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Timely forecasts of the emergence, re-emergence and elimination of human infectious diseases allow for proactive, rather than reactive, decisions that save lives. Recent theory suggests that a generic feature of dynamical systems approaching a tipping point-early warning signals (EWS) due to critical slowing down (CSD)-can anticipate disease emergence and elimination. Empirical studies documenting CSD in observed disease dynamics are scarce, but such demonstration of concept is essential to the further development of model-independent outbreak detection systems. Here, we use fitted, mechanistic models of measles transmission in four cities in Niger to detect CSD through statistical EWS. We find that several EWS accurately anticipate measles re-emergence and elimination, suggesting that CSD should be detectable before disease transmission systems cross key tipping points. These findings support the idea that statistical signals based on CSD, coupled with decision-support algorithms and expert judgement, could provide the basis for early warning systems of disease outbreaks.

The majority of known early warning indicators of critical transitions rely on asymptotic resilience and critical slowing down. In continuous systems, critical slowing down is mathematically described by a decrease in magnitude of the dominant eigenvalue of the Jacobian matrix on the approach to a critical transition. Here, we show that measures of transient dynamics, specifically, reactivity and the maximum of the amplification envelope, also change systematically as a bifurcation is approached in an important class of models for epidemics of infectious diseases. Furthermore, we introduce indicators designed to detect trends in these measures and find that they reliably classify time series of case notifications simulated from stochastic models according to levels of vaccine uptake. Greater attention should be focused on the potential for systems to exhibit transient amplification of perturbations as a critical threshold is approached, and should be considered when searching for generic leading indicators of tipping points. Awareness of this phenomenon will enrich understanding of the dynamics of complex systems on the verge of a critical transition.

Background: Despite high vaccination coverage, many childhood infections pose a growing threat to human populations. Accurate disease forecasting would be of tremendous value to public health. Forecasting disease emergence using early warning signals (EWS) is possible in non-seasonal models of infectious diseases. Here, we assessed whether EWS also anticipate disease emergence in seasonal models., Methods: We simulated the dynamics of an immunizing infectious pathogen approaching the tipping point to disease endemicity. To explore the effect of seasonality on the reliability of early warning statistics, we varied the amplitude of fluctuations around the average transmission. We proposed and analyzed two new early warning signals based on the wavelet spectrum. We measured the reliability of the early warning signals depending on the strength of their trend preceding the tipping point and then calculated the Area Under the Curve (AUC) statistic., Results: Early warning signals were reliable when disease transmission was subject to seasonal forcing. Wavelet-based early warning signals were as reliable as other conventional early warning signals. We found that removing seasonal trends, prior to analysis, did not improve early warning statistics uniformly., Conclusions: Early warning signals anticipate the onset of critical transitions for infectious diseases which are subject to seasonal forcing. Wavelet-based early warning statistics can also be used to forecast infectious disease.

There is growing interest in understanding the nature and consequences of interactions among infectious agents. Pathogen interactions can be operational at different scales, either within a co-infected host or in host populations where they co-circulate, and can be either cooperative or competitive. The detection of interactions among pathogens has typically involved the study of synchrony in the oscillations of the protagonists, but as we show here, phase association provides an unreliable dynamical fingerprint for this task. We assess the capacity of a likelihood-based inference framework to accurately detect and quantify the presence and nature of pathogen interactions on the basis of realistic amounts and kinds of simulated data. We show that when epidemiological and demographic processes are well understood, noisy time series data can contain sufficient information to allow correct inference of interactions in multi-pathogen systems. The inference power is dependent on the strength and time-course of the underlying mechanism: stronger and longer-lasting interactions are more easily and more precisely quantified. We examine the limitations of our approach to stochastic temporal variation, under-reporting, and over-aggregation of data. We propose that likelihood shows promise as a basis for detection and quantification of the effects of pathogen interactions and the determination of their (competitive or cooperative) nature on the basis of population-level time-series data.

