Population dynamics of plague vector fleas in an endemic focus: implications for plague surveillance.

Plague is a zoonotic vector-borne disease caused by the bacterium Yersinia pestis. In Madagascar, it persists in identified foci, where it is a threat to public health generally from September to April. A more complete understanding of how the disease persists could guide control strategies. Fleas are the main vector for transmission between small mammal hosts and humans, and fleas likely play a role in the maintenance of plague. This study characterized the dynamics of flea populations in plague foci alongside the occurrence of human cases. From 2018 to 2020, small mammals were trapped at sites in the central Highlands of Madagascar. A total of 2,762 small mammals were captured and 5,295 fleas were collected. The analysis examines 2 plague vector species in Madagascar (Synopsyllus fonquerniei and Xenopsylla cheopis). Generalized linear models were used to relate flea abundance to abiotic factors, with adjustments for trap location and flea species. We observed significant effects of abiotic factors on the abundance, intensity, and infestation rate by the outdoor-associated flea species, S. fonquerniei, but weak seasonality for the indoor-associated flea species, X. cheopis. A difference in the timing of peak abundance was observed between the 2 flea species during and outside the plague season. While the present study did not identify a clear link between flea population dynamics and plague maintenance, as only one collected X. cheopis was infected, the results presented herein can be used by local health authorities to improve monitoring and control strategies of plague vector fleas in Madagascar.

Respiratory viruses: New frontiers-a Keystone Symposia report.

Respiratory viruses are a common cause of morbidity and mortality around the world. Viruses like influenza, RSV, and most recently SARS-CoV-2 can rapidly spread through a population, causing acute infection and, in vulnerable populations, severe or chronic disease. Developing effective treatment and prevention strategies often becomes a race against ever-evolving viruses that develop resistance, leaving therapy efficacy either short-lived or relevant for specific viral strains. On June 29 to July 2, 2022, researchers met for the Keystone symposium "Respiratory Viruses: New Frontiers." Researchers presented new insights into viral biology and virus-host interactions to understand the mechanisms of disease and identify novel treatment and prevention approaches that are effective, durable, and broad.

Investigation of the Impact of Childhood Immune Imprinting on Birth Year-Specific Risk of Clinical Infection During Influenza A Virus Epidemics in Hong Kong.

Influenza imprinting reduces risks of influenza A virus clinical infection by 40%-90%, estimated from surveillance data in western countries. We analyzed surveillance data from 2010 to 2019 in Hong Kong. Based on the best model, which included hemagglutinin group-level imprinting, we estimated that individuals imprinted to H1N1 or H2N2 had a 17% (95% confidence interval [CI], 3%-28%) lower risk of H1N1 clinical infection, and individuals imprinted to H3N2 would have 12% (95% CI, -3% to 26%) lower risk of H3N2 clinical infection. These estimated imprinting protections were weaker than estimates in western countries. Identifying factors affecting imprinting protections is important for control policies and disease modeling.

Tracking changes in SARS-CoV-2 transmission with a novel outpatient sentinel surveillance system in Chicago, USA.

Public health indicators typically used for COVID-19 surveillance can be biased or lag changing community transmission patterns. In this study, we investigate whether sentinel surveillance of recently symptomatic individuals receiving outpatient diagnostic testing for SARS-CoV-2 could accurately assess the instantaneous reproductive number R(t) and provide early warning of changes in transmission. We use data from community-based diagnostic testing sites in the United States city of Chicago. Patients tested at community-based diagnostic testing sites between September 2020 and June 2021, and reporting symptom onset within four days preceding their test, formed the sentinel population. R(t) calculated from sentinel cases agreed well with R(t) from other indicators. Retrospectively, trends in sentinel cases did not precede trends in COVID-19 hospital admissions by any identifiable lead time. In deployment, sentinel surveillance held an operational recency advantage of nine days over hospital admissions. The promising performance of opportunistic sentinel surveillance suggests that deliberately designed outpatient sentinel surveillance would provide robust early warning of increasing transmission.

