Considering humans as habitat reveals evidence of successional disease ecology among human pathogens.

The realization that ecological principles play an important role in infectious disease dynamics has led to a renaissance in epidemiological theory. Ideas from ecological succession theory have begun to inform an understanding of the relationship between the individual microbiome and health but have not yet been applied to investigate broader, population-level epidemiological dynamics. We consider human hosts as habitat and apply ideas from succession to immune memory and multi-pathogen dynamics in populations. We demonstrate that ecologically meaningful life history characteristics of pathogens and parasites, rather than epidemiological features alone, are likely to play a meaningful role in determining the age at which people have the greatest probability of being infected. Our results indicate the potential importance of microbiome succession in determining disease incidence and highlight the need to explore how pathogen life history traits and host ecology influence successional dynamics. We conclude by exploring some of the implications that inclusion of successional theory might have for understanding the ecology of diseases and their hosts.

A mathematical representation of the reactive scope model.

Researchers have long sought to understand and predict an animal's response to stressful stimuli. Since the introduction of the concept of homeostasis, a variety of model frameworks have been proposed to describe what is necessary for an animal to remain within this stable physiological state and the ramifications of leaving it. Romero et al. (Horm Behav 55(3):375-389, 2009) introduced the reactive scope model to provide a novel conceptual framework for the stress response that assumes an animal's ability to tolerate a stressful stimulus may degrade over time in response to the stimulus. We provide a mathematical formulation for the reactive scope model using a system of ordinary differential equations and show that this model is capable of recreating existing experimental data. We also provide an experimental method that may be used to verify the model as well as several potential additions to the model. If future experimentation provides the necessary data to estimate the model's parameters, the model presented here may be used to make quantitative predictions about physiological mediator levels during a stress response and predict the onset of homeostatic overload.

How Drivers of Seasonality in Respiratory Infections May Impact Vaccine Strategy: A Case Study in How Coronavirus Disease 2019 (COVID-19) May Help Us Solve One of Influenza's Biggest Challenges.

Vaccines against seasonal infections like influenza offer a recurring testbed, encompassing challenges in design, implementation, and uptake to combat a both familiar and ever-shifting threat. One of the pervading mysteries of influenza epidemiology is what causes the distinctive seasonal outbreak pattern. Proposed theories each suggest different paths forward in being able to tailor precision vaccines and/or deploy them most effectively. One of the greatest challenges in contrasting and supporting these theories is, of course, that there is no means by which to actually test them. In this communication we revisit theories and explore how the ongoing coronavirus disease 2019 (COVID-19) pandemic might provide a unique opportunity to better understand the global circulation of respiratory infections. We discuss how vaccine strategies may be targeted and improved by both isolating drivers and understanding the immunological consequences of seasonality, and how these insights about influenza vaccines may generalize to vaccines for other seasonal respiratory infections.

Capturing complex interactions in disease ecology with simplicial sets.

Network approaches have revolutionized the study of ecological interactions. Social, movement and ecological networks have all been integral to studying infectious disease ecology. However, conventional (dyadic) network approaches are limited in their ability to capture higher-order interactions. We present simplicial sets as a tool that addresses this limitation. First, we explain what simplicial sets are. Second, we explain why their use would be beneficial in different subject areas. Third, we detail where these areas are: social, transmission, movement/spatial and ecological networks and when using them would help most in each context. To demonstrate their application, we develop a novel approach to identify how pathogens persist within a host population. Fourth, we provide an overview of how to use simplicial sets, highlighting specific metrics, generative models and software. Finally, we synthesize key research questions simplicial sets will help us answer and draw attention to methodological developments that will facilitate this.

Carceral Amplification of COVID-19: Impacts for Community, Corrections Officer, and Incarcerated Population Risks.

COVID-19 is challenging many societal institutions, including our criminal justice systems. Some have proposed or enacted (e.g., the State of New Jersey) reductions in the jail and/or prison populations. We present a mathematical model to explore the epidemiologic impact of such interventions in jails and contrast them with the consequences of maintaining unaltered practices. We consider infection risk and likely in-custody deaths, and estimate how within-jail dynamics lead to spill-over risks, not only affecting incarcerated people but increasing exposure, infection, and death rates for both corrections officers and the broader community beyond the justice system. We show that, given a typical jail-community dynamic, operating in a business-as-usual way results in substantial, rapid, and ongoing loss of life. Our results are consistent with the hypothesis that large-scale reductions in arrest and speeding of releases are likely to save the lives of incarcerated people, jail staff, and the wider community.