Although many infectious diseases of humans and wildlife are transmitted via an environmental reservoir, the theory of environmental transmission remains poorly elaborated. Here we introduce an SIR-type multi-strain disease transmission model with perfect cross immunity where environmental transmission is broadly defined by three axioms. We establish the conditions under which a multi-strain endemic state is invaded by another strain which is both directly and environmentally transmitted. We discuss explicit forms for environmental transmission terms and apply our newly derived invasion conditions to a two-strain system. Then, we consider the case of two strains with matching basic reproduction numbers (i.e., R(0)), one directly transmitted only and the other both directly and environmentally transmitted, invading each other's endemic state. We find that the strain which is only directly transmitted can invade the endemic state of the strain with mixed transmission. However, the endemic state of the first strain is neutrally stable to invasion by the second strain. Thus, our results suggest that environmental transmission makes the endemic state less resistant to invasion., (Copyright (c) 2010 Elsevier Ltd. All rights reserved.)

While most of the world has enjoyed exponential economic growth, more than one-sixth of the world is today roughly as poor as their ancestors were many generations ago. Widely accepted general explanations for the persistence of such poverty have been elusive and are needed by the international development community. Building on a well-established model of human infectious diseases, we show how formally integrating simple economic and disease ecology models can naturally give rise to poverty traps, where initial economic and epidemiological conditions determine the long-term trajectory of the health and economic development of a society. This poverty trap may therefore be broken by improving health conditions of the population. More generally, we demonstrate that simple human ecological models can help explain broad patterns of modern economic organization.

Measles epidemics in human populations exhibit what is perhaps the best empirically characterized, and certainly the most studied, stochastic persistence threshold in population biology. A critical community size (CCS) of around 250,000-500,000 separates populations where measles is predominantly persistent from smaller communities where there are frequent extinctions of measles between major epidemics. The fundamental mechanisms contributing to this pattern of persistence, which are long-lasting immunity to re-infection, recruitment of susceptibles, seasonality in transmission, age dependence of transmission and the spatial coupling between communities, have all been quantified and, to a greater or lesser level of success, captured by theoretical models. Despite these successes there has not been a consensus over whether simple models can successfully predict the value of the CCS, or indeed which mechanisms determine the persistence of measles over a broader range of population sizes. Specifically, the level of the CCS has been thought to be particularly sensitive to the detailed stochastic dynamics generated by the waiting time distribution (WTD) in the infectious and latent periods. We show that the relative patterns of persistence between models with different WTDs are highly sensitive to the criterion of comparison-in particular, the statistical measure of persistence that is employed. To this end, we introduce two new statistical measures of persistence-fade-outs post epidemic and fade-outs post invasion. Contrary to previous reports, we demonstrate that, no matter the choice of persistence measure, appropriately parametrized models of measles demonstrate similar predictions for the level of the CCS.

Patterns of morbidity and mortality around the globe are determined by interactions between infectious diseases and systematic human socioeconomic processes. The most obvious of these patterns is that the greatest burdens of infectious diseases are found among the poor, who lack the basic resources for disease prevention and treatment. Yet, it is becoming increasingly clear that many infectious diseases are themselves causes of poverty owing to their effects on labour productivity. A particularly subtle phenomenon that receives little attention in the epidemiology literature and is especially important for poor communities is the role of the birth rate as an important direct cause of high disease burdens. Because of their high rates of transmission and life-long immunity, the persistence of many child diseases such as measles relies on high rates of reproduction as their source of susceptible individuals. Thus, there are significant direct health benefits of lower fertility rates, which are further enhanced by interactions with economic processes. Indeed, fertility, poverty and disease all interact with each other in important and predictable ways that can be built into traditional disease ecology models. We present such a model here that provides insights into the long-term effect of policy interventions. For example, because of indirect income effects, herd immunity may be acquired with lower vaccine coverage than previously thought. Reductions in the disease burden can also occur through lower fertility. Our model thus provides a disease ecology framework that is useful for the analysis of demographic transitions.

