Transmission of SARS-CoV-2 by Children
Background: Six months into the COVID-19 pandemic, children appear largely spared from the direct effects of disease, suggesting age as an important predictor of infection and severity. They remain, however, impacted by far-reaching public health interventions. One crucial question often posed is whether children generally transmit SARS-CoV-2 effectively.
Methods: We assessed the components of transmission and the different study designs and considerations necessary for valid assessment of transmission dynamics. We searched for published evidence about transmission of SARS-CoV-2 by children employing a narrative review methodology through 25 June, 2020.
Results: Transmission dynamics must be studied in representative pediatric populations with a combination of study designs including rigorous epidemiological studies (e.g. in households, schools, daycares, clinical settings) and laboratory studies while taking into account the social and socio-economic contexts. Viral load (VL) estimates from representative pediatric samples of infected children are missing so far. Currently available evidence suggests that the secondary attack rate stratified by age of the infector is lower for children, however this age pattern needs to be better quantified and understood.
Conclusion: A generalizable pediatric evidence base is urgently needed to inform policy making now, later when facing potential subsequent waves, and extending through a future in which endemicity alongside vaccination may become the enduring reality.
Age has been one of the most important factors in the prognosis of those affected by COVID-19 (e1). Differential SARS-CoV-2 infection, transmission and clinical manifestation by age have important implications for social policy decisions, such as closure of schools. By April 2020, interventions to limit SARS-CoV-2 transmission left over 90% of children confined at home worldwide (e2). Such closures reduce contacts not only between students, but also prevent parents from working (1) and have proven effective during influenza epidemics (2).
It quickly became clear, however, that children were relatively spared by SARS-CoV-2. They account for a small proportion of confirmed cases, with symptoms absent or mild and fleeting (e3) and have half the odds of infection compared to adults (3). We review evidence about transmission of SARS-CoV-2 by children, and how relevant parameters may be ascertained through epidemiologic and laboratory techniques. It is essential to know if children need to adapt their individual contacts with close family members at risk for severe COVID-19. This evidence is also required for rational policies that balance harms and benefits of education and recreation, as well prioritization of future vaccination efforts. Most acutely, continued interruption of formal education carries a huge social cost to health and development of children (4), and to social and professional functioning of parents (e4). Evidence to guide balancing of those risks against potential epidemic spread is therefore urgently needed.
We assessed the components of transmission and the different study designs and requirements necessary for assessment of transmission dynamics. We searched for the evidence base of transmission of SARS-CoV-2 by children using narrative review methodology (e5, e6) until 25 June 2020, including a PubMed search using the terms “child”, “SARS-CoV-2” and “transmission” (eBox).
Components of Transmission
Disease transmission is a complex interaction between infectious and susceptible hosts, an agent and the environment (5). For a child to become a possible source of infection, they need to have been previously exposed to, replicate and effectively shed the infectious virus. Components of transmission dynamics, the spread of the infection over time, are biological, behavioral and contextual. Knowledge of all three components is required to define effective transmission and its evolution over time.
Children differ from adults in biological and behavioral characteristics related to transmission. For example, they differ in contact type, rate, duration and intensity and they primarily interact with other children (e7). It remains unknown which variables play critical roles in the transmission dynamics of SARS-CoV-2 (6). Current consensus is that respiratory droplet expulsion is key, especially during speech (7), but this has not been studied among children. There is presently insufficient evidence for feco-oral transmission. Context also matters, and most documented transmission occurs in adult settings like bars, conferences, meat-plants and ships (8). Like other infectious diseases, SARS-CoV-2 data suggest that ~20% of the cases are responsible for ~80% of local transmission (9). So far, these ~20% appear to be predominantly adults, not children (8, e8).
Most work on transmission dynamics focuses on susceptible hosts and calculates risk of becoming infected. To know whether children transmit the SARS-CoV-2 virus as effectively as adults, one needs to know the secondary attack rate (i.e. number of new cases an initial case infects, per 100 exposed individuals [e9]) stratified by age of infector. This is the most direct measure of the infectiousness of a particular agent.
Epidemiological and Experimental Designs
The question of pediatric contagiousness is not a theoretical one, but a real-world phenomenon in the specific context of the current pandemic. A natural approach is via epidemiologic designs, observing populations directly and measuring chains of transmission to ascertain how many cases stem from an infected child.
