u003eImpact of Timing of Policy Relaxation on Changes in COVID-19 Cases Across Age Groups\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e* The gray shading in the background of each plot indicates the model-estimated immunity rate at each time point, calculated based on the share of individuals in the S, V1, V2, H, and H2 compartments. Fixed durations of protection were used to account for waning immunity over time.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e*A: 0–17 years, B: 18–59 years, C: 60 years and above\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6852152/v1/178c2632e027131515ba3c30.png"},{"id":85647978,"identity":"5ac2ea3e-ca74-40b4-a90e-48053026ff75","added_by":"auto","created_at":"2025-06-30 08:52:11","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":24966,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between immunity rate and also Rt and changes in numbers of COVID-19 cases\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6852152/v1/a09e37ec617c39d389fa2ba2.png"},{"id":107928104,"identity":"d60a702a-5f52-42c5-88df-dc99b7b604a4","added_by":"auto","created_at":"2026-04-27 16:07:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":680464,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6852152/v1/11375186-add0-422e-abc6-78c8e41d67c1.pdf"},{"id":85647974,"identity":"d9fbf520-08d9-4917-8c8f-81d1678074a1","added_by":"auto","created_at":"2025-06-30 08:52:11","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":18991,"visible":true,"origin":"","legend":"","description":"","filename":"MaskpolicySupplementmaterial0609submit.docx","url":"https://assets-eu.researchsquare.com/files/rs-6852152/v1/e8f8b58abd2a7b9223508f8b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impacts of relaxed mask policies on COVID-19 epidemics: A modeling study in South Korea","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eCOVID-19, caused by the novel coronavirus SARS-CoV-2, has resulted in more than 760\u0026nbsp;million cases and 6.9\u0026nbsp;million deaths worldwide as of August 9, 2023 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The first case of COVID-19 in South Korea was confirmed on January 20, 2020 and, since then, the country has experienced several waves of outbreaks, with about 34\u0026nbsp;million confirmed cases and 35,000 deaths as of August 31, 2023 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the absence of effective pharmaceutical treatments at the onset of the pandemic, countries implemented various non-pharmaceutical interventions (NPIs) to slow the spread of the virus and protect healthcare capacity [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. These measures included social distancing, mask-wearing, testing, contact tracing, and isolation of confirmed cases. South Korea adopted a proactive and adaptive strategy, adjusting the intensity of NPIs based on real-time risk assessments and epidemiological trends [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. While initial efforts focused on suppression, the prolonged nature of the pandemic, along with the rollout of mass vaccination programs, led to a gradual shift toward strategies that balanced infection control with social and economic sustainability.\u003c/p\u003e \u003cp\u003eDetermining the appropriate timing for easing public health measures is a complex and critical task [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Premature relaxation may lead to a resurgence in cases and overwhelm healthcare systems, whereas prolonged restrictions can result in economic burdens and mental health challenges [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, assessing the impact of NPIs on the dynamics of an epidemic presents significant challenges. These challenges arise from the complex and dynamic nature of disease transmission, the diverse and interconnected nature of NPIs, and the potential confounders and biases present in observational data [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this context, mathematical modeling emerges as a valuable tool. It enables the construction of counterfactual scenarios that can help quantify the potential outcomes of various NPIs under different assumptions and uncertainties [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Such modeling not only provides information about the direct effects of specific interventions but can also aid the planning of more effective public health responses for future epidemics.\u003c/p\u003e \u003cp\u003eIn this study, we focused on mask policies as one of the most prominent NPIs during the COVID-19 pandemic [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Masks serve a dual role, limiting transmission from infected individuals while also shielding uninfected individuals from infection [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Moreover, mask-wearing conveys a sense of collective responsibility and reinforces protective behavior at the population level [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Understanding the epidemiological role of mask-wearing, particularly in determining when and how mandates should be lifted, is critical for effective infection control. Between May 2022 and June 2023, the South Korean government implemented a phased relaxation of mask mandates at five key time points, referred to as policy adjustment points (PAP1\u0026ndash;PAP5), which included the stepwise lifting of outdoor and indoor mask requirements. Despite the significance of these policy changes, few studies have evaluated their epidemiological impact in the South Korean context. This study addresses that gap through a modeling-based counterfactual analysis.\u003c/p\u003e \u003cp\u003eWe conducted a counterfactual analysis of the effects of mask-wearing policies on the COVID-19 epidemic in South Korea, using a mathematical model. We compared the observed epidemic curves with simulated scenarios under different levels and timings of mask-wearing compliance and estimated the number of cases and deaths averted or delayed by these NPIs.