Traditionally, epidemiological studies have focused on understanding the dynamics of a single pathogen, assuming no interactions with other pathogens. Recently, a large body of work has begun to explore the effects of immune-mediated interactions, arising from cross-immunity and antibody-dependent enhancement, between related pathogen strains. In addition, ecological processes such as a temporary period of convalescence and pathogen-induced mortality have led to the concept of ecological interference between unrelated diseases. There remains, however, the need for a systematic study of both immunological and ecological processes within a single framework. In this paper, we develop a general two-pathogen single-host model of pathogen interactions that simultaneously incorporates these mechanisms. We are then able to mechanistically explore how immunoecological processes mediate interactions between diseases for a pool of susceptible individuals. We show that the precise nature of the interaction can induce either competitive or cooperative associations between pathogens. Understanding the dynamic implications of multi-pathogen associations has potentially important public health consequences. Such a framework may be especially helpful in disentangling the effects of partially cross-immunizing infections that affect populations with a pre-disposition towards immunosuppression such as children and the elderly.

Seasonal variations in temperature, rainfall and resource availability are ubiquitous and can exert strong pressures on population dynamics. Infectious diseases provide some of the best-studied examples of the role of seasonality in shaping population fluctuations. In this paper, we review examples from human and wildlife disease systems to illustrate the challenges inherent in understanding the mechanisms and impacts of seasonal environmental drivers. Empirical evidence points to several biologically distinct mechanisms by which seasonality can impact host-pathogen interactions, including seasonal changes in host social behaviour and contact rates, variation in encounters with infective stages in the environment, annual pulses of host births and deaths and changes in host immune defences. Mathematical models and field observations show that the strength and mechanisms of seasonality can alter the spread and persistence of infectious diseases, and that population-level responses can range from simple annual cycles to more complex multiyear fluctuations. From an applied perspective, understanding the timing and causes of seasonality offers important insights into how parasite-host systems operate, how and when parasite control measures should be applied, and how disease risks will respond to anthropogenic climate change and altered patterns of seasonality. Finally, by focusing on well-studied examples of infectious diseases, we hope to highlight general insights that are relevant to other ecological interactions.

There is growing evidence that communicable diseases constitute a strong selective force on the evolution of social systems. It has been suggested that infectious diseases may determine upper limits of host sociality by, for example, inducing territoriality or early juvenile dispersal. Here we use game theory to model the evolution of host sociality in the context of communicable diseases. Our model is then augmented with the evolution of virulence to determine coevolutionarily stable strategies of host sociality and pathogen virulence. In contrast to a controversial hypothesis by Ewald (1994), our analysis indicates that pathogens may become more virulent when contact rates are low, and their prevalence can ultimately induce greater sociality.

Background: Mathematical models have become invaluable management tools for epidemiologists, both shedding light on the mechanisms underlying observed dynamics as well as making quantitative predictions on the effectiveness of different control measures. Here, we explain how substantial biases are introduced by two important, yet largely ignored, assumptions at the core of the vast majority of such models., Methods and Findings: First, we use analytical methods to show that (i) ignoring the latent period or (ii) making the common assumption of exponentially distributed latent and infectious periods (when including the latent period) always results in underestimating the basic reproductive ratio of an infection from outbreak data. We then proceed to illustrate these points by fitting epidemic models to data from an influenza outbreak. Finally, we document how such unrealistic a priori assumptions concerning model structure give rise to systematically overoptimistic predictions on the outcome of potential management options., Conclusion: This work aims to highlight that, when developing models for public health use, we need to pay careful attention to the intrinsic assumptions embedded within classical frameworks.

Fisheries and aquaculture provide food and economic security, especially in the developing world, but both face challenges from infectious disease. Here, we consider management of disease issues from a structured decision‐making perspective to examine how infectious disease can threaten seafood production and influence management decisions. For both wild fisheries and aquaculture, disease‐management objectives generally aim to mitigate the severity and economic burden of outbreaks. General management strategies include manipulating host densities, reducing system connectivity, conserving or improving habitat, and implementing direct treatments or some other biological interventions. To inform decisions, mathematical models can be used to explore disease dynamics and to forecast the potential effectiveness of alternative management actions. Developing and implementing disease‐management strategies also involve considering uncertainties and balancing competing stakeholder interests and risk tolerances. We conclude by outlining several steps for applying structured decision making that are broadly useful to decision makers facing issues related to disease. [ABSTRACT FROM AUTHOR]

