extracted_captions/cmmidcovid19workinggroupAgedependentEffectsTransmission2020.json

{"CAPTION FIG1\"(NOISY)\".png": "'\\n\\n**Fig. 11 Fit of different model variants to data from Wuhan City, China.**,**,**. Model diagram and duration of disease states in days, where \\\\(d\\\\) parameters represent the duration of time in each disease state (see Methods), \\\\(y\\\\), is the fraction of infections that manifest as clinical cases in age group \\\\(\\\\downarrow\\\\lambda\\\\) is the force of infection in age group \\\\(\\\\downarrow P\\\\), is the incubation period and \\\\(\\\\rho_{i}\\\\) is the serial interval (see Methods). **b**, Susceptibility by age for the three models, with mean (lines), 50% (darker shading) and 95% (lighter shading) credible intervals shown. Age-specific values were estimated for model 1 (orange). Susceptibility is defined as the probability of infection on contact with an infectious person. **c**, Clinical fraction (\\\\(y\\\\)) by age for the three models. Age-specific values were estimated for model 2 (blue) and fixed at 0.5 for models 1 and 3. **d**, Fitted contact multipliers for holiday (\\\\(q_{0}\\\\)) and restricted periods (\\\\(q_{1}\\\\)) for each model showed an increase in non-school contacts beginning on 12 January (start of the Lunar New Year) and a decrease in contacts following restrictions on 23 January. **e**, Estimated \\\\(R_{0}\\\\) values for each model. The red barplot shows the inferred window of spillover of infection. **f**, Incident reported cases (black) and modeled incidence of reported clinical cases for the three models fitted to cases reported by China Centers for Disease Control (CCDCY) with onset on or before 1 February 2020. Lines mark the mean and the shaded window is the 95% highest density interval (HDI). **g**, Age distribution of cases by onset date as fitted to the age distributions reported by Li et al.2(first three panels) and CCDC (fourth panel). Data are shown in open bars and model predictions in filled bars, where the dot marks the mean posterior estimate. **h**, Implied distribution of subclinical cases by age for each model. Credible intervals on modeled values show the 95% HDIs; credible intervals on data for **g** and **h** show 95% HDIs for the proportion of cases in each age group.\\n\\n'", "CAPTION FIG1.png": "'\\n\\n**Fig. 11 Fit of different model variants to data from Wuhan City, China.**,**,**. Model diagram and duration of disease states in days, where \\\\(d\\\\) parameters represent the duration of time in each disease state (see Methods), \\\\(y\\\\), is the fraction of infections that manifest as clinical cases in age group \\\\(\\\\downarrow\\\\lambda\\\\) is the force of infection in age group \\\\(\\\\downarrow P\\\\), is the incubation period and \\\\(\\\\rho_{i}\\\\) is the serial interval (see Methods). **b**, Susceptibility by age for the three models, with mean (lines), 50% (darker shading) and 95% (lighter shading) credible intervals shown. Age-specific values were estimated for model 1 (orange). Susceptibility is defined as the probability of infection on contact with an infectious person. **c**, Clinical fraction (\\\\(y\\\\)) by age for the three models. Age-specific values were estimated for model 2 (blue) and fixed at 0.5 for models 1 and 3. **d**, Fitted contact multipliers for holiday (\\\\(q_{0}\\\\)) and restricted periods (\\\\(q_{1}\\\\)) for each model showed an increase in non-school contacts beginning on 12 January (start of the Lunar New Year) and a decrease in contacts following restrictions on 23 January. **e**, Estimated \\\\(R_{0}\\\\) values for each model. The red barplot shows the inferred window of spillover of infection. **f**, Incident reported cases (black) and modeled incidence of reported clinical cases for the three models fitted to cases reported by China Centers for Disease Control (CCDCY) with onset on or before 1 February 2020. Lines mark the mean and the shaded window is the 95% highest density interval (HDI). **g**, Age distribution of cases by onset date as fitted to the age distributions reported by Li et al.2(first three panels) and CCDC (fourth panel). Data are shown in open bars and model predictions in filled bars, where the dot marks the mean posterior estimate. **h**, Implied distribution of subclinical cases by age for each model. Credible intervals on modeled values show the 95% HDIs; credible intervals on data for **g** and **h** show 95% HDIs for the proportion of cases in each age group.