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Preplanned Studies: Transmission Dynamics and Epidemiological Characteristics of the SARS-CoV-2 Delta Variant ― Hunan Province, China, 2021

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  • Summary

    What is already known about this topic?

    Little is known about the epidemiology, natural history, and transmission patterns of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Delta variant. Monitoring the evolution of viral fitness of SARS-CoV-2 in the host population is key for preparedness and response planning.

    What is added by this report?

    We analyzed a successfully contained local outbreak of Delta that took place in Hunan, China, and provided estimates of time-to-key event periods, infectiousness over time, and risk factors for SARS-CoV-2 infection and transmission for a still poorly understood variant.

    What are the implications for public health practice?

    Our findings simultaneously shed light on both the characteristics of the Delta variant, by identifying key age groups, risk factors, and transmission pathways, and planning a future response effort against SARS-CoV-2.

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  • Funding: Supported by grants from the Key Program of the National Natural Science Foundation of China (82130093), Shanghai Municipal Science and Technology Major Project (ZD2021CY001), Hunan Provincial Innovative Construction Special Fund: Emergency response to COVID-19 outbreak (No. 2020SK3012), and Chinese Academy of Medical Sciences Coronavirus Disease 2019 Science and Technology Research Project in 2020 (No. 2020HY320003)
  • [1] Hu SX, Wang W, Wang Y, Litvinova M, Luo KW, Ren LS, et al. Infectivity, susceptibility, and risk factors associated with SARS-CoV-2 transmission under intensive contact tracing in Hunan, China. Nat Commun 2021;12(1):1533. http://dx.doi.org/10.1038/s41467-021-21710-6CrossRef
    [2] Office of Changde Municipal People’s Government. An asymptomatic case was reported in Changde city, Hunan province. https://www.changde.gov.cn/cdzx/cdyw/content_850758. [2022-7-26].https://www.changde.gov.cn/cdzx/cdyw/content_850758
    [3] Kang M, Xin HL, Yuan J, Ali ST, Liang ZM, Zhang JY, et al. Transmission dynamics and epidemiological characteristics of SARS-CoV-2 Delta variant infections in Guangdong, China, May to June 2021. Euro Surveill 2022;27(10):2100815. http://dx.doi.org/10.2807/1560-7917.ES.2022.27.10.2100815CrossRef
    [4] Ogata T, Tanaka H, Irie F, Hirayama A, Takahashi Y. Shorter incubation period among unvaccinated delta variant coronavirus disease 2019 patients in Japan. Int J Environ Res Public Health 2022;19(3):1127. http://dx.doi.org/10.3390/IJERPH19031127CrossRef
    [5] Del Águila-Mejía J, Wallmann R, Calvo-Montes J, Rodríguez-Lozano J, Valle-Madrazo T, Aginagalde-Llorente A. Secondary attack rate, transmission and incubation periods, and serial interval of SARS-CoV-2 omicron variant, spain. Emerg Infect Dis 2022;28(6):1224 − 8. http://dx.doi.org/10.3201/eid2806.220158CrossRef