The spread of SARS-CoV-2 has been geographically uneven. To understand the drivers of this spatial variation in SARS-CoV-2 transmission, in particular the role of stochasticity, we used the early stages of the SARS-CoV-2 invasion in Washington state as a case study. We analysed spatially-resolved COVID-19 epidemiological data using two distinct statistical analyses. The first analysis involved using hierarchical clustering on the matrix of correlations between county-level case report time series to identify geographical patterns in the spread of SARS-CoV-2 across the state. In the second analysis, we used a stochastic transmission model to perform likelihood-based inference on hospitalised cases from five counties in the Puget Sound region. Our clustering analysis identifies five distinct clusters and clear spatial patterning. Four of the clusters correspond to different geographical regions, with the final cluster spanning the state. Our inferential analysis suggests that a high degree of connectivity across the region is necessary for the model to explain the rapid inter-county spread observed early in the pandemic. In addition, our approach allows us to quantify the impact of stochastic events in determining the subsequent epidemic. We find that atypically rapid transmission during January and February 2020 is necessary to explain the observed epidemic trajectories in King and Snohomish counties, demonstrating a persisting impact of stochastic events. Our results highlight the limited utility of epidemiological measures calculated over broad spatial scales. Furthermore, our results make clear the challenges with predicting epidemic spread within spatially extensive metropolitan areas, and indicate the need for high-resolution mobility and epidemiological data. Author summary: Geographic structure in human populations has been widely recognised as a key determinant in the spread of infectious diseases, however the mechanisms underlying it are hard to disentangle. Using two distinct statistical analyses, we sought to address questions surrounding the impact of movement patterns and stochastic events on the spatial spread of SARS-CoV-2 in Washington state. Through our first analysis, which made use of a clustering algorithm, we uncovered a relationship between spatial proximity and the similarity of epidemic trajectories. Clusters of similar trajectories formed a clear geographical pattern. In our second analysis, we performed statistical inference on time series data from the Puget Sound region of Washington state. From our inference, we found that both a high degree of connectivity and atypically fast transmission within the first few weeks of the outbreak were necessary to explain the rapid inter-county spread. Furthermore, this stochastic early spread had a lasting effect on the subsequent epidemic course as non-pharmaceutical interventions were insufficient to temporarily eradicate the virus from the region. [ABSTRACT FROM AUTHOR]

When modeling infectious diseases, it is common to assume that infection-derived immunity is either (1) non-existent or (2) perfect and lifelong. However there are many diseases in which infection-derived immunity is known to be present but imperfect. There are various ways in which infection-derived immunity can fail, which can ultimately impact the probability that an individual be reinfected by the same pathogen, as well as the long-run population-level prevalence of the pathogen. Here we discuss seven different models of imperfect infection-derived immunity, including waning, leaky and all-or-nothing immunity. For each model we derive the probability that an infected individual becomes reinfected during their lifetime, given that the system is at endemic equilibrium. This can be thought of as the impact that each of these infection-derived immunity failures have on reinfection. This measure is useful because it provides us with a way to compare different modes of failure of infection-derived immunity. [ABSTRACT FROM AUTHOR]

The pandemic of COVID-19 has become one of the greatest threats to human health, causing severe disruptions in the global supply chain, and compromising health care delivery worldwide. Although government authorities sought to contain the spread of SARS-CoV-2, by restricting travel and in-person activities, failure to deploy time-sensitive strategies in ramping-up of critical resource production exacerbated the outbreak. Here, we developed a mathematical model to analyze the effects of the interaction between supply chain disruption and infectious disease dynamics using coupled production and disease networks built on global data. Analysis of the supply chain model suggests that time-sensitive containment strategies could be created to balance objectives in pandemic control and economic losses, leading to a spatiotemporal separation of infection peaks that alleviates the societal impact of the disease. A lean resource allocation strategy can reduce the impact of supply chain shortages from 11.91 to 1.11% in North America. Our model highlights the importance of cross-sectoral coordination and region-wise collaboration to optimally contain a pandemic and provides a framework that could advance the containment and model-based decision making for future pandemics. [ABSTRACT FROM AUTHOR]

