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    Children tend to make more social contacts than adults 21 and hence, all else being equal, should contribute more to transmission than adults 22 , If the number of infections or cases depends strongly on the role of children, countries with different age distributions could exhibit substantially different epidemic profiles and overall impact of COVID epidemics.

    The higher contact rates in children are why school closures are considered a key intervention for epidemics of respiratory infections 22 , but the impact of school closure depends on the role of children in transmission. The particular context of SARS-CoV-2 in Wuhan, China, could have resulted in a skewed age distribution because early cases were concentrated in adults over 40 years of age 24 , and assortative mixing between adults could have reduced transmission to children in the very early stages of the outbreak.

    Outside China, COVID outbreaks may have been initially seeded by working-age travelers entering the country 25 , 26 , producing a similar excess of adults in early phases of local epidemics. In both cases, the school closures that occurred subsequently potentially further decreased transmission among children, but to what degree is unclear.

    We developed an age-stratified transmission model with heterogeneous contact rates between age groups Fig. We fitted to two data sources from the Wuhan epidemic: a time series of reported cases 1 and four snapshots of the age distribution of cases 1 , 27 Fig. We assumed that initial cases were in adults, and accounted for school closures in the model by decreasing the school contacts of children starting on 12 January , when schools were closed for the Lunar New Year holiday.

    We also estimated the effect of the Lunar New Year holiday period on non-school contact rates from 12 January to 22 January , as well as the impact on transmission of travel and movement restrictions in Wuhan, which came into effect on 23 January Fig. We found that, under each hypothesis, the basic reproduction number R 0 was initially 2.

    Age-specific values were estimated for model 1 orange. Susceptibility is defined as the probability of infection on contact with an infectious person. Age-specific values were estimated for model 2 blue and fixed at 0. The red barplot shows the inferred window of spillover of infection.

    Data are shown in open bars and model predictions in filled bars, where the dot marks the mean posterior estimate. All model variants fitted the daily incident number of confirmed cases equally well Fig. In this model, the number of cases in children was overestimated and cases in older adults were underestimated Fig. Age-dependent severity has been demonstrated in hospitalized confirmed cases 16 , 28 , which suggests that subclinical infection in individuals aged over 70 years is probably rare and supports that the clinical fraction increases with age.

    Comparison using the deviance information criterion 6 DIC showed that the age-varying susceptibility DIC, and age-varying clinical fraction DIC, model variants were preferred over the model with neither DIC, Both age-varying susceptibility and age-varying clinical fraction could contribute in part to the observed age patterns.

    A fourth model variant in which both susceptibility and clinical fraction vary by age was able to reproduce the epidemic in Wuhan, and was statistically preferred to any other model variant DIC, ; Extended Data Fig. However, because decreased susceptibility and decreased clinical fraction have a similar effect on the age distribution of cases, it is necessary to use additional sources of data to disentangle the relative contribution of each to the observed patterns.

    We used age-specific case data from 32 settings in six countries China 1 , 29 , Japan 30 , 31 , Italy 32 , Singapore 25 , Canada 33 and South Korea 26 and data from six studies giving estimates of infection rates and symptom severity across ages 16 , 19 , 34 , 35 , 36 , 37 , to simultaneously estimate susceptibility and clinical fraction by age Fig.

    We fitted the stationary distribution of the next-generation matrix to these data sources, using setting-specific demographics, with measured contact matrices where possible and synthetic contact matrices otherwise see Methods The age-dependent clinical fraction was markedly lower in younger age groups in all regions Fig.

    The age-specific susceptibility profile suggested that those aged under 20 years were half as susceptible to SARS-CoV-2 infection as those aged over 20 years Extended Data Fig. Specifically, relative susceptibility to infection was 0. The overall consensus fit is shown in gray. To determine whether this consensus age-specific profile of susceptibility and clinical fraction for COVID was capable of reproducing epidemic dynamics, we fitted our dynamic model to the incidence of clinical cases in Beijing, Shanghai, South Korea and Italy Fig.

    The consensus age-specific susceptibility and clinical fraction were largely capable of reproducing the age distribution of cases, although there are some outliers, for example in the to year-old age group in South Korea. This could, however, be the result of clustered transmission within a church group in this country 4. The predicted age distribution of cases for Italy is also less skewed toward adults, especially those over 70 years, than reported cases show, suggesting potential differences in age-specific testing in Italy Locally estimated age-varying susceptibility and clinical fraction captured these patterns more precisely Fig.