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eData\u003c/h2\u003e\n \u003cp\u003eWe utilized data from confirmed cases from April 1, 2020, to August 31, 2023 [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e]. The initial phase of COVID-19 in South Korea, which covers the period from January 20, 2020, to March 31, 2020, was excluded from the analysis. This decision was based on the assessment that, during this early stage, the local transmission of the virus was not as significant as in subsequent months. By focusing on the data from April 2020 onwards, we aimed to capture the dynamics of the pandemic during periods when various public health interventions were being actively implemented and adjusted.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePolicy adjustment points\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePolicy adjustment point\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePolicy\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePAP 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2022. 5. 2.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOutdoor mask mandates are lifted, except for outdoor events with 50 attendants or more, such as at rallies, concerts, and sports stadiums\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePAP 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2022. 9. 26.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOutdoor mask mandates fully lifted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePAP 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2023. 1. 30.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndoor mask mandates are lifted, except in hospitals, pharmacies, and on public transit systems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePAP 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2023. 3. 20.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndoor mask mandates for public transit and pharmacies located within supermarkets and train stations are lifted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePAP 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2023. 6. 1.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndoor mask mandates for clinics and pharmacies are lifted \u003cem\u003e(*Mask mandates are maintained at hospitals and higher-level medical institutions, as well as inpatient facilities vulnerable to infections.)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003ePolicy adjustment points\u003c/h3\u003e\n\u003cp\u003eFive PAPs were identified based on the dates when the government announced significant changes to mask-wearing policies (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe first major shift occurred in May 2022 (PAP 1), when the government lifted outdoor mask mandates following the end of social distancing measures and the reclassification of COVID-19 as a Class 2 infectious disease. This decision was grounded in scientific evidence that outdoor transmission posed a relatively low risk and was supported by declining case numbers and rising immunity levels across the population [\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]. Building on this, the complete removal of outdoor mask requirements in September 2022 (PAP 2) followed the stabilization of the BA.5 subvariant wave and a sustained improvement in key indicators such as severe cases and deaths. At the time, international trends toward relaxing mask mandates and growing expert discussions around easing measures also influenced policy decisions [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eIn January 2023 (PAP 3), the government initiated the first phase of indoor mask relaxation as multiple indicators pointed to improved epidemiological stability. Case numbers, severe outcomes, and deaths were all on a downward trajectory, and the healthcare system maintained sufficient capacity to manage critical care needs. Furthermore, seroprevalence surveys indicated that approximately 98.6% of the population had developed antibodies through vaccination or natural infection, suggesting strong collective immunity and a lower risk of widespread resurgence. These conditions provided a supportive context for easing indoor mask requirements [\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e]. By March 2023 (PAP 4), new daily cases and severe outcomes had continued to decline significantly even after the initial indoor mask easing, indicating that the overall epidemic situation remained stable. Based on this continued downward trend and absence of emerging high-risk variants, the government proceeded to lift mandates in public transportation while maintaining protections in settings with vulnerable populations [\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e]. Finally, in June 2023 (PAP 5), Korea marked its formal transition to endemic management by lowering the national COVID-19 crisis alert level from \u0026ldquo;serious\u0026rdquo; to \u0026ldquo;alert\u0026rdquo;. This shift accompanied the removal of most remaining mask mandates and signaled a full transition from government-enforced measures to voluntary, individual-level practices [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eThese five PAPs represent pivotal policy shifts that occurred under distinct epidemiological and institutional conditions. To assess their short-term impacts systematically, we defined standardized observational windows around each policy change.\u003c/p\u003e\n\u003ch3\u003eDescriptive analysis\u003c/h3\u003e\n\u003cp\u003eTo evaluate the effects of relaxation policies, we analyzed the variation in the reproduction number (Rt) before and after the introduction of such policies. This value was estimated using the EpiEstim package in R, assuming a parametric serial interval with a mean of 2.9 days and standard deviation of 1.6 days, based on the characteristics of the Omicron variant [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]. We computed the average Rt values for 2 weeks preceding and succeeding the implementation of a relaxation policy. Subsequently, we assessed the rates of change.