Epidemiological features and risk factors of SARS-CoV-2 reinfection: a retrospective cohort analysis in Xiamen, China

Introduction

Although the World Health Organization (WHO) announced that coronavirus disease 2019 (COVID-19) was no longer classified as a public health emergency of international concern (PHEIC) on May 5, 2023 (1), the emergence of highly contagious variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to drive increasing infections globally, remaining a significant global health concern.

In particular, the emerging dominant SARS-CoV-2 variant Omicron has demonstrated heightened transmissibility and infectivity than its predecessor, the dominant Delta variant, as extensively discussed in prior research (2-4). The numerous mutations in the viral spike protein of Omicron facilitate immune evasion from both previous infections and vaccinations, resulting in successive waves of infections worldwide (3,5).

In China, notably after the conclusion of the zero-COVID-19 policy on January 8, 2023, the strategy for managing COVID-19 shifted toward employing interventions typically utilized for Class B infectious diseases rather than Class A infectious diseases (6,7). This change could lead to a larger scale of population infection than that in the era of the zero-COVID-19 policy (7), leading to an elevated risk of SARS-CoV-2 reinfection (RI).

Rationale and knowledge gap

After the first cases of SARS-CoV-2 refection worldwide were reported on 24 August, in Hong Kong, China (8), this phenomenon garnered global attention and raised concerns about the potential for unprecedented pandemic waves or a complete shift in the immune barrier established through vaccination or prior natural infection.

Several real-world studies have been conducted to capture the epidemiological features and potential mechanisms of RI worldwide (9-15), including in the United States and Saudi Arabia. However as the country with the first reported RI cases of SARS-CoV-2 worldwide, China has seen few large observational studies on this emerging issue.

Furthermore, an increasing trend in the SARS-CoV-2 test-positive rate was observed across national sentinel hospitals from June to August 2024, as indicated in the COVID-19 infection reports published by the Chinese Center for Disease Control and Prevention (accessible at: https://www.chinacdc.cn/jkzt/crb/zl/szkb_11803/jszl_13141/). This trend has raised considerable public concern over the potential for multiple infections with SARS-CoV-2.

Therefore, we aimed to evaluate the epidemiological features and analyze the risk of RI comprehensively in China. Here, a population-based observational retrospective cohort study was conducted in Xiamen city, China (see Appendix 1 and Figures S1,S2). We present this article in accordance with the STROBE reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-24-1703/rc).

Methods

Study design and cohort construction

This study comprises three sections: cohort construction, epidemiological feature analysis, and risk factor analysis (Figure S3). This retrospective cohort study was based on registry data from the Diseases Surveillance Point System (DSPs) (see Appendix 2). The study period spanned from September 1, 2021 to January 8, 2023.

According to our prior investigations (16-18), there were two significant epidemic waves during the observational period, the first wave occurred between September and October 2021, dominated by the Delta variant (B.1.617.2), and the second wave occurred in August 2022, dominated by the Omicron variant (BA.2 and BA.5). Additionally, a minor outbreak wave was observed from March to April 2022, dominated by the Omicron variant (BA.2). Consequently, two subcohorts were established to analyze the features and risk of RIs across different variants.

The inclusion criterion was confirmed SARS-CoV-2 cases with documented positive nucleic acid amplification test (NAAT) results during the study period. The participants in cohort 1 were randomly matched based on sex and age with individuals from cohort 2.

To differentiate it from post-COVID-19 conditions such as long COVID (19), RI was defined as individuals who had previously been infected, recovered from SARS-CoV-2, and subsequently tested positive on a NAAT or rapid antigen test (RAT) at least 90 days after the primary infection (PI). The study outcome focused on whether individuals experienced RI at the study deadline. A total of 327 eligible individuals were included in the study. For a detailed description of the cohort design, please see Figure S4. The detailed definition of this study can be found in Appendix 3.

The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the institutional ethics committee of the Xiamen Center for Disease Control and Prevention, Fujian, China [XJK/LLSC(2024)017] and individual consent for this retrospective analysis was waived.

Epidemiological feature analysis

The descriptive analysis to investigate the epidemiological features of RI, the epidemiological distributions of the two sub-cohorts are compared.

