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Systematic analysis of infectious disease outcomes by age shows lowest severity in school-age children


The COVID-19 pandemic has ignited interest in age-specific manifestations of infection but surprisingly little is known about relative severity of infectious disease between the extremes of age. In a systematic analysis we identified 142 datasets with information on severity of disease by age for 32 different infectious diseases, 19 viral and 13 bacterial. For almost all infections, school-age children have the least severe disease, and severity starts to rise long before old age. Indeed, for many infections even young adults have more severe disease than children, and dengue was the only infection that was most severe in school-age children. Together with data on vaccine response in children and young adults, the findings suggest peak immune function is reached around 5–14 years of age. Relative immune senescence may begin much earlier than assumed, before accelerating in older age groups. This has major implications for understanding resilience to infection, optimal vaccine scheduling, and appropriate health protection policies across the life course.


A striking feature of the COVID-19 pandemic is that disease and death are both much less common in children than in adults1. This unexplained phenomenon has renewed interest in age patterns of disease. Before COVID-19, most contemporary discussion focussed on vulnerabilities at the extremes of age, due to the immature immune system in infants, and immune deterioration or ‘immunosenescence’ in the elderly2. There has been little investigation of the comparative response to infection in between theses extremes: indeed, it is generally assumed that the response is broadly constant over this period and that immune function remains stable until around 60 years of age2.

Infections which have been noted to increase in severity in young adults, such as Spanish influenza and tuberculosis, are typically discussed as outliers, and explanations sought. However the medical literature a century ago noted that the case fatality rate (deaths/cases) by age is J-shaped for a number of infections, with a ‘honeymoon period’ in school-age children and rising fatality throughout adulthood3,4. This pattern, which has never been systematically examined, appears to have been largely forgotten, and the explanation for it is unknown.

We present a systematic analysis of severity of infectious disease by age, concentrating on the age at which severity starts to rise after childhood. We investigated case fatality rates and other measures of severity for a wide range of viral and bacterial infections. We are interested in the effect of age on the outcome of infection, so do not include population mortality rates, as they would be influenced by different exposures to infection by age. For infections for which there are effective treatments or vaccines, older studies were sought, as the interest is in the natural host response. We have included all studies identified by our search that fit our inclusion criteria.

We find that for almost all infections, school-age children have the lowest severity, severity starts to rise well before old age, and for many infections, severity is already higher in young adults than in children.


Information on at least one measure of severity in at least one study was found for 32 infections. Severity is measured as case fatality rate, or the proportion of cases with severe disease (e.g. the proportion hospitalised), or as the proportion with symptomatic disease among those with infection or known exposure. For ease of comparison, a figure from the largest dataset for each infection is presented in Figs. 14. Details of all identified studies for each infection are given in the Supplementary Material1,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143, together with figures, including the number of cases in each age group, where known, and 95% confidence intervals where these could be calculated. Results are shown separately by sex when this was available and the sample size was sufficient.

Fig. 1
figure 1

Severity of infectious disease by age for polio, typhoid, tuberculosis, smallpox, chickenpox, measles, infectious mononucleosis and HIV. The 95% confidence intervals on the estimates are shown where they are known or could be calculated. (a) Polio in England and Wales 1947–50, notified deaths/notified cases (23,143 cases)12; (b) Typhoid in small towns in New York State, US, 1915–24, notified deaths/notified cases13; (c) Pulmonary TB in Denmark, 1925–34, percent of notified cases dying by 31st December 193421; (d) Measles in England and Wales, 1970–88, notified deaths/notified cases29; (e) Smallpox in London Smallpox Hospital, UK, 1836–51, case fatality rate among unvaccinated patients35; (f) Chickenpox in France 1997–9, estimated hospitalisation rates based on national databases and surveillance40; (g) Infectious mononucleosis in Rochester, Minnesota, US, 1950–69, percent hospitalised43; (h) HIV in Europe, North America and Australia, 1983–1996, percent dying within 10 years, mother-to-child infections excluded (12,910 cases, survival estimated from graph)44.

Fig. 2
figure 2

Severity of infectious disease by age for influenza, pertussis, Salmonellosis, yellow fever, typhus, scarlet fever, Ebola, and Meningococcal meningitis. The 95% confidence intervals on the estimates are shown where they are known or could be calculated. (a) Influenza in Maryland, US, 1918–19, case fatality rate from household surveys47; (b) Pertussis in small towns in New York State, US, 1915–24, notified deaths/notified cases13; (c) Salmonellosis in Spain 1997–2006, case fatality rate among hospitalised patients (numbers estimated from graph and population data)55; (d) Yellow fever in New Orleans, US, 1878, case fatality rate63; (e) Typhus in London Fever Hospital 1848–57, case fatality rate15; (f) Scarlet fever in Pennsylvania, US, 1907–12, notified deaths/notified cases9; (g) Ebola in Guinea, Liberia and Sierra Leone, 2013–15. case fatality rate66; (h) Meningococcal meningitis in Cyprus, 1908–9, case fatality rate70.

Fig. 3
figure 3

Severity of infectious disease by age for Japanese encephalitis, cholera, Lassa fever, brucellosis, hepatitis B, plague, hepatitis A, and SARS The 95% confidence intervals on the estimates are shown where they are known or could be calculated. (a) Japanese encephalitis in Korea, 1955–66, notified deaths/notified cases73; (b) Cholera in Munich, Germany, 1873–4, notified deaths/notified cases76; (c) Lassa fever in Nigeria, 2018, case fatality rate in laboratory confirmed cases91; (d) Brucellosis in Malta, 1936, case fatality rate96; (e) Hepatitis B in Alaska, 1971–6, proportion of new infections with symptomatic hepatitis99; (f) Plague in 75 villages in Jalandhar District, India, 1897–8, notified deaths/notified cases102; (g) Hepatitis A in England and Wales, 1979–85, notified deaths/notified cases111; (h) SARS in Hong Kong, 2003, case fatality rate (numbers estimated from incidence and population data)114.

Fig. 4
figure 4

Severity of infectious disease by age for COVID-19, MERS-CoV, St Louis encephalitis, Western equine encephalitis, diphtheria, Escherichia coli, and dengue. The 95% confidence intervals on the estimates are shown where they are known or could be calculated. (a) COVID-19 in Spain 2020, notified deaths/notified confirmed cases, as of 11 May 2020117; (b) MERS-CoV in Saudi Arabia June 2012-July 2014 and 2017–18, case fatality rate124,125; (c) S Louis encephalitis in St Louis, US, 1933, case fatality rate126, (d) Campylobacter in Canada, 2001–4, percent hospitalised57; (e) Western equine encephalitis in Mannitoba, Canada, 1941, case fatality rate134; (f) Diphtheria in London, UK, 1894–1903, notified deaths/notified cases135; (g) E coli 0104/H4 Hamburg, Germany, 2011, proportion developing haemolytic uraemic syndrome138; (h) Dengue in Brazil 2000–2014, proportion haemorrhagic, with any complications, and hospitalised142.

For most infections clinical severity is relatively high in infancy, drops in early childhood and rises in adulthood and more steeply into old age. Figures 14, Supplementary Figures S1–32 and online-only Table 1 are arranged in order of the age at which the severity starts to rise.

For many infections the rise in severity is apparent by age 20 years. For polio, typhoid, tuberculosis and measles (Fig. 1a–d, Supplementary Figures S14) some datasets suggest that the case fatality rate is already higher by age 10–14 years than in younger children9,11,12,13,21,29. A rise in severity by age 15–19 or 15–24 years is seen clearly in large datasets for case fatality rates for smallpox35, HIV44, Spanish influenza47,48, pertussis13, and Salmonella55, for hospitalisation rates for chickenpox40, and in smaller datasets for hospitalisation for infectious mononucleosis43, and case fatality rate for Yellow fever63 (Figs. 1d–h, 2a–d, Supplementary Figures S512).