    [6] Manica M, Litvinova M, De Bellis A, Guzzetta G, Mancuso P, Vicentini M, et al. Estimation of the incubation period and generation time of SARS-CoV-2 Alpha and Delta variants from contact tracing data. Epidemiol Infect 2022. https://doi.org/10.1017/S0950268822001947.https://doi.org/10.1017/S0950268822001947
    [7] Hart WS, Miller E, Andrews NJ, Waight P, Maini PK, Funk S, et al. Generation time of the alpha and delta SARS-CoV-2 variants: an epidemiological analysis. Lancet Infect Dis 2022;22(5):603 − 10. http://dx.doi.org/10.1016/S1473-3099(22)00001-9CrossRef
    [8] Park SW, Sun KY, Champredon D, Li M, Bolker BM, Earn DJD, et al. Forward-looking serial intervals correctly link epidemic growth to reproduction numbers. Proc Natl Acad Sci USA 2021;118(2):e2011548118. http://dx.doi.org/10.1073/pnas.2011548118CrossRef
    [9] Riley S, Wang HW, Eales O, Haw D, Walters CE, Ainslie KEC, et al. REACT-1 round 12 report: resurgence of SARS-CoV-2 infections in England associated with increased frequency of the Delta variant. medRxiv 2021. http://medrxiv.org/content/early/2021/06/21/2021.06.17.21259103.abstract.http://medrxiv.org/content/early/2021/06/21/2021.06.17.21259103.abstract
    [10] Kang M, Yi Y, Li Y, Sun LM, Deng AP, Hu T, et al. Effectiveness of inactivated COVID-19 vaccines against illness caused by the B. 1.617.2 (Delta) variant during an outbreak in Guangdong, China: a cohort study. Ann Intern Med 2022;175(4):533 − 40. http://dx.doi.org/10.7326/M21-3509CrossRef
    [11] Chen XH, Yan XM, Sun KY, Zheng N, Sun RJ, Zhou JX, et al. Estimation of disease burden and clinical severity of COVID-19 caused by Omicron BA.2 in Shanghai, February-June 2022. Emerg Microbes Infect 2022;11(1):2800-7. http://dx.doi.org/10.1080/22221751.2022.2128435.http://dx.doi.org/10.1080/22221751.2022.2128435
    [12] Chen ZY, Deng XW, Fang LQ, Sun KY, Wu YP, Che TL, et al. Epidemiological characteristics and transmission dynamics of the outbreak caused by the SARS-CoV-2 Omicron variant in Shanghai, China: a descriptive study. Lancet Reg Health West Pacific 2022;29:100592. http://dx.doi.org/10.1016/j.lanwpc.2022.100592CrossRef
    [13] Bager P, Wohlfahrt J, Bhatt S, Stegger M, Legarth R, Møller CH, et al. Risk of hospitalisation associated with infection with SARS-CoV-2 omicron variant versus delta variant in Denmark: an observational cohort study. Lancet Infect Dis 2022;22(7):967 − 76. http://dx.doi.org/10.1016/S1473-3099(22)00154-2CrossRef
  • FIGURE 1.  Key time-to-event intervals of SARS-CoV-2 Delta infections in Hunan, China. (A) Estimated incubation period distribution by log-normal distributions and generation time by gamma distributions based on 71 confirmed cases. (B) Estimated distribution of the serial interval by Weibull distributions and of the infectiousness profile by gamma distributions based on 54 transmission pairs. (C) Time intervals from symptom onset to hospitalization, isolation, first time of sampling, and diagnosis of symptomatic cases before and after the implementation of NPIs.