Risk factor analysis

To evaluate the relationship between the RI and the selected variables, we utilized an ensemble model (see Appendix 4 and Table S1) combined with the host-pathogen-environment structure model (HPESM) (see Appendix 5 and Figure S5).

Statistical analysis

Continuous variables are presented as means [standard deviations (SD)] or medians [interquartile ranges (IQR)], based on the normality test results. Categorical variables are presented as absolute and relative frequencies. Appropriate statistical tests, including the chi-square test, Fisher exact test, or Wilcoxon test, were employed based on the type of data as well as the results of normality and homogeneity of variance tests. Statistical significance was assessed at a two-tailed significance level of P<0.10 for the normality and homogeneity of variance tests, whereas all other tests were evaluated at a significance level of P<0.05.

The database was constructed in Excel 2020, and all the statistical analyses and modeling were conducted via Python 3.9 within Jupyter Notebook 6.4.12. Details of the Python packages employed can be found in Table S2.

Results

Epidemiological features of SARS-CoV-2 reinfection

Between September 1, 2021 and January 8, 2023, the overall RI rate was 14.68% (48/327), with 22.51% (43/191) for cohort 1 and 3.68% (5/136) for cohort 2. Males were less likely to experience reinfection than females, with RI rates of 14.09% (21/149) versus 15.17% (27/178) and 20.88% (19/91) versus 24.00% (24/100) in cohort 1 and 3.45% (2/58) versus 3.85% (3/78) in cohort 2. The highest RI rates were observed in the 15–64 years age group at 15.49% (44/284) and 23.95% (40/167) in cohort 1, whereas cohort 2 had the highest RI rate of 7.14% (1/14) in the 65+ years age group (Tables 1,2, Table S3).

The PI cases were predominantly located in the Tongan district of cohort 1 (87.96%, 168/191) and the Siming district of cohort 2 (52.94%, 72/136). Higher RI rates were noted in the Jimei district (100.00%, 2/2) and Haicang district (83.33%, 5/6) in cohort 1. In cohort 2, the Jimei district also presented higher RI rates, at 20.00% (2/10), than the other districts did. No RI cases were reported in the Xiang’an District (Figures S6-S8 and Tables 1,2).

A total of 83.79% (274/327) of individuals received at least one dose of the vaccine, with the highest RI rates observed in those who received 2 (23.23%, 23/99) or 3 doses (12.74%, 20/157), though without significant statistical differences (Tables 1,2, Table S3).

A total of 48.93% (160/327) of individuals were employed in enterprises, while higher RI rates were observed in hospitals (50.00%, 5/10) and the government (36.36%, 4/11), as well as in hospitals (83.33%, 5/6) and government offices (80.00%, 4/5) in cohort 1. In cohort 2, higher RI rates were noted in commercial centers (7.14%, 3/42) and households (5.41%, 2/37) (Tables 1,2, Table S3).

The PI was more likely to manifest symptoms than the RI was (99.69%, 326/327 vs. 47.92%, 23/48), with 100.00% (191/191) and 44.19% (19/43) in cohort 1 and 99.26% (135/136) and 80.00% (4/5) in cohort 2, respectively. Hospitalization was necessary for 34.25% (112/327) of individuals during PI, which was significantly reduced to 4.17% (2/48) for RIs, with consistent proportions across cohorts (56.54%, 108/191 vs. 4.65%, 2/43 in cohort 1; 2.94%, 4/136 vs. 0.00%, 0/5 in cohort 2). The RI rate was significantly higher in individuals who were hospitalized during PI (24.11%, 27/112 vs. 9.77%, 21/215), but the difference was not statistically significant across cohorts (Tables 1,2, Table S3).

In cohort 1, 58.12% (111/191) of individuals experienced persistent infection during the PI, whereas 0.74% (1/136) of those in cohort 2 experienced persistent infection. Higher RI rates were observed in individuals with persistent infection during the PI (18.75%, 21/112), whereas higher RI rates were also noted in those without persistent infection during the PI for cohort 1 (27.50%, 22/80) and cohort 2 (3.70%, 5/135). No deaths attributed to COVID-19 were recorded during the follow-up period (Tables 1,2). The two major epidemic waves mentioned above and the PI cases and public health and social measures (PHSM) strength increased shapely because of initial inexperience with COVID-19 management, followed by a swift response to the emergence of the Delta (B.1.617.2) variant in Xiamen city, China. Subsequently, there was a decline in the PI cases concurrent with the increase in the PHSMs during the Omicron-dominated period. Moreover, 83.33% (40/48) of RI cases occurred following the “10 new measures” released as the strict PHSM gradually relaxed (20) (Figure 1A).