Commentary on the use of the reproduction number R during the COVID-19 pandemic.

Since the beginning of the COVID-19 pandemic, the reproduction number [Formula: see text] has become a popular epidemiological metric used to communicate the state of the epidemic. At its most basic, [Formula: see text] is defined as the average number of secondary infections caused by one primary infected individual. [Formula: see text] seems convenient, because the epidemic is expanding if [Formula: see text] and contracting if [Formula: see text]. The magnitude of [Formula: see text] indicates by how much transmission needs to be reduced to control the epidemic. Using [Formula: see text] in a naïve way can cause new problems. The reasons for this are threefold: (1) There is not just one definition of [Formula: see text] but many, and the precise definition of [Formula: see text] affects both its estimated value and how it should be interpreted. (2) Even with a particular clearly defined [Formula: see text], there may be different statistical methods used to estimate its value, and the choice of method will affect the estimate. (3) The availability and type of data used to estimate [Formula: see text] vary, and it is not always clear what data should be included in the estimation. In this review, we discuss when [Formula: see text] is useful, when it may be of use but needs to be interpreted with care, and when it may be an inappropriate indicator of the progress of the epidemic. We also argue that careful definition of [Formula: see text], and the data and methods used to estimate it, can make [Formula: see text] a more useful metric for future management of the epidemic.

Correction: Practical considerations for measuring the effective reproductive number, Rt.

[This corrects the article DOI: 10.1371/journal.pcbi.1008409.].

Influenza immune escape under heterogeneous host immune histories.

In a pattern called immune imprinting, individuals gain the strongest immune protection against the influenza strains encountered earliest in life. In many recent examples, differences in early infection history can explain birth year-associated differences in susceptibility (cohort effects). Susceptibility shapes strain fitness, but without a clear conceptual model linking host susceptibility to the identity and order of past infections general conclusions on the evolutionary and epidemic implications of cohort effects are not possible. Failure to differentiate between cohort effects caused by differences in the set, rather than the order (path), of past infections is a current source of confusion. We review and refine hypotheses for path-dependent cohort effects, which include imprinting. We highlight strategies to measure their underlying causes and emergent consequences.

Practical considerations for measuring the effective reproductive number, Rt.

Estimation of the effective reproductive number Rt is important for detecting changes in disease transmission over time. During the Coronavirus Disease 2019 (COVID-19) pandemic, policy makers and public health officials are using Rt to assess the effectiveness of interventions and to inform policy. However, estimation of Rt from available data presents several challenges, with critical implications for the interpretation of the course of the pandemic. The purpose of this document is to summarize these challenges, illustrate them with examples from synthetic data, and, where possible, make recommendations. For near real-time estimation of Rt, we recommend the approach of Cori and colleagues, which uses data from before time t and empirical estimates of the distribution of time between infections. Methods that require data from after time t, such as Wallinga and Teunis, are conceptually and methodologically less suited for near real-time estimation, but may be appropriate for retrospective analyses of how individuals infected at different time points contributed to the spread. We advise caution when using methods derived from the approach of Bettencourt and Ribeiro, as the resulting Rt estimates may be biased if the underlying structural assumptions are not met. Two key challenges common to all approaches are accurate specification of the generation interval and reconstruction of the time series of new infections from observations occurring long after the moment of transmission. Naive approaches for dealing with observation delays, such as subtracting delays sampled from a distribution, can introduce bias. We provide suggestions for how to mitigate this and other technical challenges and highlight open problems in Rt estimation.

Beyond clinical trials: Evolutionary and epidemiological considerations for development of a universal influenza vaccine.