\\n\\n'", "CAPTION FIG10.png": "'\\n\\n**Extended Data Fig. 10 | Contact matrices used in transmission model.** Contact matrices used for Figs. 1-3 of the main text. We have not shown matrices for all 12 regions of Italy modeled, nor for all 13 provinces of China modeled, as these show similar patterns to the matrices for Milan and for Wuhan, Beijing and Shanghai, respectively.\\n\\n'", "CAPTION FIG2 \"(NOISY)\".png": "'\\nFigure 2: **Estimating the age-specific symptomatic rate from age-specific case counts for six countries.****a.** Age-specific reported cases from 13 provinces of China, 12 regions of Italy, Japan, Singapore, South Korea and Ontario, Canada. Open bars are data and the colored lines are model fits with 95% HD. **b.** Fitted mean (lines) and 95% HD (shaded areas) for the age distribution in the clinical fraction (solid lines) and the age distribution of susceptibility (dashed lines) for all countries. The overall consensus fit is shown in gray. **c.** Fitted incidence of confirmed cases and resulting age distribution of cases using either the consensus (gray) or country-specific (color) age-specific clinical fraction from **b.**\\n\\n'", "CAPTION FIG2.png": "'\\nFigure 2: **Estimating the age-specific symptomatic rate from age-specific case counts for six countries.****a.** Age-specific reported cases from 13 provinces of China, 12 regions of Italy, Japan, Singapore, South Korea and Ontario, Canada. Open bars are data and the colored lines are model fits with 95% HD. **b.** Fitted mean (lines) and 95% HD (shaded areas) for the age distribution in the clinical fraction (solid lines) and the age distribution of susceptibility (dashed lines) for all countries. The overall consensus fit is shown in gray. **c.** Fitted incidence of confirmed cases and resulting age distribution of cases using either the consensus (gray) or country-specific (color) age-specific clinical fraction from **b.**\\n\\n'", "CAPTION FIG3.png": "'\\n\\n**Fig. 3 [Effect of school closure under different demographics and subclinical infectiousness.****a**, Age dependence in clinical fraction (severity) and susceptibility to infection on contact for COVID-19 and for the influenza-like scenarios (simplified, based on ref. [4]) considered here. **b**, Age structure for the three exemplar cities. **c**, Age-specific clinical case rate for COVID-19 and influenza-like infections, assuming \\\\(\\\\leq\\\\)50% infectiousness of subclinical infections. **d**, Daily incidence of clinical cases in exemplar cities for COVID-19 versus influenza-like infections. \\\\(R_{0}\\\\) is fixed at 2.4. The rows show the effect of varying the infectiousness of subclinical infections to be 0%, 50% or 100% as infectious as clinical cases while keeping \\\\(R_{0}\\\\) fixed **e**, Change in peak timing and peak cases for the three cities, for either COVID-19 or influenza-like infections. **f**, Change in median COVID-19 peak timing and peak cases for the three cities, depending on the infectiousness of subclinical infections.\\n\\n'", "CAPTION FIG4.png": "'\\n\\n**Fig. 4 : Implications for global preparedness.****a**, Expected clinical case attack rate (mean and 95% HDD) and peak in clinical case incidence for 146 countries in the Global Burden of Disease (GBD) country groupings(r) for an unmitigated epidemic. **b**, Expected subclinical case attack rate and peak in subclinical cases. **c**, Estimated basic reproduction number (_R_o) in the capital city of each country assuming the age-specific clinical fraction shown in Fig. 2b and 50% infectiousness of subclinically infected people. **d**, Proportion of clinical cases in each age group at times relative to the peak of the epidemic. The 146 city epidemics were aligned at the peak, and colors mark the GBD groupings in **a**, **e**, Age distribution of the first and last thirds of clinical cases for 146 countries in GBD country groupings.