The evolutionary emergence of pandemic influenza

Fogarty International Center collaborative networks in infectious disease modeling: Lessons learnt in research and capacity building

Due to a combination of ecological, political, and demographic factors, the emergence of novel pathogens has been increasingly observed in animals and humans in recent decades. Enhancing global capacity to study and interpret infectious disease surveillance data, and to develop data-driven computational models to guide policy, represents one of the most cost-effective, and yet overlooked, ways to prepare for the next pandemic. Epidemiological and behavioral data from recent pandemics and historic scourges have provided rich opportunities for validation of computational models, while new sequencing technologies and the 'big data' revolution present new tools for studying the epidemiology of outbreaks in real time. For the past two decades, the Division of International Epidemiology and Population Studies (DIEPS) of the NIH Fogarty International Center has spearheaded two synergistic programs to better understand and devise control strategies for global infectious disease threats. The Multinational Influenza Seasonal Mortality Study (MISMS) has strengthened global capacity to study the epidemiology and evolutionary dynamics of influenza viruses in 80 countries by organizing international research activities and training workshops. The Research and Policy in Infectious Disease Dynamics (RAPIDD) program and its precursor activities has established a network of global experts in infectious disease modeling operating at the research-policy interface, with collaborators in 78 countries. These activities have provided evidence-based recommendations for disease control, including during large-scale outbreaks of pandemic influenza, Ebola and Zika virus. Together, these programs have coordinated international collaborative networks to advance the study of emerging disease threats and the field of computational epidemic modeling. A global community of researchers and policy-makers have used the tools and trainings developed by these programs to interpret infectious disease patterns in their countries, understand modeling concepts, and inform control policies. Here we reflect on the scientific achievements and lessons learnt from these programs (h-index = 106 for RAPIDD and 79 for MISMS), including the identification of outstanding researchers and fellows; funding flexibility for timely research workshops and working groups (particularly relative to more traditional investigator-based grant programs); emphasis on group activities such as large-scale modeling reviews, model comparisons, forecasting challenges and special journal issues; strong quality control with a light touch on outputs; and prominence of training, data-sharing, and joint publications.

The coexistence or replacement of two subtypes of influenza

A pandemic subtype of influenza A sometimes replaces but sometimes coexists with the previous seasonal subtype. For example, the 1957 pandemic subtype H2N2 replaced the seasonal subtype H1N1; whereas after 1977 subtypes H1N1 (from the pandemic) and H3N2 continue to coexist. In an attempt to understand these alternatives, a hybrid model for the dynamics of influenza A is formulated. During an epidemic season the model takes into account cross-immunity of strains depending on the most recent seasonal infection. This cross-immunity reduces susceptibility to related strains of the seasonal subtype, and wanes with time due to virus drift. The population is assumed to reach an equilibrium distribution in susceptibility after several seasons, and then a pandemic subtype appears. Individuals who have been infected by the seasonal subtype all have the same cross-immunity to the pandemic subtype. A combination of theoretical and numerical analyses shows that for very strong cross-immunity between the subtypes the pandemic cannot invade, whereas for strong and weak cross-immunity there is coexistence for the season following the pandemic, and for intermediate levels of cross-immunity the pandemic may replace the seasonal subtype. This replacement depends on the basic reproduction numbers of seasonal and pandemic influenza. Vaccination against the seasonal subtype is found to slightly increase this range for pandemic replacement, with the range increasing with increasing vaccine protection and with the length of time that vaccine-induced immunity lasts.

Evolution and emergence of plant viruses

Viruses are common agents of plant infectious diseases. During last decades, worldwide agriculture production has been compromised by a series of epidemics caused by new viruses that spilled over from reservoir species or by new variants of classic viruses that show new pathogenic and epidemiological properties. Virus emergence has been generally associated with ecological change or with intensive agronomical practices. However, the complete picture is much more complex since the viral populations constantly evolve and adapt to their new hosts and vectors. This chapter puts emergence of plant viruses into the framework of evolutionary ecology, genetics, and epidemiology. We will stress that viral emergence begins with the stochastic transmission of preexisting genetic variants from the reservoir to the new host, whose fate depends on their fitness on each hosts, followed by adaptation to new hosts or vectors, and finalizes with an efficient epidemiological spread.

Evaluating methods for estimating existential risks

Researchers and commissions contend that the risk of human extinction is high, but none of these estimates have been based upon a rigorous methodology suitable for estimating existential risks. This article evaluates several methods that could be used to estimate the probability of human extinction. Traditional methods evaluated include: simple elicitation; whole evidence Bayesian; evidential reasoning using imprecise probabilities; and Bayesian networks. Three innovative methods are also considered: influence modeling based on environmental scans; simple elicitation using extinction scenarios as anchors; and computationally intensive possible-worlds modeling. Evaluation criteria include: level of effort required by the probability assessors; level of effort needed to implement the method; ability of each method to model the human extinction event; ability to incorporate scientific estimates of contributory events; transparency of the inputs and outputs; acceptability to the academic community (e.g., with respect to intellectual soundness, familiarity, verisimilitude); credibility and utility of the outputs of the method to the policy community; difficulty of communicating the method's processes and outputs to nonexperts; and accuracy in other contexts. The article concludes by recommending that researchers assess the risks of human extinction by combining these methods.