Developing methods for anticipating the emergence or reemergence of infectious diseases is both important and timely; however, traditional model-based approaches are stymied by uncertainty surrounding the underlying drivers. Here, we demonstrate an operational, mechanism-agnostic detection algorithm for disease (re-)emergence based on early warning signals (EWSs) derived from the theory of critical slowing down. Specifically, we used computer simulations to train a supervised learning algorithm to detect the dynamical footprints of (re-)emergence present in epidemiological data. Our algorithm was then challenged to forecast the slowly manifesting, spatially replicated reemergence of mumps in England in the mid-2000s and pertussis post-1980 in the United States. Our method successfully anticipated mumps reemergence 4 years in advance, during which time mitigation efforts could have been implemented. From 1980 onwards, our model identified resurgent states with increasing accuracy, leading to reliable classification starting in 1992. Additionally, we successfully applied the detection algorithm to 2 vector-transmitted case studies, namely, outbreaks of dengue serotypes in Puerto Rico and a rapidly unfolding outbreak of plague in 2017 in Madagascar. Taken together, these findings illustrate the power of theoretically informed machine learning techniques to develop early warning systems for the (re-)emergence of infectious diseases. This study develops an operational algorithm for the detection of the (re-)emergence of infectious disease. The authors illustrate its utility by successfully applying it to four (re-)emerging threats—mumps, pertussis, dengue and plague, providing early warning that could enable intervention measures. [ABSTRACT FROM AUTHOR]

Despite medical advances, the emergence and re-emergence of infectious diseases continue to pose a public health threat. Low-dimensional epidemiological models predict that epidemic transitions are preceded by the phenomenon of critical slowing down (CSD). This has raised the possibility of anticipating disease (re-)emergence using CSD-based early-warning signals (EWS), which are statistical moments estimated from time series data. For EWS to be useful at detecting future (re-)emergence, CSD needs to be a generic (model-independent) feature of epidemiological dynamics irrespective of system complexity. Currently, it is unclear whether the predictions of CSD—derived from simple, low-dimensional systems—pertain to real systems, which are high-dimensional. To assess the generality of CSD, we carried out a simulation study of a hierarchy of models, with increasing structural complexity and dimensionality, for a measles-like infectious disease. Our five models included: i) a nonseasonal homogeneous Susceptible-Exposed-Infectious-Recovered (SEIR) model, ii) a homogeneous SEIR model with seasonality in transmission, iii) an age-structured SEIR model, iv) a multiplex network-based model (Mplex) and v) an agent-based simulator (FRED). All models were parameterised to have a herd-immunity immunization threshold of around 90% coverage, and underwent a linear decrease in vaccine uptake, from 92% to 70% over 15 years. We found evidence of CSD prior to disease re-emergence in all models. We also evaluated the performance of seven EWS: the autocorrelation, coefficient of variation, index of dispersion, kurtosis, mean, skewness, variance. Performance was scored using the Area Under the ROC Curve (AUC) statistic. The best performing EWS were the mean and variance, with AUC > 0.75 one year before the estimated transition time. These two, along with the autocorrelation and index of dispersion, are promising candidate EWS for detecting disease emergence. Author summary: Emerging and re-emerging infectious diseases, such as Ebola and measles, present urgent public health challenges and threaten the progress made towards eliminating the global burden of disease. Consequently, a crucial activity in modern epidemiology is developing methods of anticipating (re-)emerging disease outbreaks. Early-warning signals (EWS) are a proposed method for detecting disease (re-)emergence, based on critical slowing down (CSD), a dynamical phenomenon present in systems approaching transition points. The presence of CSD preceding disease (re-)emergence has been comprehensively demonstrated in a range of low-dimensional epidemiological models. For EWS to be useful, however, CSD needs to be a generic feature of (re-)emerging disease transmission dynamics, rather than being limited to specific models. To assess the generality of CSD, we carried out a simulation study of a hierarchy of models of a re-emerging measles-like infectious disease. We found that CSD is present in the dynamics of all the models studied, supporting its generality. In addition, we studied seven candidate EWS, and found that four are strong candidates for use in monitoring systems to detect disease (re-)emergence. [ABSTRACT FROM AUTHOR]