\\n\\n'", "CAPTION FIG5.png": "\"\\n\\n**Extended Data Fig. 5 Posterior distributions for Beijing, Shanghai, South Korea, and Lombardy.** Prior and posterior distributions for the epidemics in a, Beijing and Shanghai, **b**, South Korea and **c**, Lombardy using the 'consensus' fit for age-specific clinical fraction and assuming subclinical infections are 50% as infectious as clinical infections (see Fig. 2c, main text). For (**a**), times are in days after December 1st, 2019; for (**b**) and (**c**), times are in days after January 1st, 2019. Note, seed_d is the inferred duration of the seeding event. See also Supplementary Table 4.\\n\\n\"", "CAPTION FIG6.png": "'\\n\\n**Extended Data Fig. 6 [ Global projections assuming greater severity in lower-income countries.****a**. Schematic age-specific clinical fraction for higher-income and lower-income countries. **b-f**, illustrative results of the projections for 146 capital cities assuming a higher age-varying clinical fraction in lower-income countries. See Fig. 4 (main text) for details.\\n\\n'", "CAPTION FIG7.png": "'\\n\\n**Extended Data Fig. 7 [ Consensus age-specific clinical fraction and susceptibility under varying assumptions for subclinical infectiousness.** Line and rhobens show mean and 95% HDI for clinical fraction and susceptibility, assuming subclinical infections are 0%, 25%, 50%, 75%, or 100% as infectious as clinical infections.\\n\\n'", "CAPTION FIG8.png": "'\\n\\n**Extended Data Fig. 8 | Projections for capital cities depending upon subclinical infectiousness.****a**, Projected total and peak clinical case attack rate for 146 capital cities, under different assumptions for the infectiousness of subclinical infections. **b**, Projected total and peak subclinical infection attack rate for 146 capital cities, under different assumptions for the infectiousness of subclinical infections. **c**, Projected differences in \\\\(R_{a}\\\\) among 146 capital cities, under different assumptions for the infectiousness of subclinical infections. Mean and 95% HDI shown.\\n\\n'", "CAPTION FIG9.png": "'\\n\\n**Extended Data Fig. 9 | School closures with fixed susceptibility across cities.** Comparison of school closures in three exemplar cities when susceptibility is fixed across settings instead of 80. See main text Fig. 3 for details.\\n\\n'", "CAPTION FIGS1.png": "'\\n\\n**Extended Data Fig. 1 Posterior distributions for Wuhan.** Prior distributions (gray dotted lines) and posterior distributions (colored histograms) for model parameters fitting to the early epidemic in Wuhan (Fig. 1, main text); seed_start is measured in days after November 1st, 2019. **a**, Model 1(age-varying contact patterns and susceptibility); **b**, Model 2 (age-varying contact patterns and clinical fraction); **c**, Model 3 (age-varying contact patterns only). See also Supplementary Table 4.\\n\\n'", "CAPTION FIGS10.png": "'\\n\\n**Extended Data Fig. 10 | Contact matrices used in transmission model.** Contact matrices used for Figs. 1-3 of the main text. We have not shown matrices for all 12 regions of Italy modeled, nor for all 13 provinces of China modeled, as these show similar patterns to the matrices for Milan and for Wuhan, Beijing and Shanghai, respectively.\\n\\n'", "CAPTION FIGS2.png": "'\\n\\n**Extended Data Fig. 2 [ Simultaneous estimation of age-varying susceptibility and clinical fraction to epidemic data from Wuhan City, China.