One world, one health.

The past year has underscored the threat that emerging viruses pose to global health. The 2021 John Dirks Canada Gairdner Global Health award recognizes the contributions of Joseph Sriyal Malik Peiris and Yi Guan toward understanding the origins and options for control of newly emerging infectious disease outbreaks in Asia, notably zoonotic influenza and severe acute respiratory syndrome (SARS). Nicole Neuman of Cell corresponded with Malik Peiris about his path to studying emerging infectious diseases and the challenges of this work. Excerpts of their exchange are included here.

A meta-analysis on global change drivers and the risk of infectious disease.

Anthropogenic change is contributing to the rise in emerging infectious diseases, which are significantly correlated with socioeconomic, environmental and ecological factors1. Studies have shown that infectious disease risk is modified by changes to biodiversity2-6, climate change7-11, chemical pollution12-14, landscape transformations15-20 and species introductions21. However, it remains unclear which global change drivers most increase disease and under what contexts. Here we amassed a dataset from the literature that contains 2,938 observations of infectious disease responses to global change drivers across 1,497 host-parasite combinations, including plant, animal and human hosts. We found that biodiversity loss, chemical pollution, climate change and introduced species are associated with increases in disease-related end points or harm, whereas urbanization is associated with decreases in disease end points. Natural biodiversity gradients, deforestation and forest fragmentation are comparatively unimportant or idiosyncratic as drivers of disease. Overall, these results are consistent across human and non-human diseases. Nevertheless, context-dependent effects of the global change drivers on disease were found to be common. The findings uncovered by this meta-analysis should help target disease management and surveillance efforts towards global change drivers that increase disease. Specifically, reducing greenhouse gas emissions, managing ecosystem health, and preventing biological invasions and biodiversity loss could help to reduce the burden of plant, animal and human diseases, especially when coupled with improvements to social and economic determinants of health.

Thinking clearly about social aspects of infectious disease transmission.

Social and cultural forces shape almost every aspect of infectious disease transmission in human populations, as well as our ability to measure, understand, and respond to epidemics. For directly transmitted infections, pathogen transmission relies on human-to-human contact, with kinship, household, and societal structures shaping contact patterns that in turn determine epidemic dynamics. Social, economic, and cultural forces also shape patterns of exposure, health-seeking behaviour, infection outcomes, the likelihood of diagnosis and reporting of cases, and the uptake of interventions. Although these social aspects of epidemiology are hard to quantify and have limited the generalizability of modelling frameworks in a policy context, new sources of data on relevant aspects of human behaviour are increasingly available. Researchers have begun to embrace data from mobile devices and other technologies as useful proxies for behavioural drivers of disease transmission, but there is much work to be done to measure and validate these approaches, particularly for policy-making. Here we discuss how integrating local knowledge in the design of model frameworks and the interpretation of new data streams offers the possibility of policy-relevant models for public health decision-making as well as the development of robust, generalizable theories about human behaviour in relation to infectious diseases.

The probability of epidemic burnout in the stochastic SIR model with vital dynamics.

We present an approach to computing the probability of epidemic "burnout," i.e., the probability that a newly emergent pathogen will go extinct after a major epidemic. Our analysis is based on the standard stochastic formulation of the Susceptible-Infectious-Removed (SIR) epidemic model including host demography (births and deaths) and corresponds to the standard SIR ordinary differential equations (ODEs) in the infinite population limit. Exploiting a boundary layer approximation to the ODEs and a birth-death process approximation to the stochastic dynamics within the boundary layer, we derive convenient, fully analytical approximations for the burnout probability. We demonstrate-by comparing with computationally demanding individual-based stochastic simulations and with semi-analytical approximations derived previously-that our fully analytical approximations are highly accurate for biologically plausible parameters. We show that the probability of burnout always decreases with increased mean infectious period. However, for typical biological parameters, there is a relevant local minimum in the probability of persistence as a function of the basic reproduction number [Formula: see text]. For the shortest infectious periods, persistence is least likely if [Formula: see text]; for longer infectious periods, the minimum point decreases to [Formula: see text]. For typical acute immunizing infections in human populations of realistic size, our analysis of the SIR model shows that burnout is almost certain in a well-mixed population, implying that susceptible recruitment through births is insufficient on its own to explain disease persistence.

Phylogenetic inference of inter-population transmission rates for infectious diseases.