The prospect of universal influenza vaccines is generating much interest and research at the intersection of immunology, epidemiology, and viral evolution. While the current focus is on developing a vaccine that elicits a broadly cross-reactive immune response in clinical trials, there are important downstream questions about global deployment of a universal influenza vaccine that should be explored to minimize unintended consequences and maximize benefits. Here, we review and synthesize the questions most relevant to predicting the population benefits of universal influenza vaccines and discuss how existing information could be mined to begin to address these questions. We review three research topics where computational modeling could bring valuable evidence: immune imprinting, viral evolution, and transmission. We address the positive and negative consequences of imprinting, in which early childhood exposure to influenza shapes and limits immune responses to future infections via memory of conserved influenza antigens. However, the mechanisms at play, their effectiveness, breadth of protection, and the ability to "reprogram" already imprinted individuals, remains heavily debated. We describe instances of rapid influenza evolution that illustrate the plasticity of the influenza virus in the face of drug pressure and discuss how novel vaccines could introduce new selective pressures on the evolution of the virus. We examine the possible unintended consequences of broadly protective (but infection-permissive) vaccines on the dynamics of epidemic and pandemic influenza, compared to conventional vaccines that have been shown to provide herd immunity benefits. In conclusion, computational modeling offers a valuable tool to anticipate the benefits of ambitious universal influenza vaccine programs, while balancing the risks from endemic influenza strains and unpredictable pandemic viruses. Moving forward, it will be important to mine the vast amount of data generated in clinical studies of universal influenza vaccines to ensure that the benefits and consequences of these vaccine programs have been carefully modeled and explored.

Estimating prevalence and test accuracy in disease ecology: How Bayesian latent class analysis can boost or bias imperfect test results.

Obtaining accurate estimates of disease prevalence is crucial for the monitoring and management of wildlife populations but can be difficult if different diagnostic tests yield conflicting results and if the accuracy of each diagnostic test is unknown. Bayesian latent class analysis (BLCA) modeling offers a potential solution, providing estimates of prevalence levels and diagnostic test accuracy under the realistic assumption that no diagnostic test is perfect.In typical applications of this approach, the specificity of one test is fixed at or close to 100%, allowing the model to simultaneously estimate the sensitivity and specificity of all other tests, in addition to infection prevalence. In wildlife systems, a test with near-perfect specificity is not always available, so we simulated data to investigate how decreasing this fixed specificity value affects the accuracy of model estimates.We used simulations to explore how the trade-off between diagnostic test specificity and sensitivity impacts prevalence estimates and found that directional biases depend on pathogen prevalence. Both the precision and accuracy of results depend on the sample size, the diagnostic tests used, and the true infection prevalence, so these factors should be considered when applying BLCA to estimate disease prevalence and diagnostic test accuracy in wildlife systems. A wildlife disease case study, focusing on leptospirosis in California sea lions, demonstrated the potential for Bayesian latent class methods to provide reliable estimates under real-world conditions.We delineate conditions under which BLCA improves upon the results from a single diagnostic across a range of prevalence levels and sample sizes, demonstrating when this method is preferable for disease ecologists working in a wide variety of pathogen systems.

Practical considerations for measuring the effective reproductive number, R t.

Estimation of the effective reproductive number, R t , is important for detecting changes in disease transmission over time. During the COVID-19 pandemic, policymakers and public health officials are using R t to assess the effectiveness of interventions and to inform policy. However, estimation of R t from available data presents several challenges, with critical implications for the interpretation of the course of the pandemic. The purpose of this document is to summarize these challenges, illustrate them with examples from synthetic data, and, where possible, make recommendations. For near real-time estimation of R t , we recommend the approach of Cori et al. (2013), which uses data from before time t and empirical estimates of the distribution of time between infections. Methods that require data from after time t, such as Wallinga and Teunis (2004), are conceptually and methodologically less suited for near real-time estimation, but may be appropriate for retrospective analyses of how individuals infected at different time points contributed to spread. We advise against using methods derived from Bettencourt and Ribeiro (2008), as the resulting R t estimates may be biased if the underlying structural assumptions are not met. Two key challenges common to all approaches are accurate specification of the generation interval and reconstruction of the time series of new infections from observations occurring long after the moment of transmission. Naive approaches for dealing with observation delays, such as subtracting delays sampled from a distribution, can introduce bias. We provide suggestions for how to mitigate this and other technical challenges and highlight open problems in R t estimation.