** This figure replicates Fig. 1 of the main text, but comparing model variants 1 and 2 to a fourth model variant in which both susceptibility and clinical fraction vary by age. **a**, Model diagram (see Fig. 1, main text). **b**, Susceptibility by age for the three models. Age-specific values were estimated for models 1 (orange) and 4 (pink). Susceptibility is defined as the probability of infection on contact with an infectious person. Mean (lines), 50% (darker shading) and 95% (lighter shading) credible intervals shown. **c**, Clinical fraction (_y_) by age for the three models. Age-specific values were estimated for model 2 (blue) and 4 (pink), and fixed at 0.5 for model 1. **d**, Fitted contact multiplex for holiday (_a_b_) and restricted periods (_a_b_) for each model showed an increase in non-school contacts beginning on January 12th (start of Lunar New Year) and a decrease in contacts following restrictions on January 23rd. **e**, Estimated \\\\(R\\\\) values for each model. The red barplot shows the inferred window of spillover of infection. **f**, Incident reported cases (black), and modeled incidence of reported clinical cases for the three models fitted to cases reported by China Centers for Disease Control (CCDC) with onset on or before February 1st, 2020. Line marks mean and shaded window is the 95% highest density interval (HDI). **g**, Age distribution of cases by onset date as fitted to the age distributions reported by Li et al. (first three panels) and CCDC (fourth panel). Data are shown in the hollow bars, and model predictions in filled bars, where the dot marks the mean posterior estimate. **h**, Implied distribution of subclinical cases by age for each model. Credible intervals on modeled values show the 95% HDIs; credible intervals on data for panels g and h show 95% HDIs for the proportion of cases in each age group.\\n\\n'", "CAPTION FIGS3.png": "'\\n\\n**Extended Data Fig. 3 | Impact of data sources used.** _Analysis showing how the inferred age-varying susceptibility (first column) and age-varying clinical fraction (second column) depend upon the additional data sources used.\\n\\n'", "CAPTION FIGS4.png": "'\\n\\n**Extended Data Fig. 4 -- Posterior estimates for the consensus susceptibility and clinical fraction from 6 countries.** Note that susceptibility is a relative measure.\\n\\n'", "CAPTION FIGS5.png": "\"\\n\\n**Extended Data Fig. 5 Posterior distributions for Beijing, Shanghai, South Korea, and Lombardy.** Prior and posterior distributions for the epidemics in a, Beijing and Shanghai, **b**, South Korea and **c**, Lombardy using the 'consensus' fit for age-specific clinical fraction and assuming subclinical infections are 50% as infectious as clinical infections (see Fig. 2c, main text). For (**a**), times are in days after December 1st, 2019; for (**b**) and (**c**), times are in days after January 1st, 2019. Note, seed_d is the inferred duration of the seeding event. See also Supplementary Table 4.\\n\\n\"", "CAPTION FIGS6.png": "'\\n\\n**Extended Data Fig. 6 [ Global projections assuming greater severity in lower-income countries.****a**. Schematic age-specific clinical fraction for higher-income and lower-income countries. **b-f**, illustrative results of the projections for 146 capital cities assuming a higher age-varying clinical fraction in lower-income countries. See Fig. 4 (main text) for details.\\n\\n'", "CAPTION FIGS7.png": "'\\n\\n**Extended Data Fig. 7 [ Consensus age-specific clinical fraction and susceptibility under varying assumptions for subclinical infectiousness.** Line and rhobens show mean and 95% HDI for clinical fraction and susceptibility, assuming subclinical infections are 0%, 25%, 50%, 75%, or 100% as infectious as clinical infections.\\n\\n'", "CAPTION FIGS8.png": "'\\n\\n**Extended Data Fig. 8 | Projections for capital cities depending upon subclinical infectiousness.****a**, Projected total and peak clinical case attack rate for 146 capital cities, under different assumptions for the infectiousness of subclinical infections. **b**, Projected total and peak subclinical infection attack rate for 146 capital cities, under different assumptions for the infectiousness of subclinical infections. **c**, Projected differences in \\\\(R_{a}\\\\) among 146 capital cities, under different assumptions for the infectiousness of subclinical infections. Mean and 95% HDI shown.\\n\\n'", "CAPTION FIGS9.png": "'\\n\\n**Extended Data Fig. 9 | School closures with fixed susceptibility across cities.** Comparison of school closures in three exemplar cities when susceptibility is fixed across settings instead of 80. See main text Fig. 3 for details.\\n\\n'", "CAPTION TAB1.png": "'\\n\\n**Figure Captions**'"}