Estimating transmission rates is a challenging yet essential aspect of comprehending and controlling the spread of infectious diseases. Various methods exist for estimating transmission rates, each with distinct assumptions, data needs, and constraints. This study introduces a novel phylogenetic approach called transRate, which integrates genetic information with traditional epidemiological approaches to estimate inter-population transmission rates. The phylogenetic method is statistically consistent as the sample size (i.e. the number of pathogen genomes) approaches infinity under the multi-population susceptible-infected-recovered model. Simulation analyses indicate that transRate can accurately estimate the transmission rate with a sample size of 200 ~ 400 pathogen genomes. Using transRate, we analyzed 40,028 high-quality sequences of SARS-CoV-2 in human hosts during the early pandemic. Our analysis uncovered significant transmission between populations even before widespread travel restrictions were implemented. The development of transRate provides valuable insights for scientists and public health officials to enhance their understanding of the pandemic's progression and aiding in preparedness for future viral outbreaks. As public databases for genomic sequences continue to expand, transRate is increasingly vital for tracking and mitigating the spread of infectious diseases.

Update on Cardiovascular Implantable Electronic Device Infections and Their Prevention, Diagnosis, and Management: A Scientific Statement From the American Heart Association: Endorsed by the International Society for Cardiovascular Infectious Diseases.

The American Heart Association sponsored the first iteration of a scientific statement that addressed all aspects of cardiovascular implantable electronic device infection in 2010. Major advances in the prevention, diagnosis, and management of these infections have occurred since then, necessitating a scientific statement update. An 11-member writing group was identified and included recognized experts in cardiology and infectious diseases, with a career focus on cardiovascular infections. The group initially met in October 2022 to develop a scientific statement that was drafted with front-line clinicians in mind and focused on providing updated clinical information to enhance outcomes of patients with cardiovascular implantable electronic device infection. The current scientific statement highlights recent advances in prevention, diagnosis, and management, and how they may be incorporated in the complex care of patients with cardiovascular implantable electronic device infection.

Inference of Infectious Disease Transmission through a Relaxed Bottleneck Using Multiple Genomes Per Host.

In recent times, pathogen genome sequencing has become increasingly used to investigate infectious disease outbreaks. When genomic data is sampled densely enough amongst infected individuals, it can help resolve who infected whom. However, transmission analysis cannot rely solely on a phylogeny of the genomes but must account for the within-host evolution of the pathogen, which blurs the relationship between phylogenetic and transmission trees. When only a single genome is sampled for each host, the uncertainty about who infected whom can be quite high. Consequently, transmission analysis based on multiple genomes of the same pathogen per host has a clear potential for delivering more precise results, even though it is more laborious to achieve. Here, we present a new methodology that can use any number of genomes sampled from a set of individuals to reconstruct their transmission network. Furthermore, we remove the need for the assumption of a complete transmission bottleneck. We use simulated data to show that our method becomes more accurate as more genomes per host are provided, and that it can infer key infectious disease parameters such as the size of the transmission bottleneck, within-host growth rate, basic reproduction number, and sampling fraction. We demonstrate the usefulness of our method in applications to real datasets from an outbreak of Pseudomonas aeruginosa amongst cystic fibrosis patients and a nosocomial outbreak of Klebsiella pneumoniae.

Considering humans as habitat reveals evidence of successional disease ecology among human pathogens.

The realization that ecological principles play an important role in infectious disease dynamics has led to a renaissance in epidemiological theory. Ideas from ecological succession theory have begun to inform an understanding of the relationship between the individual microbiome and health but have not yet been applied to investigate broader, population-level epidemiological dynamics. We consider human hosts as habitat and apply ideas from succession to immune memory and multi-pathogen dynamics in populations. We demonstrate that ecologically meaningful life history characteristics of pathogens and parasites, rather than epidemiological features alone, are likely to play a meaningful role in determining the age at which people have the greatest probability of being infected. Our results indicate the potential importance of microbiome succession in determining disease incidence and highlight the need to explore how pathogen life history traits and host ecology influence successional dynamics. We conclude by exploring some of the implications that inclusion of successional theory might have for understanding the ecology of diseases and their hosts.

Infection patterns in simple and complex contagion processes on networks.