How public reaction to disease information across scales and the impacts of vector control methods influence disease prevalence and control efficacy.

With the development of social media, the information about vector-borne disease incidence over broad spatial scales can cause demand for local vector control before local risk exists. Anticipatory intervention may still benefit local disease control efforts; however, infection risks are not the only focal concerns governing public demand for vector control. Concern for environmental contamination from pesticides and economic limitations on the frequency and magnitude of control measures also play key roles. Further, public concern may be focused more on ecological factors (i.e., controlling mosquito populations) or on epidemiological factors (i.e., controlling infection-carrying mosquitoes), which may lead to very different control outcomes. Here we introduced a generic Ross-MacDonald model, incorporating these factors under three spatial scales of disease information: local, regional, and global. We tailored and parameterized the model for Zika virus transmitted by Aedes aegypti mosquito. We found that sensitive reactivity caused by larger-scale incidence information could decrease average human infections per patch breeding capacity, however, the associated increase in total control effort plays a larger role, which leads to an overall decrease in control efficacy. The shift of focal concerns from epidemiological to ecological risk could relax the negative effect of the sensitive reactivity on control efficacy when mosquito breeding capacity populations are expected to be large. This work demonstrates that, depending on expected total mosquito breeding capacity population size, and weights of different focal concerns, large-scale disease information can reduce disease infections without lowering control efficacy. Our findings provide guidance for vector-control strategies by considering public reaction through social media.

High prevalence does not necessarily equal maintenance species: Avoiding biased claims of disease reservoirs when using surveillance data.

Many pathogens of public health and conservation concern persist in host communities. Identifying candidate maintenance and reservoir species is therefore a central component of disease management. The term maintenance species implies that if all species but the putative maintenance species were removed, then the pathogen would still persist. In the absence of field manipulations, this statement inherently requires a causal or mechanistic model to assess. However, we lack a systematic understanding of (i) how often conclusions are made about maintenance and reservoir species without reference to mechanistic models (ii) what types of biases may be associated with these conclusions and (iii) how explicitly invoking causal or mechanistic modelling can help ameliorate these biases. Filling these knowledge gaps is critical for robust inference about pathogen persistence and spillover in multihost-parasite systems, with clear implications for human and wildlife health. To address these gaps, we performed a literature review on the evidence previous studies have used to make claims regarding maintenance or reservoir species. We then developed multihost-parasite models to explore and demonstrate common biases that could arise when inferring maintenance potential from observational prevalence data. Finally, we developed new theory to show how model-driven inference of maintenance species can minimize and eliminate emergent biases. In our review, we found that 83% of studies used some form of observational prevalence data to draw conclusions on maintenance potential and only 6% of these studies combined observational data with mechanistic modelling. Using our model, we demonstrate how the community, spatial and temporal context of observational data can lead to substantial biases in inferences of maintenance potential. Importantly, our theory identifies that model-driven inference of maintenance species elucidates other streams of observational data that can be leveraged to correct these biases. Model-driven inference is an essential, yet underused, component of multidisciplinary studies that make inference about host reservoir and maintenance species. Better integration of wildlife disease surveillance and mechanistic models is necessary to improve the robustness and reproducibility of our conclusions regarding maintenance and reservoir species.

Linking Immuno-Epidemiology Principles to Violence.

BACKGROUND: Societies have always struggled with violence, but recently there has been a push to understand violence as a public health issue. This idea has unified professionals in medicine, epidemiological, and psychology with a goal to end violence and heal those exposed to it. Recently, analogies have been made between community-level infectious disease epidemiology and how violence spreads within a community. Experts in public health and medicine suggest an epidemiological framework could be used to study violence. METHODS: Building upon results from community organizations which implement public health-like techniques to stop violence spread, we look to formalize the analogies between violence and infectious diseases. Then expanding on these ideas and using mathematical epidemiological principals, we formulate a susceptible-exposed-infected model to capture violence spread. Further, we ran example numerical simulations to show how a mathematical model can provide insight on prevention strategies. RESULTS: The preliminary simulations show negative effects of violence exposure have a greater impact than positive effects of preventative measures. For example, our simulation shows that when the impact of violence exposure is reduced by half, the amount of violence in a community drastically decreases in the long-term; but to reach this same outcome through an increase in the amount of after exposure support, it must be approximately fivefold. Further, we note that our simulations qualitatively agree with empirical studies. CONCLUSIONS: Having a mathematical model can give insights on the effectiveness of different strategies for violence prevention. Based on our example simulations, the most effective use of community funding is investing in protective factors, instead of support after violence exposure, but of course these results do not stand in isolation and will need to be contextualized with the rest of the research in the field.