Estimated effectiveness of traveller screening to prevent international spread of 2019 novel coronavirus (2019-nCoV).

Traveller screening is being used to limit further global spread of 2019 novel coronavirus (nCoV) following its recent emergence. Here, we project the impact of different travel screening programs given remaining uncertainty around the values of key nCoV life history and epidemiological parameters. Even under best-case assumptions, we estimate that screening will miss more than half of infected travellers. Breaking down the factors leading to screening successes and failures, we find that most cases missed by screening are fundamentally undetectable, because they have not yet developed symptoms and are unaware they were exposed. These findings emphasize the need for measures to track travellers who become ill after being missed by a travel screening program. We make our model available for interactive use so stakeholders can explore scenarios of interest using the most up-to-date information. We hope these findings contribute to evidence-based policy to combat the spread of nCoV, and to prospective planning to mitigate future emerging pathogens.

Estimated effectiveness of symptom and risk screening to prevent the spread of COVID-19.

Traveller screening is being used to limit further spread of COVID-19 following its recent emergence, and symptom screening has become a ubiquitous tool in the global response. Previously, we developed a mathematical model to understand factors governing the effectiveness of traveller screening to prevent spread of emerging pathogens (Gostic et al., 2015). Here, we estimate the impact of different screening programs given current knowledge of key COVID-19 life history and epidemiological parameters. Even under best-case assumptions, we estimate that screening will miss more than half of infected people. Breaking down the factors leading to screening successes and failures, we find that most cases missed by screening are fundamentally undetectable, because they have not yet developed symptoms and are unaware they were exposed. Our work underscores the need for measures to limit transmission by individuals who become ill after being missed by a screening program. These findings can support evidence-based policy to combat the spread of COVID-19, and prospective planning to mitigate future emerging pathogens.

Childhood immune imprinting to influenza A shapes birth year-specific risk during seasonal H1N1 and H3N2 epidemics.

Across decades of co-circulation in humans, influenza A subtypes H1N1 and H3N2 have caused seasonal epidemics characterized by different age distributions of cases and mortality. H3N2 causes the majority of severe, clinically attended cases in high-risk elderly cohorts, and the majority of overall deaths, whereas H1N1 causes fewer deaths overall, and cases shifted towards young and middle-aged adults. These contrasting age profiles may result from differences in childhood imprinting to H1N1 and H3N2 or from differences in evolutionary rate between subtypes. Here we analyze a large epidemiological surveillance dataset to test whether childhood immune imprinting shapes seasonal influenza epidemiology, and if so, whether it acts primarily via homosubtypic immune memory or via broader, heterosubtypic memory. We also test the impact of evolutionary differences between influenza subtypes on age distributions of cases. Likelihood-based model comparison shows that narrow, within-subtype imprinting shapes seasonal influenza risk alongside age-specific risk factors. The data do not support a strong effect of evolutionary rate, or of broadly protective imprinting that acts across subtypes. Our findings emphasize that childhood exposures can imprint a lifelong immunological bias toward particular influenza subtypes, and that these cohort-specific biases shape epidemic age distributions. As a consequence, newer and less "senior" antibody responses acquired later in life do not provide the same strength of protection as responses imprinted in childhood. Finally, we project that the relatively low mortality burden of H1N1 may increase in the coming decades, as cohorts that lack H1N1-specific imprinting eventually reach old age.

Mechanistic dose-response modelling of animal challenge data shows that intact skin is a crucial barrier to leptospiral infection.