The primary design for this purpose is the study of households, which are well-defined settings that offer the closest and most intense contact with and between children (e10, e11). These may be incidence or prevalence studies, using PCR or serology, or both. Pediatric transmission can also be studied in outbreaks at schools, daycares, or in clinical settings. In a subset of investigations, phylogenetic tools (e12) can also be used to enhance specificity. Further inference can be derived from intervention studies, using longitudinal data to evaluate the impact of prevention measures on reducing transmission. Finally, the epidemiologic profile is enhanced by population surveys, either of acute or past infection (10). These designs share the virtue of being direct observations of the series of events that culminate with transmission, and therefore provide direct answers to scientific questions about epidemic spread. The results of epidemiologic designs may be subsequently used in mathematical models, to expand inference to hypothetical and counterfactual scenarios, and for the purpose of forecasting (e4). In contrast to the artificial sterility of the laboratory, “epidemiology is the gold standard to measure transmission potential of patients”(11).
Because inference about transmission dynamics occurs at the population level, attention needs to be given to how and which study participants are recruited (Table 1) (12). Systematic differences in recruitment can lead to bias in population estimates, for example if only symptomatic children are followed, or only hospitalized cases are recruited (e13). This generates a skewed impression of the true frequencies of events and representativeness issues, and also leads to biases that arise from associations in the sample that are artifacts of the recruitment process (13). Analyses must also account for imperfect measurement of case status as a function of diagnostic test accuracy (14, e14). These potential biases may be exacerbated when they are age-dependent, for example if test accuracy varies by age, or pediatric cases are rare. Small sample sizes in addition result in imprecise estimates.
Results from laboratories, animal models and controlled experiments on SARS-CoV-2 and other respiratory pathogens contribute to knowledge about transmission, but issues of generalization remain central (e15). For example, how does transmission between hamsters translate to transmission dynamics and intervention effects in a 4th grade classroom (15)? Experiments offer the advantage of controlled environments but never approximate real-world conditions. These studies therefore provide important mechanistic insights, but do not directly inform policy decisions. For example, environmental studies provided crucial information about duration of SARS-CoV-2 on different types of surfaces (16). To what extent transmission and infection occur from such media remains unknown, with epidemiology suggesting it may be relatively infrequent (e16). A recent study estimates that a minute of loud speaking generates at least 1000 virion-containing droplet nuclei that can remain airborne for more than 8 minutes (7). This suggests a plausible mechanism behind super-spreading events, but actual transmission cannot be predicted without accounting for real-world contextual factors such as airflow patterns (e17).
The presence of sufficient infectious virus is a necessary condition for transmission, and this minimal number of viable organisms required to infect a non-immune individual is expressed as the median tissue culture infective dose. This is an in vitro phenomenon and remains unknown for SARS-CoV-2. It is cumbersome to measure and therefore high levels of viral replication are commonly quantified using a more convenient proxy, the viral load (VL) expressed in copies/ml (e18).
The VL provides a quantitative estimate of the amount of target RNA obtained from clinical samples and can be indirectly measured by PCR testing. Finding RNA does not imply presence of viable and replicating virus, however. Virus quantification by PCR testing uses a backwards calculation of cycle threshold (Ct) values (e19). The Ct value is the cycle number during the amplification phase when fluorescence of a PCR product can be detected above the background signal. The lower the value, the larger the initial amount of viral genetic material present in the sample. The minimal VL necessary to infect a secondary case is not known for SARS-CoV-2 and likely varies across hosts (17), although presumably more virus translates into increased infectivity. Pediatric cases with high VL have been reported (e20), but transmission is never guaranteed even with a large number of viral copies detected in the respiratory tract (18).
To quantify the amount of virus present in a typical infected child, one needs to account for pre-analytical and analytical sample and assay characteristics (19, e21). The respiratory specimen type, such as nasopharyngeal versus nasal swab, determines the sensitivity and specificity of a PCR test and measured VL will therefore vary (19). The ideal specimen to diagnose SARS-COV-2 infection in children has not been determined and could potentially be different from adults.