A slightly later rise, from around 20 years, is seen for case fatality rates for typhus15, scarlet fever9, Ebola66, meningococcal meningitis70, and Japanese encephalitis73 (Figs. 2e–h, 3a, Supplementary Figures S1317). For cholera, case fatality rates were lowest around 10-24 years in a number of datasets (Supplementary Figure S18)75,76, but mortality rates in the large 19th century waterborne outbreaks, in which all age groups would have been exposed, were lowest at 5-14 years88,89. For Lassa fever, case fatality rates in Nigeria91,92 increased with age from 20–30 years but no particular pattern with age was seen in Sierra Leone (Supplementary Figure S19)93,94.

For most of the remaining infections for which information was available, the severity rises with age from 30 years (seasonal influenza48, brucellosis96, plague102, probably hepatitis B acute infection99, Fig. 3d–f, Supplementary Figures S9c, S2022), 40 years (Hepatitis A111, SARS114, COVID-19117, MERS-CoV124,125, St Louis encephalitis126, Figs. 3g,h4a–c, Supplementary Figures S2327) or 50 years (Campylobacter57, Western equine encephalitis134, Fig. 4c,d Supplementary Figures S28, 29).

Three infections show different patterns, with greater severity in children than in young adults. The case fatality rate from diphtheria drops steeply with age in childhood, and continues to fall during adolescence, so the lowest case fatality rate is in the 20 s and 30 s, before rising from age 40 years (Fig. 4f, Supplementary Figure S30)9,135. The proportion of people with Shiga-toxin producing E coli who develop the haemolytic uraemic syndrome follows a similar pattern, with a drop during childhood, and the lowest proportion in the 40 s and 50 s before rising from age 60 years (Fig. 4g, Supplementary Figure S31)138,139. For Dengue the proportion with more severe disease (measured as the proportion with haemorrhagic or any complications or the proportion hospitalised) is greatest in school-age children, dropping to a nadir in the 20–30 s before rising again (Fig. 4h, Supplementary Figure S32)142.


Our findings show that the severity of most infectious diseases is at its lowest in school-age children. Strikingly, the severity was higher among young adults than among school-age children for polio12, typhoid13,14,15, tuberculosis21,22, measles29, smallpox34,35, chickenpox40,41, infectious mononucleosis43, HIV44, influenza47,48, pertussis13, Salmonella55, Yellow fever62,63, typhus15, scarlet fever9,65, Ebola144, meningococcal meningitis70, Japanese encephalitis73 cholera76, and perhaps Lassa fever91. Some infections show a slower rise of severity with age after childhood (brucellosis96, plague102, coronavirus infections113,115,117,124,125, acute hepatitis B99 and hepatitis A111, St Louis encephalitis126, Campylobacter57, and Western equine encephalitis134,) but for most this rise begins well before old age. For Diphtheria9,135 and Shiga toxin-producing E coli138,139 severity remains raised in childhood and adolescents compared to young or middle-age adults, and for Dengue142 the greatest severity is in school-age children.

Since exposure may vary for children and adults we examined case fatality rates and other measures of severity, not mortality rates. Many reports relied on clinical diagnoses or were based on surveillance data or were limited to hospitalised patients, and it is possible that diagnoses, notifications and hospitalisations differ for children and adults. In order to measure outcomes that are unaffected by vaccination or effective treatments, some of the included studies are old. But even some older studies were carefully conducted. House-to-house surveys of smallpox34 and influenza47,48 ensured mild cases and all ages are included. Direct estimates of fatalities in well-characterized outbreaks of Yellow fever62,63, meningococcal meningitis70, and viral encephalitides73,126 avoid any strain differences or notification differences by age. Long-term follow-up studies of patients with tuberculosis21,22 ensured all deaths are included. For each infection the different datasets tell a consistent story. The general pattern is remarkable, and unlikely to be explained by biases in the data.

A number of factors may explain these observations. The severity of infectious disease depends on the virulence of the infecting organism, the dose or route of infection, and the response of the host. Variation in strain virulence by age is unlikely, and many of the studies reported outbreaks of single strains (e.g. Spanish influenza, Ebola, smallpox, typhoid, cholera). Most infections have a single route or mode of infection, and for HIV the mode of infection does not explain the age pattern44.

Adolescents and adults may be exposed to higher doses of infectious agents than are younger children, through caring responsibilities or because they eat more. Greater exposure often increases the risk of acquiring an infection, but the relationship with disease severity is less consistent. An association between infecting dose and severity of disease has been suggested for measles145 and perhaps chickenpox146,147, and for Salmonella food-poisoning but not typhoid148,149,150. Fatal cases of SARS and MERS had a slightly shorter incubation period, consistent with a higher infectious dose, but this was not found for influenza151,152,153, and an association between severity and incubation period could be explained by host susceptibility. For Ebola, infecting dose (as estimated by degree of exposure to people with Ebola virus disease and their bodily fluids) is strongly associated with the risk of disease, but did not influence the case fatality rate67,69. Infectious dose cannot explain the continued rise in severity throughout adulthood.

The J-shaped pattern of severity, high in infancy, dropping in early childhood to a minimum around age 10 years, and rising in adolescence and through adulthood, with a steep increment in old age mirrors that seen for all-cause mortality across populations154,155. It is also seen for many of the more frequent causes of death including respiratory disease, diarrhoeal disease, and tuberculosis154, consistent with a changing age-specific host response.

Co-morbidities tend to increase with age but are generally low in young adults. Interestingly, in a large study of trauma in the US, the case fatality rate, adjusted for severity of injury, only rose from age 55 years onwards156, suggesting that the change in resilience to infection at younger ages is immunological, not dependent only on co-morbidities or physiological changes.

Further support comes from the immunological response to vaccine challenge by age. In clinical trials of human papilloma virus vaccine, the antibody levels to 9 different component vaccine sub-types measured 7 months post-vaccination decreased with each year increase in age from 9–26 years, with the steepest decline between ages 10 and 16 years157. A similar profile was seen in responses to Hepatitis B vaccine in China, with antibody titres in subjects vaccinated at ages 15–24 years half of those vaccinated at 5–14 years;158 and in bacteriocidal antibody responses to Meningococcal group A conjugate vaccine in West Africa, with lower titres in those aged 18–29 years at vaccination than in those aged 2–10 or 11–17 years159. However this is not universal: responses by age to the Meningococcal B vaccine varied by vaccine strain160.

Humoral responses to natural infection also indicate that immune function may be optimised in children and adolescents. Antibody titres against persistent herpesviruses such as cytomegalovirus and varicella zoster increase in response to recurrent episodes of viral reactivation so higher titres indicate less effective immune control, or re-infection. In the Gambia, where most people are infected with cytomegalovirus in infancy, cytomegalovirus-specific antibody levels show a ‘J-shaped’ pattern with age, with the lowest levels (suggesting optimal control) in adolescents, rising after age 20 years161. Similarly, Varicella zoster is ubiquitous where there is no vaccination, and studies in different settings suggest that antibody levels in seropositive individuals are at their lowest (suggesting optimal control) at 8–9 or 15–19 years162,163.

The immune system changes in infancy and old age are well described2. After birth the weak innate immune system of neonates strengthens rapidly, and the adaptive immune system develops an expanded repertoire of memory B and T cells triggered by infections (and vaccinations), the microbiome, and allergens in food and the environment2. With ageing, there is a decline in naïve T-cell numbers and T-cell receptor diversity, cytokine production by CD4 and CD8 T-cells is impaired, the CD4 to CD8 ratio is inverted, and the cells of the innate immune response function less well164. It is likely that the majority of the data presented here reflect the response to initial pathogen exposure, as those who are already fully protected by acquired immunity will not be included as cases, and those who are partially protected may have mild disease and not be diagnosed. The primary immune response may be particularly affected by ageing. The very high case fatality rates in elderly people observed in response to novel pathogens such as COVID-19, MERS, SARS, St Louis encephalitis, and Ebola, may result from restriction of the naïve T-cell repertoire.

