C. Jessica E. Metcalf

Assessing the global threat from Zika virus

Global spread of Zika virus Zika virus was identified in Uganda in 1947; since then, it has enveloped the tropics, causing disease of varying severity. Lessler et al. review the historical literature to remind us that Zikas neurotropism was observed in mice even before clinical case reports in Nigeria in 1953. What determines the clinical manifestations; how local conditions, vectors, genetics, and wild hosts affect transmission and geographical spread; what the best control strategy is; and how to develop effective drugs, vaccines, and diagnostics are all critical questions that are begging for data. Science, this issue p. 663 Assessing the global threat from Zika virus. BACKGROUND First discovered in 1947, Zika virus (ZIKV) received little attention until a surge in microcephaly cases was reported after a 2015 outbreak in Brazil. The size of the outbreak and the severity of associated birth defects prompted the World Health Organization (WHO) to declare a Public Health Emergency of International Concern on 1 February 2016. In response, there has been an explosion in research and planning as the global health community has turned its attention to understanding and controlling ZIKV. Still, much of the information needed to evaluate the global health threat from ZIKV is lacking. The global threat posed by any emerging pathogen depends on its epidemiology, its clinical features, and our ability to implement effective control measures. Whether introductions of ZIKV result in epidemics depends on local ecology, population immunity, regional demographics, and, to no small degree, random chance. The same factors determine whether the virus will establish itself as an endemic disease. The burden of ZIKV spread on human health is mediated by its natural history and pathogenesis, particularly during pregnancy, and our ability to control the virus’s spread. In this Review, we examine the empirical evidence for a global threat from ZIKV through the lens of these processes, examining historic and current evidence, as well as parallel processes in closely related viruses. ADVANCES Because ZIKV was not recognized as an important disease in humans until recently, it was little studied before the recent crisis. Nevertheless, the limited data from the decades following its discovery provide important clues into ZIKV’s epidemiology and suggest that some populations were at risk for the virus for years in the mid-20th century, although this risk may predominantly have been the result of spillover infections from a sylvatic reservoir. Recent outbreaks on Yap Island (2007) and in French Polynesia (2014) provide the only previous observations of large epidemics and are the basis for the little that we do know about ZIKV’s acute symptoms (e.g., rash, fever, conjunctivitis, and arthralgia), the risk of birth defects, such as microcephaly (estimated to be 1 per 100 in French Polynesia), and the incidence of severe neurological outcomes (e.g., Guillain-Barré is estimated to occur in approximately 2 out of every 10,000 cases). The observation of an association between ZIKV and a surge in microcephaly cases in Brazil and the subsequent declaration of a Public Health Emergency of International Concern by the WHO have rapidly accelerated research into the virus. Small, but very important, studies have begun to identify the substantial risk the virus can pose throughout a pregnancy, and careful surveillance has established that ZIKV can be transmitted sexually. Numerous modeling studies have helped to estimate the potential range of ZIKV and measured its reproductive number R0 (estimates range from 1.4 to 6.6), a key measure of transmissibility in a number of settings. Still, it remains unclear whether the recent epidemic in the Americas is the result of fundamental changes in the virus or merely a chance event. OUTLOOK ZIKV research is progressing rapidly, and over the coming months and years our understanding of the virus will undoubtedly deepen considerably. Key questions about the virus’s range, its ability to persist, and its clinical severity will be answered as the current epidemic in the Americas runs its course. Moving forward, it is important that information on ZIKV be placed within the context of its effect on human health and that we remain cognizant of the structure of postinvasion epidemic dynamics as we respond to this emerging threat. The effect of ZIKV is a function of the local transmission regime and viral pathogenesis. (A) Many countries cannot maintain ongoing vector-mediated ZIKV transmission and are only at risk from importation by travelers and limited onward transmission (e.g., through sex). (B) If conditions are appropriate, importations can lead to postinvasion epidemics with high incidence across age ranges, after which the virus may go locally extinct or remain endemic