Observations and conversations: how communities learn about infection risk can impact the success of non-pharmaceutical interventions against epidemics.

BACKGROUND: Individual behavioural decisions are responses to a person's perceived social norms that could be shaped by both their physical and social environment. In the context of the COVID-19 pandemic, these environments correspond to epidemiological risk from contacts and the social construction of risk by communication within networks of friends. Understanding the circumstances under which the influence of these different social networks can promote the acceptance of non-pharmaceutical interventions and consequently the adoption of protective behaviours is critical for guiding useful, practical public health messaging. METHODS: We explore how information from both physical contact and social communication layers of a multiplex network can contribute to flattening the epidemic curve in a community. Connections in the physical contact layer represent opportunities for transmission, while connections in the communication layer represent social interactions through which individuals may gain information, e.g. messaging friends. RESULTS: We show that maintaining focus on awareness of risk among each individual's physical contacts promotes the greatest reduction in disease spread, but only when an individual is aware of the symptoms of a non-trivial proportion of their physical contacts (~ ≥ 20%). Information from the social communication layer without was less useful when these connections matched less well with physical contacts and contributed little in combination with accurate information from physical contacts. CONCLUSIONS: We conclude that maintaining social focus on local outbreak status will allow individuals to structure their perceived social norms appropriately and respond more rapidly when risk increases. Finding ways to relay accurate local information from trusted community leaders could improve mitigation even where more intrusive/costly strategies, such as contact-tracing, are not possible.

Propinquity drives the emergence of network structure and density.

The lack of large-scale, continuously evolving empirical data usually limits the study of networks to the analysis of snapshots in time. This approach has been used for verification of network evolution mechanisms, such as preferential attachment. However, these studies are mostly restricted to the analysis of the first links established by a new node in the network and typically ignore connections made after each node's initial introduction. Here, we show that the subsequent actions of individuals, such as their second network link, are not random and can be decoupled from the mechanism behind the first network link. We show that this feature has strong influence on the network topology. Moreover, snapshots in time can now provide information on the mechanism used to establish the second connection. We interpret these empirical results by introducing the "propinquity model," in which we control and vary the distance of the second link established by a new node and find that this can lead to networks with tunable density scaling, as found in real networks. Our work shows that sociologically meaningful mechanisms are influencing network evolution and provides indications of the importance of measuring the distance between successive connections.

Genomic signatures of selection in bats surviving white-nose syndrome.

Rapid evolution of advantageous traits following abrupt environmental change can help populations recover from demographic decline. However, for many introduced diseases affecting longer-lived, slower reproducing hosts, mortality is likely to outpace the acquisition of adaptive de novo mutations. Adaptive alleles must therefore be selected from standing genetic variation, a process that leaves few detectable genomic signatures. Here, we present whole genome evidence for selection in bat populations that are recovering from white-nose syndrome (WNS). We collected samples both during and after a WNS-induced mass mortality event in two little brown bat populations that are beginning to show signs of recovery and found signatures of soft sweeps from standing genetic variation at multiple loci throughout the genome. We identified one locus putatively under selection in a gene associated with the immune system. Multiple loci putatively under selection were located within genes previously linked to host response to WNS as well as to changes in metabolism during hibernation. Results from two additional populations suggested that loci under selection may differ somewhat among populations. Through these findings, we suggest that WNS-induced selection may contribute to genetic resistance in this slowly reproducing species threatened with extinction.

The sensitivity of a honeybee colony to worker mortality depends on season and resource availability.