Studies of immune senescence have focussed largely on those over 60 years, but our data indicate that important changes in immune function are apparent at a much younger age. This could be explained by relative ‘senescence’ of the immune system starting in young adulthood or even earlier165,166. Peak immune function in childhood and adolescence, at around age 5–14 years, might represent the intersection of improved function due to maturity and increasing antigenic exposure, and decreasing function due to early senescence. This is supported by assessment of immune parameters, some of which change almost linearly over the lifespan167, and by changes in the thymic cortex and medulla which peak in size at age 4–11 years168, or earlier169.

The severity of infectious disease depends not only on the control of the infectious agent but also on pathological damage arising from the immune response. Dengue is an example where strong immune responses in childhood are associated with increased clinical complications due to antibody dependent enhancement170. It has been suggested that the balance between an insufficient immune response and an over-active immunopathological response is generally more favourable in children4. However it seems surprising that a similar balance would be required for the wide range of viral and bacterial infections with similar age-severity patterns presented here, including infections in which protection is mainly antibody-mediated (e.g. smallpox) and those in which it is predominantly cell-mediated (e.g. tuberculosis).

The increase in infectious disease severity during adolescence suggests that puberty could have a role. Sex hormones influence immune responses (reviewed in171,172) but in different ways and with inconsistent findings between studies. Overall, testosterone tends to suppress and oestrogens to enhance the immune response172,173. These associations may contribute to the generally higher mortality rate from infectious disease in males and higher incidence of autoimmune disease in females173,174. Numerous differences in immune marker concentrations and response to infection have been described between males and females, and these increase after puberty175. However, where data are available, the increase in severity of disease in young adults compared to children and adolescents was seen in both males and females (e.g. Ebola, tuberculosis, Spanish influenza, polio). The decreased immune response to human papilloma virus vaccine with age, and the profile of cytomegalovirus-specific immunity with age, are also similar in males and females157,161,176.

Environmental exposures and heterologous infections may modulate the magnitude and quality of the immune response at different ages. For tuberculosis it has been argued that not only HIV but some (unknown) sexually and parenterally transmitted virus might trigger progression to disease and explain the age distribution177.

Persistent infections, such as herpesviruses, may establish early in life, or increase with age, especially with the changing social contact patterns of adolescence. Cytomegalovirus seropositivity has been associated with immunosenescent changes, including a reduction in the naïve T-cell pool and commensurate increase in CD8 + memory T-cells with a reduced CD4:CD8 ratio165,166,178,179,180,181, as well as with increased risk of mortality in older age182,183. However, the functional consequence of persistent cytomegalovirus infection appears to vary with age and cytomegalovirus may enhance responses to heterologous infections in younger people166,184,185,186. In most countries (and probably everywhere at the time when some of the data we present were collected) almost everyone is infected with cytomegalovirus in early childhood161,187, and continuing exposure and multiple infections are likely. So if cytomegalovirus has a role in explaining the age-pattern of severity, it may reflect immunological attrition due to the burden of persistent infection or reinfection rather than simple presence or absence of infection188.

The age pattern of severity suggests that natural selection has acted to optimise immune function in childhood and adolescence with a subsequent decline during adulthood. Peak function before reproduction seems counter-intuitive, but according to life history theory there is a trade-off between the energy required for immune response and that required for other functions, such as reproduction and growth189,190. Whilst the energy costs of innate immunity are spread throughout the lifetime189,190 the adaptive immune system has high upfront but lower running energy costs. Thymic involution conserves energy when sufficient naïve T-cells have been generated189, facilitating the metabolic demands of the adolescent growth spurt and secondary sexual development191, but leaving the elderly more vulnerable.

SARS-CoV and SARS-CoV-2 appear to have more extreme variation in severity by age than other infections, with predominantly mild disease in children, and very high case fatality rates in the elderly. Unusually, there is no evidence of higher severity in infants. The reasons for this extreme variation are not yet known, but should be considered in the context of the findings of this review.

Appreciating how responses to infections vary by age outside the extremes of age can have major implications. Understanding the prevalence and transmission of SARS-CoV-2 in children, and how this differs from influenza, is crucial in predicting the role of school closures in the response192. Vaccination programmes that increase the average age of infection, can paradoxically increase the numbers of people with severe disease193. This has led to targeted vaccination of older children for measles and rubella. The higher vaccine response of younger children to human papilloma virus vaccine may lead to a shift in target age groups.

The finding that many infectious diseases are least severe in school-age children suggests that relative immune ageing starts far earlier than has previously been recognised. Improved understanding of the mechanisms underlying this observation may provide novel opportunities for intervention strategies. Children tend to have lower mortality than adults from sepsis, and comparison of their responses is being used in drug discovery194; similar approaches could be used for other infections. Comparison of immune response to infections which increase in severity in young adults and those that only increase later may give clues to drug and vaccine design. In the midst of a pandemic and with increasing antimicrobial resistance, new approaches such as these are desperately needed.


We sought information on severity of infectious disease by age for a wide range of different infections.

Defining outcome measures

The focus of this study is the host response to infection by age. We are interested in the outcome of infections or exposure. We have therefore used:

  • Case fatality rates estimated directly from cohorts of individuals

  • Case fatality rates estimated from surveillance data, comparing numbers of deaths and numbers of cases

  • Proportion of cases with severe disease (e.g. the proportion hospitalised)

  • Proportion with symptomatic disease among those with infection or known exposure

Note that mortality rates, though more readily available, would be misleading, as exposure is likely to vary by age.

Sources of data and possible biases

The most accurate data come from situations in which there is active case ascertainment to ensure mild cases are not missed, and active follow-up to ensure outcomes are recorded, ideally proving infection through serology or microbiology. But such studies are rare, and imposing such strict criteria would leave very little data.

Hospital-based data exclude mild cases, and admission criteria may vary with age, but outcomes are likely to be accurately recorded.

Surveillance data provide some of the largest studies but interpreting the age patterns assumes diagnosis and notification are not influenced by age group. Case fatality rates measured from surveillance data can also be biased because of the lag between incidence and death. This is a particular problem in ongoing epidemics, and for diseases with long duration, but will not necessarily bias the age pattern.

Strains of different virulence could affect different sections of the population. Although it is unlikely that strains would differ systematically by age group, studies of single outbreaks of disease avoid this theoretical problem, but are only available for some infections.

For infections for which effective vaccines or treatments are available there may be very few cases or few severe cases, and differential access to or efficacy of vaccines and treatment by age could influence the results. For these infections estimates from the older (e.g. pre-antibiotic or pre-vaccine) literature can be helpful.

For endemic infections leading to life-long immunity, disease in adults may be rare or atypical, so estimates from isolated (e.g. island) populations are most informative. Similarly, newly-emerged infections, or infections in areas where they have not been seen before, will not be affected by partial immunity in older individuals.

Search strategy

Finding sufficiently detailed age-specific information for different diseases is not straightforward and a conventional systematic review is not possible. Very few studies are done to look at the effect of age on disease outcomes. Most outbreak or surveillance reports do not include enough detail, giving age only as a median and range, or giving the age breakdown only for deaths or only for cases. Relevant data are occasionally included as background information in studies on other topics.

Information on different infections was found in different ways. PubMed was used to find reports on specific infections, searching with “case fatality” or “severity” or “hospital*”and “age”, or looking at reports of large outbreaks. Outbreak reports were also searched through the Morbidity and Mortality Weekly Report (MMWR) produced by the US Centers for Disease Control.

To find suitable studies in the older literature, the journal Public Health was hand-searched from volume 1 (1888) to volume 59 (1946), the Public Health Bulletin from 1904–1949 and the issues of the Journal of Tropical Medicine and Hygiene available on-line (1915–19, 1921, 1923). All pamphlets and reports in the London School of Hygiene & Tropical Medicine library open shelves and books in the Wellcome collection library open shelves relating to infectious diseases were hand-searched. These include reports from the 19th century onwards. We also searched books and reports available electronically from the United States Public Health Service, US National Library of Medicine Digital Collections, the London School of Hygiene & Tropical Medicine, or Google on plague, cholera and yellow fever. The authors’ literature collection was searched, and relevant references followed (snowball searching).