Epidemic transitions are an important feature of infectious disease systems. As the transmissibility of a pathogen increases, the dynamics of disease spread shifts from limited stuttering chains of transmission to potentially large scale outbreaks. One proposed method to anticipate this transition are early-warning signals (EWS), summary statistics which undergo characteristic changes as the transition is approached. Although theoretically predicted, their mathematical basis does not take into account the nature of epidemiological data, with are typically aggregated into periodic case reports and subject to reporting error. The viability of EWS for epidemic transitions therefore remains uncertain. Here we demonstrate that most EWS can predict emergence even when calculated from imperfect data. We quantify performance using the area under the curve (AUC) statistic, a measure of how well an EWS distinguishes between numerical simulations of an emerging disease and one which is stationary. Values of the AUC statistic are compared across a range of different reporting scenarios. We find that different EWS respond to imperfect data differently. The mean, variance and first differenced variance all perform well unless reporting error is highly overdispersed. The autocorrelation, autocovariance and decay time perform well provided that the aggregation period of the data is larger than the serial interval and reporting error is not highly overdispersed. The coefficient of variation, skewness and kurtosis are found to be unreliable indicators of emergence. Overall, we find that seven of ten EWS considered perform well for most realistic reporting scenarios. We conclude that imperfect epidemiological data is not a barrier to using EWS for many potentially emerging diseases. [ABSTRACT FROM AUTHOR]

More than a century of ecological studies have demonstrated the importance of demography in shaping spatial and temporal variation in population dynamics. Surprisingly, the impact of seasonal recruitment on infectious disease systems has received much less attention. Here, we present data encompassing 78 years of monthly natality in the USA, and reveal pronounced seasonality in birth rates, with geographical and temporal variation in both the peak birth timing and amplitude. The timing of annual birth pulses followed a latitudinal gradient, with northern states exhibiting spring/summer peaks and southern states exhibiting autumn peaks, a pattern we also observed throughout the Northern Hemisphere. Additionally, the amplitude of United States birth seasonality was more than twofold greater in southern states versus those in the north. Next, we examined the dynamical impact of birth seasonality on childhood disease incidence, using a mechanistic model of measles. Birth seasonality was found to have the potential to alter the magnitude and periodicity of epidemics, with the effect dependent on both birth peak timing and amplitude. In a simulation study, we fitted an susceptible-exposed-infected-recovered model to simulated data, and demonstrated that ignoring birth seasonality can bias the estimation of critical epidemiological parameters. Finally, we carried out statistical inference using historical measles incidence data from New York City. Our analyses did not identify the predicted systematic biases in parameter estimates. This may be owing to the well-known frequency-locking between measles epidemics and seasonal transmission rates, or may arise from substantial uncertainty in multiple model parameters and estimation stochasticity. [ABSTRACT FROM AUTHOR]

Prediction and control of the geographical spread of emerging pathogens has become a central public health issue. Because these infectious diseases are by definition novel, there are few data to characterize their dynamics. One possible solution to this problem is to apply lessons learnt from analyses of historical data on familiar and epidemiologically similar pathogens. However, the portability of the spatial ecology of an infectious disease in a different epoch to other infections remains unexamined. Here, we study this issue by taking advantage of the recent re-emergence of pertussis in the United States to compare its spatial transmission dynamics throughout the 1950s with the past decade. We report 4-year waves, sweeping across the continent in the 1950s. These waves are shown to emanate from highly synchronous foci in the northwest and northeast coasts. In contrast, the recent resurgence of the disease is characterized by 5.5-year epidemics with no particular spatial structure. We interpret this to be the result of dramatic changes in patterns of human movement over the second half of the last century, together with changing age distribution of pertussis. We conclude that extrapolation regarding the spatial spread of contemporaneous pathogens based on analyses of historical incidence may be potentially very misleading. [ABSTRACT FROM AUTHOR]