    School closures during epidemics 40 , 41 and pandemics 42 , 43 aim to decrease transmission among children 22 and might also have whole-population effects if children are major contributors to community transmission rates. The effect of school closures will depend on the fraction of the population that are children, the contacts they have with other age groups, their susceptibility to infection and their infectiousness if infected. More clinical cases were in adults aged over 20 years in Milan compared with the other cities, with a markedly younger age distribution of cases in the simulated epidemic in Bulawayo.

    R 0 is fixed at 2. This pattern could be generalizable to other low-income settings. Because children have lower susceptibility and exhibit more mildly symptomatic cases for COVID, school closures were slightly more effective at reducing transmission of COVID when the infectiousness of subclinical infections was assumed to be high. Age dependence in susceptibility and clinical fraction has implications for the projected global burden of COVID We simulated COVID epidemics in capital cities and found that the total expected number of clinical cases in an unmitigated epidemic varied between cities depending on the median age of the population, which is a proxy for the age structure of the population Fig.

    There were more clinical cases per capita projected in cities with older populations Fig. However, the mean estimated basic reproduction number, R 0 , did not substantially differ by median age Fig. Our finding that cities with younger populations are expected to show fewer cases than cities with older populations depends on all cities having the same age-dependent clinical fraction.

    However, the relationship between age and clinical symptoms could differ across settings because of a different distribution of comorbidities 46 or setting-specific comorbidities such as human immunodeficiency virus HIV 47 , for example. If children in low-income and lower—middle-income countries tend to show a higher clinical fraction than children in higher-income countries, then there could be higher numbers of clinical cases in these cities Extended Data Fig.

    The city epidemics were aligned at the peak, and colors mark the GBD groupings in a. The expected age distribution of cases shifted substantially during the simulated epidemics. In the early phase there were more cases in the central age group 20—59 years and after the peak a higher proportion of cases in those younger than 20 years and those older than 60 years Fig.

    The magnitude of the shift was higher in those countries with a higher median age, which affects projections for likely healthcare burdens at different phases of the epidemic Fig. For a number of other pathogens, there is evidence that children except for the very youngest, 0—4 years of age have lower rates of symptomatic disease 12 and mortality 26 , so the variable age-specific clinical fraction for COVID we find here is consistent with other studies We have quantified the age-specific susceptibility from available data, and other study types will be needed to build the evidence base for the role of children, including serological surveys and close follow-up of those in infected households.

    The age-specific distribution of clinical infection we have found is similar in shape but larger in scale to that generally assumed for pandemic influenza, but the age-specific susceptibility is inverted. These differences have a large effect on how effective school closures could be in limiting transmission, delaying the peak of expected cases and decreasing the total and peak numbers of cases.

    For COVID, school closures are likely to be much less effective than for influenza-like infections. It is critical to determine how infectious subclinical infections are compared with clinically apparent infections so as to properly assess predicted burdens both with and without interventions.

    It is biologically plausible that milder cases are less transmissible, for example, because of an absence of cough 16 , 28 , but direct evidence is limited 49 and viral load is high in both clinical and subclinical cases If those with no or mild symptoms are efficient transmitters of infection compared with those with fully symptomatic infections, the overall burden is higher than if they are not as infectious. At the same time, lower relative infectiousness would reduce the impact of interventions targeting children, such as school closure.

    By analyzing epidemic dynamics before and after school closures, or close follow-up in household studies, it might be possible to estimate the infectiousness of subclinical infections, but this analysis will rely on granular data by age and time. A great deal of concern has been directed toward the expected burden of COVID in low- and middle-income countries LMICs , which generally have a lower population median age than many high-income countries.

    Our results show that these demographic differences, coupled with a lower susceptibility and clinical fraction in younger ages, can result in proportionally fewer clinical cases than would be expected in high-income countries with flatter demographic pyramids. This finding should not be interpreted as fewer cases in LMICs, because the projected epidemics remain large. Moreover, the relationships found between age, susceptibility and clinical fraction are drawn from high-income and middle-income countries and might reflect not only age, but also the increasing frequency of comorbidities with age.