\u003c/p\u003e\n\u003ch3\u003eCounterfactual scenarios\u003c/h3\u003e\n\u003cp\u003eWe conducted a counterfactual analysis using a mathematical model to assess the impact of mask-wearing policy relaxations on the COVID-19 epidemic in South Korea. This approach allowed us to estimate the potential outcomes of alternative policy timings and gauge the effectiveness of mask mandates in mitigating transmission.\u003c/p\u003e\n\u003cp\u003eThe counterfactual analysis focused on the 14-day periods preceding and following each PAP, approximately amounting to 1 month. This approach was designed to capture the immediate and short-term effects of policy changes on the pandemic\u0026rsquo;s trajectory, based on the premise that policy interventions typically exhibit their impacts in the weeks following their implementation.\u003c/p\u003e\n\u003cp\u003eTo assess the effectiveness of these policy adjustments, we compared the actual observed data with the scenarios generated by the model. The primary metrics for this comparison were the differences in the rates of confirmed cases, severe cases, and deaths between the actual and counterfactual scenarios. The numbers of severe cases and deaths were derived from age- and period-specific severity and fatality rates, estimated in a national cohort study conducted in South Korea for different age groups: 0\u0026ndash;17 years, 18\u0026ndash;59 years, and 60 years and above [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e] (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePeriod/age group-specific severity and fatality rate\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e* Case severity rate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOmicron (2022)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOmicron (2023)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0\u0026ndash;17 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e18\u0026ndash;59 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e60 years and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00511\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e* Case fatality rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOmicron (2022)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOmicron (2023)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0\u0026ndash;17 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e18\u0026ndash;59 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e60 years and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00442\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00207\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch3\u003eModel description\u003c/h3\u003e\n\u003cp\u003eWe developed a discrete-time, age-structured, Susceptible\u0026ndash;Exposed\u0026ndash;Infectious\u0026ndash;Vaccinated\u0026ndash;Recovered (SEIVR) compartmental model to simulate the transmission dynamics of COVID-19 in South Korea. The model stratified the population into the three age groups defined above, reflecting differences in contact patterns, susceptibility, and vaccine coverage. Transitions between compartments were based on established epidemiological assumptions, including defined durations for latent and infectious periods, vaccine-induced protection, and waning of immunity. Age-specific vaccination data and coverage rates were incorporated, and immunity levels were updated dynamically.\u003c/p\u003e\n\u003cp\u003eThe model considered two distinct periods during the Omicron-dominant phase of the epidemic: January 16 to December 31, 2022 (Omicron 2022) and January 1 to August 31, 2023 (Omicron 2023). Each period was modeled and calibrated separately to account for differences in circulating subvariants, public health policies, and behavioral changes. Daily age-specific case counts and vaccination records were used to fit the model. Initial compartment sizes for each simulation period were obtained from the final state of a prior calibrated simulation (Delta period for 2022; early Omicron period for 2023), ensuring consistency with the epidemic dynamics prior to the evaluation period.\u003c/p\u003e\n\u003cp\u003eThe model also allowed time-varying contact rates both within and between age groups. Within-group contact rates were estimated weekly, and between-group contact rates were estimated monthly. These contact matrices were dynamically constructed based on fitted transmission parameters and empirical age contact proportions. Parameter estimation was performed using maximum likelihood estimation with 100,000 simulation iterations.\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e shows a schematic of the meta-population SEIVR model used in this study. Each age group had separate compartments for different vaccination statuses (V1, V2, V3), exposure (E), infectiousness (I), and recovery (R), allowing for realistic simulation of immunity waning and reinfection. The mathematical formulation of the SEIVR model, including all compartment transitions and force of infection equations, is provided in Supplementary Material 1. The parameters used in this study and their values are summarized in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eParameters used in this study\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSymbols\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eValue\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003evac1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVaccination rate for first dose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.428\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003evac2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVaccination rate for second dose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.655\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003evac3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVaccination rate for third dose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.672\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEffective contact rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecalibrated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026micro;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbability of being detected\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.13\u0026thinsp;+\u0026thinsp;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026theta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRate of becoming infectious (1/incubation period)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.5-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gamma;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRecovery rate (1/infectious period)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecalibrated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gamma; _und\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRecovery rate for undetected infections\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.13\u0026thinsp;+\u0026thinsp;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAssumed\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026omega;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eImmune waning period\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eV1, V2: 138 days; R: 480 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ek\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRelative infectiousness (I₂ vs. I₁)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAssumed\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026beta; _min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLower bound of effective contact rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAssumed\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eTo assess the validity of the model, we conducted a model fitting process using observed age-specific daily case data during the Omicron-dominant periods in 2022 and 2023. The fitting results demonstrated that the model closely captured the temporal dynamics across all age groups. Additionally, we estimated time-varying effective contact rates by age group using a calibration procedure. The model fitting results and estimated contact rates are shown in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows the estimated Rt over the Omicron-dominant period, with vertical lines marking the five PAPs (PAP1\u0026ndash;PAP5). The value showed notable fluctuations over time, corresponding to changes in transmission patterns following policy adjustments. A sharp increase was observed prior to PAP1, followed by elevated levels after the policy change. Similarly, Rt rose around PAP2 and PAP4. In contrast, it remained relatively stable or declined around PAP3 and PAP5.\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e summarizes the changes in the 2-week mean Rt before and after each PAP during the Omicron variant-dominant period. Overall, most policy relaxations were associated with a slight increase in Rt, although the degree of change varied across different periods. Notably, Rt increased after the relaxation of mask mandates at PAP1 (May 2, 2022), PAP2 (September 26, 2022), and PAP4 (March 20, 2023), with percentage increases of +\u0026thinsp;8.62%, +\u0026thinsp;2.73%, and +\u0026thinsp;3.35%, respectively. In contrast, PAP3 (January 30, 2023) and PAP5 (June 1, 2023) were followed by slight decreases in Rt, by \u0026minus;\u0026thinsp;0.77% and \u0026minus;\u0026thinsp;0.82%, respectively.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eChanges in 2-week mean Rt before vs. after the policy adjustment point\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePolicy adjustment point\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2-week mean Rt\u003c/p\u003e\n \u003cp\u003ebefore relaxation (A)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2-week mean Rt\u003c/p\u003e\n \u003cp\u003eafter relaxation (B)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePercentage change\u003c/p\u003e\n \u003cp\u003e(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\varvec{B}-\\varvec{A}}{\\varvec{A}}\\varvec{*}100\\)\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePAP1 (2022. 5. 2.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.858\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.932\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u0026thinsp;8.62%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePAP2 (2022. 9. 26.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.916\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.941\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u0026thinsp;2.73%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePAP3 (2023. 1. 30.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.903\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.77%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePAP4 (2023. 3. 20.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u0026thinsp;3.35%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePAP5 (2023. 6. 1.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.981\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.82%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\u003cem\u003e*Rt during the dominance period of the Omicron variant among all infections (from 2022-02-19 onwards), assuming a serial interval of 2.9 days (std 1.6 days)\u003c/em\u003e [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e compares the change in COVID-19 cases under hypothetical scenarios where policy relaxations were implemented either 2 weeks earlier (\u0026ldquo;advance\u0026rdquo;) or 2 weeks later (\u0026ldquo;delay\u0026rdquo;) than the actual date, across the three age groups. In all groups, the advance scenarios generally resulted in greater increases in case numbers, while the \u0026ldquo;delay\u0026rdquo; scenarios showed either smaller increases or actual decreases. For instance, in the child group, advancing the policy relaxation led to up to a 29.51% increase in cases, whereas delaying it resulted in a 28.79% decrease. Model-estimated total immunity was 61.0% in May 2022, dropped to 54.7% by September 2022, and further declined to 43.1% by June 2023. Across all points, immunity was consistently higher in children and lower in older adults (Supplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e presents scatter plots showing the relationship between both immunity levels and Rt and the increase in confirmed COVID-19 cases. Pearson correlation analysis showed that immunity levels were not significantly correlated with the rate of increase in confirmed cases (r = \u0026minus;\u0026thinsp;0.44, p\u0026thinsp;=\u0026thinsp;0.46), while Rt was positively correlated with the rate of increase (r\u0026thinsp;=\u0026thinsp;0.88, p\u0026thinsp;=\u0026thinsp;0.05).