It has been shown that variation in VL is dependent on the timing of specimen collection in relation to initial exposure, (14, 19), with high VL early in the disease course (20). This is confirmed in samples from pediatric case-studies and case series (e20). Infectious virus is no longer detected after 7 days since onset of symptoms (21). An estimated 56.4% (34.9–78.0%) (e22) of adult transmission takes place in the pre-symptomatic phase (22). It remains unclear if this is mainly because of high peak VL at that moment, or because infector and infectee are unaware of the infection and therefore taking no precautions, or both, and if this plays out similarly in children.
To estimate the distribution of VLs in children attending schools, one needs to follow basic epidemiologic principles of generalizability (Table 1) (e23), using a study sample with representative demography, including age, sex, and social class. For valid population inference, selection into the study should not be based on presence of symptoms, severity of disease or presence of relevant co-morbidities. Diligent use of laboratory data in conjunction with epidemiological investigation illustrates the real potential to learn about transmission probabilities and effective transmission in relation to the skewed curve of VL (23).
Knowledge Synthesis from Existing Studies
Published household transmission studies show that children are rarely the index case and investigations of cases and clusters suggest that children with SARS-CoV-2 seldom cause secondary cases (24). A review of household cluster studies compared household transmission during H5N1 influenza epidemics where 30/56 of the index cases were children, to the COVID-19 pandemic, where 3/31 clusters had a child who potentially infected a secondary case (25).
Most of the household cases published to date are from countries affected early in the pandemic and include confined children with consequently restricted exposure. In a Swiss study (26), 3/39 of households had a child who developed symptoms prior to other household contacts, but without evidence of transmission from the child. A preprint from Israel (e24) including 3353 people in 637 households, estimated children up to age 20 to be 85% as infective as adults (that is, relative 15% less infective). Published data from the first 13 families show an adult index case in all but one instance (27).
In settings where schools remained open or using data prior to closures, there is little evidence of outbreaks or major transmission into the community. In Australian schools (28) two children contracted COVID-19 after exposure to 9 infected students and 9 infected staff among 735 students and 128 staff. An outbreak in a French high-school is described among 15–17 year olds, with limited cases among sibling contacts (29). An Irish study (30) describes exposure to an infected child in primary school, two in high-school and 3 infected adults, and yet follow-up and testing detected no cases. In Sweden, where schools for children up to 15 years remained open, only hospitalization data are published (e25), during a period in which cross-sectional PCR surveillance showed overall positivity <3% in both children and adults (e26). Data free from selection bias on the impact on broader transmission and a useful comparison remain lacking.
Elsewhere, prevalence data using serology from published sero-surveys of population samples and their households (31), from residual blood sample data (e27) and national public health agency initiatives (32), reveal lower sero-prevalence in children than adults. A pre-print from the Paris region reports a high sero-prevalence of 10.7% among 605 children ages 0–15, sampled at visits to ambulatory pediatricians. This is a highly selected sample in which 17.1% and 32.3% of the included children had confirmed or suspected COVID-19 positive household contacts, respectively (33). This is substantially higher than the Paris average, and shows the sample to be unrepresentative of the overall population. Many more reports are posted by public health agencies (3) but do not adequately describe sampling and methods to infer pediatric rates.
Although VL estimates from representative pediatric samples remain unavailable, symptomatic children and adults showed similar VL in one descriptive study without formal statistical investigation (34). VL above a putative infectiousness threshold were present in 29.0% of 38 patients ages 0–6 years versus 51.4% among 3153 adults in a German study (35), but with insufficient information about sampling to suggest generalizability.
Modeling studies have shown effects confining children among a suite of interventions (36), however, even assuming equal infectiousness in children compared to adults, they show limited impact at the peak and the need for prolonged closure to control transmission (37). More recent models include lower susceptibility and infectivity and decreased impact (38).
Despite limited evidence, the general pattern emerges that transmission from children occurs, but contributes much less to evolution of the epidemic than do contacts between adults, and school-re-openings have not lead to transmission spikes in low transmission countries (e28). Possible changes in transmission among older students, as suggested using German data, warrant further assessment. Unlike other respiratory diseases such as influenza, it seems quite clear that the secondary attack rate from pediatric cases is substantially lower than for adults, and that mechanisms underlying this difference require elucidation.