    This relationship could therefore differ in LMICs for two key reasons. First, the distribution of non-communicable comorbid conditions—which are already known to increase the risk of severe disease from COVID 18 —might be differently distributed by age 50 , along with other risk factors such as undernutrition Second, communicable comorbidities such as HIV 47 , tuberculosis co-infection which has been suggested to increase risk 52 and others 53 could alter the distribution of severe outcomes by age.

    Observed severity and burden in LMICs might also be higher than in HICs due to a lack of health system capacity for intensive treatment of severe cases. There are some limitations to the study. Information drawn from the early stages of the epidemic is subject to uncertainty; however, age-specific information in our study is drawn from several regions and countries, and clinical studies 1 , 54 support the hypothesis presented here.

    We assumed that clinical cases are reported at a fixed fraction throughout the time period, although there may have been changes in reporting and testing practices that affected case ascertainment by age. We assumed that subclinical infections are less infectious than clinically apparent infections. We tested the effects of differences in infectivity on our findings Extended Data Figs. The sensitivity analyses showed very similar clinical fraction and susceptibility with age, and we demonstrated the effect of this parameter on school closure and global projections Fig.

    We used mixing matrices from the same country, but not the same location as the fitted data. We used contact matrices that combined physical and conversational contacts. We therefore implicitly assume that they are a good reflection of contact relevant for the transmission of SARS-CoV However, if fomite or fecal—oral routes are important contributors to transmission, these contact matrices might not be representative of overall transmission risk.

    The role of age in transmission is critical to designing interventions aiming to decrease transmission in the population as a whole and to projecting the expected global burden. Our findings, together with early evidence 16 , suggest that there is age dependence in susceptibility and in the risk of clinical symptoms following infection with SARS-CoV Understanding if and by how much subclinical infections contribute to transmission has implications for predicted global burden and the effectiveness of control interventions.

    We used an age-structured deterministic compartmental model Fig. Compartments in the model are stratified by infection state S, E, I P , I C , I S or R , age band and the number of time steps remaining before transition to the next infection state. We assume that people are initially susceptible S and become exposed E after effective contact with an infectious person. After a latent period, exposed individuals either develop a clinical or subclinical infection; an exposed age- i individual develops a clinical infection with probability y i , otherwise developing a subclinical infection.

    Clinical cases are preceded by a preclinical that is, pre-symptomatic but infectious I P state; from the preclinical state, individuals develop full symptoms and become clinically infected I C. Based on evidence for other respiratory infections 20 , we assume that subclinical infections I S are less infectious compared with preclinical and clinical infections, and that subclinical individuals remain in the community until they recover.

    Isolated and recovered individuals eventually enter the removed state R ; we assume these individuals are no longer infectious and are immune to re-infection. The force of infection for an individual in age group i at time t is. Contacts vary over time t depending on the modeled impact of school closures and movement restrictions see below. To calculate the basic reproductive number, R 0 , we define the next-generation matrix as. R 0 is the absolute value of the dominant eigenvalue of the next-generation matrix.

    We use the local age distribution for each city or region being modeled and synthetic or measured contact matrices for mixing between age groups Supplementary Table 1. The mixing matrices have four types of contact: home, school, work and other contacts. We contrasted three model variants. Susceptibility and clinical fraction curves were fitted using three control points for young, middle and old age, interpolating between them with a half-cosine curve see the following for details.

    We assumed that the initial outbreak in Wuhan was seeded by introducing one exposed individual per day of a randomly drawn age between A min and A max for 14 days starting on a day t seed in November 29 , We used the age distribution of Wuhan City prefecture in 55 and contact matrices measured in Shanghai 31 as a proxy for large cities in China.

    This contact matrix is stratified into school, home, work and other contacts. We aggregated the last three categories into non-school contacts and estimated how components of the contact matrix changed early in the epidemic in response to major changes.

    Schools closed on 12 January for the Lunar New Year holiday, so we decreased school contacts, but the holiday period may have changed non-school contacts, so we estimate this effect by inferring the change in non-school contact types, q H. Large-scale restrictions started on 23 January following restrictions on travel and movement imposed by the authorities, and we inferred the change in contact patterns during this period, q L.

    We fitted the model to incident confirmed cases from the early phase of the epidemic in China 8 December to 1 February reported by China CDC 1. During this period, the majority of cases were from Wuhan City, and we truncated the data after 1 February because there were more cases in other cities after this time.