All identified studies that gave information on the age-pattern of severity were included if they satisfied the following criteria:

  • Included children and adults

  • Data stratified in 10-year age groups or less

  • At least 100 cases included

  • Source of data given

All eligible studies were included whatever age-severity pattern they showed. Where the number of cases and outcomes was presented, or could be derived, by age group, binomial exact 95% confidence intervals were calculated for the proportions. For each infection, the relative risk of the outcome (death or other measure of severity) in adults was compared to that in children using the largest dataset with information on numbers of individuals in the relevant age groups.

Data availability

All datasets are available in an Excel file195. This links to the supplementary material, and includes the origin of each dataset.

Code availability

There was no custom code for this analysis


  1. The Novel Coronavirus Pneumonia Emergency Response Epidemiology Team. The epidemiological characteristics of an outbreak of 2019 Novel Coronavirus Diseases (COVID-19) - China, 2020. China CDC Weekly 2, 1–10 (2020).

    Article  Google Scholar 

  2. Simon, A. K., Hollander, G. A. & McMichael, A. Evolution of the immune system in humans from infancy to old age. Proc Biol Sci 282, 20143085, (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  3. Stallybrass, C. O. The principles of epidemiology and the process of infection. (George Routledge & Son Ltd, 1931).

  4. Ahmed, R., Oldstone, M. B. & Palese, P. Protective immunity and susceptibility to infectious diseases: lessons from the 1918 influenza pandemic. Nat Immunol 8, 1188–1193 (2007).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  5. Nathanson, N. & Kew, O. M. From emergence to eradication: the epidemiology of poliomyelitis deconstructed. Am J Epidemiol 172, 1213–1229, (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Department of Health for Scotland. Poliomyelitis. A survey of the outbreak in Scotland in 1947. (His Majesty’s Stationary Office, 1950).

  7. Donovan, C. R. & Bowman, M. Some epidemiological features of poliomyelitis and encephalitis. Manitoba 1941. Canadian Public Health Journal 33, 241–314 (1942).

    Google Scholar 

  8. Freyche, M.-J. & Nielsen, J. In Poliomyelitis (WHO Monograph Series, no. 26) (World Health Organization, 1955).

  9. Lavinder, C. H., Freeman, A. W. & Frost, W. H. Epidemiologic studies of poliomyelitis in New York City and the North Eastern United States during the year 1916. Public Health Bulletin 91, 1–310 (1918).

  10. Lumsden, L. L. Epidemiological studies of poliomyelitis in Kentucky. (United States Government Printing Office, 1936).

  11. Olin, G. In Poliomyelitis. Papers and discussions presented at the second international poliomyelitis conference 367–374 (J. B. Lippincott Company, 1952).

  12. Logan, W. P. D. Distribution of poliomyelitis by sex, age and geographical area. Mon Bull Minist Health Public Health Lab Serv 11, 147–173 (1952).

    CAS  PubMed  Google Scholar 

  13. Godfrey, E. S. The Age Distribution of Communicable Disease According to Size of Community. Am J Public Health Nations Health 18, 616–631 (1928).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  14. Picken, R. M. F. One aspect of the transmission of enteric fever. Public Health 29, 2–6 (1915).

    Article  Google Scholar 

  15. Murchison, C. Contributions to the etiology of continued fever: or an investigation of various causes which influence the prevalence and mortality of its different forms. Medico-Chirurgical Transactions 41, 219–306 (1858).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  16. Davies, S. An epidemic of enteric fever. Public Health 8, 119–124 (1896).

    Google Scholar 

  17. Holden, O. M. The Croydon typhoid outbreak. (A summary of the chief clinical features). Public Health 52, 135–146 (1939).

    Article  Google Scholar 

  18. Guest Gornall, J. The prevalence of enteric fever in Warrington during 1899. Public Health 12, 841–850 (1900).

    Article  Google Scholar 

  19. Porter, C. History of an epidemic of Typhoid fever including consideration of the means of prevention of the disease in Midden towns. Public Health 68, 80–84 (1893).

    Google Scholar 

  20. Merrillees, C. R. Report on typhoid fever in the City of Moorabbin 1943. (Melbourne, 1943).

  21. Lindhardt, M. The statistics of pulmonary tuberculosis in Denmark 1925–1934. A statistical investigation on the occurrence of pulmonary tuberculosis in the period 1925-1934, worked out on the basis of the Danish National Health Service file of notified cases and deaths., (Ejnar Munksgaard, 1939).

  22. MacGregor, A. S. M. Studies in the epidemiology of Phthisis. Public Health 27, 269–278 (1924).

    Article  Google Scholar 

  23. Picken, R. M. F. Age and sex incidence, as distinct from mortality, in respiratory tuberculosis. Public Health 54, 42–46 (1940).

    Article  Google Scholar 

  24. Marais, B. J. et al. The clinical epidemiology of childhood pulmonary tuberculosis: a critical review of literature from the pre-chemotherapy era. Int J Tuberc Lung Dis 8, 278–285 (2004).

    CAS  PubMed  Google Scholar 

  25. Ferebee, S. H. & Mount, F. W. Tuberculosis morbidity in a controlled trial of the prophylactic use of isoniazid among household contacts. Am Rev Respir Dis 85, 490–510 (1962).

    CAS  PubMed  Google Scholar 

  26. Comstock, G. W., Livesay, V. T. & Woolpert, S. F. The prognosis of a positive tuberculin reaction in childhood and adolescence. Am J Epidemiol 99, 131–138 (1974).

    CAS  PubMed  Article  Google Scholar 

  27. Panum, P. L. Observations made during the epidemic of measles on the Faroe Islands in the year 1846. (Delta Omega Society, 1940).

  28. Wilson, G. N. Measles: its prevalence and mortality in Aberdeen. Public Health 18, 65–82 (1905).

    Article  Google Scholar 

  29. Ramsay, M. et al. The epidemiology of measles in England and Wales: rationale for the 1994 national vaccination campaign. Commun Dis Rep CDR Rev 4, R141–146 (1994).

    CAS  PubMed  Google Scholar 

  30. Institutul National de Sanatate Publica Romania. (2019).

  31. Institutul National de Sanatate Publica Romania. (2019).

  32. Muscat, M. et al. The measles outbreak in Bulgaria, 2009-2011: An epidemiological assessment and lessons learnt. Euro surveill 21, 30152, (2016).

    Article  PubMed  Google Scholar 

  33. Woudenberg, T. et al. Large measles epidemic in the Netherlands, May 2013 to March 2014: changing epidemiology. Euro surveill 22, (2017).

  34. Barry, D. Report on an epidemic of small-pox at Sheffield during 1887-88. (HMSO, 1889).

  35. Marson, J. F. In General Board of Health. Papers relating to the history and practice of vaccination (ed J. Simon) (HMSO, 1857).

  36. Killinck Mallard, C. The Leicester Method of dealing with small-pox. Public Health 16, 607–629 (1904).

    Article  Google Scholar 

  37. Dingle, C. V. The story of the Middlesborough small-pox epidemic and some of its lessons. Public Health 11, 173–192 (1898).

    Article  Google Scholar 

  38. Niven, J. Small-pox problems. Public Health 64, 324–7 (1893).

  39. Hill, A. The small-pox experience of Birmingham 1893-5. Public Health 8, 413–414 (1896).

    Article  Google Scholar 

  40. Boelle, P. Y. & Hanslik, T. Varicella in non-immune persons: incidence, hospitalization and mortality rates. Epidemiol Infect 129, 599–606 (2002).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  41. Guess, H. A., Broughton, D. D., Melton, L. J. 3rd & Kurland, L. T. Population-based studies of varicella complications. Pediatrics 78, 723–727 (1986).