What limits the evolutionary emergence of pathogens?

The ability of a pathogen to cause an epidemic when introduced in a new host population often relies on its ability to adapt to this new environment. Here, we give a brief overview of recent theoretical and empirical studies of such evolutionary emergence of pathogens. We discuss the effects of several ecological and genetic factors that may affect the likelihood of emergence: migration, life history of the infectious agent, host heterogeneity, and the rate and effects of mutations. We contrast different modelling approaches and indicate how details in the way we model each step of a life cycle can have important consequences on the predicted probability of evolutionary emergence. These different theoretical perspectives yield important insights into optimal surveillance and intervention strategies, which should aim for a reduction in the emergence (and re-emergence) of infectious diseases.

Virulence attenuation during an influenza A/H5N1 pandemic

More than 15 years after the first human cases of influenza A/H5N1 in Hong Kong, the world remains at risk for an H5N1 pandemic. Preparedness activities have focused on antiviral stockpiling and distribution, development of a human H5N1 vaccine, operationalizing screening and social distancing policies, and other non-pharmaceutical interventions. The planning of these interventions has been done in an attempt to lessen the cumulative mortality resulting from a hypothetical H5N1 pandemic. In this theoretical study, we consider the natural limitations on an H5N1 pandemic's mortality imposed by the virus' epidemiological–evolutionary constraints. Evolutionary theory dictates that pathogens should evolve to be relatively benign, depending on the magnitude of the correlation between a pathogen's virulence and its transmissibility. Because the case fatality of H5N1 infections in humans is currently 60 per cent, it is doubtful that the current viruses are close to their evolutionary optimum for transmission among humans. To describe the dynamics of virulence evolution during an H5N1 pandemic, we build a mathematical model based on the patterns of clinical progression in past H5N1 cases. Using both a deterministic model and a stochastic individual-based simulation, we describe (i) the drivers of evolutionary dynamics during an H5N1 pandemic, (ii) the range of case fatalities for which H5N1 viruses can successfully cause outbreaks in humans, and (iii) the effects of different kinds of social distancing on virulence evolution. We discuss two main epidemiological–evolutionary features of this system (i) the delaying or slowing of an epidemic which results in a majority of hosts experiencing an attenuated virulence phenotype and (ii) the strong evolutionary pressure for lower virulence experienced by the virus during a period of intense social distancing.

Dynamics of Single-City Influenza with Seasonal Forcing: From Regularity to Chaos

Seasonal and epidemic influenza continue to cause concern, reinforced by connections between human and avian influenza, and H1N1 swine influenza. Models summarize ideas about disease mechanisms, help understand contributions of different processes, and explore interventions. A compartment model of single-city influenza is developed. It is mechanism-based on lower-level studies, rather than focussing on predictions. It is deterministic, without non-disease-status stratification. Categories represented are susceptible, infected, sick, hospitalized, asymptomatic, dead from flu, recovered, and one in which recovered individuals lose immunity. Most categories are represented with sequential pools with first-order kinetics, giving gamma-function progressions with realistic dynamics. A virus compartment allows representation of environmental effects on virus lifetime, thence affecting reproductive ratio. The model's behaviour is explored. It is validated without significant tuning against data on a school outbreak. Seasonal forcing causes a variety of regular and chaotic behaviours, some being typical of seasonal and epidemic flu. It is suggested that models use sequential stages for appropriate disease categories because this is biologically realistic, and authentic dynamics is required if predictions are to be credible. Seasonality is important indicating that control measures might usefully take account of expected weather.

The modes of evolutionary emergence of primal and late pandemic influenza virus strains from viral reservoir in animals: an interdisciplinary analysis

Based on a wealth of recent findings, in conjunction with earliest chronologies pertaining to evolutionary emergences of ancestral RNA viruses, ducks, Influenzavirus A (assumingly within ducks), and hominids, as well as to the initial domestication of mallard duck (Anas platyrhynchos), jungle fowl (Gallus gallus), wild turkey (Meleagris gallopavo), wild boar (Sus scrofa), and wild horse (Equus ferus), presumed genesis modes of primordial pandemic influenza strains have multidisciplinarily been configured. The virological fundamentality of domestication and farming of those various avian and mammalian species has thereby been demonstrated and broadly elucidated, within distinctive coevolutionary paradigms. The mentioned viral genesis modes were then analyzed, compatibly with common denominators and flexibility that mark the geographic profile of the last 18 pandemic strains, which reputedly emerged since 1510, the antigenic profile of the last 10 pandemic strains since 1847, and the genomic profile of the last 5 pandemic strains since 1918, until present. Related ecophylogenetic and biogeographic aspects have been enlightened, alongside with the crucial role of spatial virus gene dissemination by avian hosts. A fairly coherent picture of primary and late evolutionary and genomic courses of pandemic strains has thus been attained, tentatively. Specific patterns underlying complexes prone to generate past and future pandemic strains from viral reservoir in animals are consequentially derived.