    Note: The infectiousness profile describes the infectiousness of an individual over time since the onset of symptoms.

    Abbreviation: NPIs=nonpharmaceutical interventions; SARS-CoV-2=severe acute respiratory syndrome coronavirus 2.

    TABLE 1.  Characteristics of SARS-CoV-2 Delta infected individuals and their close contacts in Hunan Province, China, 2021.

    CharacteristicsInfections
    (n=129)
    Close contacts
    (n=2,118)
    Secondary infection attack rate
    (%, 95% CI)*
    Age, years
    Median (IQR)34 (15, 48)34 (20, 46)
    Age group, years
    0–919 (14.7%)140 (6.6%)10.7 (6.1, 17.1)
    10–1719 (14.7%)293 (13.8%)4.8 (2.6, 7.9)
    18–6487 (67.4%)1,595 (75.3%)3.2 (2.4, 4.2)
    >654 (3.1%)90 (4.2%)4.4 (1.2, 11)
    Sex
    Male58 (45.0%)920 (43.4%)4.6 (3.3, 6.1)
    Female71 (55.0%)1,198 (56.6%)3.5 (2.5, 4.7)
    Clinical outcome
    Not infected2,034 (96.0%)
    Confirmed asymptomatic infection19 (14.7%)9 (0.4%)
    Confirmed symptomatic infection110 (85.3%)75 (3.5%)
    Mild48 (43.6%)39 (52.0%)
    Moderate61 (55.5%)35 (46.7%)
    Severe1 (0.9%)1 (1.3%)
    Vaccination history
    Unvaccinated66 (51.2%)993 (46.9%)5.2 (3.9, 6.8)
    Partially vaccinated27 (20.9%)290 (13.7%)4.8 (2.7, 8.0)
    Time interval between last vaccination and symptoms onset or last exposure, days§
    Median (IQR)26 (21, 35)25 (21, 28)
    Fully vaccinated36 (27.9%)835 (39.4%)2.2 (1.3, 3.4)
    Time interval between last vaccination and symptoms onset or last exposure, days§
    Median (IQR)49 (40, 54)48 (41, 56)
    Mode of detection
    Passive surveillance14 (10.9%)
    Contact tracing80 (62.0%)
    Community screening35 (27.1%)
    Note: “−” means data not available.The last column provided the secondary infection attack rates across different groups of age group, sex, and vacciantion history.
    Abbreviation: IQR=interquartile range; CI=confidence interval; SARS-CoV-2=severe acute respiratory syndrome coronavirus 2; COVID-19=coronavirus disease 2019.
    * The secondary infection attack rate was calculated by dividing the number of infections by the total number of close contacts.
    Vaccination history: 1) unvaccinated (i.e., individuals who did not receive any COVID-19 vaccines or received 1 dose of COVID-19 vaccine less than 14 days before the date of the last known contact); 2) partially vaccinated (i.e., individuals who had received either 1 dose of a COVID-19 vaccine or received 2 doses of vaccines with the date of the second dose less than 14 days before the date of the last known contact) and 3) fully vaccinated (i.e., individuals who completed the full 2-dose course vaccination more than 14 days before the date of the last known contact).
    § For the asymptomatic subjects and the cases diagnosed by imaging characteristics, we used the date of the first sample collection with a positive test instead of the date of symptom onset.
    Download: CSV

    TABLE 2.  Estimating the association of potential risk factors with the risk of acquiring and transmitting the SARS-CoV-2 Delta variant.

    CharacteristicUnivariate analysisMultivariate analysis
    OR (95% CI)P valueOR (95% CI)P value
    Age (years) and vaccination status of contacts
    Unvaccinated children (0–9 years)3.42 (1.49, 7.89)0.004**2.44 (1.03, 5.80)0.044*
    Unvaccinated adolescents (10–17 years)1.56 (0.66, 3.69)0.3101.20 (0.46, 3.10)0.707
    Unvaccinated adults (18–64 years)ReferenceReference
    Vaccinated adults (18–64 years)0.49 (0.25, 0.95)0.035*0.46 (0.23, 0.93)0.030*
    Age (years) and vaccination status of infectors§
    Unvaccinated children (0–9 years)1.79 (0.45, 7.19)0.4100.82 (0.19, 3.46)0.784
    Unvaccinated adolescents (10–17 years)0.32 (0.07, 1.51)0.1490.63 (0.16, 2.52)0.513
    Unvaccinated adults (18–64 years)ReferenceReference
    Vaccinated adults (18–64 years)0.53 (0.17, 1.61)0.2600.51 (0.19, 1.40)0.192
    Type of contact
    Household contact7.54 (3.67, 15.48)<0.001***6.79 (3.20, 14.44)<0.001***
    Health care0.44 (0.10, 1.93)0.2740.22 (0.04, 1.11)0.067
    Social contactReferenceReference
    Workplace contact0.64 (0.15, 2.75)0.5500.73 (0.17, 3.20)0.678
    Other0.07 (0.02, 0.20)<0.001***0.05 (0.02, 0.15)<0.001***
    Sex of contacts
    FemaleReferenceReference
    Male1.03 (0.62, 1.73)0.9060.72 (0.40, 1.28)0.261
    Sex of infectors
    FemaleReferenceReference
    Male1.04 (0.39, 2.79)0.9420.67 (0.27, 1.67)0.391
    Clinical severity of primary cases
    Asymptomatic infectionReference
    Symptomatic infection1.05 (0.23, 4.75)0.951
    Note: “−” in the column of “OR (95% CI)” indicated that the corresponding variable was not incorporated into the analysis. All children and adolescents (under 18 years) were unvaccinated, as they were not covered by the COVID-19 vaccination program in the Chinese mainland during the Delta outbreak. Vaccinated adults denote fully vaccinated adults, while partially vaccinated adults were deemed unvaccinated adults in the analysis.
    Abbreviation: CI=confidence interval; OR=odds ratio; COVID-19=coronavirus disease 2019; SARS-CoV-2=severe acute respiratory syndrome coronavirus 2.
    * P<0.05,
    ** P<0.01.
    *** P<0.001.
    Contacts who were aged at or over 65 years or whose infectors were aged at or over 65 years were excluded from this analysis due to the small sample size.
    § For a contact who had interacted with more than one SARS-CoV-2 infection, we chose the first infected individual they had been exposed to as the potential infector. If they had exposed to multiple infectors at the same time, we randomly chose one from among these infectors as the potential infector.
    Download: CSV