    CAS  PubMed  Google Scholar 

  42. Brisson, M. et al. Epidemiology of varicella zoster virus infection in Canada and the United Kingdom. Epidemiol Infect 127, 305–314 (2001).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  43. Henke, C. E., Kurland, L. T. & Elveback, L. R. Infectious mononucleosis in Rochester, Minnesota, 1950 through 1969. Am J Epidemiol 98, 483-490 (1973).

  44. Collaborative group on AIDS incubation and HIV survival including the CASCADE EU Concerted Action. Time from HIV-1 seroconversion to AIDS and death before widespread use of highly-active antiretroviral therapy: a collaborative re-analysis. Lancet 355, 1131–1137 (2000).

    Article  Google Scholar 

  45. Darby, S. C., Ewart, D. W., Giangrande, P. L., Spooner, R. J. & Rizza, C. R. Importance of age at infection with HIV-1 for survival and development of AIDS in UK haemophilia population. UK Haemophilia Centre Directors’ Organisation. Lancet 347, 1573–1579 (1996).

    CAS  PubMed  Article  Google Scholar 

  46. Taubenberger, J. K. & Morens, D. M. 1918 Influenza: the mother of all pandemics. Emerg Infect Dis 12, 15–22 (2006).

    PubMed  PubMed Central  Article  Google Scholar 

  47. Frost, W. H. & Sydenstricker, E. Influenza in Maryland. Preliminary statistics of certain localities. Public Health Reports 34, 491–504 (1919).

    Article  Google Scholar 

  48. Collins, S. D. Age and sex incidence of influenza and pneumonia morbidity and mortality in the epidemic of 1928-29 with comparative data for the epidemic of 1918-19. Public Health Reports 46, 1909–1937 (1931).

    Article  Google Scholar 

  49. Chowell, G., Simonsen, L., Flores, J., Miller, M. A. & Viboud, C. Death patterns during the 1918 influenza pandemic in Chile. Emerg Infect Dis 20, 1803-1811 (2014).

  50. Chowell, G. et al. The 1918-19 influenza pandemic in Boyaca, Colombia. Emerg Infect Dis 18, 48–56 (2012).

    PubMed  PubMed Central  Article  Google Scholar 

  51. Chowell, G. et al. The 1918-1920 influenza pandemic in Peru. Vaccine 29(Suppl 2), B21–26, (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Mamelund, S. E. Geography may explain adult mortality from the 1918–20 influenza pandemic. Epidemics 3, 46–60 (2011).

    PubMed  Article  Google Scholar 

  53. McMorrow, M. L. et al. Severe Acute Respiratory Illness Deaths in Sub-Saharan Africa and the Role of Influenza: A Case Series From 8 Countries. J Infect Dis 212, 853–860 (2015).

    PubMed  Article  Google Scholar 

  54. Laing, J. S. & Hay, M. Whooping cough: its prevalence and mortality in Aberdeen. Public Health 14, 584 (1902).

    Article  Google Scholar 

  55. Gil Prieto, R., Alejandre, C. G., Meca, A. A., Barrera, V. H. & de Miguel, A. G. Epidemiology of hospital-treated Salmonella infection; data from a national cohort over a ten-year period. J Infect 58, 175–181 (2009).

    PubMed  Article  Google Scholar 

  56. Chen, P. L. et al. Epidemiology, disease spectrum and economic burden of non-typhoidal Salmonella infections in Taiwan, 2006-2008. Epidemiol Infect 140, 2256–2263 (2012).

    CAS  PubMed  Article  Google Scholar 

  57. Ruzante, J. M., Majowicz, S. E., Fazil, A. & Davidson, V. J. Hospitalization and deaths for select enteric illnesses and associated sequelae in Canada, 2001-2004. Epidemiol Infect 139, 937–945 (2011).

    CAS  PubMed  Article  Google Scholar 

  58. Wilson, H. L., Kennedy, K. J. & Moffatt, C. R. M. Epidemiology of non-typhoid Salmonella infection in the Australian Capital Territory over a 10-year period. Intern Med J 48, 316–323 (2018).

    CAS  PubMed  Article  Google Scholar 

  59. Fisker, N., Vinding, K., Molbak, K. & Hornstrup, M. K. Clinical review of nontyphoid Salmonella infections from 1991 to 1999 in a Danish county. Clin Infect Dis 37, e47–52, (2003).

    Article  PubMed  Google Scholar 

  60. Le Bacq, F., Louwagie, B. & Verhaegen, J. Salmonella typhimurium and Salmonella enteritidis: changing epidemiology from 1973 until 1992. Eur J Epidemiol 10, 367–371 (1994).

    PubMed  Article  Google Scholar 

  61. Bille, B., Mellbin, T. & Nordbring, F. An Extensive Outbreak of Gastroenteritis Caused by Salmonella Newport. I. Some Observations of 745 Known Cases. Acta Med Scand 175, 557–567 (1964).

    CAS  PubMed  Article  Google Scholar 

  62. Procter, J. R. Notes on the Yellow Fever epidemic at Hickman, Ky., 1878. (Frankfort, 1879).

  63. Sternberg, G. M. Report on the etiology and prevention of yellow fever. (Government Printing Office, 1890).

  64. Woods, H. M. Epidemiological study of scarlet fever in England and Wales since 1900. Medical Research Council Special Report Series No. 180. (His Majesty’s Stationary Office, London, 1933).

    Google Scholar 

  65. Anon. The case of mortality (fatality) of scarlet fever. Public Health, 333-334 (1895).

  66. WHO Ebola Response Team. Ebola Virus Disease among Male and Female Persons in West Africa. N Engl J Med 374, 95–96 (2016).

    Article  Google Scholar 

  67. Bower, H. et al. Exposure-specific and age-specific attack rates for Ebola virus disease in Ebola-affected households, Sierra Leone. Emerg Infect Dis 22, 1403–1412 (2016).

    PubMed  PubMed Central  Article  Google Scholar 

  68. Glynn, J. R. et al. Asymptomatic infection and unrecognised Ebola Virus Disease: seroprevalence of antibodies to Ebola virus in a large cross-sectional study in Ebola-affected households, Sierra Leone, using a new non-invasive assay. Lancet Infect Dis 17, 645–653 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  69. Bower, H. et al. Deaths, late deaths, and role of infecting dose in Ebola virus disease in Sierra Leone: retrospective cohort study. BMJ 353, i2403, (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  70. Williamson, G. A. Report on the outbreak of epidemic cerebro-spinal meningitis, December 1908 to May 1909, (Publisher not identified, 1909).

  71. Dickie, D. Transactions of the twenty-seventh annual conference of state and territorial health officers with the United States Public Health Service. Public Health Bulletin No. 194. (United States Government Printing Office, Washington, 1930).

    Google Scholar 

  72. Heiman, H. & Feldstein, S. Meningococcus meningitis. (JB Lippincott Company, 1913).

  73. Kono, R. & Kim, K. H. Comparative epidemiological features of Japanese encephalitis in the Republic of Korea, China (Taiwan) and Japan. Bull World Health Organ 40, 263–277 (1969).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. Kumar Pant, D., Tenzin, T., Chand, R., Kumar Sharma, B. & Raj Bist, P. Spatio-temporal epidemiology of Japanese encephalitis in Nepal, 2007-2015. PLoS ONE 12, e0180591 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  75. Reincke. In The Local Government Board. Reports and Papers on Cholera in England in 1893. (Her Majesty’s Stationary Office, 1894).

  76. Sticker, G. Abhandlungen aus der Seuchengeschichte und Seuchenlehre. II. Band: Die Cholera. (A. Töpelmann, 1912).

  77. Sibley, S. W. Report on the cholera patients admitted into the hospital during the year 1854. (James Truscott, 1855).

  78. Acland, H. W. Memoir on the cholera at Oxford in the year 1854 with considerations suggested by the epidemic. (John Churchill, 1856).

  79. Mahoney, O. B. Pathological and practial treatise on epidemic cholera, its history, causes, various forms, and treatment. (John Churchill, 1853).