\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study evaluated the short-term epidemiological impact of five sequential mask mandate relaxations in South Korea during the Omicron period, by mathematically modeling counterfactual scenarios. Although some increases in Rt were detected following specific policy adjustments, these changes did not translate into substantial epidemic growth. The counterfactual scenarios suggested that earlier relaxation of mask mandates could have led to substantial increases in COVID-19 cases, particularly among children and older adults, whereas delaying the policy changes by 2 weeks consistently reduced the projected incidence. These findings suggest that the timing of South Korea\u0026rsquo;s mask policy relaxations was relatively effective in minimizing transmission risk, as advance relaxation scenarios consistently led to higher case projections, particularly among vulnerable groups. This implies that the observed policy schedule may have helped avoid potential resurgences while facilitating gradual social recovery.\u003c/p\u003e \u003cp\u003eAcross the five PAPs, changes in the Rt value were modest overall. It increased slightly after some policy changes, such as in May and September 2022 (PAP1 and PAP2), and March 2023 (PAP4), but the increases remained within a limited range (2.7\u0026ndash;8.6%) and did not lead to uncontrolled epidemic growth. In other instances, it remained stable or even decreased after the policy relaxation in January and June 2023 (PAP3 and PAP5). These limited shifts in transmission may be partly explained by the fact that policy relaxations were implemented when a certain degree of population immunity had already been established, and when real-time surveillance indicators, such as Rt and case trends, suggested relatively stable epidemic conditions [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. This finding aligns with modeling studies showing that well-timed policy relaxation, guided by real-time indicators such as Rt, can mitigate epidemic resurgence [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough population immunity may have influenced transmission at certain points, it was not a consistent predictor of policy impact. For example, some increases in Rt occurred despite relatively high immunity levels, while stable trends followed periods of declining immunity. This aligns with our Pearson correlation results, which showed no significant association between immunity rate and changes in case numbers (r = \u0026minus;\u0026thinsp;0.44, p\u0026thinsp;=\u0026thinsp;0.46). This may reflect limitations in how immunity was estimated, such as the use of fixed waning assumptions and the exclusion of hybrid or age-specific immune responses as well as external factors including real-time transmission dynamics [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], behavioral adaptation [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], and seasonal variation in virus spread [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In contrast, Rt showed a strong positive correlation with case increases (r\u0026thinsp;=\u0026thinsp;0.88, p\u0026thinsp;=\u0026thinsp;0.05), supporting its use as a more reliable indicator for policy timing.\u003c/p\u003e \u003cp\u003eIn contrast, Rt at the time of policy adjustment showed a clearer and more consistent association with subsequent transmission patterns. When policy relaxation occurred during periods of low Rt, case numbers remained stable or declined; in contrast, relaxation during high Rt was often followed by increased transmission. This suggests that real-time transmission intensity, as captured by Rt, may serve as a more reliable and actionable indicator than population immunity when determining the timing of non-pharmaceutical intervention withdrawal. Estimating population immunity presents considerable challenges due to waning vaccine protection, hybrid immunity, and emerging variants [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Given these uncertainties, real-time indicators such as Rt may provide a more practical and timely basis for policy decisions [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAge-specific findings further highlight the importance of policy timing. Our counterfactual analysis revealed that advancing mask relaxation by 2 weeks could have led to significant increases in case numbers, particularly among children (up to 29.5%) and older adults (up to 25.2%). These groups, characterized by lower vaccine coverage and higher risk of severe illness, respectively, appeared more sensitive to premature relaxation [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In contrast, a 2-week delay in policy shifts was consistently associated with reductions in projected incidence, especially among working-age adults. These findings emphasize that even small shifts in timing can disproportionately affect vulnerable populations, and that age-specific dynamics must be considered in future policy planning [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe observed stability in Rt values and the absence of marked increases in case numbers suggest that the timing of policy changes may have contributed to preventing a rapid escalation in transmission compared to advance implementation scenarios [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. By relaxing mask mandates gradually and in response to a combination of declining transmission trends, manageable healthcare burden, and behavioral readiness, policymakers were able to minimize epidemiological risk while facilitating a transition to social normalcy.