Modeling infectious disease dynamics in the complex landscape of global health

Mathematical modeling of infectious diseases The spread of infectious diseases can be unpredictable. With the emergence of antibiotic resistance and worrying new viruses, and with ambitious plans for global eradication of polio and the elimination of malaria, the stakes have never been higher. Anticipation and measurement of the multiple factors involved in infectious disease can be greatly assisted by mathematical methods. In particular, modeling techniques can help to compensate for imperfect knowledge, gathered from large populations and under difficult prevailing circumstances. Heesterbeek et al. review the development of mathematical models used in epidemiology and how these can be harnessed to develop successful control strategies and inform public health policy. Science, this issue 10.1126/science.aaa4339 BACKGROUND Despite many notable successes in prevention and control, infectious diseases remain an enormous threat to human and animal health. The ecological and evolutionary dynamics of pathogens play out on a wide range of interconnected temporal, organizational, and spatial scales that span hours to months, cells to ecosystems, and local to global spread. Some pathogens are directly transmitted between individuals of a single species, whereas others circulate among multiple hosts, need arthropod vectors, or persist in environmental reservoirs. Many factors, including increasing antimicrobial resistance, human connectivity, population growth, urbanization, environmental and land-use change, as well as changing human behavior, present global challenges for prevention and control. Faced with this complexity, mathematical models offer valuable tools for understanding epidemiological patterns and for developing and evaluating evidence for decision-making in global health. ADVANCES During the past 50 years, the study of infectious disease dynamics has matured into a rich interdisciplinary field at the intersection of mathematics, epidemiology, ecology, evolutionary biology, immunology, sociology, and public health. The practical challenges range from establishing appropriate data collection to managing increasingly large volumes of information. The theoretical challenges require fundamental study of many-layered, nonlinear systems in which infections evolve and spread and where key events can be governed by unpredictable pathogen biology or human behavior. In this Review, we start with an examination of real-time outbreak response using the West African Ebola epidemic as an example. Here, the challenges range from underreporting of cases and deaths, and missing information on the impact of control measures to understanding human responses. The possibility of future zoonoses tests our ability to detect anomalous outbreaks and to estimate human-to-human transmissibility against a backdrop of ongoing zoonotic spillover while also assessing the risk of more dangerous strains evolving. Increased understanding of the dynamics of infections in food webs and ecosystems where host and nonhost species interact is key. Simultaneous multispecies infections are increasingly recognized as a notable public health burden, yet our understanding of how different species of pathogens interact within hosts is rudimentary. Pathogen genomics has become an essential tool for drawing inferences about evolution and transmission and, here but also in general, heterogeneity is the major challenge. Methods that depart from simplistic assumptions about random mixing are yielding new insights into the dynamics of transmission and control. There is rapid growth in estimation of model parameters from mismatched or incomplete data, and in contrasting model output with real-world observations. New data streams on social connectivity and behavior are being used, and combining data collected from very different sources and scales presents important challenges. All these mathematical endeavors have the potential to feed into public health policy and, indeed, an increasingly wide range of models is being used to support infectious disease control, elimination, and eradication efforts. OUTLOOK Mathematical modeling has the potential to probe the apparently intractable complexity of infectious disease dynamics. Coupled to continuous dialogue between decision-makers and the multidisciplinary infectious disease community, and by drawing on new data streams, mathematical models can lay bare mechanisms of transmission and indicate new approaches to prevention and control that help to shape national and international public health policy. Modeling for public health. Policy questions define the model’s purpose. Initial model design is based on current scientific understanding and the available relevant data. Model validation and fit to disease data may require further adaptation; sensitivity and uncertainty analysis can point to requirements for collection of additional specific data. Cycles of model testing and analysis thus lead to policy advice and improved scientific understanding. Despite some notable successes in the control of infectious diseases, transmissible pathogens still pose an enormous threat to human and animal health. The ecological and evolutionary dynamics of infections play out on a wide range of interconnected temporal, organizational, and spatial scales, which span hours to months, cells to ecosystems, and local to global spread. Moreover, some pathogens are directly transmitted between individuals of a single species, whereas others circulate among multiple hosts, need arthropod vectors, or can survive in environmental reservoirs. Many factors, including increasing antimicrobial resistance, increased human connectivity and changeable human behavior, elevate prevention and control from matters of national policy to international challenge. In the face of this complexity, mathematical models offer valuable tools for synthesizing information to understand epidemiological patterns, and for developing quantitative evidence for decision-making in global health.