    We jointly fitted the model to the age distribution of cases at three time windows 8 December to 22 January reported by Li et al. Because there was a large spike of incident cases reported on 1 February that were determined to have originated from the previous week, we amalgamated all cases from 25 January to 1 February, including those in the large spike, into a single data point for the week.

    We used a Markov chain Monte Carlo method to jointly fit each hypothesis to the two sets of empirical observations from the epidemic in Wuhan City, China Supplementary Table 2. We used a negative binomial likelihood for incident cases and a Dirichlet-multinomial likelihood for the age distribution of cases, using the likelihood.

    We set the precision of each distribution to to capture additional uncertainty in data points that would not be captured with a Poisson or multinomial likelihood model. For all Bayesian inference shown in Figs. We then ran 2,—3, samples of burn-in, and generated at least 10, samples post-burn-in. Recovered posterior distributions, with prior distributions overlaid, are shown in Extended Data Fig.

    We distinguished fitted models using the DIC criterion To infer the age-specific clinical fraction and susceptibility from reported case distributions, we assumed that reported cases follow the stationary distribution of cases reached in the early phase of an epidemic. Using our dynamic model would allow modeling any transient emphasis in the case distribution associated with the age of the individuals who seeded infection in a given region, but because the age of the true first cases is not generally known, we used the stationary distribution instead.

    Specifically, we used Bayesian inference to fit age-specific susceptibility and clinical fraction to the reported case distribution by first generating the expected case distribution k i from 1 the age-specific susceptibility u i , 2 the age-specific clinical fraction y i , 3 the measured or estimated contact matrix for the country and 4 the age structure of the country or region.

    We then used the likelihood. Above, Q C is a fitted dispersion parameter to capture the variation in observed case distributions among countries. The age-specific susceptibility u i and age-specific clinical fraction y i were estimated by evaluating the expected case distribution c i according to the likelihood functions given above.

    It is not possible to identify both u i and y i from case data alone. Accordingly, we inferred the age-specific clinical fraction, y i , from surveillance data from Italy reporting the age-specific number of cases that were asymptomatic, paucisymptomatic, mild, severe and critical Accordingly, we applied the likelihood penalty. Here, mild i is the number of mild cases reported in age group i , sev i the number of severe cases in age group i and so on. Therefore the age-specific clinical fraction reflected the proportion of infections reported by Riccardo et al.

    Above, Q X is a fitted dispersion parameter to capture the variation in clinical fraction among countries. To estimate a value for the inflation factor z compatible with empirical data on the severity of infections, we applied a further likelihood penalty when estimating the consensus fit for clinical fraction and susceptibility so as to match information on age-specific susceptibility collected from recent contact-tracing studies 34 , 35 , 36 , A leave-one-out analysis showed that these additional data allowed the model fitting procedure to converge on a consistent profile for both u i and y i Extended Data Fig.

    We extracted age-specific case data from the following sources. For provinces of China, we used age-specific case numbers reported by China CDC 1 as well as line list data compiled by the Shanghai Observer For South Korea, we used the line list released by Kim et al. Beijing and Shanghai incidence data were given by case onset, so we assumed no delay between reported and true case onsets.

    Incidence data for South Korea were given by the date of confirmation only, and we assumed the reporting delay followed a gamma distribution with a 7-day mean. Incidence data for Italy were given separately for case onset and case confirmation, with only a subset of onset dates available; accordingly, we fit the proportion of confirmed cases with onset dates and the delay from onset to confirmation. We adjusted the size parameter of the negative binomial distribution used to model case incidence to 10 to reflect greater variability among fewer data points for these countries than for Wuhan.

    Beijing and Shanghai were fitted jointly, with separate dates of introduction but the same fitted susceptibility, large-scale restriction date and large-scale restriction magnitude. South Korea and Italy were each fitted separately; we fitted a large-scale restriction date and magnitude for both South Korea and Italy. For both the line list fitting and validation, we assumed that schools were closed in China, but remained open in South Korea, Japan, Italy, Singapore and Canada, as schools were open for the majority of the period covered by the data in the latter five countries.

    To determine the impact in other cities with different demographic profiles we used the inferred parameters from our line list analysis to parameterize our transmission model for projections to other cities. We chose these to compare projections for a city with a high proportion of elderly individuals Milan, Italy , a moderately aged population Birmingham, UK and a city in a low-income country with a high proportion of young individuals Bulawayo, Zimbabwe.