Contagion processes, representing the spread of infectious diseases, information, or social behaviors, are often schematized as taking place on networks, which encode for instance the interactions between individuals. The impact of the network structure on spreading process has been widely investigated, but not the reverse question: do different processes unfolding on a given network lead to different infection patterns? How do the infection patterns depend on a model's parameters or on the nature of the contagion processes? Here we address this issue by investigating the infection patterns for a variety of models. In simple contagion processes, where contagion events involve one connection at a time, we find that the infection patterns are extremely robust across models and parameters. In complex contagion models instead, in which multiple interactions are needed for a contagion event, non-trivial dependencies on models parameters emerge, as the infection pattern depends on the interplay between pairwise and group contagions. In models involving threshold mechanisms moreover, slight parameter changes can significantly impact the spreading paths. Our results show that it is possible to study crucial features of a spread from schematized models, and inform us on the variations between spreading patterns in processes of different nature.

Ethical frameworks should be applied to computational modelling of infectious disease interventions.

This perspective is part of an international effort to improve epidemiological models with the goal of reducing the unintended consequences of infectious disease interventions. The scenarios in which models are applied often involve difficult trade-offs that are well recognised in public health ethics. Unless these trade-offs are explicitly accounted for, models risk overlooking contested ethical choices and values, leading to an increased risk of unintended consequences. We argue that such risks could be reduced if modellers were more aware of ethical frameworks and had the capacity to explicitly account for the relevant values in their models. We propose that public health ethics can provide a conceptual foundation for developing this capacity. After reviewing relevant concepts in public health and clinical ethics, we discuss examples from the COVID-19 pandemic to illustrate the current separation between public health ethics and infectious disease modelling. We conclude by describing practical steps to build the capacity for ethically aware modelling. Developing this capacity constitutes a critical step towards ethical practice in computational modelling of public health interventions, which will require collaboration with experts on public health ethics, decision support, behavioural interventions, and social determinants of health, as well as direct consultation with communities and policy makers.

Generating synthetic population for simulating the spatiotemporal dynamics of epidemics.

Agent-based models have gained traction in exploring the intricate processes governing the spread of infectious diseases, particularly due to their proficiency in capturing nonlinear interaction dynamics. The fidelity of agent-based models in replicating real-world epidemic scenarios hinges on the accurate portrayal of both population-wide and individual-level interactions. In situations where comprehensive population data are lacking, synthetic populations serve as a vital input to agent-based models, approximating real-world demographic structures. While some current population synthesizers consider the structural relationships among agents from the same household, there remains room for refinement in this domain, which could potentially introduce biases in subsequent disease transmission simulations. In response, this study unveils a novel methodology for generating synthetic populations tailored for infectious disease transmission simulations. By integrating insights from microsample-derived household structures, we employ a heuristic combinatorial optimizer to recalibrate these structures, subsequently yielding synthetic populations that faithfully represent agent structural relationships. Implementing this technique, we successfully generated a spatially-explicit synthetic population encompassing over 17 million agents for Shenzhen, China. The findings affirm the method's efficacy in delineating the inherent statistical structural relationship patterns, aligning well with demographic benchmarks at both city and subzone tiers. Moreover, when assessed against a stochastic agent-based Susceptible-Exposed-Infectious-Recovered model, our results pinpointed that variations in population synthesizers can notably alter epidemic projections, influencing both the peak incidence rate and its onset.

A mathematical, classical stratification modeling approach to disentangling the impact of weather on infectious diseases: A case study using spatio-temporally disaggregated Campylobacter surveillance data for England and Wales.

Disentangling the impact of the weather on transmission of infectious diseases is crucial for health protection, preparedness and prevention. Because weather factors are co-incidental and partly correlated, we have used geography to separate out the impact of individual weather parameters on other seasonal variables using campylobacteriosis as a case study. Campylobacter infections are found worldwide and are the most common bacterial food-borne disease in developed countries, where they exhibit consistent but country specific seasonality. We developed a novel conditional incidence method, based on classical stratification, exploiting the long term, high-resolution, linkage of approximately one-million campylobacteriosis cases over 20 years in England and Wales with local meteorological datasets from diagnostic laboratory locations. The predicted incidence of campylobacteriosis increased by 1 case per million people for every 5° (Celsius) increase in temperature within the range of 8°-15°. Limited association was observed outside that range. There were strong associations with day-length. Cases tended to increase with relative humidity in the region of 75-80%, while the associations with rainfall and wind-speed were weaker. The approach is able to examine multiple factors and model how complex trends arise, e.g. the consistent steep increase in campylobacteriosis in England and Wales in May-June and its spatial variability. This transparent and straightforward approach leads to accurate predictions without relying on regression models and/or postulating specific parameterisations. A key output of the analysis is a thoroughly phenomenological description of the incidence of the disease conditional on specific local weather factors. The study can be crucially important to infer the elusive mechanism of transmission of campylobacteriosis; for instance, by simulating the conditional incidence for a postulated mechanism and compare it with the phenomenological patterns as benchmark. The findings challenge the assumption, commonly made in statistical models, that the transformed mean rate of infection for diseases like campylobacteriosis is a mere additive and combination of the environmental variables.