Figure 1 Epidemiological features of the SARS-CoV-2 reinfection. (A) The epidemic curve of the study population. (B) The distribution of key time intervals of SARS-CoV-2 reinfection. COVID-19, coronavirus disease 2019; PI, primary infection; RI, reinfection; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2; Tpr, the time interval between primary infection and reinfection; Tvr, the time interval from the most recent vaccination to the occurrence of reinfection.

A total of 89.58% (43/48) of RI cases were from cohort 1, with a median time interval between primary infection and reinfection (Tpr) of 462.0 (IQR, 448.5–475.5) days, and the time from the most recent vaccination to the reinfection (Tvr) was 381.3 (IQR, 167.9–594.7) days. In comparison, cohort 2 had a median Tpr of 280.0 (IQR, 159.0–401.0) days, and the median Tvr was 395.0 (IQR, 319.0–471.0) days. Notably, the Tpr for unvaccinated individuals was 295 days (Figure 1B, Figure S9, Tables S4,S5).

Risk factor analysis for SARS-CoV-2 reinfection

Univariate logistic regression revealed that individuals residing in less developed areas face greater RI risk than those in developing areas [odds ratio (OR) 2.70, 95% confidence interval (CI): 1.16–6.28]. Conversely, individuals in developed areas face a lower RI risk than those in developing areas (OR 0.39, 95% CI: 0.18–0.85). Specifically, residents in the Siming district have a lower RI risk than those in the Xiangan district (OR 0.39, 95% CI: 0.16–0.95). Healthcare workers (HCWs) face a greater RI risk than farmers (OR 6.37, 95% CI: 1.77–22.93). Individuals who received 1–2 vaccine doses have a greater RI risk than unvaccinated individuals (OR 2.48, 95% CI: 1.33–4.62). Those hospitalized during the PI have a higher RI risk than those not hospitalized (OR 2.93, 95% CI: 1.57–5.48). Individuals whose PIs with Omicron are elevated have a lower RI risk than those infected with Delta (OR 0.13, 95% CI: 0.05–0.34) (Table S6).

The optimal multivariate logistic regression (Model 6) demonstrated that residents in developed areas (OR 3.85, 95% CI: 1.36–10.91) and less developed areas (OR 19.09, 95% CI: 4.67–78.06) face greater RI risk than those in developing areas. For individuals with persistent infection during the PI, the RI risk is lower than that for those without persistent infection (OR 0.40, 95% CI: 0.17–0.94). Notably, individuals whose PIs with Omicron presented a lower RI risk than those whose PI with Delta (OR 0.03, 95% CI: 0.01–0.11) (Figure 2A, Table S7). Full details of model selection and collinearity assessments are available in Tables S8-S10.

Figure 2 Risk factors analysis of the SARS-CoV-2 reinfection. (A) Result of the optimal multivariate logistic regression for SARS-CoV-2 reinfection. (B) Result of the optimal GAM for SARS-CoV-2 reinfection. GAM, generalized additive model; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2.

The optimal Generalized Additive Model (GAM) (Model 5) indicates that individuals with mild symptoms when PI face increased RI risk, while those with moderate and severe symptoms exhibit decreased RI risk. Individuals hospitalized when the PI presents a greater RI risk than those not hospitalized. The remaining disparity is consistent with the multivariate logistic regression (Figure 2B, Table S11). The full details of model selection and collinearity assessments are available in Tables S9,S10,S12.

The survival curve indicated that the RI risk increased at 113 days and peaked at 464 days post-PI, with a final cumulative survival rate of 0.74 (Figure 3A). Univariate survival analysis indicated that the RI risk increased at
161 days for developed areas, at 113 days for less developed areas, and at 365 days for developing areas, peaking at 475, 468, and 464 days respectively, with final cumulative survival rates of 0.53, 0.11, and 0.81, respectively. Moreover, RI risk increased at 365 and 113 days, peaking at 365 and 468 days for individuals with and without persistent infection during the PI, with final cumulative survival rates of 0.79 and 0.69, respectively (Figure 3B).