    For this analysis, we compared an outbreak of COVID, for which the burden and transmission is concentrated in relatively older individuals, with an outbreak of pandemic influenza, for which the burden and transmission is concentrated in relatively younger individuals. To model Milan, we used the age distribution of Milan in 59 and a contact matrix measured in Italy in To model Birmingham, we used the age distribution of Birmingham in 60 and a contact matrix measured in the UK in To model Bulawayo, we used the age distribution of Bulawayo Province in 61 and a contact matrix measured in Manicaland, Zimbabwe in We assumed that the epidemic was seeded by two infectious individuals in a random age group per week for five weeks.

    This produced qualitatively similar results Extended Data Fig. We projected the impact of school closure by setting the contact multiplier for school contacts, school t , to 0. Complete removal of school contacts may overestimate the impact of school closures because of alternate contacts children make when out of school This will, however, give the maximum impact of school closures in the model to demonstrate the differences.

    For simplicity, we assumed that capital cities followed the demographic structure of their respective countries and took the total population of each capital city from the R package maps. We simulated 20 outbreaks in each city, drawing the age-specific clinical fraction y i from the posterior of the estimated overall clinical fraction from our line list analysis Fig.

    We took the first third and the last third of clinical cases in each city to compare the early and late stages of the epidemic. Wherever possible, we used measured contact matrices Supplementary Table 3. We adapted each of these mixing matrices, using 5-year age bands, to specific regions of the countries in which they were measured by reprocessing the original contact surveys with the population demographics of the local regions.

    The contact matrices we used for Figs. The contact survey in Shanghai 64 allowed respondents to record both individual one-on-one and group contacts, the latter with approximate ages. Although individual contacts were associated with a context home, work, school and so on , group contacts were not, and so we assumed that all group contacts that involved individuals aged 0—19 years occurred at school.

    We assumed schools were closed during the epidemic in China because schools closed for the Lunar New Year holiday and remained closed , but open in Italy, Singapore, South Korea, Japan and Canada, because we used data from the early part of the epidemics in those countries, at which time schools were open. Because the infectiousness of subclinical individuals was not identifiable from the data we have available, in Fig. In Extended Data Fig. We did not find a marked difference in the findings or estimates.

    In Fig. We tested the sensitivity of our findings to the findings of the other studies by conducting a leave-one-out sensitivity analysis. The results are provided in Extended Data Fig. This means that the higher rates of contact measured in surveys in Milan and Bulawayo compared with Birmingham were not included. We also tested the sensitivity of findings on school closure.

    The conclusions regarding the relative effectiveness of school closures for COVID versus influenza are similar. However, a higher rate of comorbidities in lower-income countries could change the age-specific probability of developing clinical symptoms upon infection. We repeated the analyses with these functions and found increased burden in lower-income countries, which could exceed the burden of clinical cases in higher-income countries.

    Finally, we repeated our projections for country-specific burdens of COVID assuming different values for the relative infectiousness of subclinical infections. We found that this had a small effect on the relationship between median age and case burden across countries Extended Data Fig.

    Further information on research design is available in the Nature Research Reporting Summary linked to this Article. Contact matrix data are available at Zenodo 21 , Liu, Z. Google Scholar. Sun, K. Early epidemiological analysis of the coronavirus disease outbreak based on crowdsourced data: a population-level observational study.

    Lancet Digit. Health 2 , e—e Article Google Scholar. Cereda, D. Shim, E. Dong, Y. Epidemiological characteristics of 2, pediatric patients with coronavirus disease in China. Pediatrics , e Zhao, X.

    Incidence, clinical characteristics and prognostic factor of patients with COVID a systematic review and meta-analysis. Anderson, R. Epidemiology, transmission dynamics and control of SARS: the — epidemic. Lipsitch, M. Defining the epidemiology of Covid—studies needed. Nickbakhsh, S. Epidemiology of seasonal coronaviruses: establishing the context for the emergence of coronavirus disease Kissler, S.

    Science , — Huang, A. A systematic review of antibody mediated immunity to coronaviruses: antibody kinetics, correlates of protection and association of antibody responses with severity of disease. Cowling, B.

    Increased risk of noninfluenza respiratory virus infections associated with receipt of inactivated influenza vaccine. Tsagarakis, N. Age-related prevalence of common upper respiratory pathogens, based on the application of the FilmArray Respiratory panel in a tertiary hospital in Greece.

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