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Changes in contact patterns shape the dynamics of the COVID-19 outbreak in China

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Science  29 Apr 2020:
eabb8001
DOI: 10.1126/science.abb8001
  • Fig. 1 Contact matrices by age.

    (A) Baseline period contact matrix for Wuhan (regular weekday only). Each cell of the matrix represents the mean number of contacts that an individual in a given age group has with other individuals, stratified by age groups. The color intensity represents the number of contacts. To construct the matrix we performed bootstrap sampling with replacement of survey participants weighted by the age distribution of the actual population of Wuhan. Every cell of the matrix represents an average over 100 bootstrapped realizations. (B) Same as (A), but for the outbreak contact matrix for Wuhan. (C) Difference between the baseline period contact matrix and the outbreak contact matrix in Wuhan. (D) Same as (A), but for Shanghai. (E and F) Same as (B) and (C), but for Shanghai.

  • Fig. 2 Effect of contact patterns on the epidemic spread.

    (A) Estimated R0 during the outbreak (mean and 95%CI), as a function of baseline R0 (i.e., that derived by using the contact matrix estimated for the baseline period). The figure refers to Wuhan and include both the scenario accounting for the estimated susceptibility to infection by age and assuming that all individuals are equally susceptible to infection. The distribution of the transmission rate is estimated through the next generation matrix approach by using 100 bootstrapped contact matrices for the baseline period in order to obtain the desired R0 values. We then use the estimate distribution of the transmission rate the bootstrapped outbreak contact matrices to estimate R0 for the outbreak period. The 95% confidence intervals account for the uncertainty on the distribution of the transmission rate, mixing patterns, and susceptibility to infection by age. (B) As (A), but for Shanghai. (C) Infection attack rate one year after the initial case of COVID-19 (mean and 95%CI) as a function of the baseline R0. The estimates are by simulating the SIR transmission model (see supplementary materials) using the contact matrix for the baseline period and considering the estimated susceptibility to infection by age and assuming that all individuals are equally susceptible to infection. The 95% confidence intervals account for the uncertainty on the mixing patterns and susceptibility to infection by age. (D) As (C), but for Shanghai.

  • Fig. 3 Effect of limiting school contacts on the epidemic spread.

    (A) Estimated R0 during the outbreak (mean and 95%CI), as a function of baseline R0 (i.e., that derived by using the contact matrix estimated for the baseline period). The figure refers to Shanghai and the scenario accounting for the estimated susceptibility to infection by age. Three contact patterns are considered: i) as estimated during the COVID-19 outbreak, ii) as estimated during school vacations (7) and iii) as estimated for the baseline period, but suppressing all contacts at school. (B) Daily incidence of new SARS-CoV-2 infections (mean and 95%CI) as estimated by the SIR model assuming age-specific susceptibility to infection (see supplementary materials). Three mixing patterns are considered: i) as estimated for the baseline period, ii) as estimated during school vacations (7) and iii) as estimated for the baseline period, but suppressing all contacts at school. The inset shows the infection attack rate one year after the introduction of the first COVID-19 case (mean and 95%CI). (C) As (A), but assuming equal susceptibility to infection by age. (D) As (B), but assuming equal susceptibility to infection by age.

  • Table 1 Number of contacts by demographic characteristics and location.