Taking connected mobile-health diagnostics of infectious diseases to the field.

Mobile health, or 'mHealth', is the application of mobile devices, their components and related technologies to healthcare. It is already improving patients' access to treatment and advice. Now, in combination with internet-connected diagnostic devices, it offers novel ways to diagnose, track and control infectious diseases and to improve the efficiency of the health system. Here we examine the promise of these technologies and discuss the challenges in realizing their potential to increase patients' access to testing, aid in their treatment and improve the capability of public health authorities to monitor outbreaks, implement response strategies and assess the impact of interventions across the world.

Bayesian nonparametric inference for heterogeneously mixing infectious disease models.

SignificanceMathematical models of infectious disease transmission continue to play a vital role in understanding, mitigating, and preventing outbreaks. The vast majority of epidemic models in the literature are parametric, meaning that they contain inherent assumptions about how transmission occurs in a population. However, such assumptions can be lacking in appropriate biological or epidemiological justification and in consequence lead to erroneous scientific conclusions and misleading predictions. We propose a flexible Bayesian nonparametric framework that avoids the need to make strict model assumptions about the infection process and enables a far more data-driven modeling approach for inferring the mechanisms governing transmission. We use our methods to enhance our understanding of the transmission mechanisms of the 2001 UK foot and mouth disease outbreak.

Severe Infection and Risk of Cardiovascular Disease: A Multicohort Study.

BACKGROUND: The excess risk of cardiovascular disease associated with a wide array of infectious diseases is unknown. We quantified the short- and long-term risk of major cardiovascular events in people with severe infection and estimated the population-attributable fraction. METHODS: We analyzed data from 331 683 UK Biobank participants without cardiovascular disease at baseline (2006-2010) and replicated our main findings in an independent population from 3 prospective cohort studies comprising 271 329 community-dwelling participants from Finland (baseline 1986-2005). Cardiovascular risk factors were measured at baseline. We diagnosed infectious diseases (the exposure) and incident major cardiovascular events after infections, defined as myocardial infarction, cardiac death, or fatal or nonfatal stroke (the outcome) from linkage of participants to hospital and death registers. We computed adjusted hazard ratios (HRs) and 95% CIs for infectious diseases as short- and long-term risk factors for incident major cardiovascular events. We also calculated population-attributable fractions for long-term risk. RESULTS: In the UK Biobank (mean follow-up, 11.6 years), 54 434 participants were hospitalized for an infection, and 11 649 had an incident major cardiovascular event at follow-up. Relative to participants with no record of infectious disease, those who were hospitalized experienced increased risk of major cardiovascular events, largely irrespective of the type of infection. This association was strongest during the first month after infection (HR, 7.87 [95% CI, 6.36-9.73]), but remained elevated during the entire follow-up (HR, 1.47 [95% CI, 1.40-1.54]). The findings were similar in the replication cohort (HR, 7.64 [95% CI, 5.82-10.03] during the first month; HR, 1.41 [95% CI, 1.34-1.48] during mean follow-up of 19.2 years). After controlling for traditional cardiovascular risk factors, the population-attributable fraction for severe infections and major cardiovascular events was 4.4% in the UK Biobank and 6.1% in the replication cohort. CONCLUSIONS: Infections severe enough to require hospital treatment were associated with increased risks for major cardiovascular disease events immediately after hospitalization. A small excess risk was also observed in the long-term, but residual confounding cannot be excluded.

The Leaders in Epidemiology, Antimicrobial Stewardship, and Public Health (LEAP) Fellowship, a Novel Training Program in Public Health for Infectious Diseases Physicians.

The severe acute respiratory syndrome coronavirus 2 pandemic demonstrated a critical need for partnerships between practicing infectious diseases (ID) physicians and public health departments. The soon-to-launch combined ID and Epidemic Intelligence Service fellowship can only address a fraction of this need, and otherwise US ID training lacks development pathways for physicians aiming to make careers working with public health departments. The Leaders in Epidemiology, Antimicrobial Stewardship, and Public Health (LEAP) fellowship is a model compatible with the current training paradigm with a proven track record of developing careers of long-term collaboration. Established in 2017 by the ID Society of America, Society for Healthcare Epidemiology of America, Pediatric ID Society, and supported by the Centers for Disease Control and Prevention, LEAP is a single-year in-place, structured training for senior trainees and early career ID physicians. In this viewpoint, we describe the LEAP fellowship, its outcomes, and how it could be adapted into ID training.