Figure 3 The survival analysis for the SARS-CoV-2 reinfection. (A) The survival curve of the population examined for SARA-COV-2 reinfection. (B) Result of the univariate survival analysis and log-rank test for SARS-CoV-2 reinfection. (C) Result of the hierarchical analysis and log-rank test for SARS-CoV-2 reinfection. (D) Result of the optimal the optimal Cox proportional-hazards model for SARS-CoV-2 reinfection. KM, Kaplan-Meier; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2.

For workplace disparity, farmers displayed stable RI risk with a final cumulative survival rate of 1.00 post-PI. Conversely, the RI risk increased at 455, 161, 365, 295, 429, and 454 days, and peaked at 469, 462, 365, 468, 479, and 454 days, with the final cumulative survival rate of 0.20, 0.75, 0.79, 0.75, 0.81 and 0.17 post-PIs for individuals working on the government, commercial centers, enterprises, households, schools, and hospitals, respectively (Figure 3B).

For symptom disparity during the PI, asymptomatic individuals maintained a stable RI risk with a final cumulative survival rate of 1.00 post-PI. Conversely, the RI risk increased at 161, 113, and 469 days, and peaked at 479, 365, and 469 days, with the final cumulative survival rate of 0.53, 0.80, and 0.75 post-PI for individuals with mild, moderate and severe symptoms respectively (Figure 3B).

For variants disparity, the RI risk increased at 365 and 113 days, and peaked at 464 and 295 days, with the final cumulative survival rate of 0.76 and 0.85 post-PI for individuals PI with Delta variant and Omicron variant, respectively (Figure 3B).

Statistical tests are shown in Table S13. Pairwise comparisons for statistically significant variables are presented in Table S14. Further hierarchical analysis reveals that HCWs not hospitalized during the PI, unvaccinated males, unvaccinated elderly individuals, and unvaccinated hospitalized patients are at increased risk (Figure 3C, Table S15).

The optimal Cox proportional-hazards model (Model 3) reveals that residents in developed areas [risk ratio (RR) 5.87, 95% CI: 2.54–13.58] and less developed areas (RR 2.62, 95% CI: 1.13–6.10) face greater RI risk than those in developing areas. Individuals with persistent infection have a lower RI risk than those without persistent infection during the PI (RR 0.50, 95% CI: 0.25–0.99) (Figure 3D, Table S16), Full details of the model selection and collinearity assessments are available in Tables S9,S10,S17.

Discussion

Here, we found that the overall RI rate was 14.68%, with 22.51% for Delta-Omicron infections and 3.68% for Omicron-Omicron infections. These findings closely align with global reports, which indicate that RI rates range from 0.17% to 16% (9,10,13-15,21-23).

Between September 1, 2021 and January 8, 2023, a total of 1137 confirmed SARS-CoV-2 cases without any fatalities were recorded, indicating a relatively low COVID-19 disease burden in comparison with that in other regions worldwide. Furthermore, we observed an inverse relationship between the strictness of the PHSM and epidemic intensity, highlighting the efficacy of stringent PHSM during the zero-COVID-19 policy in controlling and managing the virus. However, 83.33% of the RI cases emerged after the implementation of the “10 new measures” and the subsequent relaxation of the PHSM. As society progressively returns to prepandemic norms, SARS-CoV-2 infections, including reinfections, are likely increasingly common due to the easing of strict PHSM.

Furthermore, compared with PIs, RIs are associated with reduced likelihood of symptom development (99.69% vs. 47.92%) and hospital admission (34.25% vs. 4.17%). This finding is consistent with the previous studies that RI was associated with a lower risk of serious illness and death than the PI was (24,25). However, data from the U.S. Veterans Health Administration’s electronic health database suggest an increased risk of death and hospitalization from repeated SARS-CoV-2 infections among individuals aged over 60 years (26). Additionally, reinfections increase the likelihood of long COVID (27). The risk associated with multiple SARS-CoV-2 infections in the broader population remains unclear. Therefore, it is crucial to adopt preventive measures such as mask-wearing and social distancing, especially to protect vulnerable groups such as elderly individuals to reduce the spread of SARS-CoV-2.