\u003c/p\u003e \u003cp\u003eAlthough this study focused only on COVID-19, mandatory mask wearing should affect other infectious diseases whose transmission routes are via droplets or direct contact similarly. Previous studies have reported decreased incidence of other diseases during the COVID-19 epidemic period; these include respiratory syncytial virus (RSV) infection [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], and influenza [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Other diseases including hand, foot, and mouth diseases, varicella, mumps, pertussis, hepatitis B, and tuberculosis showed similar trends in multiple countries [\u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Therefore, the public health benefits of mask wearing policy should include its universal impact on other diseases as well.\u003c/p\u003e \u003cp\u003ePotential adverse effects from mandatory mask wearing should also be considered. Previous studies have suggested a concept of immunity debt [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], referring to a lack of immunity to various pathogens. Strict NPIs lead to lower exposure rates to viruses and bacteria, leading to expansion of the na\u0026iuml;ve population. Resurgences of respiratory infections, such as influenza-like illness [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] or RSV [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], could support the phenomenon of immunity debt. In addition, wearing masks could have other adverse effects such as cardiopulmonary stress [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] or dermatitis [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], although the effects should be short-term [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Population-level assessment of the public health costs should also be conducted to support optimal decision making.\u003c/p\u003e \u003cp\u003eSeveral limitations of this study should be considered. First, the impact of mask wearing was assessed based on assumed efficacy rather than direct measurement. Because we evaluated relaxation effects by increasing effective contact rates within the models, the resulting increase in cases in each scenario does not fully represent the increased burden. Therefore, the interpretation of findings should focus on the comparative effects between periods rather than the specific estimates themselves. Second, the effective contact rate was determined through data calibration rather than empirical measurements or prior research findings. While calibrating contact rates or transmission coefficients is a standard approach in mathematical modeling, the inherent uncertainty of this method should be acknowledged. Further studies can improve our approach by reducing uncertainty using Bayesian methods or incorporating empirical data on the number of people who wear masks or the mobility of the population into the models. Third, we did not account for the social and economic costs associated with the implementation or relaxation of mask mandates. While such considerations are critical for comprehensive policy assessment, our analyses were constrained to epidemiological outcomes due to limitations in data availability and modeling scope. Future research should incorporate these broader societal impacts.\u003c/p\u003e \u003cp\u003eNonetheless, our findings indicate that the timing of mandatory mask mandate removal plays a crucial role in shaping the resulting effects. The primary obstacle to evidence-based policymaking during the relaxation period was the extremely limited time available for decision-making. Conducting post-hoc analyses, such as this study, is essential for generating relevant evidence and refining optimal approaches and methodologies. These discussions are critical for enhancing preparedness for future pandemics.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis study highlights the critical role of timing in the relaxation of mask mandates during a pandemic. The findings show that even a slight shift in policy implementation, such as a 2-week difference, can result in significant variation in infection rates, particularly among vulnerable populations such as children and older adults. Real-time indicators such as Rt offer a practical foundation for policy decisions, especially when population immunity levels remain uncertain. Future public health responses should integrate dynamic, evidence-based modeling to fine-tune the timing and scope of NPIs. These insights contribute to pandemic preparedness strategies and emphasize the need for real-time monitoring for adaptive, data-driven policymaking in future outbreaks.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCOVID-19: Coronavirus disease 2019\u003c/p\u003e\n\u003cp\u003eNPIs: Non-pharmaceutical interventions\u003c/p\u003e\n\u003cp\u003ePAP: Policy adjustment point\u003c/p\u003e\n\u003cp\u003eRt: Effective Reproduction Number\u003c/p\u003e\n\u003cp\u003eSARS-CoV-2: Severe acute respiratory syndrome coronavirus 2\u003c/p\u003e\n\u003cp\u003eSEIVR: Susceptible\u0026ndash;exposed\u0026ndash;infectious\u0026ndash;vaccinated\u0026ndash;recovered\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed during the current study are publicly available from the Korea Disease Control and Prevention Agency (KDCA) website at https://ncov.kdca.go.kr/pot/cv/trend/dmstc/selectMntrgSttus.do. All relevant details are described in the Methods section.