  80. Hayden, T. & Cruise, F. R. Report on the cholera epidemic of 1866: as treated in the Mater Misericordiae Hospital, Dublin; with general remarks on the disease. (Fannin and Company, 1867).

  81. Finger, D. Die Cholera Epidemica nach Beobachtungen. (Herman Fritzsche, Leipzig, 1851).

    Google Scholar 

  82. Parkin, J. Statistical report of the epidemic cholera in Jamaica. (William H Allen, 1852).

  83. Cox, S. M. Report on an outbreak of asiatic cholera in Shanghai during the summer of 1907. (Methodist Publishing House, 1908).

  84. Rogers, L. Cholera and its treatment. (Oxford University Press, 1911).

  85. Page, A. L. et al. Geographic distribution and mortality risk factors during the cholera outbreak in a rural region of Haiti, 2010-2011. PLoS Negl Trop Dis 9, e0003605, (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  86. Dizon, J. J. et al. Studies of cholera El Tor in the Philippines. I. Characteristics of cholera El Tor in Negros Occidental Province, November 1961 to September 1962. Bull World Health Organ 33, 627–636 (1965).

    CAS  PubMed  PubMed Central  Google Scholar 

  87. Umoh, J. U., Adesiyun, A. A., Adekeye, J. O. & Nadarajah, M. Epidemiological features of an outbreak of gastroenteritis/cholera in Katsina, Northern Nigeria. J Hyg (Lond) 91, 101–111 (1983).

    CAS  Article  Google Scholar 

  88. Gull, W. W. In Reports on epidemic cholera drawn up at the desire of the cholera committee of The Royal College of Physicians (eds W. Baly & W. W. Gull) (John Churchill, 1854).

  89. Anon. Report on the cholera in Paris. (Samuel S & William Wood, 1849).

  90. Townsend, S. C. Report on the epidemic of cholera of 1875.1876 in the Central Provinces. (Chief Commissioner’s Office Press, 1878).

  91. Ilori, E. A. et al. Epidemiologic and Clinical Features of Lassa Fever Outbreak in Nigeria, January 1-May 6, 2018. Emerg Infect Dis 25 (2019).

  92. Okokhere, P. et al. Clinical and laboratory predictors of Lassa fever outcome in a dedicated treatment facility in Nigeria: a retrospective, observational cohort study. Lancet Infect Dis 18, 684–695 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  93. McCormick, J. B. et al. A case-control study of the clinical diagnosis and course of Lassa fever. J Infect Dis 155, 445–455 (1987).

    CAS  PubMed  Article  Google Scholar 

  94. Shaffer, J. G. et al. Lassa fever in post-conflict Sierra Leone. PLoS Negl Trop Dis 8, e2748, (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  95. McCormick, J. B., Webb, P. A., Krebs, J. W., Johnson, K. M. & Smith, E. S. A prospective study of the epidemiology and ecology of Lassa fever. J Infect Dis 155, 437–444 (1987).

    CAS  PubMed  Article  Google Scholar 

  96. Debono, J. E. In Brucellosis in Man and Animals (ed I. Forest Huddleson) (The Commonwealth Fund, 1939).

  97. McCulloch, T. & Weir, J. C. In Reports of the Commission appointed by the Admirality, the War Office, and the Civil Government of Malta, for their investigation of Mediterranean Fever, under the supervision of an advisory committee of The Royal Society. Part VII (Harrison and Sons, 1907).

  98. Pearce, N., Milne, A. & Moyes, C. Hepatitis B virus: the importance of age at infection. N Z Med J 101, 788–790 (1988).

    CAS  PubMed  Google Scholar 

  99. McMahon, B. J. et al. Acute hepatitis B virus infection: relation of age to the clinical expression of disease and subsequent development of the carrier state. J Infect Dis 151, 599–603 (1985).

    CAS  PubMed  Article  Google Scholar 

  100. Edmunds, W. J., Medley, G. F., Nokes, D. J., Hall, A. J. & Whittle, H. C. The influence of age on the development of the hepatitis B carrier state. Proc Biol Sci 253, 197–201 (1993).

    CAS  PubMed  Article  Google Scholar 

  101. Benedict, C. Bubonic plague in nineteenth century China. PhD thesis. (UMI, 1992).

  102. Sticker, G. Abhandlungen aus der Seuchengeschichte und Seuchenlehre. I. Band: Die Pest. (A. Töpelmann, 1910).

  103. Burnet Ham, B. Report on Plague in Queensland, 1900-1907. (Department of Health, 1907).

  104. Anon. Plague cases treated in the Kennedy Town Hospital, Hong Kong. (Govenrment Report, 1903).

  105. Choksy, N. H. The treatment of plague with Prof. Lustig’s serum. (Eagle Printing Office, 1903).

  106. Hill, E. Report on the plague in Natal 1902-3. (Cassell and Company, 1904).

  107. Macchiavello, A. Contribuciones al estudio de la peste bubonica en el nordeste del Brasil. (Oficina Sanitaria Panamericana, 1941).

  108. Thomson, G. S. & Thomson, J. A treatise on plague. The conditions for its causation, prevalence, incidence, immunity, prevention, and treatment. (Swan Sonnenschein and Co. Lim., 1901).

  109. Wu, L. T. A treatise on pneumonic plague. (League of Nations, 1926).

  110. Lednar, W. M. et al. Frequency of illness associated with epidemic hepatitis A virus infections in adults. Am J Epidemiol 122, 226–233 (1985).

    CAS  PubMed  Article  Google Scholar 

  111. Forbes, A. & Williams, R. Increasing age–an important adverse prognostic factor in hepatitis A virus infection. J R Coll Physicians Lond 22, 237–239 (1988).

    CAS  PubMed  PubMed Central  Google Scholar 

  112. Shim, J. J., Chin, S. O., Lee, C. K., Jang, J. Y. & Kim, B. H. Epidemiological changes in hepatitis A in Korea: increasing age and its effect on clinical outcomes. Epidemiol Infect 140, 2182–2189 (2012).

    CAS  PubMed  Article  Google Scholar 

  113. Lau, E. H. et al. A comparative epidemiologic analysis of SARS in Hong Kong, Beijing and Taiwan. BMC Infect Dis 10, 50, (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  114. Chan-Yeung, M. & Xu, R. H. SARS: epidemiology. Respirology 8(Suppl), S9–14 (2003).

    PubMed  PubMed Central  Article  Google Scholar 

  115. Cao, W. C., de Vlas, S. J. & Richardus, J. H. The severe acute respiratory syndrome epidemic in mainland China dissected. Infect Dis Rep 3, e2, (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  116. Stockman, L. J. et al. Severe acute respiratory syndrome in children. Pediatr Infect Dis J 26, 68–74 (2007).

    PubMed  Article  Google Scholar 

  117. Centro de Coordinación de Alertas y Emergencias Sanitarias. Actualización n° 103. Enfermedad por el coronavirus (COVID-19). 12.05.2020 (datos consolidados a las 21:00 horas del 11.05.2020) Situación en España. (2020).

  118. The Government of the Republic of Korea. Tackling COVID-19. Health, quarantine and economic measures: Korean experience. (2020).

  119. Alipio, M. M. & Pregoner, J. D. M. Epidemiological characteristics of an outbreak of Coronavirus Disease 2019 in the Philippines. Preprint at (2020).

  120. Bignami-Van Assche, S., Ghio, D. & Van Assche, A. Estimates of COVID-19 case-fatality risk from individual-level data. Preprint at (2020).

  121. Onder, G., Rezza, G. & Brusaferro, S. Case-Fatality Rate and Characteristics of Patients Dying in Relation to COVID-19 in Italy. JAMA, (2020).

  122. Solis, P. & Carreño, H. COVID-19 Fatality and Comorbidity Risk Factors among Diagnosed Patients in Mexico. Preprint at (2020).