Systematic Review of Scales for Measuring Infectious Disease-Related Stigma.

Infectious disease outbreaks are associated with substantial stigma, which can have negative effects on affected persons and communities and on outbreak control. Thus, measuring stigma in a standardized and validated manner early in an outbreak is critical to disease control. We reviewed existing scales used to assess stigma during outbreaks. Our findings show that many different scales have been developed, but few have been used more than once, have been adequately validated, or have been tested in different disease and geographic contexts. We found that scales were usually developed too slowly to be informative early during an outbreak and were published a median of 2 years after the first case of an outbreak. A rigorously developed, transferable stigma scale is needed to assess and direct responses to stigma during infectious disease outbreaks.

Trends in Nationally Notifiable Infectious Diseases in Humans and Animals during COVID-19 Pandemic, South Korea.

We investigated trends in notifiable infectious diseases in both humans and animals during the COVID-19 pandemic in South Korea and compared those data against expected trends had nonpharmaceutical interventions (NPIs) not been implemented. We found that human respiratory infectious diseases other than COVID-19 decreased by an average of 54.7% after NPIs were introduced. On the basis of that trend, we estimated that annual medical expenses associated with respiratory infections other than COVID-19 also decreased by 3.8% in 2020 and 18.9% in 2021. However, human gastrointestinal infectious diseases and livestock diseases exhibited similar or even higher incidence rates after NPIs were instituted. Our investigation revealed that the preventive effect of NPIs varied among diseases and that NPIs might have had limited effectiveness in reducing the spread of certain types of infectious diseases. These findings suggest the need for future, novel public health interventions to compensate for such limitations.

Long-term variations of urban-Rural disparities in infectious disease burden of over 8.44 million children, adolescents, and youth in China from 2013 to 2021: An observational study.

BACKGROUND: An accelerated epidemiological transition, spurred by economic development and urbanization, has led to a rapid transformation of the disease spectrum. However, this transition has resulted in a divergent change in the burden of infectious diseases between urban and rural areas. The objective of our study was to evaluate the long-term urban-rural disparities in infectious diseases among children, adolescents, and youths in China, while also examining the specific diseases driving these disparities. METHODS AND FINDINGS: This observational study examined data on 43 notifiable infectious diseases from 8,442,956 cases from individuals aged 4 to 24 years, with 4,487,043 cases in urban areas and 3,955,913 in rural areas. The data from 2013 to 2021 were obtained from China's Notifiable Infectious Disease Surveillance System. The 43 infectious diseases were categorized into 7 categories: vaccine-preventable, bacterial, gastrointestinal and enterovirus, sexually transmitted and bloodborne, vectorborne, zoonotic, and quarantinable diseases. The calculation of infectious disease incidence was stratified by urban and rural areas. We used the index of incidence rate ratio (IRR), calculated by dividing the urban incidence rate by the rural incidence rate for each disease category, to assess the urban-rural disparity. During the nine-year study period, most notifiable infectious diseases in both urban and rural areas exhibited either a decreased or stable pattern. However, a significant and progressively widening urban-rural disparity in notifiable infectious diseases was observed. Children, adolescents, and youths in urban areas experienced a higher average yearly incidence compared to their rural counterparts, with rates of 439 per 100,000 compared to 211 per 100,000, respectively (IRR: 2.078, 95% CI [2.075, 2.081]; p < 0.001). From 2013 to 2021, this disparity was primarily driven by higher incidences of pertussis (IRR: 1.782, 95% CI [1.705, 1.862]; p < 0.001) and seasonal influenza (IRR: 3.213, 95% CI [3.205, 3.220]; p < 0.001) among vaccine-preventable diseases, tuberculosis (IRR: 1.011, 95% CI [1.006, 1.015]; p < 0.001), and scarlet fever (IRR: 2.942, 95% CI [2.918, 2.966]; p < 0.001) among bacterial diseases, infectious diarrhea (IRR: 1.932, 95% CI [1.924, 1.939]; p < 0.001), and hand, foot, and mouth disease (IRR: 2.501, 95% CI [2.491, 2.510]; p < 0.001) among gastrointestinal and enterovirus diseases, dengue (IRR: 11.952, 95% CI [11.313, 12.628]; p < 0.001) among vectorborne diseases, and 4 sexually transmitted and bloodborne diseases (syphilis: IRR 1.743, 95% CI [1.731, 1.755], p < 0.001; gonorrhea: IRR 2.658, 95% CI [2.635, 2.682], p < 0.001; HIV/AIDS: IRR 2.269, 95% CI [2.239, 2.299], p < 0.001; hepatitis C: IRR 1.540, 95% CI [1.506, 1.575], p < 0.001), but was partially offset by lower incidences of most zoonotic and quarantinable diseases in urban areas (for example, brucellosis among zoonotic: IRR 0.516, 95% CI [0.498, 0.534], p < 0.001; hemorrhagic fever among quarantinable: IRR 0.930, 95% CI [0.881, 0.981], p = 0.008). Additionally, the overall urban-rural disparity was particularly pronounced in the middle (IRR: 1.704, 95% CI [1.699, 1.708]; p < 0.001) and northeastern regions (IRR: 1.713, 95% CI [1.700, 1.726]; p < 0.001) of China. A primary limitation of our study is that the incidence was calculated based on annual average population data without accounting for population mobility. CONCLUSIONS: A significant urban-rural disparity in notifiable infectious diseases among children, adolescents, and youths was evident from our study. The burden in urban areas exceeded that in rural areas by more than 2-fold, and this gap appears to be widening, particularly influenced by tuberculosis, scarlet fever, infectious diarrhea, and typhus. These findings underscore the urgent need for interventions to mitigate infectious diseases and address the growing urban-rural disparity.