The increasing number of zoonotic diseases spilling over from a range of wild animal species represents a particular concern for public health, especially in light of the current dramatic trend of biodiversity loss. To understand the ecology of these multi-host pathogens and their response to environmental degradation and species extinctions, it is necessary to develop a theoretical framework that takes into account realistic community assemblages. Here, we present a multi-host species epidemiological model that includes empirically determined patterns of diversity and composition derived from community ecology studies.We use this framework to study the interaction between wildlife diversity and directly transmitted pathogen dynamics. First, we demonstrate that variability in community composition does not affect significantly the intensity of pathogen transmission. We also show that the consequences of community diversity can differentially impact the prevalence of pathogens and the number of infectious individuals. Finally, we show that ecological interactions among host species have a weaker influence on pathogen circulation than inter-species transmission rates. We conclude that integration of a community perspective to study wildlife pathogens is crucial, especially in the context of understanding and predicting infectious disease emergence events. [ABSTRACT FROM AUTHOR]

Infectious diseases have been a prime testing ground for ecological theory. However, the ecological perspective is increasingly recognized as essential in epidemiology. Long-term, spatially resolved reliable data on disease incidence and the ability to test them using mechanistic models have been critical in this cross-fertilization. Here, we review some of the key intellectual developments in epidemiology facilitated by long-term data. We identify research frontiers at the interface of ecology and epidemiology and their associated data needs. [Copyright &y& Elsevier]

Case notifications of pertussis have shown an increase in a number of countries with high rates of routine pediatric immunization. This has led to significant public health concerns over a possible pertussis re-emergence. A leading proposed explanation for the observed increase in incidence is the loss of immunity to pertussis, which is known to occur after both natural infection and vaccination. Little is known, however, about the typical duration of immunity and its epidemiological implications. Here, we analyze a simplemathematicalmodel, exploring specifically the inter-epidemic period and fade-out frequency. These predictions are then contrasted with detailed incidence data for England and Wales. We find model output to be most sensitive to assumptions concerning naturally acquired immunity, which allows us to estimate the average duration of immunity. Our results support a period of natural immunity that is, on average, long-lasting (at least 30 years) but inherently variable. [ABSTRACT FROM AUTHOR]

Avian influenza virus (AIV) persists in North American wild waterfowl, exhibiting major outbreaks every 2-4 years. Attempts to explain the patterns of periodicity and persistence using simple direct transmission models are unsuccessful. Motivated by empirical evidence, we examine the contribution of an overlooked AIV transmission mode: environmental transmission. It is known that infectious birds shed large concentrations of virions in the environment, where virions may persist for a long time. We thus propose that, in addition to direct fecal/oral transmission, birds may become infected by ingesting virions that have long persisted in the environment. We design a new host-pathogen model that combines within-season transmission dynamics, between-season migration and reproduction, and environmental variation. Analysis of the model yields three major results. First, environmental transmission provides a persistence mechanism within small communities where epidemics cannot be sustained by direct transmission only (i.e., communities smaller than the critical community size). Second, environmental transmission offers a parsimonious explanation of the 2-4 year periodicity of avian influenza epidemics. Third, very low levels of environmental transmission (i.e., few cases per year) are sufficient for avian influenza to persist in populations where it would otherwise vanish. [ABSTRACT FROM AUTHOR]

Abstract: Ecological interference between unrelated diseases, caused by the temporary or permanent removal of individuals susceptible to one disease following infection with another, might be an important mechanism underlying epidemics. In this paper, we explore the potential dynamic consequences of interference by analyzing a two-disease model. By studying the stability domain of the model''s equilibria, we find that the stable region of the two-disease endemic state becomes increasingly smaller as the strength of interference (largely determined by the disease-induced mortality) increases. When seasonal changes are included in the transmission rates, the bifurcation structure of the model''s periodic cycles reveals that when the two diseases have similar mean transmission rates, multiple attractors in which the two diseases are strongly correlated can coexist, and that when the two diseases have very different mean transmission rates, the one with higher mean transmission rate may determine the dynamics of the system, with the other infection mimicking the behavior. We conclude that ecological interference can have important effects on the dynamical pattern of interacting diseases, the extent of which is determined by the epidemiological features of the diseases, their mean transmission rates in particular. [Copyright &y& Elsevier]

Focuses on a study which investigated the dramatic changes in patterns of epidemics of infections. Mechanisms that sustain oscillations in the incidence of diseases; Cycles of measles epidemics in places where birth rate is high; Relation between recruitment rate and effective mean transmission rate; Measles incidence in four representative large cities.

