Fig. 3 Effect of limiting school contacts on the epidemic spread in Changes in contact patterns shape the dynamics of the COVID-19 outbreak in China
(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.

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Fig. 1 Contact matrices by age in Changes in contact patterns shape the dynamics of the COVID-19 outbreak in China
Fig. 3 Effect of limiting school contacts on the epidemic spread in Changes in contact patterns shape the dynamics of the COVID-19 outbreak in China
Table 1. Number of contacts by demographic characteristics and location in Changes in contact patterns shape the dynamics of the COVID-19 outbreak in China
Fig. 2 Effect of contact patterns on the epidemic spread in Changes in contact patterns shape the dynamics of the COVID-19 outbreak in China