Influenza emergence in the face of evolutionary constraints

Different influenza subtypes can evolve at very different rates, but the causes are not well understood. In this paper, we explore whether differences in transmissibility between subtypes can play a role if there are fitness constraints on antigenic evolution. We investigate the problem using a mathematical model that separates the interaction of strains through cross-immunity from the process of emergence for new antigenic variants. Evolutionary constraints are also included with antigenic mutation incurring a fitness cost. We show that the transmissibility of a strain can become disproportionately important in dictating the rate of antigenic drift: strains that spread only slightly more easily can have a much higher rate of emergence. Further, we see that the effect continues when vaccination is considered; a small increase in the rate of transmission can make it much harder to control the frequency at which new strains emerge. Our results not only highlight the importance of considering both transmission and fitness constraints when modelling influenza evolution, but may also help in understanding the differences between the emergence of H1N1 and H3N2 subtypes.

An agent-based model to study the epidemiological and evolutionary dynamics of Influenza viruses

Background

Influenza A viruses exhibit complex epidemiological patterns in a number of mammalian and avian hosts. Understanding transmission of these viruses necessitates taking into account their evolution, which represents a challenge for developing mathematical models. This is because the phrasing of multi-strain systems in terms of traditional compartmental ODE models either requires simplifying assumptions to be made that overlook important evolutionary processes, or leads to complex dynamical systems that are too cumbersome to analyse.

Results

Here, we develop an Individual-Based Model (IBM) in order to address simultaneously the ecology, epidemiology and evolution of strain-polymorphic pathogens, using Influenza A viruses as an illustrative example.

Conclusions

We carry out careful validation of our IBM against comparable mathematical models to demonstrate the robustness of our algorithm and the sound basis for this novel framework. We discuss how this new approach can give critical insights in the study of influenza evolution.

Risk factors for the evolutionary emergence of pathogens

Recent outbreaks of novel infectious diseases (e.g. SARS, influenza H1N1) have highlighted the threat of cross-species pathogen transmission. When first introduced to a population, a pathogen is often poorly adapted to its new host and must evolve in order to escape extinction. Theoretical arguments and empirical studies have suggested various factors to explain why some pathogens emerge and others do not, including host contact structure, pathogen adaptive pathways and mutation rates. Using a multi-type branching process, we model the spread of an introduced pathogen evolving through several strains. Extending previous models, we use a network-based approach to separate host contact patterns from pathogen transmissibility. We also allow for arbitrary adaptive pathways. These generalizations lead to novel predictions regarding the impact of hypothesized risk factors. Pathogen fitness depends on the host population in which it circulates, and the ‘riskiest’ contact distribution and adaptive pathway depend on initial transmissibility. Emergence probability is sensitive to mutation probabilities and number of adaptive steps required, with the possibility of large adaptive steps (e.g. simultaneous point mutations or recombination) having a dramatic effect. In most situations, increasing overall mutation probability increases the risk of emergence; however, notable exceptions arise when deleterious mutations are available.

Bacteriophages as model organisms for virus emergence research

Viruses fully emerge by gaining the ability to sustainably infect new host populations. When the hosts are humans, emerging viruses can present major public health issues, as exemplified by the AIDS pandemic. Therefore, heuristic approaches to identify nascent diseases before they become pandemic would be valuable. Unfortunately, the current patient-based and epidemiological approaches are ill-suited in this regard because they are largely responsive and not predictive. Alternative approaches based on virus evolutionary ecology might have greater potential to predict virus emergence. However, given the difficulties encountered when studying metazoan viruses in this context, the development of new model systems is greatly desirable. Here, I highlight studies that show that bacteriophages are appropriate model organisms for virus emergence research because of the ease in which important population parameters can be manipulated. Ideally this research will permit identifying major factors determining the persistence or extinction of emerging viruses. If such viruses could be recognized in advance, patient-based and epidemiological strategies could be better mobilized to deal with them.

Avian influenza viruses in wild birds: a moving target

The long-standing evolutionary and ecological relationships between wild birds and influenza A viruses has created a broad pool of viral genetic diversity and a reservoir of potentially transmissible viruses. An understanding of these relationships can help us identify and modify critical control points to reduce transmission of avian influenza viruses into animal and human populations.