Presents a correction to the article "Appropriate Models for the Management of Infectious Diseases" that was previously published in "PLoS Medicine" journal.

Abstract: Dynamical systems theory predicts that inherently oscillatory systems undergoing periodic forcings will exhibit resonance phenomena, which are characterized by qualitative dynamical consequences resulting from the amplification of small external perturbations. In this paper we use extensive numerical simulations to demonstrate that the periodic nature of pulse vaccination strategies can make disease dynamics resonate. We proceed step by step in order to tease apart the dynamical consequences of (i) the intrinsic nonlinearity of the host–pathogen system, (ii) the seasonal variation in transmission and (iii) the additional forcing caused by vaccinating in pulses. We document that the resonance phenomenon associated with pulse vaccination can have quantitative epidemiological implications and produce perverse effects such as an unexpected increase in the number of infectives as the vaccination frequency increases. Our findings emphasize the importance of carefully taking into account the dynamical properties of the disease when designing a pulse vaccination strategy. [Copyright &y& Elsevier]

Recent pertussis resurgence in numerous countries may be driven by asymptomatic infections. Most pertussis surveillance studies are cross-sectional and cannot distinguish asymptomatic from pre-symptomatic infections. Longitudinal surveillance could overcome this barrier, providing more information about the true burden of pertussis at the population level. Here we analyze 17,442 nasopharyngeal samples from a longitudinal cohort of 1320 Zambian mother/infant pairs. Our analysis has two elements. First, we demonstrate that the full range of IS481 qPCR CT values provides insight into pertussis epidemiology, showing concordance of low and high CT results over time, within mother/infant pairs, and in relation to symptomatology. Second, we exploit these full-range qPCR data to demonstrate a high incidence of asymptomatic pertussis, including among infants. Our results demonstrate a wider burden of pertussis infection than we anticipated in this population, and expose key limitations of threshold-based interpretation of qPCR results in infectious disease surveillance. [ABSTRACT FROM AUTHOR]

Despite high vaccine coverage in many parts of the world, pertussis is resurging in a number of areas in which acellular vaccines are the primary vaccine administered to infants and young children. This is attributed in part to the suboptimal and short-lived immunity elicited by acellular pertussis vaccines and to their inability to prevent nasal colonization and transmission of the etiologic agent Bordetella pertussis. In response to this escalating public health concern, the National Institute of Allergy and Infectious Diseases held the workshop "Overcoming Waning Immunity in Pertussis Vaccines" in September 2019 to identify issues and possible solutions for the defects in immunity stimulated by acellular pertussis vaccines. Discussions covered aspects of the current problem, gaps in knowledge and possible paths forward. This review summarizes presentations and discussions of some of the key points that were raised by the workshop. [ABSTRACT FROM AUTHOR]

The recent resurgence of pertussis in England and Wales has been marked by infant deaths and rising cases in teens and adults. To understand which age cohorts are most responsible for these trends, we employed three separate statistical methods to analyze high-resolution pertussis reports from 1982 to 2012. The fine-grained nature of the time-series allowed us to describe the changes in age-specific incidence and contrast the transmission dynamics in the 1980s and during the resurgence era. Our results identified infants and school children younger than 10 years of age as a core group, prior to 2002: pertussis incidence in these populations was predictive of incidence in other age groups. After 2002, no core groups were identifiable. This conclusion is independent of methodology used. Because it is unlikely that the underlying contact patterns substantially changed over the study period, changes in predictability likely result from the introduction of more stringent diagnostics tests that may have inadvertently played a role in masking the relative contributions of core transmission groups. [ABSTRACT FROM AUTHOR]