Long-term measles-induced immunomodulation increases overall childhood infectious disease mortality

Extra dividends from measles vaccine Vaccination against measles has many benefits, not only lifelong protection against this potentially serious virus. Mina et al. analyzed data collected since mass vaccination began in high-income countries when measles was common. Measles vaccination is associated with less mortality from other childhood infections. Measles is known to cause transient immunosuppression, but close inspection of the mortality data suggests that it disables immune memory for 2 to 3 years. Vaccination thus does more than safeguard children against measles; it also stops other infections taking advantage of measles-induced immune damage. Science, this issue p. 694 Preventing measles prevents immune memory damage and nonspecifically safeguards against many childhood infections. Immunosuppression after measles is known to predispose people to opportunistic infections for a period of several weeks to months. Using population-level data, we show that measles has a more prolonged effect on host resistance, extending over 2 to 3 years. We find that nonmeasles infectious disease mortality in high-income countries is tightly coupled to measles incidence at this lag, in both the pre- and post-vaccine eras. We conclude that long-term immunologic sequelae of measles drive interannual fluctuations in nonmeasles deaths. This is consistent with recent experimental work that attributes the immunosuppressive effects of measles to depletion of B and T lymphocytes. Our data provide an explanation for the long-term benefits of measles vaccination in preventing all-cause infectious disease. By preventing measles-associated immune memory loss, vaccination protects polymicrobial herd immunity.

Advancing population ecology with integral projection models: a practical guide

Summary 1. Integral projection models (IPMs) use information on how an individual’s state influences its vital rates – survival, growth and reproduction – to make population projections. IPMs are constructed from regression models predicting vital rates from state variables (e.g. size or age) and covariates (e.g. environment). By combining regressions of vital rates, an IPM provides mechanistic insight into emergent ecological patterns such as population dynamics, species geographic distributions or life-history strategies. 2. Here, we review important resources for building IPMs and provide a comprehensive guide, with extensive R code, for their construction. IPMs can be applied to any stage-structured population; here, we illustrate IPMs for a series of plant life histories of increasing complexity and biological realism, highlighting the utility of various regression methods for capturing biological patterns. We also present case studies illustrating how IPMs can be used to predict species’ geographic distributions and life-history strategies. 3. IPMs can represent a wide range of life histories at any desired level of biological detail. Much of the strength of IPMs lies in the strength of regression models. Many subtleties arise when scaling from vital rate regressions to population-level patterns, so we provide a set of diagnostics and guidelines to ensure that models are biologically plausible. Moreover, IPMs can exploit a large existing suite of analytical tools developed for matrix projection models.

Seasonality and comparative dynamics of six childhood infections in pre-vaccination Copenhagen

Seasonal variation in infection transmission is a key determinant of epidemic dynamics of acute infections. For measles, the best-understood strongly immunizing directly transmitted childhood infection, the perception is that term-time forcing is the main driver of seasonality in developed countries. The degree to which this holds true across other acute immunizing childhood infections is not clear. Here, we identify seasonal transmission patterns using a unique long-term dataset with weekly incidence of six infections including measles. Data on age–incidence allow us to quantify the mean age of infection. Results indicate correspondence between dips in transmission and school holidays for some infections, but there are puzzling discrepancies, despite close correspondence between average age of infection and age of schooling. Theoretical predictions of the relationship between amplitude of seasonality and basic reproductive rate of infections that should result from term-time forcing are also not upheld. We conclude that where yearly trajectories of susceptible numbers are perturbed, e.g. via waning of immunity, seasonality is unlikely to be entirely driven by term-time forcing. For the three bacterial infections, pertussis, scarlet fever and diphtheria, there is additionally a strong increase in transmission during the late summer before the end of school vacations.