  123. Richardson, S. et al. Presenting Characteristics, Comorbidities, and Outcomes Among 5700 Patients Hospitalized With COVID-19 in the New York City Area. JAMA, (2020).

  124. Lessler, J. et al. Estimating the Severity and Subclinical Burden of Middle East Respiratory Syndrome Coronavirus Infection in the Kingdom of Saudi Arabia. Am J Epidemiol 183, 657–663 (2016).

    PubMed  Article  Google Scholar 

  125. World Health Organization. MERS-CoV Disease outbreak news (listings for 1 November 2018, 3 October 2018, 18 June 2018, 26 January 2018) (2018).

  126. US Treasury Department Public Health Service. Report on the St. Louis outbreak of encephalitis. Public Health Bulletin No. 214. (Washington, 1935).

  127. Luby, J. P. et al. The epidemiology of St. Louis encephalitis in Houston, Texas, 1964. Am J Epidemiol 86, 584–597 (1967).

    CAS  PubMed  Article  Google Scholar 

  128. McGowan, J. E. Jr., Bryan, J. A. & Gregg, M. B. Surveillance of arboviral encephalitis in the United States, 1955–1971. Am J Epidemiol 97, 199–207 (1973).

    PubMed  Article  Google Scholar 

  129. Hopkins, C. C. et al. The epidemiology of St. Louis encephalitis in Dallas, Texas, 1966. Am J Epidemiol 102, 1–15 (1975).

    CAS  PubMed  Article  Google Scholar 

  130. Muckenfuss, R. S. Clinical Observations and Laboratory Investigations on the 1933 Epidemic of Encephalitis in St. Louis. Bull N Y Acad Med 10, 444–453 (1934).

    CAS  PubMed  PubMed Central  Google Scholar 

  131. Nichols, G. L., Richardson, J. F., Sheppard, S. K., Lane, C. & Sarran, C. Campylobacter epidemiology: a descriptive study reviewing 1 million cases in England and Wales between 1989 and 2011. BMJ Open 2, (2012).

  132. Bradshaw, M. J., Brown, R., Swallow, J. H. & Rycroft, J. A. Campylobacter enteritis in Chelmsford. Postgrad Med J 56, 80–84 (1980).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  133. Porter, I. A. & Reid, T. M. A milk-borne outbreak of Campylobacter infection. J Hyg (Lond) 84, 415–419 (1980).

    CAS  Article  Google Scholar 

  134. Donovan, C. R. & Bowman, M. Epidemiology of Encephalitis: Western Equine Type, Manitoba, 1941. Can Med Assoc J 46, 525–530 (1942).

    CAS  PubMed  PubMed Central  Google Scholar 

  135. Ramsey Smith, W. Data from Albutt’s System of Medicine compiled from County of London Records of Diphtheria. In The official year-book of the Commonwealth of Australia no.16 (1923).

  136. Anon. Report of the Medical Superintendents upon the use of antitoxic serum in the treatment of diphtheria in the hospitals of the Board during the year 1896. (Metropolitan Asylums Board, 1897).

  137. Read, M. Recent experiences of Diphtheria. Public Health 12, 346–358 (1900).

    Article  Google Scholar 

  138. Tahden, M. et al. Epidemiological and Ecological Characterization of the EHEC O104:H4 Outbreak in Hamburg, Germany, 2011. PLoS One 11, e0164508, (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  139. Gould, L. H. et al. Hemolytic uremic syndrome and death in persons with Escherichia coli O157:H7 infection, foodborne diseases active surveillance network sites, 2000–2006. Clin Infect Dis 49, 1480–1485 (2009).

    PubMed  Article  Google Scholar 

  140. Launders, N. et al. Disease severity of Shiga toxin-producing E. coli O157 and factors influencing the development of typical haemolytic uraemic syndrome: a retrospective cohort study, 2009-2012. BMJ Open 6, e009933, (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  141. Byrne, L., Jenkins, C., Launders, N., Elson, R. & Adak, G. K. The epidemiology, microbiology and clinical impact of Shiga toxin-producing Escherichia coli in England, 2009-2012. Epidemiol Infect 143, 3475–3487 (2015).

    CAS  PubMed  Article  Google Scholar 

  142. Burattini, M. N. et al. Age and regional differences in clinical presentation and risk of hospitalization for dengue in Brazil, 2000-2014. Clinics (Sao Paulo) 71, 455–463 (2016).

    Article  Google Scholar 

  143. Guzman, M. G. et al. Effect of age on outcome of secondary dengue 2 infections. Int J Infect Dis 6, 118–124 (2002).

    PubMed  Article  Google Scholar 

  144. WHO Ebola Response Team. Ebola Virus Disease among Male and Female Persons in West Africa. N Engl J Med 374, 96–98 (2016).

    Article  Google Scholar 

  145. Aaby, P. Malnutrition and overcrowding/intensive exposure in severe measles infection: review of community studies. Rev Infect Dis 10, 478–491 (1988).

    CAS  PubMed  Article  Google Scholar 

  146. Ross, A. H. Modification of chicken pox in family contacts by administration of gamma globulin. N Engl J Med 267, 369–376 (1962).

    CAS  PubMed  Article  Google Scholar 

  147. Dunkle, L. M. et al. A controlled trial of acyclovir for chickenpox in normal children. N Engl J Med 325, 1539–1544 (1991).

    CAS  PubMed  Article  Google Scholar 

  148. Glynn, J. R. & Bradley, D. J. The relationship between infecting dose and severity of disease in reported outbreaks of Salmonella infections. Epidemiol Infect 109, 371–388 (1992).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  149. Glynn, J. R., Hornick, R. B., Levine, M. M. & Bradley, D. J. Infecting dose and severity of typhoid: analysis of volunteer data and examination of the influence of the definition of illness used. Epidemiol Infect 115, 23–30 (1995).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  150. Glynn, J. R. & Palmer, S. R. Incubation period, severity of disease, and infecting dose: evidence from a Salmonella outbreak. Am J Epidemiol 136, 1369–1377 (1992).

    CAS  PubMed  Article  Google Scholar 

  151. Virlogeux, V. et al. Brief Report: Incubation Period Duration and Severity of Clinical Disease Following Severe Acute Respiratory Syndrome Coronavirus Infection. Epidemiology 26, 666–669 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  152. Virlogeux, V., Park, M., Wu, J. T. & Cowling, B. J. Association between Severity of MERS-CoV Infection and Incubation Period. Emerg Infect Dis 22, 526–528 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  153. Virlogeux, V. et al. Association between the Severity of Influenza A(H7N9) Virus Infections and Length of the Incubation Period. PLoS One 11, e0148506, (2016).

  154. Preston, S. H. Mortality patterns in national populations. With special reference to recorded causes of death. (Academic Press, 1976).

  155. Clark, S. J. & Sharrow, D. J. Contemporary Model Life Tables for Developed Countries An Application of Model-based Clustering. Working Paper no. 107 (Washington, 2011).

  156. Ottochian, M. et al. Does age matter? The relationship between age and mortality in penetrating trauma. Injury 40, 354–357 (2009).

    PubMed  Article  Google Scholar 

  157. Petersen, L. K. et al. Impact of baseline covariates on the immunogenicity of the 9-valent HPV vaccine - A combined analysis of five phase III clinical trials. Papillomavirus Res 3, 105–115 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  158. Kang, G. et al. Comparison of the effect of increased hepatitis B vaccine dosage on immunogenicity in healthy children and adults. Hum Vaccin Immunother 12, 2312–2316 (2016).

    PubMed  PubMed Central  Article  Google Scholar 

  159. Tang, Y., Plikaytis, B. D., Preziosi, M. P. & Borrow, R. Influence of Age on Antibody Response and Persistence Following Immunization With MenAfriVac. Clin Infect Dis 61(Suppl 5), S531–539 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  160. Ostergaard, L. et al. A Bivalent Meningococcal B Vaccine in Adolescents and Young Adults. N Engl J Med 377, 2349–2362 (2017).