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no competing interests to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by grants from the Korea Disease Control and Prevention Agency (2023-03-007) and the National Research Foundation of Korea (RS-2023-00227944), awarded to Dr. Asaph Young Chun of the Seoul National University Institute for Pandemic Sciences AI.celerator. The National Research Foundation is funded by the Ministry of Science, Technology, and Telecommunication of South Korea.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJ.P. and K.D.M. conceptualized the study and developed the methodology. J.P., J.H., and S.C. curated the data; J.P. conducted the formal analysis and visualization. J.P. drafted the original manuscript. J.H., S.C., A.Y.C., and K.D.M. reviewed and edited the manuscript. K.D.M. validated the results and supervised the project. A.Y.C. and K.D.M. acquired funding. All authors reviewed and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of generative AI in scientific writing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the preparation of this work, the authors used Chat-GPT 4 for language editing and sentence refinement to improve the clarity and readability of the manuscript. 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Sci Rep. 2023;13(1):6950. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-023-32180-9\u003c/span\u003e\u003cspan address=\"10.1038/s41598-023-32180-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Pandemic, COVID-19, SARS-CoV-2, Mask, Relaxation ","lastPublishedDoi":"10.21203/rs.3.rs-6852152/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6852152/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eThe COVID-19 pandemic led to widespread use of non-pharmaceutical interventions (NPIs), including mask mandates. Although many studies have examined COVID-19 policies, there is a lack of research on the impact of mask mandate relaxation in South Korea. Retrospective analyses of this topic are essential to inform optimized policy responses in future pandemics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe used a discrete-time, age-structured Susceptible–Exposed–Infectious–Vaccinated–Recovered (SEIVR) compartmental model to simulate COVID-19 transmission in South Korea and conducted counterfactual analyses to assess the impact of five major mask policy adjustment points (PAPs). The model estimated changes in confirmed cases, severe cases, and deaths under counterfactual scenarios in which mask mandates were relaxed 2 weeks earlier or later than they were in reality. Analyses were stratified by age group to evaluate differential effects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eChanges in Rt (effective reproduction number) following mask policy relaxations were modest across all five PAPs. While some policy shifts were followed by slight increases or decreases in Rt, none led to uncontrolled epidemic growth. Counterfactual simulations showed that advancing mask relaxation by 2 weeks could have led to significantly more confirmed cases, with increases of up to 29.5% in children and 25.2% in older adults, compared to the observed timeline. Conversely, delaying relaxation reduced case numbers across all age groups. The timing of relaxation, especially when Rt was low, appeared to play a more critical role than population immunity in determining transmission outcomes. A positive association was observed between higher Rt at the time of relaxation and increased case counts, whereas immunity levels did not show a consistent correlation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThe timing of mask mandate relaxation substantially influenced short-term COVID-19 transmission dynamics. Real-time indicators such as Rt were more predictive of outcomes than estimated immunity levels, suggesting their utility for informing policy adjustments. Counterfactual evidence underscores that premature relaxation could disproportionately impact vulnerable populations. Policymakers should incorporate transmission dynamics, age-specific vulnerability, and timing considerations into future pandemic response strategies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e: Not applicable.\u003c/p\u003e","manuscriptTitle":"Impacts of relaxed mask policies on COVID-19 epidemics: A modeling study in South Korea","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-30 08:52:06","doi":"10.21203/rs.3.rs-6852152/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-29T07:49:13+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-27T20:29:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-01T22:14:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"127231073628295444503477489913800846581","date":"2025-06-26T14:31:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"195541740575424693472156533892485174620","date":"2025-06-25T06:40:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"49675192549598826077762225779371811200","date":"2025-06-24T15:55:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"148581877736956287108341861965889954016","date":"2025-06-24T13:05:29+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-24T12:45:54+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-12T06:18:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-11T01:52:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-11T01:51:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Infectious Diseases","date":"2025-06-09T08:06:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"42571150-aa00-4e21-84c3-266b6ff8a8dc","owner":[],"postedDate":"June 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T16:05:32+00:00","versionOfRecord":{"articleIdentity":"rs-6852152","link":"https://doi.org/10.1186/s12879-026-13395-3","journal":{"identity":"bmc-infectious-diseases","isVorOnly":false,"title":"BMC Infectious Diseases"},"publishedOn":"2026-04-25 15:58:44","publishedOnDateReadable":"April 25th, 2026"},"versionCreatedAt":"2025-06-30 08:52:06","video":"","vorDoi":"10.1186/s12879-026-13395-3","vorDoiUrl":"https://doi.org/10.1186/s12879-026-13395-3","workflowStages":[]},"version":"v1","identity":"rs-6852152","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6852152","identity":"rs-6852152","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}