We found that PIs with the Delta variant face a higher risk of hospitalization (56.54% vs. 2.94%) and persistent infection (58.12% vs. 0.74%) than PIs with the Omicron variant do, highlighting the increased pathogenicity and prevalence of persistent infections associated with pre-Omicron variants (28,29). Immunity from SARS-CoV-2, derived from either past infection or vaccination, is temporary, lasting approximately 496 days (range, 428–491 days) postinfection and 383 days (range, 178–578 days) postvaccination. PIs with the Delta variant are more prone to RIs with the Omicron variant than PIs with the Omicron variant are, whereas PIs with the Omicron variant tend to experience a shorter duration between infections. Consistent with prior studies, infections involving pre-Omicron variants provide less immunity than Omicron-Omicron infections do, which occur at shorter intervals than pre-Omicron to Omicron infections (30-32). This phenomenon can be attributed to the greater transmissibility of the Omicron variant than other variants, a distinctive ability to evade antibodies even for 83.79% of the individuals receiving at least one dose of the vaccine, and the fact that vaccines, developed based on the wild-type SARS-CoV-2 variant, offer limited protection against the Omicron variant (5,33-35).

Using an ensemble model, we identified regional disparities and persistent infection during PI as vital factors influencing the RI. For regional disparity, less developed areas have the highest RI risk, developed areas have a higher risk, and developing areas have the lowest risk. This disparity can be ascribed to differences in population contact patterns. Despite developed areas offering abundant employment opportunities in tertiary sectors such as tourism and commercial services, the high cost of living pushes a significant portion of the workforce to reside in less developed or developing areas, or even in adjacent cities (e.g., Quanzhou, Zhangzhou), commuting through less developed regions where critical transportation hubs are situated. With their industrial zones, less developed areas also provide numerous job opportunities. In contrast, developing areas have the lowest population density and vast farmlands.

We found that individuals with persistent infection during PI have a lower RI risk than those without persistent infection. This reduced risk is partially attributed to the ongoing immune response elicited by the presence of SARS-CoV-2-infected cells and/or viral components, which enhances antibody production (27). Furthermore, those with persistent infections often undergo isolation and treatment in specialized facilities until they are asymptomatic with a SARS-CoV-2 nucleic acid cycle threshold (Ct) value ≥35, thereby exiting isolation. After extensive isolation periods, these individuals tend to adopt more stringent preventive measures against reinfection, including vaccination, mask-wearing, and maintaining social distancing, than those without persistent infections.

Both logistic regression and the GAM indicated a greater RI risk in individuals PI with the Delta variant than those with the Omicron variant. However, the Cox proportional hazards model revealed no variant difference in RI risk. This discrepancy was associated with Delta-Omicron cases (93.75%) being predominant over Omicron-Omicron cases (6.25%) during follow-up. The greater risk of RI in individuals with PI caused by the Delta variant than in those with the Omicron variant is associated with host-pathogen‒environment interactions, in addition to the relaxation of PHSMs and prior infections with pre-Omicron variants. The heterogeneity of immune levels within a population is also an important influencing factor. The immune status varies among different demographic groups; for example, a previous study has shown that older individuals are at a greater risk of RI due to insufficient immune protection acquired during PI (36). Furthermore, populations exhibit different vaccination and natural infection conditions, and the immunity generated by vaccination and natural infection declines over time, contributing to the heterogeneity in population susceptibility (37).

GAM and univariate survival analysis revealed that individuals displaying mild symptoms during PI had a greater RI risk than those with moderate symptoms did, potentially due to the lower hospitalization rate (22.45% vs. 37.73%) in the mild symptom group. Those with moderate symptoms, necessitating hospitalization, were more likely to adopt preventive measures against reinfection. No significant difference in RI risk was observed in individuals exhibiting other symptom severities, given their minimal representation within the study (0.31% asymptomatic and 2.45% severe).

Univariate survival analysis revealed that RI risk varied by workplace type, with government and HCWs, who are crucial in implementing the zero-COVID-19 policy, facing greater RI risk than those in other sectors. Furthermore, the hierarchical analysis revealed a greater RI risk and reduced survival time among unvaccinated males, elderly individuals, and hospitalized patients during PI. These findings align with previous research showing that vaccinations reduce RI risk (38,39). This highlights the importance of vaccination to reduce RI risk, especially in high-risk demographics, including elderly individuals and individuals with comorbidities such as hypertension and diabetes.