    CharacteristicsWuhanShanghai
    Baseline periodCOVID-19 OutbreakDifferencebBaseline periodCOVID-19 OutbreakDifferenceb
    N
    (%)a
    Mean
    (95% CIc)
    N
    (%)a
    Mean
    (95% CIc)
    N
    (%)
    Mean
    (95% CIc)
    N
    (%)
    Mean
    (95% CIc)
    Overall624
    (100.0)
    14.6
    (12.9, 16.3)
    627
    (100.0)
    2
    (1.9, 2.1)
    12.6***965
    (100.0)
    18.8
    (17.8, 19.8)
    557
    (100.0)
    2.3
    (2, 2.8)
    16.4***
    Sex
     Male300
    (48.1)
    14.5
    (12.2, 17.1)
    301
    (48)
    1.8
    (1.7, 2)
    12.6***474
    (49.1)
    19
    (16.9, 21)
    286
    (51.3)
    2.1
    (1.9, 2.4)
    16.9***
     Female324
    (51.9)
    14.7
    (12.5, 17.1)
    326
    (52)
    2.1
    (2, 2.3)
    12.5***491
    (50.9)
    18.5
    (16.8, 20.4)
    271
    (48.7)
    2.6
    (2.1, 3.6)
    16***
    Age group
     0-6 y12
    (1.9)
    8.6
    (3.4, 17.4)
    12
    (1.9)
    2.2
    (1.7, 2.8)
    6.4***88
    (9.1)
    11.6
    (9.2, 14.3)
    14
    (2.5)
    1.9
    (1.7, 2.2)
    9.7***
     7-19 y79
    (12.7)
    16.2
    (12.7, 19.6)
    79
    (12.6)
    2.1
    (2, 2.2)
    14.1***141
    (14.6)
    27
    (23.1, 30.7)
    55
    (9.9)
    2.6
    (2, 3.4)
    24.5***
     20-39 y254
    (40.7)
    15.3
    (12.8, 18)
    256
    (40.8)
    2.1
    (1.9, 2.2)
    13.2***236
    (24.5)
    22.4
    (19.8, 25.9)
    254
    (45.6)
    2.2
    (2, 2.5)
    20.2***
     40-59 y221
    (35.4)
    13.8
    (11.4, 16.7)
    220
    (35.1)
    2
    (1.8, 2.2)
    11.8***233
    (24.1)
    19.9
    (17.7, 23.3)
    160
    (28.7)
    2.8
    (2, 4.1)
    17.1***
     ≥60 y58
    (9.3)
    13.9
    (7.9, 20.7)
    60
    (9.6)
    1.4
    (1.2, 1.7)
    11.6***267
    (27.7)
    12.6
    (10.8, 14.7)
    74
    (13.3)
    1.6
    (1.3, 1.8)
    11***
    Type of profession
     Pre-school12
    (1.9)
    8.6
    (3.4, 17.4)
    12
    (1.9)
    2.2
    (1.7, 2.8)
    6.4***79
    (8.2)
    10.4
    (8, 13.3)
    14
    (2.5)
    1.9
    (1.7, 2.1)
    8.5***
     Student107
    (17.1)
    14.6
    (11.4, 18.2)
    107
    (17.1)
    2.1
    (2, 2.3)
    12.5***173
    (17.9)
    26.2
    (23.1, 29.2)
    71
    (12.7)
    2.5
    (2, 3.4)
    23.7***
     Employed391
    (62.7)
    15.4
    (13.4, 17.4)
    390
    (62.2)
    2.1
    (1.9, 2.2)
    13.2***400
    (41.5)
    22.5
    (20.7, 24.4)
    354
    (63.6)
    2.5
    (2.1, 3.2)
    20***
     Unemployed30
    (4.8)
    14.1
    (5.7, 24.2)
    31
    (4.9)
    1.8
    (1.4, 2.4)
    12.2***29
    (3)
    14.5
    (7.8, 24.2)
    24
    (4.3)
    1.8
    (1.3, 2.4)
    12.6***
     Retired84
    (13.5)
    12.1
    (7.2, 17.4)
    87
    (13.9)
    1.5
    (1.3, 1.7)
    10.6***278
    (28.8)
    11.8
    (10.2, 13.2)
    94
    (16.9)
    1.6
    (1.3, 1.8)
    10.2***
    Household size
     145
    (7.2)
    10.5
    (5.3, 17.2)
    45
    (7.2)
    0.6
    (0.1, 1.5)
    9.9***35
    (3.6)
    15.2
    (10.1, 21.1)
    61
    (11)
    0.3
    (0.1, 0.5)
    14.9***
     273
    (11.7)
    12.6
    (8.2, 18.3)
    76
    (12.1)
    1.1
    (1, 1.2)
    11.5***244
    (25.3)
    14.5
    (12.7, 16.7)
    138
    (24.8)
    1.4
    (1.1, 1.7)
    13.1***
     3282
    (45.2)
    14.8
    (12.8, 17.3)
    283
    (45.1)
    1.9
    (1.8, 2)
    13***432
    (44.8)
    20.3
    (17.7, 22.4)
    216
    (38.8)
    2.2
    (2, 2.3)
    18.1***
     4133
    (21.3)
    11.9
    (9.3, 15)
    132
    (21.1)
    2.3
    (2.2, 2.5)
    9.6***117
    (12.1)
    20.3
    (16.5, 23.8)
    78
    (14)
    3
    (2.8, 3.3)
    17.3***
     ≥591
    (14.6)
    21.5
    (16.2, 27.3)
    91
    (14.5)
    3.2
    (2.9, 3.4)
    17.8***137
    (14.2)
    21.4
    (18.2, 27)
    64
    (11.5)
    5.9
    (4, 9.9)
    15.5***

    aCan differ from total sample size (n=636) as it also includes participants who had not recorded contacts during the baseline period or during the COVID-19 outbreak. Note that reduced denominators indicate missing data. Percentages may not total 100 because of rounding.

    bDifference is calculated by the subtraction of the number of contacts during the outbreak from the number of contacts during the baseline period. P-values are taken from a negative binomial regression with a single binary variable distinguishing the baseline period from the outbreak.

    cThe 95% confidence interval on the mean are calculated by bootstrap sampling.

    *p<0.05, **p<0.01, ***p<0.001.

    Supplementary Materials

    • Changes in contact patterns shape the dynamics of the COVID-19 outbreak in China

      Juanjuan Zhang, Maria Litvinova, Yuxia Liang, Yan Wang, Wei Wang, Shanlu Zhao, Qianhui Wu, Stefano Merler, Cécile Viboud, Alessandro Vespignani, Marco Ajelli, Hongjie Yu

      Materials/Methods, Supplementary Text, Tables, Figures, and/or References

      Download Supplement
      • Materials and Methods
      • Figs. S1 to S15
      • Tables S1 to S15
      • References
      MDAR Reproducibility Checklist

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