BACKGROUND: Honeybees have extraordinary phenotypic plasticity in their senescence rate, making them a fascinating model system for the evolution of aging. Seasonal variation in senescence and extrinsic mortality results in a tenfold increase in worker life expectancy in winter as compared to summer. To understand the evolution of this remarkable pattern of aging, we must understand how individual longevity scales up to effects on the entire colony. In addition, threats to the health of honey bees and other social insects are typically measured at the individual level. To predict the effects of environmental change on social insect populations, we must understand how individual effects impact colony performance. We develop a matrix model of colony demographics to ask how worker age-dependent and age-independent mortality affect colony fitness and how these effects differ by seasonal conditions. RESULTS: We find that there are seasonal differences in honeybee colony elasticity to both senescent and extrinsic worker mortality. Colonies are most elastic to extrinsic (age-independent) nurse and forager mortality during periods of higher extrinsic mortality and resource availability but most elastic to age-dependent mortality during periods of lower extrinsic mortality and lower resource availability. CONCLUSIONS: These results suggest that seasonal changes in the strength of selection on worker senescence partly explain the observed pattern of seasonal differences in worker aging in honey bees. More broadly, these results extend our understanding of the role of extrinsic mortality in the evolution of senescence to social animals and improve our ability to model the effects of environmental change on social insect populations of economic or conservation concern.

How disease constrains the evolution of social systems.

Animal populations are occasionally shocked by epidemics of contagious diseases. The ability of social systems to withstand epidemic shocks and mitigate disruptions could shape the evolution of complex animal societies. We present a mathematical model to explore the potential impact of disease on the evolutionary fitness of different organizational strategies for populations of social species whose survival depends on collaborative efficiency. We show that infectious diseases select for a specific feature in the organization of collaborative roles-cohort stability-and that this feature is costly, and therefore unlikely to be maintained in environments where infection risks are absent. Our study provides evidence for an often-stated (but rarely supported) claim that pathogens have been the dominant force shaping the complexity of division of labour in eusocial societies of honeybees and termites and establishes a general theoretical approach for assessing evolutionary constraints on social organization from disease risk in other collaborative taxa.

A generic arboviral model framework for exploring trade-offs between vector control and environmental concerns.

Effective public health measures must balance potentially conflicting demands from populations they serve. In the case of infectious disease risks from mosquito-borne infections, such as Zika virus, public concern about the pathogen may be counterbalanced by public concern about environmental contamination from chemical agents used for vector control. Here we introduce a generic framework for modeling how the spread of an infectious pathogen might lead to varying public perceptions, and therefore tolerance, of both disease risk and pesticide use. We consider how these dynamics might impact the spread of a vector-borne disease. We tailor and parameterize our model for direct application to Zika virus as spread by Aedes aegypti mosquitoes, though the framework itself has broad applicability to any arboviral infection. We demonstrate how public risk perception of both disease and pesticides may drastically impact the spread of a mosquito-borne disease in a susceptible population. We conclude that models hoping to inform public health decision making about how best to mitigate arboviral disease risks should explicitly consider the potential public demand for, or rejection of, chemical control of mosquito populations.

How resource limitations and household economics may compromise efforts to safeguard children during outbreaks.

BACKGROUND: Epidemiological models have been employed with great success to explore the efficacy of alternative strategies at combating disease outbreaks. These models have often incorporated an understanding of age-based susceptibility and severity of outcome, considering how to limit the adverse outcomes or disease burden relative to an age structure. Such models frequently recommend the preferential treatment/vaccination of children or the elderly, demonstrating how prevention of serious disease within these etiological subgroups can provide both protection within the subgroup itself and indirect protection to the broader population. However, it is most frequently the case that these target populations are consumers, rather than providers, of household resources. In areas of the globe where continued health of household members relies on continued provision of resources, these models may fail to provide the most effective overall strategies for health outcomes in both target populations and overall. This is particularly important for tropical diseases impacting rural and low-income areas in which the disease may be endemic or newly emergent, particularly in the wake of natural disasters. METHODS: We propose a modified epidemiological model with targeted treatment in resource-limited populations. We evaluate the model over a broad parameter space. RESULTS: This model demonstrates how economic limitations may shift the optimal strategy. It may be advantageous to treat populations at lesser direct risk if they are responsible for providing secondary protection to higher-risk population(s) by producing household resources. Evaluation of this model over the parameter space reveals that, in some cases, targeting treatment towards consumers may result in greater numbers of consumer infections. CONCLUSIONS: Our results demonstrate how household resource limitation can drastically affect the impact of targeted treatment strategies for limiting epidemics. Depending on the economic circumstances, it is possible that focusing treatment on consumers such as children can produce a counter-intuitive outcome in which more children contract the disease.