Rather than simply a question of viral load, policy decisions such as school reopening are complex considerations that require balancing competing risks and benefits in the broader context of fear and uncertainty (e29). School closures have negative impacts on mental, educational, nutritional and social development, and disrupt relationships between children, peers and families (e30). They most adversely compromise children with special needs and those from marginalized households, exacerbating inequalities (39).
Transmission dynamics inevitably change over time (e31) and are modified by other interventions. The contribution of children to the spread of COVID-19 is therefore a highly contingent question. Many children still have limited exposure to infection, and become infected less frequently. When infected, they are generally less sick than adults. Six months into the pandemic, children have not shown any evidence of being a significant factor in its propagation (40). As societies worldwide relax or reintroduce restrictions, pragmatic studies to measure changes in transmission in various groups, particularly children, are needed more than ever.
We thank Prof. Dr. med. Andreas Stang for helpful suggestions on the content and assistance with the German translation.
Conflict of interest statement
Joanna Merckx is employed by bioMérieux Canada as Director of Medical Affairs.
Jeremy A Labrecque and Jay S Kaufman declare that no conflict of interest exists.
Manuscript received on 11 June 2020, revised version accepted on
2 July 2020
Jay S. Kaufman, Ph.D
Department of Epidemiology, Biostatistics, and Occupational Health
1020 Pine Ave West
Montreal, Quebec H3A 1A2
Cite this as:
Merckx J, Labrecque JA, Kaufman JS: Transmission of
SARS-CoV-2 by children. Dtsch Arztebl Int 2020; 117: 553–60. DOI: 10.3238/arztebl.2020.0553
A retrospective closed cohort study. medRxiv 2020; DOI: 10.1101/2020.04.18.20071134 (ePub ahead of print) CrossRef
Department of Epidemiology, Erasmus Medical Center, Rotterdam, Netherlands: Jeremy A Labrecque, PhD
|1.||Viner RM, Russell SJ, Croker H, et al.: School closure and management practices during coronavirus outbreaks including COVID-19: a rapid systematic review. Lancet Child Adolesc Health. 2020; 4: 397–404 CrossRef|
|2.||Jackson C, Mangtani P, Hawker J, Olowokure B, Vynnycky E: The effects of school closures on influenza outbreaks and pandemics: systematic review of simulation studies. PLoS One. 2014; 9: e97297 CrossRef MEDLINE PubMed Central|
|3.||Viner RM, Mytton OT, Bonnell C et al.: Susceptibility to SARS-CoV-2 infection amongst children and adolescents compared with adults: a systematic review and meta-analysis. medRxiv 2020; DOI:10.1101/2020.05.20.20108126 (ePub ahead of print) CrossRef|
|4.||Esposito S, Principi N. School closure during the coronavirus disease 2019 (COVID-19) pandemic: an effective intervention at the global level? JAMA Pediatr. 2020, DOI: 10.1001/jamapediatrics.2020.1892 (ePub ahead of print) CrossRef MEDLINE|
|5.||Halloran ME: Concepts of transmission and dynamics. Thomas JC, Thomas JC, Weber DJ, eds.: Epidemiologic methods for the study of infectious diseases. Oxford University Press 2001; 56: 85.|
|6.||Kissler SM, Tedijanto C, Goldstein E, Grad YH, Lipsitch M: Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period. Science 2020; 368: 860–8 CrossRef MEDLINE PubMed Central|
|7.||Stadnytskyi V, Bax CE, Bax A, Anfinrud P: The airborne lifetime of small speech droplets and their potential importance in SARS-CoV-2 transmission. Proc Natl Acad Sci U S A 2020; 117: 11875–7 CrossRef MEDLINE PubMed Central|