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Transmission Dynamics and Epidemiological Characteristics of the SARS-CoV-2 Delta Variant ― Hunan Province, China, 2021

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Summary

What is already known about this topic?

Little is known about the epidemiology, natural history, and transmission patterns of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Delta variant. Monitoring the evolution of viral fitness of SARS-CoV-2 in the host population is key for preparedness and response planning.

What is added by this report?

We analyzed a successfully contained local outbreak of Delta that took place in Hunan, China, and provided estimates of time-to-key event periods, infectiousness over time, and risk factors for SARS-CoV-2 infection and transmission for a still poorly understood variant.

What are the implications for public health practice?

Our findings simultaneously shed light on both the characteristics of the Delta variant, by identifying key age groups, risk factors, and transmission pathways, and planning a future response effort against SARS-CoV-2.

  • 1. Hunan Provincial Center for Disease Control and Prevention;Workstation for Emerging Infectious Disease Control and Prevention, Chinese Academy of Medical Sciences, Changsha City, Hunan Province, China
  • 2. Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai Mulnicipality, China
  • 3. School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai Municipality, China
  • 4. Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
  • 5. Laboratory of Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA
  • Corresponding authors:

    Shixiong Hu, 379788967@qq.com

    Hongjie Yu, yhj@fudan.edu.cn

  • Funding: Supported by grants from the Key Program of the National Natural Science Foundation of China (82130093), Shanghai Municipal Science and Technology Major Project (ZD2021CY001), Hunan Provincial Innovative Construction Special Fund: Emergency response to COVID-19 outbreak (No. 2020SK3012), and Chinese Academy of Medical Sciences Coronavirus Disease 2019 Science and Technology Research Project in 2020 (No. 2020HY320003)
  • Online Date: January 20 2023
    doi: 10.46234/ccdcw2023.011
  • Monitoring changes in the epidemiologic features between different severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants is key to understanding the evolution of viral fitness in the host population. Here, we analyzed a successfully contained local outbreak of the Delta variant that took place in Hunan, China, in July–August, 2021. Detailed data on SARS-CoV-2 infections and their contacts were collected during the outbreak. By leveraging these data, we estimated key epidemiological parameters, including the incubation period, serial interval, and generation time. We constructed a generalized linear mixed-effects model (GLMM) to quantify risk factors for Delta infection and transmission. Between July 28 and August 15, 2021, a total of 129 infections and their 2,118 close contacts were identified during the outbreak in Hunan Province. The mean incubation period, serial interval, and generation time were estimated to be 5.3 days [interquartile range (IQR): 3.0–6.5], 4.3 days (IQR: 1.7–6.8), and 4.4 days (IQR: 2.4–5.8), respectively. Infectiousness peaked 1.6 days before symptom onset, with 63.0% of the transmission events occurring during the presymptomatic phase. Household contacts had the highest risk of infection [odds ratio (OR)=6.79, 95% confidence interval (CI): 3.20–14.44]. The susceptibility to Delta infection was higher in children aged 0–9 years than in adults aged 18–64 years (OR=2.44, 95% CI: 1.03–5.80). The effectiveness of the inactivated vaccine against any confirmed infection was 54% (95% CI: 7%–77%). By providing quantitative evidence about key epidemiological features as well as infection and transmission risk factors for Delta, our findings improved our relatively scarce understanding of this variant of concern and, more broadly, the evolutionary trajectory of SARS-CoV-2.