The unfinished agenda of communicable diseases among children and adolescents before the COVID-19 pandemic, 1990-2019: a systematic analysis of the Global Burden of Disease Study 2019.

BACKGROUND: Communicable disease control has long been a focus of global health policy. There have been substantial reductions in the burden and mortality of communicable diseases among children younger than 5 years, but we know less about this burden in older children and adolescents, and it is unclear whether current programmes and policies remain aligned with targets for intervention. This knowledge is especially important for policy and programmes in the context of the COVID-19 pandemic. We aimed to use the Global Burden of Disease (GBD) Study 2019 to systematically characterise the burden of communicable diseases across childhood and adolescence. METHODS: In this systematic analysis of the GBD study from 1990 to 2019, all communicable diseases and their manifestations as modelled within GBD 2019 were included, categorised as 16 subgroups of common diseases or presentations. Data were reported for absolute count, prevalence, and incidence across measures of cause-specific mortality (deaths and years of life lost), disability (years lived with disability [YLDs]), and disease burden (disability-adjusted life-years [DALYs]) for children and adolescents aged 0-24 years. Data were reported across the Socio-demographic Index (SDI) and across time (1990-2019), and for 204 countries and territories. For HIV, we reported the mortality-to-incidence ratio (MIR) as a measure of health system performance. FINDINGS: In 2019, there were 3·0 million deaths and 30·0 million years of healthy life lost to disability (as measured by YLDs), corresponding to 288·4 million DALYs from communicable diseases among children and adolescents globally (57·3% of total communicable disease burden across all ages). Over time, there has been a shift in communicable disease burden from young children to older children and adolescents (largely driven by the considerable reductions in children younger than 5 years and slower progress elsewhere), although children younger than 5 years still accounted for most of the communicable disease burden in 2019. Disease burden and mortality were predominantly in low-SDI settings, with high and high-middle SDI settings also having an appreciable burden of communicable disease morbidity (4·0 million YLDs in 2019 alone). Three cause groups (enteric infections, lower-respiratory-tract infections, and malaria) accounted for 59·8% of the global communicable disease burden in children and adolescents, with tuberculosis and HIV both emerging as important causes during adolescence. HIV was the only cause for which disease burden increased over time, particularly in children and adolescents older than 5 years, and especially in females. Excess MIRs for HIV were observed for males aged 15-19 years in low-SDI settings. INTERPRETATION: Our analysis supports continued policy focus on enteric infections and lower-respiratory-tract infections, with orientation to children younger than 5 years in settings of low socioeconomic development. However, efforts should also be targeted to other conditions, particularly HIV, given its increased burden in older children and adolescents. Older children and adolescents also experience a large burden of communicable disease, further highlighting the need for efforts to extend beyond the first 5 years of life. Our analysis also identified substantial morbidity caused by communicable diseases affecting child and adolescent health across the world. FUNDING: The Australian National Health and Medical Research Council Centre for Research Excellence for Driving Investment in Global Adolescent Health and the Bill & Melinda Gates Foundation.