|8.||Leclerc QJ, Fuller NM, Knight LE, Funk S, Knight GM, CMMID COVID-19 Working Group: What settings have been linked to SARS-CoV-2 transmission clusters? Wellcome Open Res 2020; 5: 83 CrossRef MEDLINE PubMed Central|
|9.||Althouse BM, Wenger EA, Miller JC, et al.: Stochasticity and heterogeneity in the transmission dynamics of SARS-CoV-2. arXiv 2020; 2005: 13689 (ePub ahead of print).|
|10.||Subramanian SV, James KS: Use of the Demographic and Health Survey framework as a population surveillance strategy for COVID-19. Lancet Glob Health 2020; 8: e895 CrossRef|
|11.||Zhou F, Fan G, Liu Z, Cao B: SARS-CoV-2 shedding and infectivity— Authors’ reply. Lancet 2020; 395: 1340 CrossRef|
|12.||Pearce N, Vandenbroucke JP, VanderWeele TJ, Greenland S: Accurate statistics on COVID-19 are essential for policy guidance and decisions. Am J of Public Health 2020; 110: 949–51 CrossRef MEDLINE PubMed Central|
|13.||Griffith G, Morris TT, Tudball M, et al.: Collider bias undermines our understanding of COVID-19 disease risk and severity. medRxiv 2020; DOI:10.1101/2020.05.04.20090506 (ePub ahead of print) CrossRef|
|14.||Kucirka LM, Lauer SA, Laeyendecker O, Boon D, Lessler J: Variation in false-negative rate of reverse transcriptase polymerase chain reaction-based SARS-CoV-2 tests by time since exposure. Ann Intern Med 2020; M20–1495 (ePub ahead of print) CrossRef MEDLINE PubMed Central|
|15.||Chan JF, Yuan S, Zhang AJ, et al.: Surgical mask partition reduces the risk of non-contact transmission in a golden Syrian hamster model for coronavirus disease 2019 (COVID-19). Clin Infect Dis 2020; ciaa644 (ePub ahead of print) CrossRef MEDLINE PubMed Central|
|16.||Van Doremalen N, Bushmaker T, Morris DH, et al.: Aerosol and surface stability of SARS-CoV-2 as compared with SARS-CoV-1. N Engl J Med 2020; 38: 1564–7 CrossRef MEDLINE PubMed Central|
|17.||Patel R, Babady E, Theel ES, et al.: Report from the American Society for Microbiology COVID-19 International Summit, 23 March 2020: Value of diagnostic testing for SARS-CoV-2/COVID-19. mBio. 2020; 11: e00722–20 CrossRef MEDLINEPubMed Central|
|18.||Danis K, Epaulard O, Bénet T, et al.: Cluster of coronavirus disease 2019 (Covid-19) in the French Alps, 2020. Clin Infec Dis 2020; ciaa424 (ePub ahead of print).|
|19.||Hanson KE, Caliendo AM, Arias CA, et al.: Infectious Diseases Society of America Guidelines on the diagnosis of COVID-19. IDSA,Clin Infect Dis 2020, ciaa760 (ePub ahead of print) CrossRef MEDLINE PubMed Central|
|20.||To KK, Tsang OT, Leung WS, et al.: Temporal profiles of viral load in posterior oropharyngeal saliva samples and serum antibody responses during infection by SARS-CoV-2: an observational cohort study. Lancet Infect Dis 2020; 20: 565–74 CrossRef|
|21.||Bullard J, Dust K, Funk D, et al.: Predicting infectious SARS-CoV-2 from diagnostic samples. Clin Infect Dis 2020; ciaa638 (ePub ahead of print) CrossRef MEDLINE PubMed Central|
|22.||Nishiura H, Linton NM, Akhmetzhanov AR: Serial interval of novel coronavirus (COVID-19) infections. Int J Infect Dis 2020; 93: 284–6 CrossRef MEDLINE PubMed Central|
|23.||He X, Lau EH, Wu P, et al.: Temporal dynamics in viral shedding and transmissibility of COVID-19. Nat Med 2020; 26: 672–5 CrossRef MEDLINE|
|24.||Ludvigsson JF: Children are unlikely to be the main drivers of the COVID-19 pandemic–a systematic review. Acta Paediatrica 2020; 10.1111/apa.15371 (ePub ahead of print) CrossRef MEDLINE PubMed Central|
|25.||Zhu Y, Bloxham CJ, Hulme KD, et al.: Children are unlikely to have been the primary source of household SARS-CoV-2 infections. Lancet 2020, available at SSRN: https://ssrn.com/abstract=3564428 or http://dx.doi.org/10.2139/ssrn.3564428 (ePub ahead of print) CrossRef|