(Meta)population dynamics determine effective spatial distributions of mosquito-borne disease control.

Recent epidemics of mosquito-borne dengue and Zika viruses demonstrate the urgent need for effective measures to control these diseases. The best method currently available to prevent or reduce the size of outbreaks is to reduce the abundance of their mosquito vectors, but there is little consensus on which mechanisms of control are most effective, or when and where they should be implemented. Although the optimal methods are likely context dependent, broadly applicable strategies for mosquito control, such as how to distribute limited resources across a landscape in times of high epidemic risk, can mitigate (re)emerging outbreaks. We used mathematical simulations to examine how the spatial distribution of larval mosquito control affects the size of disease outbreaks, and how mosquito metapopulation dynamics and demography might impact the efficacy of different spatial distributions of control. We found that the birth rate and mechanism of density-dependent regulation of mosquito populations affected the average outbreak size across all control distributions. These factors also determined whether control distributions favoring the interior or the edges of the landscape most effectively reduced human infections. Thus, understanding local mosquito population regulation and dispersion can lead to more effective control strategies.

How Life History Shapes Optimal Patterns of Senescence: Implications from Individuals to Societies.

One evolutionary view of aging, the disposable soma theory, suggests that an organism's rate of senescence depends on the amount of energy invested in somatic maintenance. Since organisms have limited energy to allocate among growth, maintenance, and reproduction, the optimal amount of energy to invest in maintenance is influenced by the probability of death from extrinsic causes and the effect of somatic investment on survival. In eusocial animals, the disposable soma theory can be used to explain colonies' energy investment in the longevity of workers, who act as the somatic elements of a superorganism. There have been few theoretical considerations of how changes in the costliness of worker maintenance or in the effect of individual life span on group fitness influence a colony's investment in worker longevity. We develop a decision theory model to evaluate how changing the marginal costs and benefits of longevity and extrinsic mortality influence optimal worker life span in a social insect colony. Our model predicts that higher extrinsic mortality favors shorter life span. However, increased life span is favored when marginal benefits are an increasing function of longevity. In honeybees, this explains how greater somatic investment is sometimes favored despite high mortality. Our approach expands the disposable soma theory to make quantitative predictions about the selective pressures shaping senescence in social systems.

A general structured model of a hermaphrodite population.

Both empirical and theoretical studies, have dealt with the question how to best optimize reproductive fitness for hermaphrodites, using models such as game theory or complicated energetic costs and benefits of mating displays. However, hermaphrodites exhibit a broad spectrum of sexual behaviors like simultaneous, sequential or lifetime gonochorist that cannot be explained using a unique formalism. A possible explanation of this diversity relies on the way these species maximize their fitness: Does the individual hermaphrodite split its time between strategies maximizing its instantaneous reproductive fitness or its evolutionary fitness? Here, we compare these two points of view and extend a game theoretical formalism to a sex allocation model that underlies all sexual behaviors as a result of a dynamic game whose payoff depends on the costs and benefits of sexual reproduction. Using this formalism, we prove that a simultaneous hermaphrodites strategy is stable even for high values of sex changing costs. Moreover, we prove that the stability of a sequential hermaphrodite is linked to the average energy allocated to the pure female strategy.

Application of network methods for understanding evolutionary dynamics in discrete habitats.

In populations occupying discrete habitat patches, gene flow between habitat patches may form an intricate population structure. In such structures, the evolutionary dynamics resulting from interaction of gene-flow patterns with other evolutionary forces may be exceedingly complex. Several models describing gene flow between discrete habitat patches have been presented in the population-genetics literature; however, these models have usually addressed relatively simple settings of habitable patches and have stopped short of providing general methodologies for addressing nontrivial gene-flow patterns. In the last decades, network theory - a branch of discrete mathematics concerned with complex interactions between discrete elements - has been applied to address several problems in population genetics by modelling gene flow between habitat patches using networks. Here, we present the idea and concepts of modelling complex gene flows in discrete habitats using networks. Our goal is to raise awareness to existing network theory applications in molecular ecology studies, as well as to outline the current and potential contribution of network methods to the understanding of evolutionary dynamics in discrete habitats. We review the main branches of network theory that have been, or that we believe potentially could be, applied to population genetics and molecular ecology research. We address applications to theoretical modelling and to empirical population-genetic studies, and we highlight future directions for extending the integration of network science with molecular ecology.