    During the 2021 Delta outbreak in Hunan, field epidemiological investigations allowed the prompt identification of the population at risk, including contacts of positive individuals. Here we provided descriptive statistics of positive cases’ characteristics and close contacts. We estimated the incubation period (i.e., the time delay from infection to illness onset), serial interval (i.e., the time interval between the onset of symptoms in a primary infector and their onset in secondary infections), generation time (i.e., the time interval between infection of the primary infector and their secondary infections), and infectiousness profile (i.e., the temporal distribution of the probability of transmission), following the method in reference (1). To evaluate the impact of nonpharmaceutical interventions (NPIs) on the containment of the outbreak, we also compared the distribution of the time delay from symptom onset to hospitalization, to isolation, to the collection of the first positive specimen, and laboratory confirmation before and after the implementation of NPIs using the Kolmogorov-Smirnov test (K-S test).

    A GLMM was used to quantify the effects of potential drivers of susceptibility to Delta variant infection and infectivity of infected individuals. To quantify the vaccination effectiveness against the Delta variant infection and transmission, we incorporated the vaccination status of infectors and contacts into the model. Other factors, including age, sex, and the contact setting, could be potential drivers of the susceptibility and infectivity of SARS-CoV-2 ancestral strains in previous research (1) and thus have also been considered in our models. (Supplementary Material). All vaccinated individuals included in this study received inactivated vaccines. Our primary analysis was based on 2,004 close contacts, and a supplementary analysis was conducted incorporating another 4,131 general contacts aged under 65 years. Statistical analyses were performed in R, version 4.1.1 (R Foundation for Statistical Computing, Vienna, Austria).

    The first local infection of Delta in Hunan Province, China, was identified on July 28, 2021: an individual who shared the same boat with domestically infected tourists (2). Between July 28 and August 15, 2021, 129 SARS-CoV-2 Delta infections were identified in Hunan Province, China. According to genomic data, all identified infections in this outbreak were infected by the Delta variant.

    The outbreak quickly spread from the original touristic location through other cities within Hunan Province (Supplementary Figure S1). Through epidemiological investigations and active contact tracing, 10,971 individuals categorized as the at-risk population were initially identified, of which 2,118 were further classified as close contacts of infected individuals. Another 4,513 were identified as general contacts (Supplementary Figure S2), and the reconstructed transmission chain was reported in Supplementary Figure S3. Among all infections, 19 were asymptomatic, and 110 were symptomatic, including 48 mild cases (43.6%), 61 moderate cases (55.5%), and 1 severe case (0.9%), with no critical cases or deaths reported [Table 1, see Supplementary Table S1 for an analysis of the completeness of the variables].