|26.||Posfay-Barbe KM, Wagner N, Gauthey M, et al.: COVID-19 in Children and the Dynamics of Infection in Families. Pediatrics. 2020; e20201576 (ePub ahead of print) CrossRef MEDLINE|
|27.||Somekh E, Gleyzer A, Heller E, et al.: The role of children in the dynamics of intra family coronavirus 2019 spread in densely populated area. Pediatr Infect Dis J 2020; 10.1097 (ePub ahead of print) CrossRef MEDLINE|
|28.||National Centre for Immunisation Research and Surveillance (NCIRS) Australia: COVID-19 in schools—the experience in NSW; http://ncirs.org.au/sites/default/files/2020–04/NCIRS%20NSW%20Schools%20COVID_Summary_FINAL%20public_26%20April%202020.pdf (last accessed on 26 April 2020).|
|29.||Fontanet A, Tondeur L, Madec Y, et al.: Cluster of COVID-19 in northern France: |
A retrospective closed cohort study. medRxiv 2020; DOI: 10.1101/2020.04.18.20071134 (ePub ahead of print) CrossRef
|30.||Heavey L, Casey G, Kelly C, Kelly D, McDarby G: No evidence of secondary transmission of COVID-19 from children attending school in Ireland, 2020. Euro Surveill 2020; 25: 2000903 CrossRef MEDLINE PubMed Central|
|31.||Stringhini S, Wisniak A, Piumatti G, et al.: Seroprevalence of anti-SARS-CoV-2 IgG antibodies in Geneva, Switzerland (SEROCoV-POP): a population-based study. Lancet 2020; S0140–6736(20)31304–0 (ePub ahead of print) CrossRef|
|32.||Pollán M, Pérez-Gómez B, Pastor-Barriuso R et al.: Prevalence of SARS-CoV-2 in Spain (ENE-COVID): a nationwide, population-based seroepidemiological study. The Lancet 2020 Jul 6 (ePub ahead of print).|
|33.||Cohen R, Jung C, Ouldali N, et al.: Assessment of spread of SARS-CoV-2 by RT-PCR and concomitant serology in children in a region heavily affected by COVID-19 pandemic. medRxiv 2020; DOI: 10.1101/2020.06.12.20129221 (ePub ahead of print) CrossRef|
|34.||L’Huillier AG, Torriani G, Pigny F, Kaiser L, Eckerle I: Shedding of infectious SARS-CoV-2 in symptomatic neonates, children and adolescents. medRxiv 2020; DOI: 10.1101/2020.04.27.20076778 (ePub ahead of print) CrossRef|
|35.||Jones TC, Mühlemann B, Veith T, et al.: An analysis of SARS-CoV-2 viral load by patient age. German Research network Zoonotic Infectious Diseases website (last accessed on 2 June 2020) CrossRef|
|36.||Matrajt L, Leung T: Evaluating the effectiveness of social distancing interventions to delay or flatten the epidemic curve of coronavirus disease. Emerg Infect Dis 2020; 26: 10.3201/eid2608.201093 (ePub ahead of print) CrossRef MEDLINE|
|37.||Ferguson N, Laydon D, Nedjati Gilani G, et al.: Report 9: Impact of non-pharmaceutical interventions (NPIs) to reduce COVID19 mortality and healthcare demand. Imperial College COVID-19 Response Team, London, 2020; DOI: https://doi.org/10.25561/77482.|
|38.||Davies NG, Klepac P, Liu Y, et al.: Age-dependent effects in the transmission and control of COVID-19 epidemics. Nat Med. 2020; 10.1038/s41591–020–0962–9 (ePub ahead of print) CrossRef|
|39.||Crawley E, Loades M, Feder G, Logan S, Redwood S, Macleod J: Wider collateral damage to children in the UK because of the social distancing measures designed to reduce the impact of COVID-19 in adults. BMJ Paediatr Open 2020; 4: e000701 (ePub ahead of print) CrossRefMEDLINE PubMed Central|
|40.||Munro APS, Faust SN: Children are not COVID-19 super spreaders: time to go back to school. Arch Dis Child 2020; 105: 618–9 CrossRef MEDLINE|
|e1.||Richardson S, Hirsch JS, Narasimhan M, et al.: Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized with COVID-19 in the New York City area. JAMA 2020; 323: 2052–9 CrossRef MEDLINE PubMed Central|