Evolution of delayed reproduction in uncertain environments: a life-history perspective.

Environmental uncertainty alone can select for delayed reproduction; however, its relative role in the evolution of delayed reproduction across life histories is not known. Along a life‐history spectrum from low‐survival/high‐fertility species to high‐survival/low‐fertility species, we show that the latter are more likely to evolve delayed reproduction if fertility varies over time. By contrast, if survival varies over time, low‐survival life histories are more likely to evolve delays. If there is variation in both survival and fertility, and if this variation is positively associated, the evolutionarily stable reproductive delay is decreased (relative to independent variation in survival and fertility). Conversely, if variation in survival and fertility is negatively associated, the evolutionarily stable reproductive delay is increased. We further show that environmental uncertainty can drive the evolution of delayed reproduction in an iteroparous organism but only in the special case where juvenile survival is greater than adult survival. For common iteroparous life histories (adult survival > juvenile survival), environmental uncertainty does not select for delayed reproduction. Thus, any benefits that delayed reproduction might have on reproduction or survival could be especially important in explaining the common observation of delayed reproduction in many vertebrates and perennial plants.

Measuring the Performance of Vaccination Programs Using Cross-Sectional Surveys: A Likelihood Framework and Retrospective Analysis

Justin Lessler and colleagues describe a method that estimates the fraction of a population accessible to vaccination activities, and they apply it to measles vaccination in three African countries: Ghana, Madagascar, and Sierra Leone.

Modeling the Influence of Genetic and Environmental Variation on the Expression of Plant Life Cycles across Landscapes

Organisms develop through multiple life stages that differ in environmental tolerances. The seasonal timing, or phenology, of life-stage transitions determines the environmental conditions to which each life stage is exposed and the length of time required to complete a generation. Both environmental and genetic factors contribute to phenological variation, yet predicting their combined effect on life cycles across a geographic range remains a challenge. We linked submodels of the plasticity of individual life stages to create an integrated model that predicts life-cycle phenology in complex environments. We parameterized the model for Arabidopsis thaliana and simulated life cycles in four locations. We compared multiple “genotypes” by varying two parameters associated with natural genetic variation in phenology: seed dormancy and floral repression. The model predicted variation in life cycles across locations that qualitatively matches observed natural phenology. Seed dormancy had larger effects on life-cycle length than floral repression, and results suggest that a genetic cline in dormancy maintains a life-cycle length of 1 year across the geographic range of this species. By integrating across life stages, this approach demonstrates how genetic variation in one transition can influence subsequent transitions and the geographic distribution of life cycles more generally.

The path of least resistance: aggressive or moderate treatment?

The evolution of resistance to antimicrobial chemotherapy is a major and growing cause of human mortality and morbidity. Comparatively little attention has been paid to how different patient treatment strategies shape the evolution of resistance. In particular, it is not clear whether treating individual patients aggressively with high drug dosages and long treatment durations, or moderately with low dosages and short durations can better prevent the evolution and spread of drug resistance. Here, we summarize the very limited available empirical evidence across different pathogens and provide a conceptual framework describing the information required to effectively manage drug pressure to minimize resistance evolution.

The Cinderella syndrome: why do malaria-infected cells burst at midnight?

An interesting quirk of many malaria infections is that all parasites within a host – millions of them – progress through their cell cycle synchronously. This surprising coordination has long been recognized, yet there is little understanding of what controls it or why it has evolved. Interestingly, the conventional explanation for coordinated development in other parasite species does not seem to apply here. We argue that for malaria parasites, a critical question has yet to be answered: is the coordination due to parasites bursting at the same time or at a particular time? We explicitly delineate these fundamentally different scenarios, possible underlying mechanistic explanations and evolutionary drivers, and discuss the existing corroborating data and key evidence needed to solve this evolutionary mystery.