A case study of bats and white-nose syndrome demonstrating how to model population viability with evolutionary effects.

Ecological factors generally affect population viability on rapid time scales. Traditional population viability analyses (PVA) therefore focus on alleviating ecological pressures, discounting potential evolutionary impacts on individual phenotypes. Recent studies of evolutionary rescue (ER) focus on cases in which severe, environmentally induced population bottlenecks trigger a rapid evolutionary response that can potentially reverse demographic threats. ER models have focused on shifting genetics and resulting population recovery, but no one has explored how to incorporate those findings into PVA. We integrated ER into PVA to identify the critical decision interval for evolutionary rescue (DIER) under which targeted conservation action should be applied to buffer populations undergoing ER against extinction from stochastic events and to determine the most appropriate vital rate to target to promote population recovery. We applied this model to little brown bats (Myotis lucifugus) affected by white-nose syndrome (WNS), a fungal disease causing massive declines in several North American bat populations. Under the ER scenario, the model predicted that the DIER period for little brown bats was within 11 years of initial WNS emergence, after which they stabilized at a positive growth rate (λ = 1.05). By comparing our model results with population trajectories of multiple infected hibernacula across the WNS range, we concluded that ER is a potential explanation of observed little brown bat population trajectories across multiple hibernacula within the affected range. Our approach provides a tool that can be used by all managers to provide testable hypotheses regarding the occurrence of ER in declining populations, suggest empirical studies to better parameterize the population genetics and conservation-relevant vital rates, and identify the DIER period during which management strategies will be most effective for species conservation.

Un Estudio de Caso sobre Murciélagos y el Síndrome de Nariz Blanca que Demuestra cómo Modelar la Viabilidad Poblacional con Efectos Evolutivos Resumen Los factores ecológicos afectan generalmente a la viabilidad poblacional en escalas rápidas de tiempo. Por esto los análisis tradicionales de viabilidad poblacional (AVP) se enfocan en aliviar las presiones ecológicas, lo que discontinúa los impactos evolutivos potenciales sobre los fenotipos individuales. Los estudios recientes del rescate evolutivo (RE) se enfocan en casos en los que cuellos de botella poblacionales inducidos por el ambiente disparan una respuesta evolutiva rápida, la que puede revertir potencialmente las amenazas demográficas. Los modelos de rescate evolutivo se han enfocado en la genética cambiante y la recuperación poblacional resultante, pero nadie ha explorado cómo incorporar estos hallazgos en los AVP. Integramos el RE a los AVP para identificar el intervalo de decisión crítica para el rescate evolutivo (IDRE), bajo el cual se deben aplicar las acciones de conservación enfocada para amortiguar a las poblaciones sometidas a RE ante la extinción por eventos estocásticos, y para determinar la tasa vital más apropiada para promover la recuperación de la población. Aplicamos este modelo a los pequeños murciélagos cafés (Myotis lucifugus) afectados por el síndrome de nariz blanca (SNB), una enfermedad micótica que causa declinaciones masivas en varias poblaciones norteamericanas de murciélagos. Bajo el escenario de RE, el modelo predijo que el periodo de IDRE para estos murciélagos estaba dentro de once años del surgimiento inicial del síndrome, después del cual se estabilizaban a una tasa positiva de crecimiento (λ = 1.05). Al comparar nuestros resultados del modelo con las trayectorias poblacionales de múltiples sitios de hibernación infectados a lo largo de la extensión del SNB, concluimos que el RE es una explicación potencial de las trayectorias observadas de pequeños murciélagos cafés a lo largo de múltiples sitios de hibernación dentro de la extensión afectada. Nuestra estrategia proporciona una herramienta que puede ser usada por todos los manejadores para proporcionar hipótesis comprobables con respecto a la aparición del RE en las poblaciones declinantes, sugerir estudios empíricos que mejoren los parámetros de la genética de poblaciones y las tasas vitales relevantes para la conservación, y para identificar el periodo IDRE durante el cual las estrategias de manejo serán más efectivas para la conservación de la especie.