    CAS  PubMed  Article  Google Scholar 

  161. Stockdale, L. et al. Human cytomegalovirus epidemiology and relationship to tuberculosis and cardiovascular disease risk factors in a rural Ugandan cohort. PLoS One 13, e0192086, (2018).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  162. van Lier, A. et al. Varicella zoster virus infection occurs at a relatively young age in The Netherlands. Vaccine 31, 5127–5133 (2013).

    PubMed  Article  Google Scholar 

  163. Cohen, D. I. et al. Seroepidemiology of Varicella zoster in Israel prior to large-scale use of varicella vaccines. Infection 34, 208–213 (2006).

    CAS  PubMed  Article  Google Scholar 

  164. Yoshida, K. et al. Aging-related changes in human T-cell repertoire over 20 years delineated by deep sequencing of peripheral T-cell receptors. Exp Gerontol 96, 29–37 (2017).

    CAS  PubMed  Article  Google Scholar 

  165. Ben-Smith, A. et al. Differences between naive and memory T cell phenotype in Malawian and UK adolescents: a role for Cytomegalovirus? BMC Infect Dis 8, 139, (2008).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  166. Miles, D. J. et al. Cytomegalovirus infection induces T-cell differentiation without impairing antigen-specific responses in Gambian infants. Immunology 124, 388–400 (2008).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  167. Carr, E. J. et al. The cellular composition of the human immune system is shaped by age and cohabitation. Nat Immunol 17, 461–468 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  168. Boyd, E. Weight of the thymus and its component parts and number of Hassall corpuscles in health and in disease. Am J Dis Child 51, 313–335 (1936).

    Google Scholar 

  169. Steinmann, G. G., Klaus, B. & Muller-Hermelink, H. K. The involution of the ageing human thymic epithelium is independent of puberty. A morphometric study. Scand J Immunol 22, 563–575 (1985).

    CAS  PubMed  Article  Google Scholar 

  170. Katzelnick, L. C. et al. Antibody-dependent enhancement of severe dengue disease in humans. Science 358, 929–932 (2017).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  171. Roved, J., Westerdahl, H. & Hasselquist, D. Sex differences in immune responses: Hormonal effects, antagonistic selection, and evolutionary consequences. Horm Behav 88, 95–105 (2017).

    CAS  PubMed  Article  Google Scholar 

  172. Ghosh, S. & Klein, R. S. Sex Drives Dimorphic Immune Responses to Viral Infections. J Immunol 198, 1782–1790 (2017).

    CAS  PubMed  Article  Google Scholar 

  173. Giefing-Kroll, C., Berger, P., Lepperdinger, G. & Grubeck-Loebenstein, B. How sex and age affect immune responses, susceptibility to infections, and response to vaccination. Aging Cell 14, 309–321 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  174. Bouman, A., Heineman, M. J. & Faas, M. M. Sex hormones and the immune response in humans. Hum Reprod Update 11, 411–423 (2005).

    CAS  PubMed  Article  Google Scholar 

  175. Klein, S. L. & Flanagan, K. L. Sex differences in immune responses. Nat Rev Immunol 16, 626–638 (2016).

    CAS  PubMed  Article  Google Scholar 

  176. Van Damme, P. et al. A phase III clinical study to compare the immunogenicity and safety of the 9-valent and quadrivalent HPV vaccines in men. Vaccine 34, 4205–4212 (2016).

    PubMed  Article  CAS  Google Scholar 

  177. Nagelkerke, N. J. D. Courtesans and consumption: how sexually transmitted infection drive tuberculosis epidemics. (Eburon, 2012).

  178. Turner, J. E. et al. Rudimentary signs of immunosenescence in Cytomegalovirus-seropositive healthy young adults. Age (Dordr) 36, 287–297 (2014).

    CAS  Article  Google Scholar 

  179. Kaczorowski, K. J. et al. Continuous immunotypes describe human immune variation and predict diverse responses. Proc Natl Acad Sci USA 114, E6097–E6106, (2017).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  180. Aiello, A. E., Chiu, Y. L. & Frasca, D. How does cytomegalovirus factor into diseases of aging and vaccine responses, and by what mechanisms? Geroscience 39, 261–271 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  181. Nikolich-Zugich, J., Goodrum, F., Knox, K. & Smithey, M. J. Known unknowns: how might the persistent herpesvirome shape immunity and aging? Curr Opin Immunol 48, 23–30 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  182. Savva, G. M. et al. Cytomegalovirus infection is associated with increased mortality in the older population. Aging Cell 12, 381–387 (2013).

    CAS  Article  PubMed  Google Scholar 

  183. Simanek, A. M. et al. Seropositivity to cytomegalovirus, inflammation, all-cause and cardiovascular disease-related mortality in the United States. PLoS One 6, e16103, (2011).

    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

  184. Furman, D. et al. Cytomegalovirus infection enhances the immune response to influenza. Sci Transl Med 7, 281ra243, (2015).

    Article  Google Scholar 

  185. Barton, E. S. et al. Herpesvirus latency confers symbiotic protection from bacterial infection. Nature 447, 326–329 (2007).

    ADS  CAS  PubMed  Article  Google Scholar 

  186. Pera, A. et al. CMV latent infection improves CD8+ T response to SEB due to expansion of polyfunctional CD57+ cells in young individuals. PLoS One 9, e88538, (2014).

    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

  187. Davis, M. M. & Brodin, P. Rebooting Human Immunology. Annu Rev Immunol 36, 843–864 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  188. Redeker, A. et al. The Contribution of Cytomegalovirus Infection to Immune Senescence Is Set by the Infectious Dose. Front Immunol 8, 1953, (2017).

    CAS  Article  PubMed  Google Scholar 

  189. Shanley, D. P., Aw, D., Manley, N. R. & Palmer, D. B. An evolutionary perspective on the mechanisms of immunosenescence. Trends Immunol 30, 374–381 (2009).

    CAS  PubMed  Article  Google Scholar 

  190. McDade, T. W., Georgiev, A. V. & Kuzawa, C. W. Trade-offs between acquired and innate immune defenses in humans. Evol Med Public Health 2016, 1–16 (2016).

    PubMed  PubMed Central  Article  Google Scholar 

  191. Shattuck-Heidorn, H., Reiches, M. W., Prentice, A. M., Moore, S. E. & Ellison, P. T. Energetics and the immune system. Trade-offs associated with non-acute levels of CRP in adolescent Gambian girls. Evol Med Public Health, 27-38 (2017).

  192. Viner, R. M. et al. Susceptibility to SARS-CoV-2 infection amongst children and adolescents compared with adults: a systematic review and meta-analysis Preprint at (2020).

  193. Miller, E. & Gay, N. Effect of age on outcome and epidemiology of infectious diseases. Biologicals 25, 137–142 (1997).

    CAS  PubMed  Article  Google Scholar 

  194. Joachim, R. B. et al. The relative resistance of children to sepsis mortality: from pathways to drug candidates. Mol Syst Biol 14, e7998, (2018).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  195. Glynn, J. R. Systematic analysis of infectious disease outcomes by age. London School of Hygiene & Tropical Medicine (2020).

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With thanks to Hilary Bower, Judy Breuer, Oona Campbell, Michel Caraël, Hazel Dockrell, Paul Fine, Helen Fletcher, Nigel Klein, Andrew Prentice, Milica Vukmanovic-Stejic for helpful discussions at various stages of this work. No specific funding was received for this work. JRG is funded largely by the Higher Education Funding Council of England via the London School of Hygiene & Tropical Medicine and PAHM is funded by the University of Birmingham.

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J.G.: conception, literature searching, data extraction, analysis, writing. P.M.: critical review and contribution to writing.

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Correspondence to Judith R. Glynn.

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Online-only Table

Online-only Table 1 Comparison of disease severity in children and adults.

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Glynn, J.R., Moss, P.A.H. Systematic analysis of infectious disease outcomes by age shows lowest severity in school-age children. Sci Data 7, 329 (2020).

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