    CharacteristicsInfections
    (n=129)
    Close contacts
    (n=2,118)
    Secondary infection attack rate
    (%, 95% CI)*
    Age, years
    Median (IQR)34 (15, 48)34 (20, 46)
    Age group, years
    0–919 (14.7%)140 (6.6%)10.7 (6.1, 17.1)
    10–1719 (14.7%)293 (13.8%)4.8 (2.6, 7.9)
    18–6487 (67.4%)1,595 (75.3%)3.2 (2.4, 4.2)
    >654 (3.1%)90 (4.2%)4.4 (1.2, 11)
    Sex
    Male58 (45.0%)920 (43.4%)4.6 (3.3, 6.1)
    Female71 (55.0%)1,198 (56.6%)3.5 (2.5, 4.7)
    Clinical outcome
    Not infected2,034 (96.0%)
    Confirmed asymptomatic infection19 (14.7%)9 (0.4%)
    Confirmed symptomatic infection110 (85.3%)75 (3.5%)
    Mild48 (43.6%)39 (52.0%)
    Moderate61 (55.5%)35 (46.7%)
    Severe1 (0.9%)1 (1.3%)
    Vaccination history
    Unvaccinated66 (51.2%)993 (46.9%)5.2 (3.9, 6.8)
    Partially vaccinated27 (20.9%)290 (13.7%)4.8 (2.7, 8.0)
    Time interval between last vaccination and symptoms onset or last exposure, days§
    Median (IQR)26 (21, 35)25 (21, 28)
    Fully vaccinated36 (27.9%)835 (39.4%)2.2 (1.3, 3.4)
    Time interval between last vaccination and symptoms onset or last exposure, days§
    Median (IQR)49 (40, 54)48 (41, 56)
    Mode of detection
    Passive surveillance14 (10.9%)
    Contact tracing80 (62.0%)
    Community screening35 (27.1%)
    Note: “−” means data not available.The last column provided the secondary infection attack rates across different groups of age group, sex, and vacciantion history.
    Abbreviation: IQR=interquartile range; CI=confidence interval; SARS-CoV-2=severe acute respiratory syndrome coronavirus 2; COVID-19=coronavirus disease 2019.
    * The secondary infection attack rate was calculated by dividing the number of infections by the total number of close contacts.
    Vaccination history: 1) unvaccinated (i.e., individuals who did not receive any COVID-19 vaccines or received 1 dose of COVID-19 vaccine less than 14 days before the date of the last known contact); 2) partially vaccinated (i.e., individuals who had received either 1 dose of a COVID-19 vaccine or received 2 doses of vaccines with the date of the second dose less than 14 days before the date of the last known contact) and 3) fully vaccinated (i.e., individuals who completed the full 2-dose course vaccination more than 14 days before the date of the last known contact).
    § For the asymptomatic subjects and the cases diagnosed by imaging characteristics, we used the date of the first sample collection with a positive test instead of the date of symptom onset.

    Table 1.  Characteristics of SARS-CoV-2 Delta infected individuals and their close contacts in Hunan Province, China, 2021.

    We analyzed 71 locally confirmed cases with clear exposure dates or exposure windows to estimate the incubation period. From the best-fitting lognormal distribution, we obtained a mean estimate of the incubation period of 5.3 days (median: 4.4, IQR: 3.0–6.5). Consistent results were obtained when fitting alternative distributions (Supplementary Table S2). The symptom onset date was available for 54 transmission pairs; the resulting serial interval had an estimated mean of 4.3 days (median: 4.2, IQR: 1.7–6.8), based on fitting a Weibull distribution, and consistent results were found by fitting alternative distributions (Supplementary Table S2). Infectiousness was estimated to peak 1.6 days before symptom onset, and the proportion of pre-symptomatic transmission was 63.0%, with 95% of transmission events occurring between −5.6 and 5.8 days after the date of symptom onset. The mean generation time was estimated to be 4.4 days (median: 3.9, IQR: 2.4–5.8) (Figure 1A–B). A marked decreasing trend was observed in all time intervals from symptom onset to hospitalization, to isolation, to the collection of the first positive specimen, and the laboratory diagnosis of symptomatic individuals after the initiation of NPIs. Among them, the time interval from symptom onset to isolation showed the most significant decrease, with a reduction from a median of 4.0 days (IQR: 2.5–7.0) to 0 days (IQR: −1.0–1.0) (Figure 1C), which may have potentially played a crucial role in the successful containment of the outbreak.

    Figure 1.  Key time-to-event intervals of SARS-CoV-2 Delta infections in Hunan, China. (A) Estimated incubation period distribution by log-normal distributions and generation time by gamma distributions based on 71 confirmed cases. (B) Estimated distribution of the serial interval by Weibull distributions and of the infectiousness profile by gamma distributions based on 54 transmission pairs. (C) Time intervals from symptom onset to hospitalization, isolation, first time of sampling, and diagnosis of symptomatic cases before and after the implementation of NPIs.