Leptospirosis is a widespread and potentially life-threatening zoonotic disease caused by spirochaetes of the genus Leptospira. Humans become infected primarily via contact with environmental reservoirs contaminated by the urine of shedding mammalian hosts. Populations in high transmission settings, such as urban slums and subsistence farming communities, are exposed to low doses of Leptospira on a daily basis. Under these conditions, numerous factors determine whether infection occurs, including the route of exposure and inoculum dose. Skin wounds and abrasions are risk factors for leptospirosis, but it is not known whether broken skin is necessary for spillover, or if low-dose exposures to intact skin and mucous membranes can also cause infection. To establish a quantitative relationship between dose, route and probability of infection, we performed challenge experiments in hamsters and rats, developed mechanistic dose-response models representing the spatial dynamics of within-host infection and persistence, and fitted models to experimental data. Results show intact skin is a strong barrier against infection, and that broken skin is the predominant route by which low-dose environmental exposures cause infection. These results identify skin integrity as a bottleneck to spillover of Leptospira and underscore the importance of barrier interventions in the prevention of leptospirosis. This article is part of the theme issue 'Dynamic and integrative approaches to understanding pathogen spillover'.

Predictive Modeling of Influenza Shows the Promise of Applied Evolutionary Biology.

Seasonal influenza is controlled through vaccination campaigns. Evolution of influenza virus antigens means that vaccines must be updated to match novel strains, and vaccine effectiveness depends on the ability of scientists to predict nearly a year in advance which influenza variants will dominate in upcoming seasons. In this review, we highlight a promising new surveillance tool: predictive models. Based on data-sharing and close collaboration between the World Health Organization and academic scientists, these models use surveillance data to make quantitative predictions regarding influenza evolution. Predictive models demonstrate the potential of applied evolutionary biology to improve public health and disease control. We review the state of influenza predictive modeling and discuss next steps and recommendations to ensure that these models deliver upon their considerable biomedical promise.

Potent protection against H5N1 and H7N9 influenza via childhood hemagglutinin imprinting.

Two zoonotic influenza A viruses (IAV) of global concern, H5N1 and H7N9, exhibit unexplained differences in age distribution of human cases. Using data from all known human cases of these viruses, we show that an individual's first IAV infection confers lifelong protection against severe disease from novel hemagglutinin (HA) subtypes in the same phylogenetic group. Statistical modeling shows that protective HA imprinting is the crucial explanatory factor, and it provides 75% protection against severe infection and 80% protection against death for both H5N1 and H7N9. Our results enable us to predict age distributions of severe disease for future pandemics and demonstrate that a novel strain's pandemic potential increases yearly when a group-mismatched HA subtype dominates seasonal influenza circulation. These findings open new frontiers for rational pandemic risk assessment.

Mapping influenza transmission in the ferret model to transmission in humans.

The controversy surrounding 'gain-of-function' experiments on high-consequence avian influenza viruses has highlighted the role of ferret transmission experiments in studying the transmission potential of novel influenza strains. However, the mapping between influenza transmission in ferrets and in humans is unsubstantiated. We address this gap by compiling and analyzing 240 estimates of influenza transmission in ferrets and humans. We demonstrate that estimates of ferret secondary attack rate (SAR) explain 66% of the variation in human SAR estimates at the subtype level. Further analysis shows that ferret transmission experiments have potential to identify influenza viruses of concern for epidemic spread in humans, though small sample sizes and biological uncertainties prevent definitive classification of human transmissibility. Thus, ferret transmission experiments provide valid predictions of pandemic potential of novel influenza strains, though results should continue to be corroborated by targeted virological and epidemiological research.

Effectiveness of traveller screening for emerging pathogens is shaped by epidemiology and natural history of infection.

During outbreaks of high-consequence pathogens, airport screening programs have been deployed to curtail geographic spread of infection. The effectiveness of screening depends on several factors, including pathogen natural history and epidemiology, human behavior, and characteristics of the source epidemic. We developed a mathematical model to understand how these factors combine to influence screening outcomes. We analyzed screening programs for six emerging pathogens in the early and late stages of an epidemic. We show that the effectiveness of different screening tools depends strongly on pathogen natural history and epidemiological features, as well as human factors in implementation and compliance. For pathogens with longer incubation periods, exposure risk detection dominates in growing epidemics, while fever becomes a better target in stable or declining epidemics. For pathogens with short incubation, fever screening drives detection in any epidemic stage. However, even in the most optimistic scenario arrival screening will miss the majority of cases.