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.

Human determinants of age-dependent patterns of death from infection.

Regardless of microbial virulence (i.e., the global infection-fatality ratio), age generally drives the prevalence of death from infection in unvaccinated humans. Four mortality patterns are recognized: the common U- and L-shaped curves of endemic infections and the unique W- and J-shaped curves of pandemic infections. We suggest that these patterns result from different sets of human genetic and immunological determinants. In this model, it is the interplay between (1) monogenic genotypes affecting immunity to primary infection that preferentially manifest early in life and related genotypes or their phenocopies, including auto-antibodies, which manifest later in life and (2) the occurrence and persistence of adaptive, acquired immunity to primary or cross-reactive infections, which shapes the age-dependent pattern of human deaths from infection.

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.

Infectious diseases and discrimination: Sicily's Lampedusa migrants.

Lampedusa, a picturesque Italian island in the Mediterranean, serves as a gateway for migrants from Africa and Asia to Europe. Despite populist rhetoric portraying migrants as carriers of disease, epidemiological data reveal very low levels of communicable diseases among migrants, challenging false narratives and xenophobic sentiments propagated by populist governments.

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.

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.

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.

rtestim: Time-varying reproduction number estimation with trend filtering.

To understand the transmissibility and spread of infectious diseases, epidemiologists turn to estimates of the instantaneous reproduction number. While many estimation approaches exist, their utility may be limited. Challenges of surveillance data collection, model assumptions that are unverifiable with data alone, and computationally inefficient frameworks are critical limitations for many existing approaches. We propose a discrete spline-based approach that solves a convex optimization problem-Poisson trend filtering-using the proximal Newton method. It produces a locally adaptive estimator for instantaneous reproduction number estimation with heterogeneous smoothness. Our methodology remains accurate even under some process misspecifications and is computationally efficient, even for large-scale data. The implementation is easily accessible in a lightweight R package rtestim.

Interacting particle models on the impact of spatially heterogeneous human behavioral factors on dynamics of infectious diseases.

Human behaviors have non-negligible impacts on spread of contagious disease. For instance, large-scale gathering and high mobility of population could lead to accelerated disease transmission, while public behavioral changes in response to pandemics may effectively reduce contacts and suppress the peak of the outbreak. In order to understand how spatial characteristics like population mobility and clustering interplay with epidemic outbreaks, we formulate a stochastic-statistical environment-epidemic dynamic system (SEEDS) via an agent-based biased random walk model on a two-dimensional lattice. The "popularity" and "awareness" variables are taken into consideration to capture human natural and preventive behavioral factors, which are assumed to guide and bias agent movement in a combined way. It is found that the presence of the spatial heterogeneity, like social influence locality and spatial clustering induced by self-aggregation, potentially suppresses the contacts between agents and consequently flats the epidemic curve. Surprisedly, disease responses might not necessarily reduce the susceptibility of informed individuals and even aggravate disease outbreak if each individual responds independently upon their awareness. The disease control is achieved effectively only if there are coordinated public-health interventions and public compliance to these measures. Therefore, our model may be useful for quantitative evaluations of a variety of public-health policies.

Detecting and quantifying heterogeneity in susceptibility using contact tracing data.

The presence of heterogeneity in susceptibility, differences between hosts in their likelihood of becoming infected, can fundamentally alter disease dynamics and public health responses, for example, by changing the final epidemic size, the duration of an epidemic, and even the vaccination threshold required to achieve herd immunity. Yet, heterogeneity in susceptibility is notoriously difficult to detect and measure, especially early in an epidemic. Here we develop a method that can be used to detect and estimate heterogeneity in susceptibility given contact by using contact tracing data, which are typically collected early in the course of an outbreak. This approach provides the capability, given sufficient data, to estimate and account for the effects of this heterogeneity before they become apparent during an epidemic. It additionally provides the capability to analyze the wealth of contact tracing data available for previous epidemics and estimate heterogeneity in susceptibility for disease systems in which it has never been estimated previously. The premise of our approach is that highly susceptible individuals become infected more often than less susceptible individuals, and so individuals not infected after appearing in contact networks should be less susceptible than average. This change in susceptibility can be detected and quantified when individuals show up in a second contact network after not being infected in the first. To develop our method, we simulated contact tracing data from artificial populations with known levels of heterogeneity in susceptibility according to underlying discrete or continuous distributions of susceptibilities. We analyzed these data to determine the parameter space under which we are able to detect heterogeneity and the accuracy with which we are able to estimate it. We found that our power to detect heterogeneity increases with larger sample sizes, greater heterogeneity, and intermediate fractions of contacts becoming infected in the discrete case or greater fractions of contacts becoming infected in the continuous case. We also found that we are able to reliably estimate heterogeneity and disease dynamics. Ultimately, this means that contact tracing data alone are sufficient to detect and quantify heterogeneity in susceptibility.

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.

How Global Collaboration Can Improve the Medical Countermeasure Life Cycle for Infectious Disease Outbreaks.

Infectious disease outbreaks have become increasingly common and require global partnership for adequate preparedness and response. During outbreaks, medical countermeasures (MCMs)-vaccines, therapeutics, and diagnostics-need to reach patients quickly. Recent outbreaks exemplify that products with regulatory approval can expand access and reach patients quicker than investigational products. Unfortunately, insufficient funding globally and differences in funders' prioritization puts gains and future efforts at risk. Of primary concern is (1) lack of a feasible regulatory path and clinical capability to achieve regulatory approval for new MCMs for many diseases; and (2) the need for partners with the mandate, funding, and capabilities to support long-term sustainment of manufacturing capability and stockpiling of licensed products. Without collaboration, the global community runs the risk of losing the capabilities built through years of investment and being underprepared to combat future threats. Synergies between funders are critical to create long-term sustainment of products to ensure access.