    Note: The infectiousness profile describes the infectiousness of an individual over time since the onset of symptoms.

    Abbreviation: NPIs=nonpharmaceutical interventions; SARS-CoV-2=severe acute respiratory syndrome coronavirus 2.

    A multivariate regression analysis was performed based on close contact data collected. The results showed that the susceptibility to infection of the adult population was lower for fully vaccinated individuals than non-vaccinated individuals (OR=0.46, 95% CI: 0.23–0.93), indicating a vaccine effectiveness of 54% (95% CI: 7%–77%) against any confirmed Delta infection. Among unvaccinated close contacts, susceptibility to Delta infection was higher in children than in adults (OR=2.44, 95% CI: 1.03–5.80). In addition, we found a higher infection risk in the household setting (OR=6.79, 95% CI: 3.20–14.44), while other potential risk factors, such as the sex of the infectors and the contacts, were not statistically significant (Table 2). In Supplementary Table S3, we present the results of a sensitivity analysis where, when the source of exposure was not resolved, the potential infector was selected at random. The consistency between these results and those of the main analysis supports the robustness of our main conclusions. A supplementary analysis was performed on 6,135 contacts, which included both close contacts and general contacts, and these results are also consistent with those of the main analysis (Supplementary Table S4).

    CharacteristicUnivariate analysisMultivariate analysis
    OR (95% CI)P valueOR (95% CI)P value
    Age (years) and vaccination status of contacts
    Unvaccinated children (0–9 years)3.42 (1.49, 7.89)0.004**2.44 (1.03, 5.80)0.044*
    Unvaccinated adolescents (10–17 years)1.56 (0.66, 3.69)0.3101.20 (0.46, 3.10)0.707
    Unvaccinated adults (18–64 years)ReferenceReference
    Vaccinated adults (18–64 years)0.49 (0.25, 0.95)0.035*0.46 (0.23, 0.93)0.030*
    Age (years) and vaccination status of infectors§
    Unvaccinated children (0–9 years)1.79 (0.45, 7.19)0.4100.82 (0.19, 3.46)0.784
    Unvaccinated adolescents (10–17 years)0.32 (0.07, 1.51)0.1490.63 (0.16, 2.52)0.513
    Unvaccinated adults (18–64 years)ReferenceReference
    Vaccinated adults (18–64 years)0.53 (0.17, 1.61)0.2600.51 (0.19, 1.40)0.192
    Type of contact
    Household contact7.54 (3.67, 15.48)<0.001***6.79 (3.20, 14.44)<0.001***
    Health care0.44 (0.10, 1.93)0.2740.22 (0.04, 1.11)0.067
    Social contactReferenceReference
    Workplace contact0.64 (0.15, 2.75)0.5500.73 (0.17, 3.20)0.678
    Other0.07 (0.02, 0.20)<0.001***0.05 (0.02, 0.15)<0.001***
    Sex of contacts
    FemaleReferenceReference
    Male1.03 (0.62, 1.73)0.9060.72 (0.40, 1.28)0.261
    Sex of infectors
    FemaleReferenceReference
    Male1.04 (0.39, 2.79)0.9420.67 (0.27, 1.67)0.391
    Clinical severity of primary cases
    Asymptomatic infectionReference
    Symptomatic infection1.05 (0.23, 4.75)0.951
    Note: “−” in the column of “OR (95% CI)” indicated that the corresponding variable was not incorporated into the analysis. All children and adolescents (under 18 years) were unvaccinated, as they were not covered by the COVID-19 vaccination program in the Chinese mainland during the Delta outbreak. Vaccinated adults denote fully vaccinated adults, while partially vaccinated adults were deemed unvaccinated adults in the analysis.
    Abbreviation: CI=confidence interval; OR=odds ratio; COVID-19=coronavirus disease 2019; SARS-CoV-2=severe acute respiratory syndrome coronavirus 2.
    * P<0.05,
    ** P<0.01.
    *** P<0.001.
    Contacts who were aged at or over 65 years or whose infectors were aged at or over 65 years were excluded from this analysis due to the small sample size.
    § For a contact who had interacted with more than one SARS-CoV-2 infection, we chose the first infected individual they had been exposed to as the potential infector. If they had exposed to multiple infectors at the same time, we randomly chose one from among these infectors as the potential infector.