|e2.||UNESCO. COVID-19 Educational Disruption and Response https://en.unesco.org/covid19/educationresponse (last accessed 25 June 2020).|
|e3.||Parri N, Lenge M, Buonsenso D: Children with Covid-19 in pediatric emergency departments in Italy. N Engl J Med 2020; NEJMc2007617 (ePub ahead of print CrossRef MEDLINE PubMed Central|
|e4.||Bayham J, Fenichel EP: Impact of school closures for COVID-19 on the US health-care workforce and net mortality: a modelling study. Lancet Public Health 2020; 5: e271–8 CrossRef|
|e5.||Tricco AC, Lillie E, Zarin W, et al.: PRISMA Extension for Scoping Reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med. 2018; 169: 467–3 CrossRef MEDLINE|
|e6.||Baethge C, Goldbeck-Wood S, Mertens S: SANRA—a scale for the quality assessment of narrative review articles. Res Integr Peer Rev 2019; 4: 5 CrossRef MEDLINE PubMed Central|
|e7.||Huskins WC: Transmission and control of infections in out-of-home child care. Pediatr Infect Dis J 2000; 19: 106-10 CrossRef MEDLINE|
|e8.||Xu XK, Liu XF, Wu Y, et al.: Reconstruction of transmission pairs for novel Coronavirus Disease 2019 (COVID-19) in mainland China: estimation of super-spreading events, serial interval, and hazard of infection. Clin Infect Dis 2020; ciaa790 (ePub ahead of print) CrossRef MEDLINE PubMed Central|
|e9.||Rampey AH Jr, Longini IM Jr, Haber M, Monto AS: A discrete-time model for the statistical analysis of infectious disease incidence data. Biometrics 1992; 48: 117–28 CrossRef|
|e10.||Longini IM Jr, Koopman JS, Haber M, Cotsonis GA: Statistical inference for infectious diseases. Risk-specific household and community transmission parameters. Am J Epidemiol 1988; 128: 845–59 CrossRef MEDLINE|
|e11.||Li W, Zhang B, Lu J, et al.: The characteristics of household transmission of COVID-19. Clin Infect Dis 2020; ciaa450 (ePub ahead of print).|
|e12.||Wang JT, Lin YY, Chang SY, et al.: The role of phylogenetic analysis in clarifying the infection source of a COVID-19 patient. J Infect 2020; 81: 147–78 CrossRef|
|e13.||Keiding N, Louis TA: Perils and potentials of self-selected entry to epidemiological studies and surveys. J R Stat Soc Ser A Stat Soc 2016; 179: 319–76 CrossRef|
|e14.||Woloshin S, Patel N, Kesselheim AS: False negative tests for SARS-CoV-2 infection—challenges and implications. N Engl J Med 2020; DOI: 10.1056/NEJMp2015897 (ePub ahead of print) CrossRef MEDLINE|
|e15.||Westreich D, Edwards JK, Lesko CR, Cole SR: Stuart EA: Target validity and the hierarchy of study designs. Am J Epidemiol 2019; 188: 438–43 CrossRef MEDLINE PubMed Central|
|e16.||CDC updates: COVID-19 transmission webpage to clarify information about types of spread https://www.cdc.gov/media/releases/2020/s0522-cdc-updates-covid-transmission.html (last accessed on 28 June 2020).|
|e17.||Lu J, Gu J, Li K, et al.: COVID-19 outbreak associated with air conditioning in restaurant, Guangzhou, China, 2020. Emerg Infect Dis 2020; DOI: 26:10.3201/eid2607.200764 (ePub ahead of print) CrossRef MEDLINE PubMed Central|
|e18.||Loeffelholz M: Clinical virology manual. John Wiley & Sons; 2016, 202 CrossRef|
|e19.||Bustin SA, Mueller R: Real-time reverse transcription PCR (qRT-PCR) and its potential use in clinical diagnosis. Clin Sci (Lond). 2005;109: 365–79 CrossRef MEDLINE|
|e20.||Kam KQ, Yung CF, Cui L, et al.: A well infant with coronavirus disease 2019 with high viral load. Clin Infect Dis 2020;ciaa201 (ePub ahead of print) CrossRef MEDLINE PubMed Central|
|e21.||Lippi G, Simundic AM, Plebani M: Potential preanalytical and analytical vulnerabilities in the laboratory diagnosis of coronavirus disease 2019 (COVID-19). Clin Chem Lab Med 2020; 58: 1070–6 CrossRef MEDLINE|