Notably, the GAM revealed that region and disease severity were non-linear associated with RI, despite the comparable goodness of fit between the GAM and logistic regression, this finding highlights potential nonlinear influences on the RI risk. Additionally, the time interval is a crucial factor in assessing RI risk, with survival analysis proving integral to understanding risk evolution across demographics over time.

Given the surge of the Omicron variant after the zero-COVID-19 policy conclusion, we urgently advise immediate vaccination for the unvaccinated and biannual boosters for those triple-vaccinated, prioritizing pre-Omicron infected individuals, elderly individuals, and other vulnerable populations. Furthermore, we endorse preventative practices for those in high-contact settings and urge HCWs to follow the National Health Commission’s latest protocol. With reduced mass laboratory screening, wastewater surveillance could effectively track SARS-CoV-2 evolution. Prompt action is essential for discovering significant viral mutations, including updating vaccines to address efficacy reductions and considering bivalent or multivalent options to prevent the spread of new strains.

The strengths of this study include the contribution of reliable evidence based on population-based observational follow-up aimed at estimating the epidemiological features and risk factors for RI. The study was conducted during the period when COVID-19 was managed as a class A disease, which allowed for comprehensive prevention and control measures covering the entire population. Detailed epidemiological investigations were carried out based on cases and their close contacts, providing reliable and unbiased sampling data. Second, we use an ensemble model that integrates a spectrum of factors—hosts, pathogens, and the environment—accounting for both linear and nonlinear interactions, thus facilitating a comprehensive analysis of RI risk determinants.

This study has several limitations. First, our definition of RI as occurring at least 90 days after PI is based on prior studies (12,13,40-42), although the optimal interval lacks consensus, ranging from 45 to 120 days used in different studies (12-15,22,25,43). Caution should thus be exercised when these results are compared with those of other studies. Second, our analysis covers the period from September 1, 2021 to January 8, 2023, a timeframe that is not fully representative of the evolving dynamics of the Omicron variant, particularly its latest mutations such as JN.1. This was due to the management of COVID-19 as a class B disease, resulting in less rigorous data collection protocols within DSPs. Additionally, although our study data were derived from DSPs during the zero-COVID-19 policy period and the data quality was relatively high, only 327 PI subjects were enrolled in this study due to the implementation of strict PHSMs during this period. The scope of the study was limited to Xiamen city, which restricts the generalizability of our findings compared with those of similar studies conducted in other regions (25,44); therefore, conclusions from this study need to be drawn cautiously. Furthermore, the noninclusion of data on underlying conditions such as diabetes and hypertension, or other variables such as marital status, ethnicity, and human-animal contact, limits the breadth of our analysis owing to data unavailability. Finally, we could not quantify some of the potential risk factors for RI, such as temperature, humidity, and other meteorological factors, at the individual level. Future larger-scale field studies are needed to integrate these broader risk factors.

Conclusions

As society returns to prepandemic norms, RIs are becoming more prevalent, driven by the relaxation of PHSM, evolutionary changes in pathogens, and shifts in population immunity and contact patterns. High-risk individuals include unvaccinated individuals, those living in densely populated areas, individuals previously infected with pre-Omicron variants and frontline COVID-19 workers. Although RIs tend to be less severe than PIs, the implications of multiple (three or more) SARS-CoV-2 infections are still not well understood. Consequently, the development of optimized and targeted public health strategies is essential to manage future infection waves and adapt effectively to evolving epidemiological dynamics.

Acknowledgments

We extend our thanks to the members of the Xiamen Center for Disease Control and Prevention, Fujian, China, and the School of Public Health, Xiamen University for their relentless efforts in anti-COVID-19 especially during the ‘zero-COVID-19 policy’ period.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-24-1703/rc

Data Sharing Statement: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-24-1703/dss

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-24-1703/prf

Funding: This work was supported by the Major Project of the Guangzhou National Laboratory (No. SRPG22-007 and No. GZNL2024A01004).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-24-1703/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the institutional ethics committee of the Xiamen Center for Disease Control and Prevention, Fujian, China [XJK/LLSC(2024)017] and individual consent for this retrospective analysis was waived.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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