    Table 2.  Estimating the association of potential risk factors with the risk of acquiring and transmitting the SARS-CoV-2 Delta variant.

    • Based on case surveillance and contact tracing data, we described the epidemiological characteristics of the SARS-CoV-2 Delta variant outbreak in Hunan Province, China, in July–August 2021. To date, only a few studies have provided estimates of the incubation period, serial interval, and generation time of Delta variant infection. These studies suggest that the mean (or median) incubation period of Delta ranges from 3.0 to 6.0 days (3-4), which contains the mean estimate obtained in our study (5.3 days); also, our estimate of the serial interval (4.3 days) falls inside the range reported in previous studies (range 2.6–5.4 days) (5-6). Regarding the generation time, we found only two studies providing estimates for the Delta variant; the first one was conducted in the UK and found a mean value of 4.7 days (7), while the second one was conducted in Italy and found a mean value of 6.6 days (6). By comparison, we estimated a mean generation time of 4.4 days. However, it is important to stress that here we provide estimates of the “realized” (i.e., observed) generation time. In contrast the two European studies estimate the intrinsic generation time (i.e., what would be observed in an infinitely large and fully susceptible population). Contact tracing could speed up the detection and isolation of infectors, and case isolation and contact quarantine could prevent potential infectors from contacting susceptible individuals, which limited our estimates to the specific conditions of the analyzed outbreak. A strength of our study is that our estimates of the time-to-event distributions are based on transmission pairs of the entire outbreak and are thus insensitive to right-censoring bias mediated by epidemic growth rates (8).

      Different from the ancestral strain, we found that children were more likely to be infected with the Delta variant, and similar observations have been found in other settings (9). This finding emphasizes the relevance of young individuals in SARS-CoV-2 outbreaks. In agreement with prior studies (10), the effectiveness of 2 doses of inactivated vaccines against Delta infection was estimated at 54% (OR=0.46, 95% CI: 0.23–0.93), while their effectiveness in preventing forward transmission was not statistically significant, possibly due to the small sample size. Our analysis did not explicitly consider the effect of the waning of vaccine protection as most [86% (31/36)] reported infections among fully vaccinated individuals were infected within two months after receiving their most recent vaccine dose. Nonetheless, our study further supports the key role of vaccination in mitigating coronavirus disease 2019 (COVID-19) burden.

      Our study has several limitations. First, the sample size of our study is relatively limited (129 confirmed infections, 2,118 contacts, and 4,513 general contacts). It thus does not allow the analysis for further stratifications, such as the estimation of the key time-to-event intervals by vaccination status. Moreover, it is possible that our sample size is insufficient to provide statistically significant results for reducing forward transmission in vaccinated individuals. Second, due to the small number of asymptomatic infections in our sample, we did not distinguish between asymptomatic and symptomatic infections in our analysis, which may mask the heterogeneity of the two modes of transmission. The low proportion of asymptomatic infections in our sample (14.7%) as compared to more recent studies on outbreaks of the Omicron variants (11) may be due to several factors, including the reliance on repeated city-wide screenings of the population to curb the spread of highly transmissible Omicron variants (12), the progressive increase of population immunity protecting against symptomatic disease, and a possible reduced intrinsic severity of the Omicron variants (13).

      Given the rapid adaptive evolution of SARS-CoV-2, having a cohesive picture of the differences and similarities between different variants is key for preparedness planning. As such, our findings not only shed light on the characteristics of the analyzed outbreak but can also be instrumental for planning future response efforts against SARS-CoV-2.

    • All the teams of field investigators of the Hunan Provincial Center for Disease Control and Prevention for their contribution.

Reference (13)

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