|e22.||Casey M, Griffin J, McAloon CG, et al.: Estimating pre-symptomatic transmission of COVID-19: a secondary analysis using published data. medRxiv 2020; DOI: 10.1101/2020.05.08.20094870 (ePub ahead of print) CrossRef|
|e23.||Lipsitch M, Swerdlow DL, Finelli L: Defining the epidemiology of Covid-19—studies needed. N Engl J Med 2020; 382: 1194–6 CrossRef MEDLINE|
|e24.||Dattner I, Goldberg Y, Katriel G, et al.: The role of children in the spread of COVID-19: Using household data from Bnei Brak, Israel, to estimate the relative susceptibility and infectivity of children. medRxiv 2020; DOI: 10.1101/2020.06.03.20121145 (ePub ahead of print) CrossRef MEDLINE|
|e25.||Hildenwall H, Luthander J, Rhedin S, et al.: Paediatric COVID-19 admissions in a region with open schools during the two first months of the pandemic. Acta Paediatr. 2020; DOI: 10.1111/apa.15432 (ePub ahead of print) CrossRef MEDLINE PubMed Central|
|e26.||Folkhälsomyndigheten (FHM): Förekomsten av covid-19 i region Stockholm, 26 mars–3 april 2020. https://www.folkhalsomyndigheten.se/publicerat-material/publikationsarkiv/f/forekomsten-av-covid-19-i-region-stockholm-26-mars3-april-2020/( last accessed on 13 July 2020).|
|e27.||Havers FP, Reed C, Lim TW, et al.: Seroprevalence of Antibodies to SARS-CoV-2 in Six Sites in the United States, March 23–May 3, 2020. medRxiv 2020; DOI: 10.1101/2020.06.25.20140384 (ePub ahead of print) CrossRef|
|e28.||Stage HB, Shinleton J, Ghosh S, Scarabel F, Pellis L, Finnie T: Shut and re-open: the role of schools in the spread of COVID-19 in Europe. medRxiv 2020; DOI:10.1101/2020.06.24.20139634 (ePub ahead of print) CrossRef PubMed Central|
|e29.||Walger P, Heininger U, Knuf M, et al.: Children and adolescents in the CoVid-19 pandemic: Schools and daycare centers are to be opened again without restrictions. The protection of teachers, educators, carers and parents and the general hygiene rules do not conflict with this. GMS Hyg Infect Control 2020; 15: Doc11.|
|e30.||Christakis DA: School reopening—the pandemic issue that is not getting its due. JAMA Pediatr 2020; DOI: 10.1001/jamapediatrics.2020.2068 (ePub ahead of print) CrossRef|
|e31.||Delamater PL, Street EJ, Leslie TF, Yang YT, Jacobsen KH: Complexity of the Basic Reproduction Number (R0). Emerg Infect Dis 2019; 25: 1–4 CrossRef MEDLINE PubMed Central|
International Journal of Health and Life Sciences, 202110.5812/ijhls.110729
Presentation of a participatory approach to develop preventive measures to reduce COVID-19 transmission in child careJournal of Occupational Medicine and Toxicology, 202110.1186/s12995-021-00316-0
SARS-CoV-2 Infection, Risk Perception, Behaviour and Preventive Measures at Schools in Berlin, Germany, during the Early Post-Lockdown Phase: A Cross-Sectional StudyInternational Journal of Environmental Research and Public Health, 202110.3390/ijerph18052739
8806 Russian patients demonstrate T cell count as better marker of COVID-19 clinical course severity than SARS-CoV-2 viral loadScientific Reports, 202110.1038/s41598-021-88714-6
Persistent Detection and Infectious Potential of SARS-CoV-2 Virus in Clinical Specimens from COVID-19 PatientsViruses, 202010.3390/v12121384
Kinder und COVID-19: Kontaktpersonen-Surveillance in Frankfurter Kitas und Schulen (August bis Dezember 2020)Monatsschrift Kinderheilkunde, 202110.1007/s00112-021-01134-8
Canadian Journal of Public Health, 202110.17269/s41997-021-00544-1
SARS-CoV-2 testing and infection control strategies in European paediatric emergency departments during the first wave of the pandemicEuropean Journal of Pediatrics, 202110.1007/s00431-020-03843-w